diff --git a/.gitignore b/.gitignore
index 51a7b58c027..30591a95750 100644
--- a/.gitignore
+++ b/.gitignore
@@ -48,3 +48,5 @@ help
 
 # Project specific
 statsmodels/version.py
+cythonize.dat
+statsmodels.egg-info/
diff --git a/.travis.orig.yml b/.travis.orig.yml
new file mode 100644
index 00000000000..933005fcd6e
--- /dev/null
+++ b/.travis.orig.yml
@@ -0,0 +1,57 @@
+# Adapted from M. Brett's .yaml file for nipy:
+# https://github.com/nipy/nipy/blob/master/.travis.yml
+#
+# We pretend to be erlang because we can't use the python support in
+# travis-ci; it uses virtualenvs, they do not have numpy, scipy, matplotlib,
+# and it is impractical to build them
+language: erlang
+notifications:
+    email:
+        - statsmodels-commits@googlegroups.com
+env:
+    # Enable python 2 and python 3 builds. Python3.2 available in Ubuntu 12.04.
+    - PYTHON=python PYSUF=''
+    - PYTHON=python3 PYSUF=3
+install:
+    - sudo apt-get update
+    - sudo apt-get install $PYTHON-dev
+    - sudo apt-get install $PYTHON-numpy
+    - sudo apt-get install $PYTHON-scipy
+    - sudo apt-get install $PYTHON-setuptools
+    - sudo apt-get install $PYTHON-nose
+    # Cython needs manual install under Python 3
+    - if [ "${PYSUF}" == "3" ]; then
+         wget http://cython.org/release/Cython-0.17.1.tar.gz ;
+         tar xfvz Cython-0.17.1.tar.gz ;
+         cd Cython-0.17.1 ;
+         sudo python3 setup.py install ;
+         cd .. ;
+      else
+         sudo apt-get install cython ;
+      fi
+    - echo ${DISTRIB_CODENAME}
+    - wget -O- http://neuro.debian.net/lists/precise.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
+    - sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9
+    - sudo apt-get update -qq
+    - sudo apt-get install $PYTHON-dateutil
+    - sudo apt-get --no-install-recommends install $PYTHON-pandas
+    - sudo apt-get --no-install-recommends install $PYTHON-pandas-lib
+    - sudo easy_install$PYSUF -U patsy
+    - if [ "${PYSUF}" != "3" ]; then sudo pip install coverage; fi
+    - if [ "${PYSUF}" != "3" ]; then sudo pip install coveralls; fi
+script:
+    - SRCDIR=$PWD
+    - sudo $PYTHON setup.py install
+    # Ubuntu 12.04 installs statsmodels under the wrong path for Python 3
+    - if [ "${PYSUF}" == "3" ]; then
+         sudo mv /usr/local/lib/python3.2/dist-packages/statsmodels-*/statsmodels /usr/local/lib/python3.2/dist-packages/statsmodels ;
+         sudo rm -rf /usr/lib/python3.2/dist-packages/statsmodels-* ;
+      fi
+    # statsmodels was installed "system-wide" thus cd under dedicated
+    # travis-test/ directory to not try using local version without built extensions
+    - mkdir -p "${SRCDIR}/travis-test"; cd "${SRCDIR}/travis-test"
+    # Compose a script to run testing with coverage
+    - echo 'import statsmodels as sm; a=sm.test(); import sys; sys.exit((len(a.failures)+len(a.errors))>0)' > test.py
+    - if [ "${PYSUF}" != "3" ]; then coverage run --rcfile=${SRCDIR}/.travis_coveragerc test.py; else $PYTHON test.py; fi
+after_success:
+    - if [ "${PYSUF}" != "3" ]; then coveralls; fi
diff --git a/.travis.yml b/.travis.yml
index 995a4de0fb4..d6c9a1cf71f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,50 +1,97 @@
-# Adapted from M. Brett's .yaml file for nipy:
-# https://github.com/nipy/nipy/blob/master/.travis.yml
-#
-# We pretend to be erlang because we can't use the python support in
-# travis-ci; it uses virtualenvs, they do not have numpy, scipy, matplotlib,
-# and it is impractical to build them
-language: erlang
-notifications:
-    email:
-        - statsmodels-commits@googlegroups.com
+# Travis script that uses miniconda in place of the system installed python
+# versions.  Allows substantial flexability for choosing versions of
+# required packages and is simpler to use to test up-to-date scientific Python
+# stack
+language: python
+
 env:
-    # Enable python 2 and python 3 builds. Python3.2 available in Ubuntu 12.04.
-    - PYTHON=python PYSUF=''
-    - PYTHON=python3 PYSUF=3
+  # Default values for common packages, override as needed
+  global:
+    - CYTHON=0.20
+    - PATSY=0.2
+    - PANDAS=0.12
+    - MATPLOTLIB=1.3
+    - OPTIONAL=
+    - COVERAGE=false
+
+matrix:
+  fast_finish: true
+  include:
+  - python: 2.7
+    env:
+    - PYTHON=2.6
+    - NUMPY="1.6.2=py26_4"
+    - SCIPY="0.11.0=np16py26_3"
+    - MATPLOTLIB=1.2
+  - python: 2.7
+    env:
+    - PYTHON=2.7
+    - NUMPY=1.7
+    - SCIPY=0.12
+    - OPTIONAL=cvxopt
+    - COVERAGE=true
+  - python: 2.7
+    env:
+    - PYTHON=3.3
+    - NUMPY=1.8
+    - SCIPY=0.13
+    - PANDAS=0.13
+  - python: 2.7
+    env:
+    - PYTHON=2.7
+    - NUMPY=
+    - SCIPY=
+    - MATPLOTLIB=
+    - PANDAS=
+
+notifications:
+  email:
+    on_success: always
+
+# Setup anaconda
+before_install:
+  - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  - chmod +x miniconda.sh
+  - ./miniconda.sh -b
+  - export PATH=/home/travis/miniconda/bin:$PATH
+  - conda update --yes --quiet conda
+  # Fix for headless TravisCI
+  - "export DISPLAY=:99.0"
+  - "sh -e /etc/init.d/xvfb start"
+  # Avoid noise from matplotlib
+  - mkdir $HOME/.config
+  - mkdir $HOME/.config/matplotlib
+  - SRCDIR=$PWD
+  - cp $SRCDIR/tools/matplotlibrc $HOME/.config/matplotlib/matplotlibrc
+  # Location for older version of matplotlib
+  - if [ ${MATPLOTLIB} = "1.2" ]; then mkdir $HOME/.matplotlib; fi
+  - if [ ${MATPLOTLIB} = "1.2" ]; then cp ${SRCDIR}/tools/matplotlibrc $HOME/.matplotlib/matplotlibrc; fi
+  # Build package list to avoid empty package=versions; only needed for versioned pacakges
+  - PKGS="python=${PYTHON}"
+  - PKGS="${PKGS} numpy"; if [ ${NUMPY} ]; then PKGS="${PKGS}=${NUMPY}"; fi
+  - PKGS="${PKGS} scipy"; if [ ${SCIPY} ]; then PKGS="${PKGS}=${SCIPY}"; fi
+  - PKGS="${PKGS} patsy"; if [ ${PATSY} ]; then PKGS="${PKGS}=${PATSY}"; fi
+  - PKGS="${PKGS} pandas"; if [ ${PANDAS} ]; then PKGS="${PKGS}=${PANDAS}"; fi
+  - PKGS="${PKGS} Cython"; if [ ${CYTHON} ]; then PKGS="${PKGS}=${CYTHON}"; fi
+  - PKGS="${PKGS} matplotlib"; if [ ${MATPLOTLIB} ]; then PKGS="${PKGS}=${MATPLOTLIB}"; fi
+
+# Install packages
 install:
-    - sudo apt-get update
-    - sudo apt-get install $PYTHON-dev
-    - sudo apt-get install $PYTHON-numpy
-    - sudo apt-get install $PYTHON-scipy
-    - sudo apt-get install $PYTHON-setuptools
-    - sudo apt-get install $PYTHON-nose
-    # Cython needs manual install under Python 3
-    - if [ "${PYSUF}" == "3" ]; then
-         wget http://cython.org/release/Cython-0.17.1.tar.gz ;
-         tar xfvz Cython-0.17.1.tar.gz ;
-         cd Cython-0.17.1 ;
-         sudo python3 setup.py install ;
-         cd .. ;
-      else
-         sudo apt-get install cython ;
-      fi
-    - echo ${DISTRIB_CODENAME}
-    - wget -O- http://neuro.debian.net/lists/precise.us-nh.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
-    - sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9
-    - sudo apt-get update -qq
-    - sudo apt-get install $PYTHON-dateutil
-    - sudo apt-get --no-install-recommends install $PYTHON-pandas
-    - sudo apt-get --no-install-recommends install $PYTHON-pandas-lib
-    - sudo easy_install$PYSUF -U patsy
+  - conda create --yes --quiet -n statsmodels-test ${PKGS} ${OPTIONAL} dateutil nose pip pyyaml setuptools
+  - source activate statsmodels-test
+  - if [ ${COVERAGE} = true ]; then pip install coverage coveralls; fi
+  - python setup.py install
+
 script:
-    - sudo $PYTHON setup.py install
-    # Ubuntu 12.04 installs statsmodels under the wrong path for Python 3
-    - if [ "${PYSUF}" == "3" ]; then
-         sudo mv /usr/local/lib/python3.2/dist-packages/statsmodels-*/statsmodels /usr/local/lib/python3.2/dist-packages/statsmodels ;
-         sudo rm -rf /usr/lib/python3.2/dist-packages/statsmodels-* ;
-      fi
-    # For some reason, Python 3 will try to work with the build directory. Get out of folder to avoid breakage
-    - cd ../
-    - sudo $PYTHON -c "import statsmodels as sm; a=sm.test(); import sys; sys.exit((len(a.failures)+len(a.errors))>0)"
+  - SRCDIR=$PWD
+  - python setup.py install
+  - mkdir -p "${SRCDIR}/travis-test"; cd "${SRCDIR}/travis-test"
+  # Show versions
+  - python -c 'import statsmodels.api as sm; sm.show_versions();'
+  # Compose a script to run testing with coverage
+  - echo 'import statsmodels as sm; a=sm.test(); import sys; sys.exit((len(a.failures)+len(a.errors))>0)' > test.py
+  - if [ ${COVERAGE} = true ]; then coverage run --rcfile=${SRCDIR}/.travis_coveragerc test.py; else python test.py; fi
 
+after_success:
+  # Coverage only for 2.7
+  - if [ ${COVERAGE} = true ]; then coveralls --rcfile=${SRCDIR}/.travis_coveragerc; fi
diff --git a/.travis_coveragerc b/.travis_coveragerc
new file mode 100644
index 00000000000..c41942d037e
--- /dev/null
+++ b/.travis_coveragerc
@@ -0,0 +1,5 @@
+
+[run]
+branch = True
+include =
+    */statsmodels/*
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index f090f07a6ca..033f01aa5da 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -7,23 +7,42 @@ by submitting patches, statistical tests, new models, or examples.
 `statsmodels` is developed on `Github <https://github.com/statsmodels/statsmodels>`_ 
 using the `Git <http://git-scm.com/>`_ version control system. 
 
-License
-~~~~~~~
+Submitting a Bug Report
+~~~~~~~~~~~~~~~~~~~~~~~
 
-Statsmodels is released under the 
-`Modified (3-clause) BSD license <http://www.opensource.org/licenses/BSD-3-Clause>`_.
+- Include a short, self-contained code snippet that reproduces the problem
+- Specify the statsmodels version used. You can do this with ``sm.version.full_version``
+- If the issue looks to involve other dependencies, also include the output of ``sm.show_versions()``
+
+Making Changes to the Code
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+For a pull request to be accepted, you must meet the below requirements. This greatly helps in keeping the job of maintaining and releasing the software a shared effort.
+
+- **One branch. One feature.** Branches are cheap and github makes it easy to merge and delete branches with a few clicks. Avoid the temptation to lump in a bunch of unrelated changes when working on a feature, if possible. This helps us keep track of what has changed when preparing a release.
+- Commit messages should be clear and concise. This means a subject line of less than 80 characters, and, if necessary, a blank line followed by a commit message body. We have an `informal commit format standard <http://statsmodels.sourceforge.net/devel/dev/maintainer_notes.html#commit-comments>`_ that we try to adhere to. You can see what this looks like in practice by ``git log --oneline -n 10``. If your commit references or closes a specific issue, you can close it by mentioning it in the `commit message <https://help.github.com/articles/closing-issues-via-commit-messages>`_.  (*For maintainers*: These suggestions go for Merge commit comments too. These are partially the record for release notes.)
+- Code submissions must always include tests. See our `notes on testing <https://statsmodels.sourceforge.net/devel/dev/test_notes.html>`_.
+- Each function, class, method, and attribute needs to be documented using docstrings. We conform to the `numpy docstring standard <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-standard>`_.
+- If you are adding new functionality, you need to add it to the documentation by editing (or creating) the appropriate file in ``docs/source``.
+- Make sure your documentation changes parse correctly. Change into the top-level ``docs/`` directory and type::
+  
+   make clean
+   make html
 
-Submitting a Patch
-~~~~~~~~~~~~~~~~~~
+  Check that the build output does not have *any* warnings due to your changes. 
+- Finally, please add your changes to the release notes. Open the ``docs/source/release/versionX.X.rst`` file that has the version number of the next release and add your changes to the appropriate section.
 
-So you want to submit a patch to `statsmodels`?. Great news! Here are the 
-steps you need to take.
+How to Submit a Pull Request
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+So you want to submit a patch to `statsmodels` but aren't too familiar with github? Here are the steps you need to take.
 
 1. `Fork <https://help.github.com/articles/fork-a-repo>`_ the `statsmodels repository <https://github.com/statsmodels/statsmodels>`_ on Github.
-2. `Create a new feature branch <http://git-scm.com/book/en/Git-Branching-Basic-Branching-and-Merging>`_
-    + Each branch must be self-contained, with a single new feature or bugfix. 
-    + Patches must always include tests. See our `notes on testing <test_notes.html>`_.
-3. `Submit a pull request <https://help.github.com/articles/using-pull-requests>`_ 
+2. `Create a new feature branch <http://git-scm.com/book/en/Git-Branching-Basic-Branching-and-Merging>`_. Each branch must be self-contained, with a single new feature or bugfix. 
+3. Make sure the test suite passes. This includes testing on Python 3. The easiest way to do this is to either enable `Travis-CI <https://travis-ci.org/>`_ on your fork, or to make a pull request and check there.
+4. Document your changes by editing the appropriate file in ``docs/source/``. If it is a big, new feature add a note and an example to the latest ``docs/source/release/versionX.X.rst`` file. See older versions for examples. If it's a minor change, it will be included automatically in our relase notes.
+5. Add an example. If it is a big, new feature please submit an example notebook by following `these instructions <http://statsmodels.sourceforge.net/devel/dev/examples.html>`_.
+6. `Submit a pull request <https://help.github.com/articles/using-pull-requests>`_ 
 
 Mailing List
 ~~~~~~~~~~~~
@@ -35,3 +54,9 @@ Learn More
 
 The ``statsmodels`` documentation's `developer page <http://statsmodels.sourceforge.net/stable/dev/index.html>`_ 
 offers much more detailed information about the process.
+
+License
+~~~~~~~
+
+Statsmodels is released under the 
+`Modified (3-clause) BSD license <http://www.opensource.org/licenses/BSD-3-Clause>`_.
diff --git a/INSTALL.txt b/INSTALL.txt
index 39e5de9f46f..db088ab4128 100644
--- a/INSTALL.txt
+++ b/INSTALL.txt
@@ -9,7 +9,7 @@ numpy >= 1.5.1
 
     www.numpy.org
 
-scipy >= 0.7
+scipy >= 0.9.0
     
     www.scipy.org
 
@@ -22,13 +22,15 @@ patsy >= 0.1.0
     patsy.readthedocs.org
 
 
-cython >= 0.15.1
+cython >= 0.20.1
 
     http://cython.org/
 
     Cython is required if you are building the source from github. However,
     if you have are building from source distribution archive then the 
-    generated C files are included and Cython is not necessary.
+    generated C files are included and Cython is not necessary. If you are 
+    building for Python 3.4, then you must use Cython >= 0.20.1. Earlier 
+    versions may be ok for Python < 3.4.
 
 Optional Dependencies
 ---------------------
@@ -52,6 +54,9 @@ nose >= 1.0.0
 
     Nose is needed to run the tests.
 
+IPython >= 1.0
+
+    Needed to build the docs.
 
 Easy Install
 ------------
diff --git a/MANIFEST.in b/MANIFEST.in
index 682069e91ae..c7a12f5ae56 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,13 +1,12 @@
-global-include *.csv *.py *.txt
-#scikits*.*
+global-include *.csv *.py *.txt *.pyx *.pxd *.pxi *.c *.h
 include MANIFEST.in
 
-#exclude docs/build/htmlhelp*
 recursive-exclude build *
 recursive-exclude dist *
 recursive-exclude tools *
 include tools/examples_rst.py
 include tools/hash_funcs.py
+include tools/cythonize.py
 
 graft statsmodels/datasets
 graft statsmodels/tests
@@ -20,28 +19,16 @@ exclude docs/source/generated/*
 recursive-include docs/sphinxext *
 recursive-include docs/themes *
 recursive-exclude docs/build *
-#recursive-include docs/build/html *
 recursive-exclude docs/build/htmlhelp *
 include statsmodels/statsmodelsdoc.chm
 include docs/make.bat
 include docs/Makefile
-#include docs mak*
-#include docs GLM*
 
 recursive-include examples *
 
-#missed files:  .npz, .npy
-include statsmodels/tsa/vector_ar/tests/results/vars_results.npz
-include statsmodels/iolib/tests/results/*
-include statsmodels/stats/tests/results/influence_lsdiag_R.json
-include statsmodels/sandbox/panel/test_data.txt
-include statsmodels/stats/tests/results/influence_measures_R.csv
-include statsmodels/stats/libqsturng/tests/results/*
-include statsmodels/stats/libqsturng/tests/bootleg.dat
-include statsmodels/stats/libqsturng/tests/bootleg.csv
-include statsmodels/stats/libqsturng/CH.r
-include statsmodels/stats/libqsturng/LICENSE.txt
-include statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt
+# Cached Cython signatures
+include cythonize.dat
 
+prune */__pycache__
 
-global-exclude *~ *.swp  *.pyc *.bak *.pyx
+global-exclude *~ *.swp  *.pyc *.pyo *.bak
diff --git a/README.txt b/README.txt
index b8ac66ecf2c..8b4285c4102 100644
--- a/README.txt
+++ b/README.txt
@@ -86,11 +86,11 @@ This can be opened from the python interpreter ::
 Discussion and Development
 ==========================
 
-Discussions take place on our mailing list. 
+Discussions take place on our mailing list.
 
     http://groups.google.com/group/pystatsmodels
 
-We are very interested in feedback about usability and suggestions for improvements. 
+We are very interested in feedback about usability and suggestions for improvements.
 
 
 Bug Reports
diff --git a/build_bdists.bat b/build_bdists.bat
index bc362686299..96aa183216f 100644
--- a/build_bdists.bat
+++ b/build_bdists.bat
@@ -4,4 +4,6 @@ call tools\build_win_bdist64-py27.bat
 call tools\build_win_bdist32-py27.bat
 call tools\build_win_bdist32-py32.bat
 call tools\build_win_bdist64-py32.bat
+call tools\build_win_bdist33-py34.bat
+call tools\build_win_bdist64-py34.bat
 call python setup.py sdist --formats=zip,gztar
diff --git a/docs/Makefile b/docs/Makefile
index d68245dc686..32d888a080f 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -7,6 +7,7 @@ SPHINXBUILD   = sphinx-build
 PAPER         =
 BUILDDIR      = build
 TOOLSPATH	  = ../tools/
+DATASETBUILD  = dataset_rst.py
 EXAMPLEBUILD  = examples_rst.py
 NOTEBOOKBUILD = nbgenerate.py
 FOLDTOC  = fold_toc.py
@@ -48,17 +49,24 @@ cleancache:
 	-rm source/examples/generated/*
 	-rm -rf source/examples/notebooks/generated/*
 	-rm -rf ../tools/hash_dict.pickle
+	-rm -rf source/datasets/generated/*
+
+notebooks:
+	@echo "Generating notebooks from examples/notebooks folder" 
+	$(TOOLSPATH)$(NOTEBOOKBUILD)
 
 html:
 	# generate the examples rst files
 	@echo "Generating reST from examples folder"
-	$(TOOLSPATH)$(EXAMPLEBUILD)
+	#$(TOOLSPATH)$(EXAMPLEBUILD)
 	@echo "Generating notebooks from examples/notebooks folder" 
 	$(TOOLSPATH)$(NOTEBOOKBUILD)
+	@echo "Generating datasets from installed statsmodels.datasets"
+	$(TOOLSPATH)$(DATASETBUILD)
 	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	$(TOOLSPATH)$(EXAMPLEBUILD)
+	#$(TOOLSPATH)$(EXAMPLEBUILD)
 	$(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/index.html
-	$(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/examples/index.html ../_static
+	#$(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/examples/index.html ../_static
 	$(TOOLSPATH)$(FOLDTOC) $(BUILDDIR)/html/dev/index.html ../_static
 	@echo
 	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
diff --git a/docs/source/_static/examples.css b/docs/source/_static/examples.css
new file mode 100644
index 00000000000..6225bd11b31
--- /dev/null
+++ b/docs/source/_static/examples.css
@@ -0,0 +1,113 @@
+.examples-page {
+    /*override div.body padding of 30px */
+    margin-left: -30px;
+    margin-right: -30px;
+}
+
+.examples-page .padleft {
+    padding-left: 30px;
+}
+
+.examples-page h1, h2, h3 {
+    padding-left: 30px; /* to make up for margin above */
+}
+
+.examples-page .toclist {
+    list-style:none;
+    margin-bottom:0px;
+    margin-top:0px;
+}
+
+/* Marketing section of Overview
+-------------------------------------------------- */
+.marketing p {
+  margin-right: 10px;
+}
+
+/* Eaxmples page
+------------------------- */
+.thumbnail {
+  margin-bottom: 9px;
+  background-color: #fff;
+}
+
+/* Example sites showcase */
+.example-sites img {
+  max-width: 100%;
+  margin: 0 auto;
+}
+
+.marketing-byline {
+  font-size: 18px;
+  font-weight: 300;
+  line-height: 24px;
+  color: #999;
+  text-align: center;
+}
+
+/* From bootstrap.css */
+.thumbnails {
+  margin-left: -20px;
+  list-style: none;
+  *zoom: 1;
+}
+
+.thumbnails:before,
+.thumbnails:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.thumbnails:after {
+  clear: both;
+}
+
+.row-fluid .thumbnails {
+  margin-left: 0;
+}
+
+.thumbnails > li {
+  float: left;
+  margin-bottom: 20px;
+  margin-left: 20px;
+}
+
+.thumbnail {
+  display: block;
+  padding: 4px;
+  line-height: 20px;
+  border: 1px solid #ddd;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+     -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+          box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+  -webkit-transition: all 0.2s ease-in-out;
+     -moz-transition: all 0.2s ease-in-out;
+       -o-transition: all 0.2s ease-in-out;
+          transition: all 0.2s ease-in-out;
+}
+
+a.thumbnail:hover,
+a.thumbnail:focus {
+  border-color: #0088cc;
+  -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+     -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+          box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+}
+
+.thumbnail > img {
+  display: block;
+  width: 360px;
+  max-width: 100%;
+  height: auto;
+  margin-right: auto;
+  margin-left: auto;
+}
+
+.thumbnail .caption {
+  padding: 9px;
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diff --git a/docs/source/about.rst b/docs/source/about.rst
new file mode 100644
index 00000000000..2ba79f54149
--- /dev/null
+++ b/docs/source/about.rst
@@ -0,0 +1,57 @@
+.. currentmodule:: statsmodels
+
+*****************
+About Statsmodels
+*****************
+
+Background
+----------
+
+The ``models`` module of ``scipy.stats`` was originally written by Jonathan 
+Taylor. For some time it was part of scipy but was later removed. During
+the Google Summer of Code 2009, ``statsmodels`` was corrected, tested,
+improved and released as a new package. Since then, the statsmodels 
+development team has continued to add new models, plotting tools, and statistical methods.
+
+Testing
+-------
+
+Most results have been verified with at least one other statistical package:
+R, Stata or SAS. The guiding principal for the initial rewrite and for 
+continued development is that all numbers have to be verified. Some 
+statistical methods are tested with Monte Carlo studies. While we strive to
+follow this test driven approach, there is no guarantee that the code is 
+bug-free and always works. Some auxiliary function are still insufficiently 
+tested, some edge cases might not be correctly taken into account, and the 
+possibility of numerical problems is inherent to many of the statistical 
+models. We especially appreciate any help and reports for these kind of 
+problems so we can keep improving the existing models.
+
+Code Stability
+~~~~~~~~~~~~~~
+
+The existing models are mostly settled in their user interface and we do not
+expect many large changes going forward. For the existing code, although 
+there is no guarantee yet on API stability, we have long deprecation periods 
+in all but very special cases, and we try to keep changes that require 
+adjustments by existing users to a minimal level. For newer models we might
+adjust the user interface as we gain more experience and obtain feedback. 
+These changes will always be noted in our release notes available in the
+documentation.
+
+Financial Support
+-----------------
+
+We are grateful for the financial support that we obtained for the
+development of statsmodels:
+
+ Google `www.google.com <https://www.google.com/>`_ : Google Summer of Code
+ (GSOC) 2009-2013.
+
+ AQR `www.aqr.com <http://www.aqr.com/>`_ : financial sponsor for the work on
+ Vector Autoregressive Models (VAR) by Wes McKinney
+
+We would also like to thank our hosting providers, `github
+<https://github.com/>`_ for the public code repository, `sourceforge
+<http://sourceforge.net/>`_ for hosting our documentation and `python.org
+<https://python.org>`_ for making our downloads available on PyPi.
diff --git a/docs/source/anova.rst b/docs/source/anova.rst
index d6172470ed1..27f591332a3 100644
--- a/docs/source/anova.rst
+++ b/docs/source/anova.rst
@@ -31,7 +31,7 @@ A more detailed example can be found here:
 .. toctree::
   :maxdepth: 1
 
-  examples/generated/example_interactions
+  examples/notebooks/generated/interactions_anova
 
 Module Reference
 ----------------
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 1ce077d3dec..c40a065e7e9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -32,8 +32,8 @@
               'sphinx.ext.inheritance_diagram',
               'matplotlib.sphinxext.plot_directive',
               'matplotlib.sphinxext.only_directives',
-              'ipython_console_highlighting',
-              'ipython_directive',
+              'IPython.sphinxext.ipython_console_highlighting',
+              'IPython.sphinxext.ipython_directive',
               'numpy_ext.numpydoc',
               'github' # for GitHub links
               ]
@@ -75,8 +75,8 @@
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
-from statsmodels.version import version, full_version
-release = version
+from statsmodels.version import short_version, full_version
+release = short_version
 # The full version, including dev tag.
 version = full_version
 
@@ -311,7 +311,7 @@
 intersphinx_mapping = {
         'numpy' : ('http://docs.scipy.org/doc/numpy/', None),
         'python' : ('http://docs.python.org/3.2', None),
-        'pydagogue' : ('http://matthew-brett.github.com/pydagogue/', None),
+        'pydagogue' : ('http://matthew-brett.github.io/pydagogue/', None),
         'patsy' : ('http://patsy.readthedocs.org/en/latest/', None),
         'pandas' : ('http://pandas.pydata.org/pandas-docs/dev/', None),
         }
@@ -321,3 +321,8 @@
 
 # ghissue config
 github_project_url = "https://github.com/statsmodels/statsmodels"
+
+# for the examples landing page
+import json
+example_context = json.load(open('examples/landing.json'))
+html_context = {'examples': example_context }
diff --git a/docs/source/contrasts.rst b/docs/source/contrasts.rst
index 801afc88db6..5f5777c9fed 100644
--- a/docs/source/contrasts.rst
+++ b/docs/source/contrasts.rst
@@ -28,12 +28,10 @@ To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS
            contr = -1./nlevels * np.ones((nlevels, nlevels-1))
            contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels
            return contr
-
        def code_with_intercept(self, levels):
            contrast = np.column_stack((np.ones(len(levels)),
                                        self._simple_contrast(levels)))
            return ContrastMatrix(contrast, _name_levels("Simp.", levels))
-
        def code_without_intercept(self, levels):
            contrast = self._simple_contrast(levels)
            return ContrastMatrix(contrast, _name_levels("Simp.", levels[:-1]))
diff --git a/docs/source/datasets/dataset_proposal.rst b/docs/source/datasets/dataset_proposal.rst
index d2a6044149e..00a7920d4f4 100644
--- a/docs/source/datasets/dataset_proposal.rst
+++ b/docs/source/datasets/dataset_proposal.rst
@@ -151,4 +151,4 @@ Although the datasets package emerged from the learn package, we try to keep it
 independant from everything else, that is once we agree on the remaining
 problems and where the package should go, it can easily be put elsewhere
 without too much trouble. If there is interest in re-using the datasets package,
-please contact the developers on the `mailing list <http://groups.google.com/group/pystatsmodels?hl=en>`_.
+please contact the developers on the `mailing list <https://groups.google.com/group/pystatsmodels?hl=en>`_.
diff --git a/docs/source/datasets/generated/anes96.rst b/docs/source/datasets/generated/anes96.rst
deleted file mode 100644
index 074047d9a4b..00000000000
--- a/docs/source/datasets/generated/anes96.rst
+++ /dev/null
@@ -1,92 +0,0 @@
-American National Election Survey 1996
-======================================
-
-Description
------------
-
-This data is a subset of the American National Election Studies of 1996.
-
-Notes
------
-
-Number of observations - 944
-Numner of variables - 10
-
-Variables name definitions::
-
-        popul - Census place population in 1000s
-        TVnews - Number of times per week that respondent watches TV news.
-        PID - Party identification of respondent.
-            0 - Strong Democrat
-            1 - Weak Democrat
-            2 - Independent-Democrat
-            3 - Independent-Indpendent
-            4 - Independent-Republican
-            5 - Weak Republican
-            6 - Strong Republican
-        age : Age of respondent.
-        educ - Education level of respondent
-            1 - 1-8 grades
-            2 - Some high school
-            3 - High school graduate
-            4 - Some college
-            5 - College degree
-            6 - Master's degree
-            7 - PhD
-        income - Income of household
-            1  - None or less than $2,999
-            2  - $3,000-$4,999
-            3  - $5,000-$6,999
-            4  - $7,000-$8,999
-            5  - $9,000-$9,999
-            6  - $10,000-$10,999
-            7  - $11,000-$11,999
-            8  - $12,000-$12,999
-            9  - $13,000-$13,999
-            10 - $14,000-$14.999
-            11 - $15,000-$16,999
-            12 - $17,000-$19,999
-            13 - $20,000-$21,999
-            14 - $22,000-$24,999
-            15 - $25,000-$29,999
-            16 - $30,000-$34,999
-            17 - $35,000-$39,999
-            18 - $40,000-$44,999
-            19 - $45,000-$49,999
-            20 - $50,000-$59,999
-            21 - $60,000-$74,999
-            22 - $75,000-89,999
-            23 - $90,000-$104,999
-            24 - $105,000 and over
-        vote - Expected vote
-            0 - Clinton
-            1 - Dole
-        The following 3 variables all take the values:
-            1 - Extremely liberal
-            2 - Liberal
-            3 - Slightly liberal
-            4 - Moderate
-            5 - Slightly conservative
-            6 - Conservative
-            7 - Extremely Conservative
-        selfLR - Respondent's self-reported political leanings from "Left"
-            to "Right".
-        ClinLR - Respondents impression of Bill Clinton's political
-            leanings from "Left" to "Right".
-        DoleLR  - Respondents impression of Bob Dole's political leanings
-            from "Left" to "Right".
-        logpopul - log(popul + .1)
-
-
-Source
-------
-
-http://www.electionstudies.org/
-
-The American National Election Studies.
-
-
-Copyright
----------
-
-This is public domain.
diff --git a/docs/source/datasets/generated/cancer.rst b/docs/source/datasets/generated/cancer.rst
deleted file mode 100644
index 40e1b2deb5e..00000000000
--- a/docs/source/datasets/generated/cancer.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-Breast Cancer Data
-==================
-
-Description
------------
-
-The number of breast cancer observances in various counties
-
-Notes
------
-
-Number of observations: 301
-Number of variables: 2
-Variable name definitions:
-
-    cancer - The number of breast cancer observances
-    population - The population of the county
-
-
-
-Source
-------
-
-This is the breast cancer data used in Owen's empirical likelihood.  It is taken from
-Rice, J.A. Mathematical Statistics and Data Analysis.
-http://www.thomsonedu.com/statistics/discipline_content/dataLibrary.html
-
-
-Copyright
----------
-
-???
diff --git a/docs/source/datasets/generated/ccard.rst b/docs/source/datasets/generated/ccard.rst
deleted file mode 100644
index 4b13fd6de1f..00000000000
--- a/docs/source/datasets/generated/ccard.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-Bill Greene's credit scoring data.
-==================================
-
-Description
------------
-
-More information on this data can be found on the
-homepage for Greene's `Econometric Analysis`. See source.
-
-
-Notes
------
-
-Number of observations - 72
-Number of variables - 5
-Variable name definitions - See Source for more information on the variables.
-
-
-Source
-------
-
-William Greene's `Econometric Analysis`
-
-More information can be found at the web site of the text:
-http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
-
-
-Copyright
----------
-
-Used with express permission of the original author, who
-retains all rights.
diff --git a/docs/source/datasets/generated/committee.rst b/docs/source/datasets/generated/committee.rst
deleted file mode 100644
index 6cb4719e31f..00000000000
--- a/docs/source/datasets/generated/committee.rst
+++ /dev/null
@@ -1,53 +0,0 @@
-First 100 days of the US House of Representatives 1995
-======================================================
-
-Description
------------
-
-The example in Gill, seeks to explain the number of bill
-assignments in the first 100 days of the US' 104th House of Representatives.
-The response variable is the number of bill assignments in the first 100 days
-over 20 Committees.  The explanatory variables in the example are the number of
-assignments in the first 100 days of the 103rd House, the number of members on
-the committee, the number of subcommittees, the log of the number of staff
-assigned to the committee, a dummy variable indicating whether
-the committee is a high prestige committee, and an interaction term between
-the number of subcommittees and the log of the staff size.
-
-The data returned by load are not cleaned to represent the above example.
-
-
-Notes
------
-Number of Observations - 20
-
-Number of Variables - 6
-
-Variable name definitions::
-
-    BILLS104 - Number of bill assignments in the first 100 days of the 104th
-               House of Representatives.
-    SIZE     - Number of members on the committee.
-    SUBS     - Number of subcommittees.
-    STAFF    - Number of staff members assigned to the committee.
-    PRESTIGE - PRESTIGE == 1 is a high prestige committee.
-    BILLS103 - Number of bill assignments in the first 100 days of the 103rd
-               House of Representatives.
-
-Committee names are included as a variable in the data file though not
-returned by load.
-
-
-Source
-------
-
-Jeff Gill's `Generalized Linear Models: A Unifited Approach`
-
-http://jgill.wustl.edu/research/books.html
-
-
-Copyright
----------
-
-Used with express permission from the original author,
-who retains all rights.
diff --git a/docs/source/datasets/generated/copper.rst b/docs/source/datasets/generated/copper.rst
deleted file mode 100644
index d37f6bcdba2..00000000000
--- a/docs/source/datasets/generated/copper.rst
+++ /dev/null
@@ -1,47 +0,0 @@
-World Copper Market 1951-1975 Dataset
-=====================================
-
-Description
------------
-
-This data describes the world copper market from 1951 through 1975.  In an
-example, in Gill, the outcome variable (of a 2 stage estimation) is the world
-consumption of copper for the 25 years.  The explanatory variables are the
-world consumption of copper in 1000 metric tons, the constant dollar adjusted
-price of copper, the price of a substitute, aluminum, an index of real per
-capita income base 1970, an annual measure of manufacturer inventory change,
-and a time trend.
-
-
-Notes
------
-
-Number of Observations - 25
-
-Number of Variables - 6
-
-Variable name definitions::
-
-    WORLDCONSUMPTION - World consumption of copper (in 1000 metric tons)
-    COPPERPRICE - Constant dollar adjusted price of copper
-    INCOMEINDEX - An index of real per capita income (base 1970)
-    ALUMPRICE - The price of aluminum
-    INVENTORYINDEX - A measure of annual manufacturer inventory trend
-    TIME - A time trend
-
-Years are included in the data file though not returned by load.
-
-
-Source
-------
-
-Jeff Gill's `Generalized Linear Models: A Unified Approach`
-
-http://jgill.wustl.edu/research/books.html
-
-
-Copyright
----------
-
-Used with express permission from the original author,
-who retains all rights.
diff --git a/docs/source/datasets/generated/cpunish.rst b/docs/source/datasets/generated/cpunish.rst
deleted file mode 100644
index f2df2ef5b92..00000000000
--- a/docs/source/datasets/generated/cpunish.rst
+++ /dev/null
@@ -1,51 +0,0 @@
-US Capital Punishment dataset.
-==============================
-
-Description
------------
-
-This data describes the number of times capital punishment is implemented
-at the state level for the year 1997.  The outcome variable is the number of
-executions.  There were executions in 17 states.
-Included in the data are explanatory variables for median per capita income
-in dollars, the percent of the population classified as living in poverty,
-the percent of Black citizens in the population, the rate of violent
-crimes per 100,000 residents for 1996, a dummy variable indicating
-whether the state is in the South, and (an estimate of) the proportion
-of the population with a college degree of some kind.
-
-
-Notes
------
-
-Number of Observations - 17
-
-Number of Variables - 7
-
-Variable name definitions::
-
-    EXECUTIONS - Executions in 1996
-    INCOME - Median per capita income in 1996 dollars
-    PERPOVERTY - Percent of the population classified as living in poverty
-    PERBLACK - Percent of black citizens in the population
-    VC100k96 - Rate of violent crimes per 100,00 residents for 1996
-    SOUTH - SOUTH == 1 indicates a state in the South
-    DEGREE - An esimate of the proportion of the state population with a
-        college degree of some kind
-
-State names are included in the data file, though not returned by load.
-
-
-Source
-------
-
-Jeff Gill's `Generalized Linear Models: A Unified Approach`
-
-http://jgill.wustl.edu/research/books.html
-
-
-Copyright
----------
-
-Used with express permission from the original author,
-who retains all rights.
diff --git a/docs/source/datasets/generated/elnino.rst b/docs/source/datasets/generated/elnino.rst
deleted file mode 100644
index 974044583c1..00000000000
--- a/docs/source/datasets/generated/elnino.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-El Nino - Sea Surface Temperatures
-==================================
-
-Description
------------
-
-This data contains the averaged monthly sea surface
-temperature in degrees Celcius of the Pacific Ocean, between 0-10 degrees South
-and 90-80 degrees West, from 1950 to 2010.  This dataset was obtained from
-NOAA.
-
-
-Notes
------
-
-Number of Observations - 61 x 12
-
-Number of Variables - 1
-
-Variable name definitions::
-
-    TEMPERATURE - average sea surface temperature in degrees Celcius
-                  (12 columns, one per month).
-
-
-
-Source
-------
-
-National Oceanic and Atmospheric Administration's National Weather Service
-
-ERSST.V3B dataset, Nino 1+2
-http://www.cpc.ncep.noaa.gov/data/indices/
-
-
-Copyright
----------
-
-This data is in the public domain.
diff --git a/docs/source/datasets/generated/fair.rst b/docs/source/datasets/generated/fair.rst
deleted file mode 100644
index f29ebba3fcd..00000000000
--- a/docs/source/datasets/generated/fair.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-Affairs dataset
-===============
-
-Description
------------
-
-Extramarital affair data used to explain the allocation
-of an individual's time among work, time spent with a spouse, and time
-spent with a paramour. The data is used as an example of regression
-with censored data.
-
-Notes
------
-
-Number of observations: 6366
-Number of variables: 9
-Variable name definitions::
-
-    rate_marriage   : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,
-                      4 = good, 5 = very good
-    age             : Age
-    yrs_married     : No. years married. Interval approximations. See
-                      original paper for detailed explanation.
-    children        : No. children
-    religious       : How relgious, 1 = not, 2 = mildly, 3 = fairly,
-                      4 = strongly
-    educ            : Level of education, 9 = grade school, 12 = high school,
-                      14 = some college, 16 = college graduate, 17 = some
-                      graduate school, 20 = advanced degree
-    occupation      : 1 = student, 2 = farming, agriculture; semi-skilled,
-                      or unskilled worker; 3 = white-colloar; 4 = teacher
-                      counselor social worker, nurse; artist, writers;
-                      technician, skilled worker, 5 = managerial,
-                      administrative, business, 6 = professional with
-                      advanced degree
-    occupation_husb : Husband's occupation. Same as occupation.
-    affairs         : measure of time spent in extramarital affairs
-
-See the original paper for more details.
-
-
-Source
-------
-
-Fair, Ray. 1978. "A Theory of Extramarital Affairs," `Journal of Political
-    Economy`, February, 45-61.
-
-The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm
-
-
-Copyright
----------
-
-Included with permission of the author.
diff --git a/docs/source/datasets/generated/grunfeld.rst b/docs/source/datasets/generated/grunfeld.rst
deleted file mode 100644
index a5607a8a733..00000000000
--- a/docs/source/datasets/generated/grunfeld.rst
+++ /dev/null
@@ -1,45 +0,0 @@
-Grunfeld (1950) Investment Data
-===============================
-
-Description
------------
-
-Grunfeld (1950) Investment Data for 11 U.S. Firms.
-
-Notes
------
-Number of observations - 220 (20 years for 11 firms)
-
-Number of variables - 5
-
-Variables name definitions::
-
-    invest  - Gross investment in 1947 dollars
-    value   - Market value as of Dec. 31 in 1947 dollars
-    capital - Stock of plant and equipment in 1947 dollars
-    firm    - General Motors, US Steel, General Electric, Chrysler,
-              Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear,
-              Diamond Match, American Steel
-    year    - 1935 - 1954
-
-Note that raw_data has firm expanded to dummy variables, since it is a
-string categorical variable.
-
-
-Source
-------
-This is the Grunfeld (1950) Investment Data.
-
-The source for the data was the original 11-firm data set from Grunfeld's Ph.D.
-thesis recreated by Kleiber and Zeileis (2008) "The Grunfeld Data at 50".
-The data can be found here.
-http://statmath.wu-wien.ac.at/~zeileis/grunfeld/
-
-For a note on the many versions of the Grunfeld data circulating see:
-http://www.stanford.edu/~clint/bench/grunfeld.htm
-
-
-Copyright
----------
-
-This is public domain.
diff --git a/docs/source/datasets/generated/heart.rst b/docs/source/datasets/generated/heart.rst
deleted file mode 100644
index a0d5170ca87..00000000000
--- a/docs/source/datasets/generated/heart.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-Transplant Survival Data
-========================
-
-Description
------------
-
-This data contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored.  
-
-
-Notes
------
-
-Number of Observations - 69
-
-Number of Variables - 3
-
-Variable name definitions::
-    death - Days after surgery until death
-    age - age at the time of surgery
-    censored - indicates if an observation is censored.  1 is uncensored
-
-
-
-
-Source
-------
- Miller, R. (1976). Least squares regression with censored dara. Biometrica, 63 (3). 449-464.
-
-
-
-Copyright
----------
-
-???
diff --git a/docs/source/datasets/generated/longley.rst b/docs/source/datasets/generated/longley.rst
deleted file mode 100644
index 5475fd2ac21..00000000000
--- a/docs/source/datasets/generated/longley.rst
+++ /dev/null
@@ -1,46 +0,0 @@
-Longley dataset
-===============
-
-Description
------------
-
-The Longley dataset contains various US macroeconomic
-variables that are known to be highly collinear.  It has been used to appraise
-the accuracy of least squares routines.
-
-Notes
------
-
-Number of Observations - 16
-
-Number of Variables - 6
-
-Variable name definitions::
-
-        TOTEMP - Total Employment
-        GNPDEFL - GNP deflator
-        GNP - GNP
-        UNEMP - Number of unemployed
-        ARMED - Size of armed forces
-        POP - Population
-        YEAR - Year (1947 - 1962)
-
-
-Source
-------
-
-The classic 1967 Longley Data
-
-http://www.itl.nist.gov/div898/strd/lls/data/Longley.shtml
-
-::
-
-    Longley, J.W. (1967) "An Appraisal of Least Squares Programs for the
-        Electronic Comptuer from the Point of View of the User."  Journal of
-        the American Statistical Association.  62.319, 819-41.
-
-
-Copyright
----------
-
-This is public domain.
diff --git a/docs/source/datasets/generated/macrodata.rst b/docs/source/datasets/generated/macrodata.rst
deleted file mode 100644
index eb629a28e9d..00000000000
--- a/docs/source/datasets/generated/macrodata.rst
+++ /dev/null
@@ -1,61 +0,0 @@
-United States Macroeconomic data
-================================
-
-Description
------------
-
-US Macroeconomic Data for 1959Q1 - 2009Q3
-
-Notes
------
-
-Number of Observations - 203
-
-Number of Variables - 14
-
-Variable name definitions::
-
-    year      - 1959q1 - 2009q3
-    quarter   - 1-4
-    realgdp   - Real gross domestic product (Bil. of chained 2005 US$,
-                seasonally adjusted annual rate)
-    realcons  - Real personal consumption expenditures (Bil. of chained 2005
-                US$,
-                seasonally adjusted annual rate)
-    realinv   - Real gross private domestic investment (Bil. of chained 2005
-                US$, seasonally adjusted annual rate)
-    realgovt  - Real federal consumption expenditures & gross investment
-                (Bil. of chained 2005 US$, seasonally adjusted annual rate)
-    realdpi   - Real private disposable income (Bil. of chained 2005
-                US$, seasonally adjusted annual rate)
-    cpi       - End of the quarter consumer price index for all urban
-                consumers: all items (1982-84 = 100, seasonally adjusted).
-    m1        - End of the quarter M1 nominal money stock (Seasonally adjusted)
-    tbilrate  - Quarterly monthly average of the monthly 3-month treasury bill:
-                secondary market rate
-    unemp     - Seasonally adjusted unemployment rate (%)
-    pop       - End of the quarter total population: all ages incl. armed
-                forces over seas
-    infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
-    realint   - Real interest rate (tbilrate - infl)
-
-
-Source
-------
-
-Compiled by Skipper Seabold. All data are from the Federal Reserve Bank of St.
-Louis [1] except the unemployment rate which was taken from the National
-Bureau of Labor Statistics [2]. ::
-
-    [1] Data Source: FRED, Federal Reserve Economic Data, Federal Reserve Bank of
-        St. Louis; http://research.stlouisfed.org/fred2/; accessed December 15,
-        2009.
-
-    [2] Data Source: Bureau of Labor Statistics, U.S. Department of Labor;
-        http://www.bls.gov/data/; accessed December 15, 2009.
-
-
-Copyright
----------
-
-This is public domain.
diff --git a/docs/source/datasets/generated/randhie.rst b/docs/source/datasets/generated/randhie.rst
deleted file mode 100644
index 1f5d43fab1c..00000000000
--- a/docs/source/datasets/generated/randhie.rst
+++ /dev/null
@@ -1,52 +0,0 @@
-RAND Health Insurance Experiment Data
-=====================================
-
-Description
------------
-
-
-
-Notes
------
-
-Number of observations - 20,190
-Number of variables - 10
-Variable name definitions::
-
-    mdvis   - Number of outpatient visits to an MD
-    lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100
-    idp     - 1 if individual deductible plan, 0 otherwise
-    lpi     - ln(max(1, annual participation incentive payment))
-    fmde    - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise
-    physlm  - 1 if the person has a physical limitation
-    disea   - number of chronic diseases
-    hlthg   - 1 if self-rated health is good
-    hlthf   - 1 if self-rated health is fair
-    hlthp   - 1 if self-rated health is poor
-    (Omitted category is excellent self-rated health)
-
-
-Source
-------
-
-The data was collected by the RAND corporation as part of the Health
-Insurance Experiment (HIE).
-
-http://www.rand.org/health/projects/hie/
-
-This data was used in::
-
-    Cameron, A.C. amd Trivedi, P.K. 2005.  `Microeconometrics: Methods
-        and Applications,` Cambridge: New York.
-
-And was obtained from: <http://cameron.econ.ucdavis.edu/mmabook/mmadata.html>
-
-See randhie/src for the original data and description.  The data included
-here contains only a subset of the original data.  The data varies slightly
-compared to that reported in Cameron and Trivedi.
-
-
-Copyright
----------
-
-This is in the public domain.
diff --git a/docs/source/datasets/generated/scotland.rst b/docs/source/datasets/generated/scotland.rst
deleted file mode 100644
index ff718c37235..00000000000
--- a/docs/source/datasets/generated/scotland.rst
+++ /dev/null
@@ -1,58 +0,0 @@
-Taxation Powers Vote for the Scottish Parliamant 1997
-=====================================================
-
-Description
------------
-
-
-This data is based on the example in Gill and describes the proportion of
-voters who voted Yes to grant the Scottish Parliament taxation powers.
-The data are divided into 32 council districts.  This example's explanatory
-variables include the amount of council tax collected in pounds sterling as
-of April 1997 per two adults before adjustments, the female percentage of
-total claims for unemployment benefits as of January, 1998, the standardized
-mortality rate (UK is 100), the percentage of labor force participation,
-regional GDP, the percentage of children aged 5 to 15, and an interaction term
-between female unemployment and the council tax.
-
-The original source files and variable information are included in
-/scotland/src/
-
-
-Notes
------
-
-Number of Observations - 32 (1 for each Scottish district)
-
-Number of Variables - 8
-
-Variable name definitions::
-
-    YES    - Proportion voting yes to granting taxation powers to the Scottish
-             parliament.
-    COUTAX - Amount of council tax collected in pounds steling as of April '97
-    UNEMPF - Female percentage of total unemployment benefits claims as of
-             January 1998
-    MOR    - The standardized mortality rate (UK is 100)
-    ACT    - Labor force participation (Short for active)
-    GDP    - GDP per county
-    AGE    - Percentage of children aged 5 to 15 in the county
-    COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF
-
-Council district names are included in the data file, though are not returned
-by load.
-
-
-Source
-------
-
-Jeff Gill's `Generalized Linear Models: A Unified Approach`
-
-http://jgill.wustl.edu/research/books.html
-
-
-Copyright
----------
-
-Used with express permission from the original author,
-who retains all rights.
diff --git a/docs/source/datasets/generated/spector.rst b/docs/source/datasets/generated/spector.rst
deleted file mode 100644
index ab14bd92fbe..00000000000
--- a/docs/source/datasets/generated/spector.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-Spector and Mazzeo (1980) - Program Effectiveness Data
-======================================================
-
-Description
------------
-
-Experimental data on the effectiveness of the personalized
-system of instruction (PSI) program
-
-Notes
------
-
-Number of Observations - 32
-
-Number of Variables - 4
-
-Variable name definitions::
-
-    Grade - binary variable indicating whether or not a student's grade
-            improved.  1 indicates an improvement.
-    TUCE  - Test score on economics test
-    PSI   - participation in program
-    GPA   - Student's grade point average
-
-
-Source
-------
-
-http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
-
-The raw data was downloaded from Bill Greene's Econometric Analysis web site,
-though permission was obtained from the original researcher, Dr. Lee Spector,
-Professor of Economics, Ball State University.
-
-Copyright
----------
-
-Used with express permission of the original author, who
-retains all rights. 
diff --git a/docs/source/datasets/generated/stackloss.rst b/docs/source/datasets/generated/stackloss.rst
deleted file mode 100644
index 2d3e5ec60f8..00000000000
--- a/docs/source/datasets/generated/stackloss.rst
+++ /dev/null
@@ -1,37 +0,0 @@
-Stack loss data
-===============
-
-Description
------------
-
-The stack loss plant data of Brownlee (1965) contains
-21 days of measurements from a plant's oxidation of ammonia to nitric acid.
-The nitric oxide pollutants are captured in an absorption tower.
-
-Notes
------
-
-Number of Observations - 21
-
-Number of Variables - 4
-
-Variable name definitions::
-
-    STACKLOSS - 10 times the percentage of ammonia going into the plant that
-                escapes from the absoroption column
-    AIRFLOW   - Rate of operation of the plant
-    WATERTEMP - Cooling water temperature in the absorption tower
-    ACIDCONC  - Acid concentration of circulating acid minus 50 times 10.
-
-
-Source
-------
-
-Brownlee, K. A. (1965), "Statistical Theory and Methodology in
-Science and Engineering", 2nd edition, New York:Wiley.
-
-
-Copyright
----------
-
-This is public domain. 
diff --git a/docs/source/datasets/generated/star98.rst b/docs/source/datasets/generated/star98.rst
deleted file mode 100644
index 133e05f1e4c..00000000000
--- a/docs/source/datasets/generated/star98.rst
+++ /dev/null
@@ -1,71 +0,0 @@
-Star98 Educational Dataset
-==========================
-
-Description
------------
-
-
-This data is on the California education policy and outcomes (STAR program
-results for 1998.  The data measured standardized testing by the California
-Department of Education that required evaluation of 2nd - 11th grade students
-by the the Stanford 9 test on a variety of subjects.  This dataset is at
-the level of the unified school district and consists of 303 cases.  The
-binary response variable represents the number of 9th graders scoring
-over the national median value on the mathematics exam.
-
-The data used in this example is only a subset of the original source.
-
-
-Notes
------
-
-Number of Observations - 303 (counties in California).
-
-Number of Variables - 13 and 8 interaction terms.
-
-Definition of variables names::
-
-    NABOVE   - Total number of students above the national median for the math
-               section.
-    NBELOW   - Total number of students below the national median for the math
-               section.
-    LOWINC   - Percentage of low income students
-    PERASIAN - Percentage of Asian student
-    PERBLACK - Percentage of black students
-    PERHISP  - Percentage of Hispanic students
-    PERMINTE - Percentage of minority teachers
-    AVYRSEXP - Sum of teachers' years in educational service divided by the
-               number of teachers.
-    AVSALK   - Total salary budget including benefits divided by the number of
-               full-time teachers (in thousands)
-    PERSPENK - Per-pupil spending (in thousands)
-    PTRATIO  - Pupil-teacher ratio.
-    PCTAF    - Percentage of students taking UC/CSU prep courses
-    PCTCHRT  - Percentage of charter schools
-    PCTYRRND - Percentage of year-round schools
-
-    The below variables are interaction terms of the variables defined above.
-
-    PERMINTE_AVYRSEXP
-    PEMINTE_AVSAL
-    AVYRSEXP_AVSAL
-    PERSPEN_PTRATIO
-    PERSPEN_PCTAF
-    PTRATIO_PCTAF
-    PERMINTE_AVTRSEXP_AVSAL
-    PERSPEN_PTRATIO_PCTAF
-
-
-Source
-------
-
-Jeff Gill's `Generalized Linear Models: A Unified Approach`
-
-http://jgill.wustl.edu/research/books.html
-
-
-Copyright
----------
-
-Used with express permission from the original author,
-who retains all rights.
diff --git a/docs/source/datasets/generated/statecrime.rst b/docs/source/datasets/generated/statecrime.rst
deleted file mode 100644
index 2dec0356ab5..00000000000
--- a/docs/source/datasets/generated/statecrime.rst
+++ /dev/null
@@ -1,56 +0,0 @@
-Statewide Crime Data 2009
-=========================
-
-Description
------------
-
-State crime data 2009
-
-Notes
------
-
-Number of observations: 51
-Number of variables: 8
-Variable name definitions:
-
-state
-    All 50 states plus DC.
-violent
-    Rate of violent crimes / 100,000 population. Includes murder, forcible
-    rape, robbery, and aggravated assault. Numbers for Illinois and Minnesota
-    do not include forcible rapes. Footnote included with the American
-    Statistical Abstract table reads:
-    "The data collection methodology for the offense of forcible
-    rape used by the Illinois and the Minnesota state Uniform Crime Reporting
-    (UCR) Programs (with the exception of Rockford, Illinois, and Minneapolis
-    and St. Paul, Minnesota) does not comply with national UCR guidelines.
-    Consequently, their state figures for forcible rape and violent crime (of
-    which forcible rape is a part) are not published in this table."
-murder
-    Rate of murders / 100,000 population.
-hs_grad
-    Precent of population having graduated from high school or higher.
-poverty
-    % of individuals below the poverty line
-white
-    Percent of population that is one race - white only. From 2009 American
-    Community Survey
-single
-    Calculated from 2009 1-year American Community Survey obtained obtained
-    from Census. Variable is Male householder, no wife present, family
-    household combined with Female household, no husband prsent, family
-    household, divided by the total number of Family households.
-urban
-    % of population in Urbanized Areas as of 2010 Census. Urbanized Areas are
-    area of 50,000 or more people.
-
-Source
-------
-
-All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below.
-
-
-Copyright
----------
-
-Public domain.
diff --git a/docs/source/datasets/generated/strikes.rst b/docs/source/datasets/generated/strikes.rst
deleted file mode 100644
index 765f1df15d6..00000000000
--- a/docs/source/datasets/generated/strikes.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-U.S. Strike Duration Data
-=========================
-
-Description
------------
-
-Contains data on the length of strikes in US manufacturing and
-unanticipated industrial production. The data is a subset of the data originally
-used by Kennan. The data here is data for the months of June only to avoid
-seasonal issues.
-
-Notes
------
-
-Number of observations - 62
-
-Number of variables - 2
-
-Variable name definitions::
-
-            duration - duration of the strike in days
-            iprod - unanticipated industrial production
-
-
-Source
-------
-
-This is a subset of the data used in Kennan (1985). It was originally
-published by the Bureau of Labor Statistics.
-
-::
-
-    Kennan, J. 1985. "The duration of contract strikes in US manufacturing.
-        `Journal of Econometrics` 28.1, 5-28.
-
-
-Copyright
----------
-
-This is public domain.
diff --git a/docs/source/datasets/generated/sunspots.rst b/docs/source/datasets/generated/sunspots.rst
deleted file mode 100644
index 101b72e76ed..00000000000
--- a/docs/source/datasets/generated/sunspots.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-Yearly sunspots data 1700-2008
-==============================
-
-Description
------------
-
-Yearly (1700-2008) data on sunspots from the National
-Geophysical Data Center.
-
-Notes
------
-
-Number of Observations - 309 (Annual 1700 - 2008)
-Number of Variables - 1
-Variable name definitions::
-
-    SUNACTIVITY - Number of sunspots for each year
-
-The data file contains a 'YEAR' variable that is not returned by load.
-
-
-Source
-------
-
-http://www.ngdc.noaa.gov/stp/SOLAR/ftpsunspotnumber.html
-
-The original dataset contains monthly data on sunspot activity in the file
-./src/sunspots_yearly.dat.  There is also sunspots_monthly.dat.
-
-
-Copyright
----------
-
-This data is public domain.
diff --git a/docs/source/datasets/index.rst b/docs/source/datasets/index.rst
index 4fb2229d798..9d056c493b4 100644
--- a/docs/source/datasets/index.rst
+++ b/docs/source/datasets/index.rst
@@ -17,7 +17,7 @@ examples, tutorials, model testing, etc.
 Using Datasets from R
 ---------------------
 
-The `Rdatasets project <http://vincentarelbundock.github.com/Rdatasets/>`__ gives access to the datasets available in R's core datasets package and many other common R packages. All of these datasets are available to statsmodels by using the :func:`get_rdataset` function. For example:
+The `Rdatasets project <http://vincentarelbundock.github.io/Rdatasets/>`__ gives access to the datasets available in R's core datasets package and many other common R packages. All of these datasets are available to statsmodels by using the :func:`get_rdataset` function. The actual data is accessible by the ``data`` attribute. For example:
 
 .. ipython:: python
 
@@ -58,7 +58,11 @@ Load a dataset:
    import statsmodels.api as sm
    data = sm.datasets.longley.load()
 
-The `Dataset` object follows the bunch pattern explained  in :ref:`proposal <dataset_proposal>`.
+The `Dataset` object follows the bunch pattern explained  in :ref:`proposal <dataset_proposal>`. The full dataset is available in the ``data`` attribute.
+
+.. ipython:: python
+
+   data.data
 
 Most datasets hold convenient representations of the data in the attributes `endog` and `exog`:
 
@@ -95,7 +99,7 @@ Loading data as pandas objects
 
 For many users it may be preferable to get the datasets as a pandas DataFrame or
 Series object. Each of the dataset modules is equipped with a ``load_pandas``
-method which returns a ``Dataset`` instance with the data as pandas objects:
+method which returns a ``Dataset`` instance with the data readily available as pandas objects:
 
 .. ipython:: python
 
@@ -103,10 +107,17 @@ method which returns a ``Dataset`` instance with the data as pandas objects:
    data.exog
    data.endog
 
+The full DataFrame is available in the ``data`` attribute of the Dataset object
+
+.. ipython:: python
+
+   data.data
+
+
 With pandas integration in the estimation classes, the metadata will be attached
 to model results:
 
-.. ipython:: python
+.. ipython:: python,okwarning
 
    y, x = data.endog, data.exog
    res = sm.OLS(y, x).fit()
diff --git a/docs/source/dev/examples.rst b/docs/source/dev/examples.rst
index 8a8fdeecf71..54a922dda21 100644
--- a/docs/source/dev/examples.rst
+++ b/docs/source/dev/examples.rst
@@ -6,50 +6,35 @@ Examples
 Examples are invaluable for new users who hope to get up and running quickly
 with `statsmodels`, and they are extremely useful to those who wish to explore
 new features of `statsmodels`. We hope to provide documentation and tutorials
-for as many models and use-cases as possible!
+for as many models and use-cases as possible! Please consider submitting an example with any PR that introduces new functionality.
 
-Most user-contributed examples/tutorials/recipes should be placed on the
-`statsmodels examples wiki page
-<https://github.com/statsmodels/statsmodels/wiki/Examples:-user-contributions>`_
+User-contributed examples/tutorials/recipes can be placed on the
+`statsmodels examples wiki page <https://github.com/statsmodels/statsmodels/wiki/Examples>`_
 That wiki page is freely editable. Please post your cool tricks,
-examples, and recipes on there! 
+examples, and recipes on there!
 
 If you would rather have your example file officially accepted to the
 `statsmodels` distribution and posted on this website, you will need to go
-through the normal `patch submission process <index.html#submitting-a-patch>`_.  
+through the normal `patch submission process <index.html#submitting-a-patch>`_ and follow the instructions that follow. 
 
 File Format
 ~~~~~~~~~~~
 
-Examples are simple runnable python scripts that go in the top-level examples
-directory. We use the `ipython_directive for Sphinx
-<http://ipython.org/ipython-doc/dev/development/ipython_directive.html>`_  to
-convert them automatically to `reStructuredText
-<http://docutils.sourceforge.net/rst.html>`_ and html at build time. 
+Examples are best contributed as IPython notebooks. Save your notebook with all output cells cleared in ``examples/notebooks``. From the notebook save the pure Python output to ``examples/python``. The first line of the Notebook *must* be a header cell that contains a title for the notebook, if you want the notebook to be included in the documentation.
 
-Each line of the script is executed; both the python code and the printed
-results are shown in the output file. Lines that are commented out using the
-hash symbol ``#`` are rendered as reST markup. 
 
-**Comments**: "True" comments that should not appear in the output file should be written on lines that start with ``#..``. 
+The Example Gallery
+~~~~~~~~~~~~~~~~~~~
 
-**Error handling**: Syntax errors in pure Python will raise an error during the build process. If you need to show a SyntaxError, an alternative would be to provide a verbatim copy of an IPython session encased in a ReST code block instead of pure Python code. 
+We have a gallery of example notebooks available `here <http://statsmodels.sourceforge.net/devel/examples/index.html>`_. If you would like your example to show up in this gallery, add a link to the notebook in ``docs/source/examples/landing.json``. For the thumbnail, take a screenshot of what you think is the best "hook" for the notebook. The image will be displayed at 360 x 225 (W x H). It's best to save the image as a PNG with a resolution that is some multiple of 360 x 225 (720 x 450 is preferred).
 
-**Suppressing lines**: To suppress a line in the built documentation, follow it with a semicolon. 
 
-**Figures**: To save a figure, prepend the line directly before the plotting command with ``#@savefig file_name.png width=4in``, for example. You do not need to call show or close.
-
-**IPython magics**: You can use IPython magics by writing a line like this: ``#%timeit X = np.empty((1000,1000))``.
-
-
-Make Life Easier
-~~~~~~~~~~~~~~~~
+Before submitting a PR
+~~~~~~~~~~~~~~~~~~~~~~
 
 To save you some time and to make the new examples nicely fit into the existing
 ones consider the following points.
 
 **Look at examples source code** to get a feel for how statsmodels examples should look like.
 
-**PEP8 syntax checker** install a [PEP8] http://pypi.python.org/pypi/pep8 syntax checker for you editor. It will not only make your code look nicer but also serves as `pre-debugger`. Note that some of doc directives explained above imply pep8 violations. Also, for the sake of readability it's a local convention not to add white spaces around power operators, e.g. `x * 2 + y**2 + z`. 
-
-**build docs** run `make html` from the docs directory to see how your example looks in the fully rendered html pages.
+**Build the docs** by running `make html` from the docs directory to see how your example looks in the fully rendered html pages.
diff --git a/docs/source/dev/git_notes.rst b/docs/source/dev/git_notes.rst
index e0075fcdb1a..ebe141e6b27 100644
--- a/docs/source/dev/git_notes.rst
+++ b/docs/source/dev/git_notes.rst
@@ -4,8 +4,8 @@ Working with the Statsmodels Code
 Github
 ------
 
-The `statsmodels` code base is hosted on `Github <https://www.github.com/statsmodels/statsmodels>`_. To
-contribute you will need to `sign up for a free Github account <https://github.com/signup/free>`_.
+The `statsmodels` code base is hosted on `Github <https://github.com/statsmodels/statsmodels>`_. To
+contribute you will need to `sign up for a free Github account <https://github.com/join>`_.
 
 Version Control and Git
 -----------------------
@@ -17,18 +17,14 @@ working on the code and allows you to easily contribute your changes to the
 codebase. It also keeps a complete history of all changes to the code, so you
 can easily undo changes or see when a change was made, by whom, and why.
 
-To install and configure Git, and to setup SSH keys, see: 
-
-+ `Linux users <http://help.github.com/linux-set-up-git/>`_
-+ `Windows users <http://help.github.com/win-set-up-git/>`_
-+ `Mac users <http://help.github.com/mac-set-up-git/>`_
+To install and configure Git, and to setup SSH keys, see `this page <https://help.github.com/articles/set-up-git>`_.
 
 To learn more about Git, you may want to visit: 
 
 + `Git documentation (book and videos) <http://git-scm.com/documentation>`_
-+ `Github help pages <http://help.github.com/>`_ 
++ `Github help pages <https://help.github.com/>`_ 
 + `NumPy documentation <http://docs.scipy.org/doc/numpy/dev/index.html>`_
-+ `Matthew Brett's Pydagogue <http://matthew-brett.github.com/pydagogue/>`_
++ `Matthew Brett's Pydagogue <http://matthew-brett.github.io/pydagogue/>`_
 
 Below, we describe the bare minimum git commands you need to contribute to
 `statsmodels`.   
@@ -166,7 +162,7 @@ this by using `compare view
    `shiny-new-feature`, respectively.
 #. From here you will see a nice overview of your changes. If anything is amiss, you can fix it.
 
-If everything looks good you are read to make a `pull request <http://help.github.com/send-pull-requests/>`__.
+If everything looks good you are read to make a `pull request <https://help.github.com/articles/using-pull-requests>`__.
 
 #. Navigate to your repository on github.
 #. Click on the `Pull Request` button.
@@ -206,7 +202,7 @@ could introduce bugs. However, if you have only a few commits, this might not
 be such a concern. One great place to start learning about rebase is
 :ref:`rebasing without tears <pydagogue:actual-rebase>`.  In particular, `heed
 the warnings
-<http://matthew-brett.github.com/pydagogue/rebase_without_tears.html#safety>`__.
+<http://matthew-brett.github.io/pydagogue/rebase_without_tears.html#safety>`__.
 Namely, **always make a new branch before doing a rebase**. This is good
 general advice for working with git. I would also add **never use rebase on
 work that has already been published**. If another developer is using your
@@ -231,7 +227,7 @@ git will know that it can safely delete your branch::
 
 Then you can just do::
 
-    git -d shiny-new-feature
+    git branch -d shiny-new-feature
 
 Make sure you use a lower-case -d. That way, git will complain if your feature
 branch has not actually been merged. The branch will still exist on github
diff --git a/docs/source/dev/index.rst b/docs/source/dev/index.rst
index 80956d88677..e234aefda3d 100644
--- a/docs/source/dev/index.rst
+++ b/docs/source/dev/index.rst
@@ -8,28 +8,51 @@ by submitting patches, statistical tests, new models, or examples.
 <https://github.com/statsmodels/statsmodels>`_ using the `Git
 <http://git-scm.com/>`_ version control system. 
 
-License
-~~~~~~~
+Submitting a Bug Report
+~~~~~~~~~~~~~~~~~~~~~~~
 
-Statsmodels is released under
-the `Modified (3-clause) BSD license <http://www.opensource.org/licenses/BSD-3-Clause>`_.
+- Include a short, self-contained code snippet that reproduces the problem
+- Specify the statsmodels version used. You can do this with ``sm.version.full_version``
+- If the issue looks to involve other dependencies, also include the output of ``sm.show_versions()``
+
+Making Changes to the Code
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+For a pull request to be accepted, you must meet the below requirements. This greatly helps the job of maintaining and releasing the software a shared effort.
+
+- **One branch. One feature.** Branches are cheap and github makes it easy to merge and delete branches with a few clicks. Avoid the temptation to lump in a bunch of unrelated changes when working on a feature, if possible. This helps us keep track of what has changed when preparing a release.
+- Commit messages should be clear and concise. This means a subject line of less than 80 characters, and, if necessary, a blank line followed by a commit message body. We have an `informal commit format standard <http://statsmodels.sourceforge.net/devel/dev/maintainer_notes.html#commit-comments>`_ that we try to adhere to. You can see what this looks like in practice by ``git log --oneline -n 10``. If your commit references or closes a specific issue, you can close it by mentioning it in the `commit message <https://help.github.com/articles/closing-issues-via-commit-messages>`_.  (*For maintainers*: These suggestions go for Merge commit comments too. These are partially the record for release notes.)
+- Code submissions must always include tests. See our notes on :ref:`testing`.
+- Each function, class, method, and attribute needs to be documented using docstrings. We conform to the `numpy docstring standard <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt#docstring-standard>`_.
+- If you are adding new functionality, you need to add it to the documentation by editing (or creating) the appropriate file in ``docs/source``.
+- Make sure your documentation changes parse correctly. Change into the top-level ``docs/`` directory and type::
+  
+   make clean
+   make html
 
-Submitting a Patch
-~~~~~~~~~~~~~~~~~~~
+  Check that the build output does not have *any* warnings due to your changes. 
+- Finally, please add your changes to the release notes. Open the ``docs/source/release/versionX.X.rst`` file that has the version number of the next release and add your changes to the appropriate section.
 
-So you want to submit a patch to `statsmodels`?. Great news! Here are the 
-steps you need to take.
+How to Submit a Pull Request
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+So you want to submit a patch to `statsmodels` but aren't too familiar with github? Here are the steps you need to take.
 
 1. `Fork <https://help.github.com/articles/fork-a-repo>`_ the `statsmodels repository <https://github.com/statsmodels/statsmodels>`_ on Github.
-2. `Create a new feature branch <http://git-scm.com/book/en/Git-Branching-Basic-Branching-and-Merging>`_
-    + Each branch must be self-contained, with a single new feature or bugfix. 
-    + Patches must always include tests. See our `notes on testing <test_notes.html>`_.
-3. `Submit a pull request <https://help.github.com/articles/using-pull-requests>`_ 
+2. `Create a new feature branch <http://git-scm.com/book/en/Git-Branching-Basic-Branching-and-Merging>`_. Each branch must be self-contained, with a single new feature or bugfix. 
+3. Make sure the test suite passes. This includes testing on Python 3. The easiest way to do this is to either enable `Travis-CI <https://travis-ci.org/>`_ on your fork, or to make a pull request and check there.
+4. `Submit a pull request <https://help.github.com/articles/using-pull-requests>`_ 
 
 Mailing List
 ~~~~~~~~~~~~
 
-Conversations about development take place on the `statsmodels mailing list <http://groups.google.com/group/pystatsmodels?hl=en>`__.
+Conversations about development take place on the `statsmodels mailing list <https://groups.google.com/group/pystatsmodels?hl=en>`__.
+
+License
+~~~~~~~
+
+Statsmodels is released under
+the `Modified (3-clause) BSD license <http://www.opensource.org/licenses/BSD-3-Clause>`_.
 
 Contents
 ~~~~~~~~
@@ -45,3 +68,4 @@ Contents
    examples
    roadmap_todo
    internal
+   testing
diff --git a/docs/source/dev/internal.rst b/docs/source/dev/internal.rst
index 5d0ba0c177b..c2f22f7f71c 100644
--- a/docs/source/dev/internal.rst
+++ b/docs/source/dev/internal.rst
@@ -33,6 +33,13 @@ define some common methods.
    ResultMixin
    GenericLikelihoodModelResults
 
+.. currentmodule:: statsmodels.stats.contrast
+
+.. autosummary::
+   :toctree: generated/
+
+   ContrastResults
+
 .. inheritance-diagram:: statsmodels.base.model statsmodels.discrete.discrete_model statsmodels.regression.linear_model statsmodels.miscmodels.count
    :parts: 3
 
diff --git a/docs/source/dev/maintainer_notes.rst b/docs/source/dev/maintainer_notes.rst
index f9a7b147362..a24098e5533 100644
--- a/docs/source/dev/maintainer_notes.rst
+++ b/docs/source/dev/maintainer_notes.rst
@@ -114,7 +114,7 @@ use::
 Handling Pull Requests
 ~~~~~~~~~~~~~~~~~~~~~~
 
-You can apply a pull request through `fetch <http://www.kernel.org/pub/software/scm/git/docs/git-fetch.html>`__ and `merge <http://www.kernel.org/pub/software/scm/git/docs/git-merge.html>`__. In your local
+You can apply a pull request through `fetch <https://www.kernel.org/pub/software/scm/git/docs/git-fetch.html>`__ and `merge <https://www.kernel.org/pub/software/scm/git/docs/git-merge.html>`__. In your local
 copy of the main repo::
 
     git checkout master
@@ -157,7 +157,7 @@ Releasing
 
     git checkout -b maintenance/0.3.x
 
-   Any bug fixes and maintenance commits for this minor release go into this branch and are merged back into master. If we have another micro release, then we make the tag and do this release in this branch and then merge the changes back into master so the tag is reachable. Check that the version number is still ok after the merge, fixing any conflicts here. 
+   Any bug fixes and maintenance commits intended for the next micro release should be made against master as usual, but tagged with the milestone for the micro release it is intended for. Then merge into master as usual. When ready to do the backports, use the file ``tools/backport_pr.py`` to identify which PRs need to be backported and to apply them to the maintenance branch. The tag for the release should be made in the maintenance branch.
 
 #. Upload the source distribution to PyPI::
 
@@ -184,3 +184,9 @@ Prefix commit messages in the master branch of the main shared repository with t
     REL: Release related commit
     TST: Change to a test, adding a test. Only used if not directly related to a bug.
     REF: Refactoring changes
+
+
+vbench
+------
+
+See :ref:`vbenchdoc` notes for working with the `statsmodels vbench suite <http://statsmodels.sourceforge.net/vbench>`_. The github repository is available `here <https://github.com/statsmodels/vbench>`_.
diff --git a/docs/source/dev/roadmap_todo.rst b/docs/source/dev/roadmap_todo.rst
index 8e1af9d8a2b..008b9ffd7f8 100644
--- a/docs/source/dev/roadmap_todo.rst
+++ b/docs/source/dev/roadmap_todo.rst
@@ -4,7 +4,7 @@ Get Involved
 Where to Start?
 ---------------
 
-Use grep or download a tool like `grin <pypi.python.org/pypi/grin>`__ to search the code for TODO notes::
+Use grep or download a tool like `grin <https://pypi.python.org/pypi/grin>`__ to search the code for TODO notes::
 
     grin -i -I "*.py*" todo
 
@@ -23,12 +23,7 @@ We currently have a large amount code in the :ref:`sandbox`. The medium term goa
 Contribute an Example
 ---------------------
 
-Link to examples documentation. Examples and technical documentation.
-
-Contribute to the Gallery
--------------------------
-
-Link to the Gallery.
+Contribute an :ref:`example <examples>`, add some technical documentation, or contribute a statistics tutorial.
 
 Roadmap to 0.6
 ==============
diff --git a/docs/source/dev/test_notes.rst b/docs/source/dev/test_notes.rst
index 6aba64339c7..a8ea396a61c 100644
--- a/docs/source/dev/test_notes.rst
+++ b/docs/source/dev/test_notes.rst
@@ -11,7 +11,7 @@ tests versus an existing statistical package, if possible.
 
 Introduction to Nose
 ~~~~~~~~~~~~~~~~~~~~
-Like many packages, statsmodels uses the `Nose testing system <http://somethingaboutorange.com/mrl/projects/nose/>`__ and the convenient extensions in `numpy.testing <http://docs.scipy.org/doc/numpy/reference/routines.testing.html>`__. Nose itself is an extension of :mod:`Python's unittest <python:unittest>`. Nose will find any file, directory, function, or class name that matches the regular expression ``(?:^|[b_./-])[Tt]est``. This is mainly functions that begin with test* and classes that begin with Test*.
+Like many packages, statsmodels uses the `Nose testing system <https://nose.readthedocs.org/en/latest>`__ and the convenient extensions in `numpy.testing <http://docs.scipy.org/doc/numpy/reference/routines.testing.html>`__. Nose itself is an extension of :mod:`Python's unittest <python:unittest>`. Nose will find any file, directory, function, or class name that matches the regular expression ``(?:^|[b_./-])[Tt]est``. This is mainly functions that begin with test* and classes that begin with Test*.
 
 .. _run-tests:
 
diff --git a/docs/source/dev/testing.rst b/docs/source/dev/testing.rst
new file mode 100644
index 00000000000..d5c9632b6b0
--- /dev/null
+++ b/docs/source/dev/testing.rst
@@ -0,0 +1,12 @@
+Testing on Build Machines
+-------------------------
+
+There are currently several places that statsmodels is automatically built and tested against different dependency and Python versions and architectures. Check these logs periodically, make sure everything looks okay, and fix any failures.:
+
+* `Travis CI <https://travis-ci.org/statsmodels/statsmodels/builds>`_
+* `Daily testing on Ubuntu via Python(x,y) <https://code.launchpad.net/~pythonxy/+recipe/statsmodels-daily-current>`_
+* `NiPy testing on SPARC Boxes <http://nipy.bic.berkeley.edu/waterfall?category=statsmodels>`_
+
+The test coverage pages are here.
+
+* `Coveralls <https://coveralls.io/r/statsmodels/statsmodels>`_
diff --git a/docs/source/dev/vbench.rst b/docs/source/dev/vbench.rst
new file mode 100644
index 00000000000..72b24f6b194
--- /dev/null
+++ b/docs/source/dev/vbench.rst
@@ -0,0 +1,145 @@
+:orphan:
+
+.. _vbenchdoc:
+
+vbench
+======
+
+`vbench`_ is a tool for benchmarking your code through time, for showing performance improvement or regressions.
+
+WARNING: ``vbench`` is not yet compatible with python3.
+
+New Dependencies
+~~~~~~~~~~~~~~~~
+
+* `vbench`_ (from github only)
+* `sqlalchemy`_
+* `gitpython`_
+* `psutil`_
+* `affinity`_ (As a fallback to psutil)
+
+.. _vbench: https://github.com/pydata/vbench
+.. _sqlalchemy: https://pypi.python.org/pypi/SQLAlchemy
+.. _gitpython: https://pypi.python.org/pypi/GitPython/
+.. _psutil: https://pypi.python.org/pypi/psutil
+.. _affinity: https://pypi.python.org/pypi/affinity
+
+Also note that you need to have sqlite3 working with python.
+
+Writing a good vbench
+~~~~~~~~~~~~~~~~~~~~~
+
+A set of related benchmarks go together in a module (a ``.py`` file).
+See ``vb_suite/discrete.py`` for an example.
+
+There's typically some boilerplate common to all the tests, which can
+be placed in a string ``common_setup``.
+
+Now we can write our specific benchmark.
+
+There are up to three items in a single benchmark:
+
+* setup specific to that benchmark (typically a string concatenated to ``common_setup``)
+* a statement to be executed, which is the first argument to the ``vbench.BenchmarkRunner`` class
+* instantiation the ``vbench.Benchmark`` class
+
+It's important to separate the setup from the statement we're interested in profiling.
+The statement ought to be concise and should profile only one thing.
+If you mix setup in with the statement to be profiled, then changes affecting the performance of the setup (which might even take place outside your library) will pollute the test.
+
+Each module must be listed in the ``suite.py`` file in the modules list.
+
+Not all tests can be run against the entire history of the project.
+For newer features, each ``Benchmark`` object takes an optional ``start_date`` parameter.
+For example:
+
+.. code-block:: python
+
+    start_date=datetime(2012, 1, 1)
+
+If a ``start_date`` is not applied for a specific benchmark, the global setting from ``vb_suite.py`` is used.
+
+Another reason that a benchmark can't be run against the entire project's history is that API's sometimes have to change in ways that are not backwards compatible.
+For these cases, the easiest way to compare performance pre- to post-API change is probably the try-except idiom:
+
+.. code-block:: python
+
+    try:
+        rng = date_range('1/1/2000', periods=N, freq='min')
+    except NameError:
+        rng = DateRange('1/1/2000', periods=N, offset=datetools.Minute())
+        date_range = DateRange
+
+Pre-PR
+~~~~~~
+
+Most contributors don't need to worry about writing a vbench or running the full suite against the project's entire history.
+Use ``test_perf.py`` to see how the performance of your PR compares against a known-to-be-good benchmark.
+
+
+Implementation
+==============
+
+There are two main uses for ``vbench.``
+The first is most useful for someone submitting a pull request that might affect the performance of the library.
+In this case, the submitter should run ``python vb_suite/test_perf.py -b base_commit -H``, where ``base_commit`` is the baseline commit hash you want to compare to.
+The ``-H`` argument says to compare the HEAD of your branch against the baseline commit.
+
+The second use-case is for measuring the long-term performance of the project.
+For this case the file of interest is ``run_suite.py``.
+Using the parameters specified in that file, the suite of benchmarks is run against the history of the project.
+The results are stored in a sqlite database.
+
+suite.py
+~~~~~~~~
+
+This is the main configuration file.
+It pulls in the benchmarks from the various modules in ``vb_suite``, reads in the user configuration, and handles the setup and tear-down of the performance tests.
+
+run_suite.py
+~~~~~~~~~~~~
+
+Useful for the maintainers of the project to track performance over time.
+Runs with no arguments from the command line.
+Persists the results in a database so that the the full suite needn't be rerun each time.
+
+User config file
+~~~~~~~~~~~~~~~~
+
+Only necessary if you're running ``run_suite.py``.
+Should look something like:
+
+repo_path: /Home/Envs/statsmodels/lib/python2.7/site~packages/statsmodels/
+repo_url: https://github.com/statsmodels/statsmodels.git
+db_path: /Homevbench/statsmodels/vb_suite/benchmarks.db
+tmp_dir: /Home/tmp
+
+
+test_perf.py
+~~~~~~~~~~~~
+
+Use before commit to check for performance regressions.
+CLT, use ``python test_perf.py -h`` for help.
+
+Most of the time you'll be giving it one or two arguments:
+
+* ``-b BASE_COMMIT``: the commit you're comparing your commit against
+* ``-t TARGET_COMMIT``: or use -H to set the target to the ``HEAD`` of your branch.
+
+
+generate_rst_files.py
+~~~~~~~~~~~~~~~~~~~~~
+
+Once you've run `run_suite.py` and generated a benchmark database, you can use ``generate_rst_files.py`` to graph performance over time.
+
+
+References:
+~~~~~~~~~~~
+
+`http://wesmckinney.com/blog/?p=373 <http://wesmckinney.com/blog/?p=373>`_
+
+`https://github.com/pydata/vbench <https://github.com/pydata/vbench>`_
+
+`https://github.com/pydata/pandas/tree/master/vb_suite <https://github.com/pydata/pandas/tree/master/vb_suite>`_
+
+`https://github.com/yarikoptic/numpy-vbench <https://github.com/yarikoptic/numpy-vbench>`_
diff --git a/docs/source/diagnostic.rst b/docs/source/diagnostic.rst
index fddd89096a7..69363b2e10a 100644
--- a/docs/source/diagnostic.rst
+++ b/docs/source/diagnostic.rst
@@ -185,7 +185,7 @@ individual outliers and might not be able to identify groups of outliers.
         ### Example for using Huber's T norm with the default
         ### median absolute deviation scaling
 
-        data = sm.datasets.stackloss.Load()
+        data = sm.datasets.stackloss.load()
         data.exog = sm.add_constant(data.exog)
         huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())
         hub_results = huber_t.fit()
diff --git a/docs/source/discretemod.rst b/docs/source/discretemod.rst
index 0b4764c38f3..9a4135f173c 100644
--- a/docs/source/discretemod.rst
+++ b/docs/source/discretemod.rst
@@ -31,7 +31,8 @@ Detailed examples can be found here:
 .. toctree::
     :maxdepth: 2
 
-    examples/generated/example_discrete
+    examples/notebooks/generated/discrete_choice_overview
+    examples/notebooks/generated/discrete_choice_example
 
 Technical Documentation
 -----------------------
diff --git a/docs/source/duration.rst b/docs/source/duration.rst
new file mode 100644
index 00000000000..6b580686efa
--- /dev/null
+++ b/docs/source/duration.rst
@@ -0,0 +1,81 @@
+.. currentmodule:: statsmodels.duration.hazard_regression
+
+
+.. _duration:
+
+Models for Survival and Duration Analysis
+=========================================
+
+currently contains Cox's Proportional Hazard Model.
+
+
+Examples
+--------
+
+::
+
+  url = "http://vincentarelbundock.github.io/Rdatasets/csv/survival/flchain.csv"
+  data = pd.read_csv(url)
+  del data["chapter"]
+  data = data.dropna()
+  data["lam"] = data["lambda"]
+  data["female"] = 1*(data["sex"] == "F")
+  data["year"] = data["sample.yr"] - min(data["sample.yr"])
+
+  status = np.asarray(data["death"])
+  mod = PHreg.from_formula("futime ~ 0 + age + female + creatinine + " +
+                           "np.sqrt(kappa) + np.sqrt(lam) + year + mgus",
+                           data, status=status, ties="efron")
+  rslt = mod.fit()
+  print(rslt.summary())
+
+Detailed examples can be found here:
+
+.. toctree::
+    :maxdepth: 2
+
+    examples/notebooks/generated/    not yet
+
+There some notebook examples on the Wiki:
+`Wiki notebooks for PHReg and Survival Analysis <https://github.com/statsmodels/statsmodels/wiki/Examples#survival-analysis>`_
+
+
+
+Technical Documentation
+-----------------------
+
+TODO
+
+
+References
+^^^^^^^^^^
+
+References for Cox proportional hazards regression model::
+
+T Therneau (1996).  Extending the Cox model.  Technical report.
+http://www.mayo.edu/research/documents/biostat-58pdf/DOC-10027288
+
+G Rodriguez (2005).  Non-parametric estimation in survival models.
+http://data.princeton.edu/pop509/NonParametricSurvival.pdf
+
+B Gillespie (2006).  Checking the assumptions in the Cox proportional
+hazards model.
+http://www.mwsug.org/proceedings/2006/stats/MWSUG-2006-SD08.pdf
+
+
+Module Reference
+----------------
+
+The model class is:
+
+.. autosummary::
+   :toctree: generated/
+
+   PHReg
+
+The result classe is:
+
+.. autosummary::
+   :toctree: generated/
+
+   PHRegResults
diff --git a/docs/source/example_formulas.rst b/docs/source/example_formulas.rst
index 99434f7abe2..050a1f57ccd 100644
--- a/docs/source/example_formulas.rst
+++ b/docs/source/example_formulas.rst
@@ -18,7 +18,7 @@ Loading modules and functions
 
 .. code:: python
 
-    import statsmodels.formula.api as sm
+    import statsmodels.formula.api as smf
     import numpy as np
     import pandas
 
@@ -31,7 +31,7 @@ accept ``formula`` and ``df`` arguments, whereas upper case ones take
 which describes the model in terms of a ``patsy`` formula. ``df`` takes
 a `pandas <http://pandas.pydata.org/>`_ data frame.
 
-``dir(sm)`` will print a list of available models.
+``dir(smf)`` will print a list of available models.
 
 Formula-compatible models have the following generic call signature:
 ``(formula, data, subset=None, *args, **kwargs)``
@@ -63,7 +63,7 @@ Fit the model:
 
 .. code:: python
 
-    mod = sm.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)
+    mod = smf.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)
     res = mod.fit()
     print res.summary()
 
@@ -111,7 +111,7 @@ operator:
 
 .. code:: python
 
-    res = sm.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()
+    res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()
     print res.params
 
 .. parsed-literal::
@@ -144,7 +144,7 @@ can remove the intercept from a model by:
 
 .. code:: python
 
-    res = sm.ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()
+    res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()
     print res.params
 
 .. parsed-literal::
@@ -167,8 +167,8 @@ multiplied together:
 
 .. code:: python
 
-    res1 = sm.ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()
-    res2 = sm.ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()
+    res1 = smf.ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()
+    res2 = smf.ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()
     print res1.params, '\n'
     print res2.params
 
@@ -192,7 +192,7 @@ You can apply vectorized functions to the variables in your model:
 
 .. code:: python
 
-    res = sm.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()
+    res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()
     print res.params
 
 .. parsed-literal::
@@ -207,7 +207,7 @@ Define a custom function:
 
     def log_plus_1(x):
         return np.log(x) + 1.
-    res = sm.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()
+    res = smf.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()
     print res.params
 
 .. parsed-literal::
@@ -277,7 +277,7 @@ To generate pandas data frames:
 
 .. code:: python
 
-    print sm.OLS(y, X).fit().summary()
+    print smf.OLS(y, X).fit().summary()
 
 .. parsed-literal::
 
diff --git a/docs/source/examples/README b/docs/source/examples/README
new file mode 100644
index 00000000000..7e9162c47b2
--- /dev/null
+++ b/docs/source/examples/README
@@ -0,0 +1 @@
+The format for landing.json should be self-explanatory. The images should be placed in docs/source/_static/images/. They will be displayed at 360 x 225 (W x H). It's best to save them as a png with a resolution of a multiple of at least 720 x 450. If you want, you can use png crush to make the images smaller.
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 7f039b3b60e..f340df6bfce 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -4,32 +4,17 @@ Statsmodels Examples
 ====================
 
 This page provides a series of examples, tutorials and recipes to help you get
-started with ``statsmodels``.
+started with ``statsmodels``. Each of the examples shown here is made available
+as an IPython Notebook and as a plain python script on the `statsmodels github
+repository <https://github.com/statsmodels/statsmodels/tree/master/examples>`_. 
 
-Each of these examples is also made available in
-IPython Notebook format on the `examples wiki page
-<https://github.com/statsmodels/statsmodels/wiki/Examples:-user-contributions>`_.
-Of course, you can edit the examples wiki page freely; please add any example,
-tutorial or cool `statsmodels` trick you feel is worth sharing. Thanks for
-helping us make this resource more useful!
-
-The script files for these examples and the notebooks are available in the
-top level examples folder in the source distribution.
-
-`Examples wiki page <https://github.com/statsmodels/statsmodels/wiki/Examples:-user-contributions>`_
+We also encourage users to submit their own examples, tutorials or cool
+`statsmodels` trick to the `Examples wiki page
+<https://github.com/statsmodels/statsmodels/wiki/Examples>`_
 
 The Examples
 ------------
 
-.. toctree::
-   :maxdepth: 3
-   :glob:
-
-   generated/*
-
-Notebook Examples
------------------
-
 .. toctree::
    :maxdepth: 3
    :glob:
diff --git a/docs/source/examples/landing.json b/docs/source/examples/landing.json
new file mode 100644
index 00000000000..27c0a23ccab
--- /dev/null
+++ b/docs/source/examples/landing.json
@@ -0,0 +1,173 @@
+[
+  {
+      "header":"Linear Regression Models",
+      "id":"regression",
+      "links":[
+        {
+          "text": "Ordinary Least Squares",
+          "target": "notebooks/generated/ols.html",
+          "img": "../_static/images/ols.png"
+        },
+        {
+          "text": "Generalized Least Squares",
+          "target": "notebooks/generated/gls.html",
+          "img": "../_static/images/gls.png"
+        },
+        {
+          "text": "Quantile Regression",
+          "target": "notebooks/generated/quantile_regression.html",
+          "img": "../_static/images/quantile_regression.png"
+        },
+        {
+          "text": "Regression Diagnostics",
+          "target": "notebooks/generated/regression_diagnostics.html",
+          "img": "../_static/images/regression_diagnostics.png"
+        },
+        {
+          "text": "Weighted Least Squares",
+          "target": "notebooks/generated/wls.html",
+          "img": "../_static/images/wls.png"
+        }
+        ]
+  },
+  {
+    "header":"Plotting",
+    "id":"plotting",
+    "links":[
+    {
+        "text": "Regression Plots",
+        "target": "notebooks/generated/regression_plots.html",
+        "img": "../_static/images/regression_plots.png"
+    },
+    {
+        "text": "Categorical Interactions",
+        "target": "notebooks/generated/categorical_interaction_plot.html",
+        "img": "../_static/images/categorical_interaction_plot.png"
+    }
+        ]
+
+  },
+  {
+    "header": "Discrete Choice Models",
+    "id":"discrete",
+    "links":[
+    {
+        "text": "Getting Started",
+        "target": "notebooks/generated/discrete_choice_overview.html",
+        "img": "../_static/images/discrete_overview.png"
+    },
+    {
+        "text": "Fair's Affairs Data",
+        "target": "notebooks/generated/discrete_choice_example.html",
+        "img": "../_static/images/fairs_data.png"
+    }
+        ]
+  },
+  {
+      "header": "Nonparametric Statistics",
+      "id":"nonparametric",
+      "links":[
+        {
+          "text": "Univariate Kernel Density Estimator",
+          "target": "notebooks/generated/kernel_density.html",
+          "img": "../_static/images/kde_overview.png"
+        }
+      ]
+  },
+  {
+    "header":"Generalized Linear Models",
+    "id":"glm",
+    "links":[
+        {
+            "text": "Generalized Linear Models Overview",
+            "target": "notebooks/generated/glm.html",
+            "img": "../_static/images/glm_overview.png"
+        },
+        {
+            "text": "Using Formulas with GLMs",
+            "target": "notebooks/generated/glm_formula.html",
+            "img": "../_static/images/glm_formulas.png"
+
+        }
+        ]
+  },
+ {
+      "header":"Robust Regression",
+      "id":"robust",
+      "links":[
+        {
+          "text": "M-estimators for Robust Regression",
+          "target": "notebooks/generated/robust_models_1.html",
+          "img": "../_static/images/rlm_overview.png"
+        },
+        {
+          "text": "Comparing OLS and RLM",
+          "target": "notebooks/generated/robust_models_0.html",
+          "img": "../_static/images/rlm.png"
+        }
+          ]
+  },
+  {
+      "header":"Statistics",
+      "id":"stats",
+      "links":[
+        {   
+            "text": "ANOVA",
+            "target": "notebooks/generated/interactions_anova.html",
+            "img": "../_static/images/anova.png"
+        }
+          ]
+  },
+  {
+      "header":"Time Series Analysis",
+      "id":"stats",
+      "links":[
+        {
+            "text": "ARMA: Sunspots Data",
+            "target": "notebooks/generated/tsa_arma_0.html",
+            "img": "../_static/images/tsa_arma0.png"
+        },
+        {
+            "text": "ARMA: Artificial Data",
+            "target": "notebooks/generated/tsa_arma_1.html",
+            "img": "../_static/images/tsa_arma1.png"
+        },
+        {
+            "text": "Time Series Filters",
+            "target": "notebooks/generated/tsa_filters.html",
+            "img": "../_static/images/tsa_filters.png"
+        }
+          ]
+  },
+  {
+    "header":"User Notes",
+    "id":"user",
+    "links":[
+    {   
+        "text": "Contrasts",
+        "target": "notebooks/generated/contrasts.html",
+        "img": "../_static/images/contrasts.png"
+    },
+    {
+        "text": "Formulas",
+        "target": "notebooks/generated/formulas.html",
+        "img": "../_static/images/formulas_intro.png"
+    },
+    {
+        "text": "Prediction",
+        "target": "notebooks/generated/predict.html",
+        "img": "../_static/images/prediction.png"
+    },
+    {
+        "text": "Generic Maximum Likelihood",
+        "target": "notebooks/generated/generic_mle.html",
+        "img": "../_static/images/generic_mle.png"
+    },
+    {
+        "text": "Dates in Time-Series Models",
+        "target": "notebooks/generated/tsa_dates.html",
+        "img": "../_static/images/tsa_dates.png"
+    }
+        ]
+  }
+]
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
new file mode 100644
index 00000000000..7a8e6b38876
--- /dev/null
+++ b/docs/source/faq.rst
@@ -0,0 +1,41 @@
+:orphan:
+
+.. currentmodule:: statsmodels
+
+.. _faq:
+
+Frequently Asked Question
+-------------------------
+
+.. _endog-exog-faq:
+
+What do endog and exog mean?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+These are shorthand for endogenous and exogenous variables. You might be more comfortable with the common ``y`` and ``X`` notation in linear models. Sometimes the endogenous variable ``y`` is called a dependent variable. Likewise, sometimes the exogenous variables ``X`` are called the independent variables. You can read about this in greater detail at :ref:`endog_exog` 
+
+
+.. _missing-faq:
+
+How does statsmodels handle missing data?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Missing data can be handled via the ``missing`` keyword argument. Every model takes this keyword. You can find more information in the docstring of :class:`statsmodels.base.Model <base.model.Model>`. 
+
+.. `Model class <http://statsmodels.sourceforge.net/devel/dev/generated/statsmodels.base.model.Model.html#statsmodels.base.model.Model>`_.
+
+.. _build-faq:
+
+Why won't statsmodels build?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you're on Python 3.4, you *must* use Cython 0.20.1. If you're still having problems, try running
+
+.. code-block:: bash
+
+    python setup.py clean
+
+What if my question isn't answered here?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You may find answers for questions that have not yet been added here on GitHub under the `FAQ issues tag <https://github.com/statsmodels/statsmodels/issues?labels=FAQ&page=1&state=open>`_. If not, please ask your question on stackoverflow using the `statsmodels tag <https://stackoverflow.com/questions/tagged/statsmodels>`_ or on the `mailing list <https://groups.google.com/forum/#!forum/pystatsmodels>`_.
diff --git a/docs/source/gettingstarted.rst b/docs/source/gettingstarted.rst
index 7ad33042986..f81cdd01f0e 100644
--- a/docs/source/gettingstarted.rst
+++ b/docs/source/gettingstarted.rst
@@ -176,9 +176,9 @@ plot of partial regression for a set of regressors by:
 
 .. ipython:: python
 
-    from statsmodels.graphics.regressionplots import plot_partregress
     @savefig gettingstarted_0.png
-    plot_partregress(res)
+    sm.graphics.plot_partregress('Lottery', 'Wealth', ['Region', 'Literacy'],
+                                 data=df, obs_labels=False)
 
 More
 ----
diff --git a/docs/source/glm.rst b/docs/source/glm.rst
index 3a7ad1c2606..ff5e25f23e2 100644
--- a/docs/source/glm.rst
+++ b/docs/source/glm.rst
@@ -29,7 +29,8 @@ Detailed examples can be found here:
 .. toctree::
    :maxdepth: 1
 
-   examples/generated/example_glm
+   examples/notebooks/generated/glm
+   examples/notebooks/generated/glm_formula
 
 Technical Documentation
 -----------------------
@@ -64,6 +65,8 @@ Results Class
 
    GLMResults
 
+.. _families:
+
 Families
 ^^^^^^^^
 
@@ -84,6 +87,8 @@ The distribution families currently implemented are
    Poisson
 
 
+.. _links:
+
 Link Functions
 ^^^^^^^^^^^^^^
 
diff --git a/docs/source/gmm.rst b/docs/source/gmm.rst
index 1033e90c120..cfb2d872a88 100644
--- a/docs/source/gmm.rst
+++ b/docs/source/gmm.rst
@@ -33,12 +33,8 @@ Module Reference
    GMM
    GMMResults
    IV2SLS
-
-not sure what the status is on the following
-
-.. autosummary::
-   :toctree: generated/
-
    IVGMM
+   IVGMMResults
+   IVRegressionResults
+   LinearIVGMM
    NonlinearIVGMM
-   DistQuantilesGMM
diff --git a/docs/source/graphics.rst b/docs/source/graphics.rst
index 775984634ce..a5a937c16e1 100644
--- a/docs/source/graphics.rst
+++ b/docs/source/graphics.rst
@@ -69,6 +69,8 @@ Time Series Plots
 
    tsaplots.plot_acf
    tsaplots.plot_pacf
+   tsaplots.month_plot
+   tsaplots.quarter_plot
 
 Other Plots
 -----------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c9f45687944..b861b6b9a42 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -64,12 +64,12 @@ Basic Documentation
 .. toctree::
     :maxdepth: 3
 
-    introduction
     release/index
     gettingstarted
     example_formulas
     install
     related
+    about
 
 Information about the structure and development of
 statsmodels:
@@ -91,10 +91,13 @@ Table of Contents
 
    regression
    glm
+   gee
    rlm
+   mixed_linear
    discretemod
    anova
    tsa
+   duration
    stats
    nonparametric
    gmm
diff --git a/docs/source/install.rst b/docs/source/install.rst
index 4c38f9a9a66..87cdff06977 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -8,18 +8,18 @@ Installation
 Using setuptools
 ~~~~~~~~~~~~~~~~
 
-To obtain the latest released version of statsmodels using `setuptools <http://pypi.python.org/pypi/setuptools>`__::
+To obtain the latest released version of statsmodels using `setuptools <https://pypi.python.org/pypi/setuptools>`__::
 
     easy_install -U statsmodels
 
-Or follow `this link to our PyPI page <http://pypi.python.org/pypi/statsmodels>`__.
+Or follow `this link to our PyPI page <https://pypi.python.org/pypi/statsmodels>`__.
 
 Obtaining the Source
 ~~~~~~~~~~~~~~~~~~~~
 
 We do not release very often but the master branch of our source code is 
 usually fine for everyday use. You can get the latest source from our 
-`github repository <https://www.github.com/statsmodels/statsmodels>`__. Or if you have git installed::
+`github repository <https://github.com/statsmodels/statsmodels>`__. Or if you have git installed::
 
     git clone git://github.com/statsmodels/statsmodels.git
 
@@ -107,15 +107,16 @@ Replace `/x64` with `/x86` and `v7.0` with `v7.1` as needed.
 Dependencies
 ~~~~~~~~~~~~
 
-* `Python <http://www.python.org>`__ >= 2.6, including Python 3.x 
+* `Python <https://www.python.org>`__ >= 2.6, including Python 3.x 
 * `NumPy <http://www.scipy.org/>`__ >= 1.5.0
 * `SciPy <http://www.scipy.org/>`__ >= 0.7
 * `Pandas <http://pandas.pydata.org/>`__ >= 0.7.1
-* `Patsy <http://patsy.readthedocs.org>`__ >= 0.1.0
-* `Cython <http://cython.org/>`__ >= 15.1, Needed if you want to build the code from github and not a source distribution.
+* `Patsy <https://patsy.readthedocs.org>`__ >= 0.1.0
+* `Cython <http://cython.org/>`__ >= 20.1, Needed if you want to build the code from github and not a source distribution. You must use Cython >= 0.20.1 if you're on Python 3.4. Earlier versions may work for Python < 3.4.
 
 Optional Dependencies
 ~~~~~~~~~~~~~~~~~~~~~
 
-* `Matplotlib <http://matplotlib.sourceforge.net/>`__ is needed for plotting functions and running many of the examples. 
-* `Nose <http://www.somethingaboutorange.com/mrl/projects/nose/>`__ is required to run the test suite.
+* `Matplotlib <http://matplotlib.org/>`__ is needed for plotting functions and running many of the examples. 
+* `Nose <https://nose.readthedocs.org/en/latest>`__ is required to run the test suite.
+* `IPython <http://ipython.org>`__ is required to build the docs locally.
diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
deleted file mode 100644
index 124392bb6a5..00000000000
--- a/docs/source/introduction.rst
+++ /dev/null
@@ -1,193 +0,0 @@
-.. currentmodule:: statsmodels
-
-************
-Introduction
-************
-
-Background
-----------
-
-Scipy.stats.models was originally written by Jonathan Taylor.
-For some time it was part of scipy but then removed from it. During
-the Google Summer of Code 2009, stats.models was corrected, tested and
-enhanced and released as a new package. Since then we have continued to
-improve the existing models and added new statistical methods.
-
-
-Main Features and Current Status
---------------------------------
-
-statsmodels 0.4 is a pure python package, with one optional cython based
-extension that provides a considerable speed improvement for ARIMA estimation.
-Future releases will depend on cython generated extensions.
-
-statsmodels includes:
-
-* regression: Generalized least squares (including weighted least squares and
-  least squares with autoregressive errors), ordinary least squares.
-* glm: Generalized linear models with support for all of the one-parameter
-  exponential family distributions.
-* discrete: regression with discrete dependent variables, including Logit, Probit,
-  MNLogit, Poisson, based on maximum likelihood estimators
-* rlm: Robust linear models with support for several M-estimators.
-* tsa: models for time series analysis
-
-  - univariate time series analysis: AR, ARIMA
-  - vector autoregressive models, VAR and structural VAR
-  - descriptive statistics and process models for time series analysis
-
-* nonparametric : (Univariate) kernel density estimators
-* datasets: Datasets to be distributed and used for examples and in testing.
-* stats: a wide range of statistical tests
-
-  - diagnostics and specification tests
-  - goodness-of-fit and normality tests
-  - functions for multiple testing
-  - various additional statistical tests
-
-* iolib: Tools for reading Stata .dta files into numpy arrays. (not yet ported to Python 3)
-* iolob: printing table output to ascii, latex, and html
-
-* miscellaneous models
-
-statsmodels contains a sandbox folder, which includes some of the original
-stats.models code that has not yet been rewritten and tested. The sandbox also
-contains models and functions that we are currently developing. This code is
-in various stages of development from early stages to almost finished, but
-not sufficiently tested or with an API that is still in flux. Some of the code
-in the advanced state covers among others Mixed (repeated measures) Models,
-GARCH models, general method of moments (GMM) estimators, kernel regression and
-kernel density estimation, and various extensions to scipy.stats.distributions.
-
-The code is written for plain NumPy arrays so that statsmodels can be used
-as a library for any kind of data structure users might have. However, in
-order to make the data handling easier, some time series specific models
-rely on pandas, and we have plans to integrate pandas in future releases of
-statsmodels.
-
-We have also included several datasets from the public domain and by
-permission for tests and examples. The datasets are set up so that it is
-easy to add more datasets.
-
-Python 3
---------
-
-statsmodels has been ported and tested for Python 3.2. Python 3
-version of the code is automatically created during setup by running 2to3.py
-over the statsmodels source (excluding examples).
-The STATA file reader and writer in iolib.foreign has not been ported yet.
-A recent development version of matplotlib for Python 3 runs without problems
-with our examples and tests.
-Running the test suite with Python 3.2 shows only one errors related to
-unported STATA file reader.
-
-Testing
--------
-
-Most results have been verified with at least one other statistical package: R,
-Stata or SAS. The guiding principal for the initial rewrite and for continued
-development is that all numbers have to be verified. Some statistical
-methods are tested with Monte Carlo studies. While we strife to follow this
-test driven approach, there is no guarantee that the code is bug-free and
-always works. Some auxiliary function are still insufficiently tested, some
-edge cases might not be correctly taken into account, and the possibility of
-numerical problems is inherent to many of the statistical models. We
-especially appreciate any help and reports for these kind of problems so we
-can keep improving the existing models.
-
-
-
-
-Looking Forward
----------------
-
-We would like to invite everyone to give statsmodels a test drive, use it, and
-report comments, possibilities for improvement and bugs to the statsmodels
-mailing list http://groups.google.com/group/pystatsmodels or file tickets on our
-issue tracker at https://github.com/statsmodels/statsmodels/issues
-
-The source code is available from https://github.com/statsmodels/statsmodels.
-
-Our plans for the future include improving the coverage of statistical
-models, methods and tests that any basic statistics package should provide.
-But the main direction for the expansion of statsmodels depends on the
-requirements and interests of the developers and contributers.
-
-The current maintainers are mostly interested in econometrics and time series
-analysis, but we would like to invite any users or developers to contribute
-their own extensions to existing models, or new models. To speed up
-improvements that are waiting in the sandbox, any help with providing test
-cases, reviewing or improving the code would be very appreciated.
-
-Planned Extensions
-~~~~~~~~~~~~~~~~~~
-
-Big changes that are planned for the next release will improve the
-usability of statsmodels especially for interactive work.
-
-* Metainformation about data and models: Currently the models essentially
-  use no information about the design matrix and just treat it as numpy
-  array. Some information like variable names are included with the wrapper
-  for use with Pandas or other data structures.
-* Formulas similar to R: This will provide a faster way to interactively
-  define models and contrast matrices, and will provide additional
-  information especially for categorical variables. (Nathaniel Smith)
-
-Various models that are work in progress where the time to inclusion in
-statsmodels proper will depend on the available developer time and interests:
-
-Bayesian dynamic linear models (Wes)
-
-more Kalman filter based time series analysis (Skipper)
-
-New models (roughly in order of completeness):
-general method of moments (GMM) estimators, kernel regression,
-kernel density estimation, various extensions to scipy.stats.distributions,
-GARCH models, copulas, system of equation models, panel data models,
-more discrete choice models, mixed effects models, survival models.
-
-Resampling approaches like bootstrap and permutation for tests and estimator
-statistics.
-
-
-Code Stability
-~~~~~~~~~~~~~~
-
-The existing models are mostly settled in their user interface and we do not
-expect many changes anymore. One area that will need adjustment is how
-formulas and meta information are included. New models that have just been
-included might require adjustments as we gain more experience and obtain
-feedback by users. As we expand the range of models, we keep improving the
-framework for different estimators and statistical tests, so further changes
-will be necessary.
-
-In 0.3 we reorganized the internal location of the code and
-import paths which will make future enhancements less interruptive. In 0.4
-most models obtained a wrapper that stores and returns additional information
-from richer data structures like data structures in Pandas and structured
-arrays. In 0.4 also prediction has been improved in many cases and made more
-consistent across models.
-
-Although there is no guarantee yet on API stability, we try to keep changes
-that require adjustments by existing users to a minimal level.
-
-Financial Support
------------------
-
-We are grateful for the financial support that we obtained for the
-developement of statsmodels:
-
- Google `www.google.com <http://www.google.com/>`_ : two Google Summer of Code,
- GSOC 2009, GSOC 2010 and GSOC 2011
-
- AQR `www.aqr.com <http://www.aqr.com/>`_ : financial sponsor for the work on
- Vector Autoregressive Models (VAR) by Wes McKinney
-
-We would also like to thank our hosting providers, `github
-<http://github.com/>`_ for the public code repository, `sourceforge
-<http://sourceforge.net/>`_ for hosting our documentation and `python.org
-<http://python.org>`_ for making our downloads available on pypi.
-
-
-Josef Perktold and Skipper Seabold
-(maintainers)
diff --git a/docs/source/iolib.rst b/docs/source/iolib.rst
index 17a842f62bf..6d3581a6150 100644
--- a/docs/source/iolib.rst
+++ b/docs/source/iolib.rst
@@ -31,3 +31,14 @@ Module Reference
    table.csv2st
    smpickle.save_pickle
    smpickle.load_pickle
+
+
+The following are classes and functions used to return the summary of
+estimation results, and mostly intended for internal use. There are currently
+two versions for creating summaries.
+
+.. autosummary::
+   :toctree: generated/
+
+   summary.Summary
+   summary2.Summary
diff --git a/docs/source/mixed_linear.rst b/docs/source/mixed_linear.rst
new file mode 100644
index 00000000000..3c10275a53f
--- /dev/null
+++ b/docs/source/mixed_linear.rst
@@ -0,0 +1,177 @@
+.. currentmodule:: statsmodels.regression.mixed_linear_model
+
+
+.. _mixedlmmod:
+
+Linear Mixed Effects Models
+===========================
+
+
+Linear Mixed Effects models are used for regression analyses involving
+dependent data.  Such data arise when working with longitudinal and
+other study designs in which multiple observations are made on each
+subject.  Two specific mixed effects models are "random intercepts
+models", where all responses in a single group are additively shifted
+by a value that is specific to the group, and "random slopes models",
+where the values follow a mean trajectory that is linear in observed
+covariates, with both the slopes and intercept being specific to the
+group.  The Statsmodels MixedLM implementation allows arbitrary random
+effects design matrices to be specified for the groups, so these and
+other types of random effects models can all be fit.
+
+The Statsmodels LME framework currently supports post-estimation
+inference via Wald tests and confidence intervals on the coefficients,
+profile likelihood analysis, likelihood ratio testing, and AIC.  Some
+limitations of the current implementation are that it does not support
+structure more complex on the residual errors (they are always
+homoscedastic), and it does not support crossed random effects.  We
+hope to implement these features for the next release.
+
+Examples
+--------
+
+::
+
+  # Load the data from Spector and Mazzeo (1980)
+  spector_data = sm.datasets.spector.load()
+  spector_data.exog = sm.add_constant(spector_data.exog)
+
+  data = pd.read_csv("http://vincentarelbundock.github.io/Rdatasets/csv/geepack/dietox.csv")
+  md = MixedLM.from_formula("Weight ~ Time", data, groups=data["Pig"])
+  mdf = md.fit()
+  print(mdf.summary())
+
+Detailed examples can be found here:
+
+.. toctree::
+    :maxdepth: 2
+
+    examples/notebooks/generated/    not yet
+
+There some notebook examples on the Wiki:
+`Wiki notebooks for MixedLM <https://github.com/statsmodels/statsmodels/wiki/Examples#linear-mixed-models>`_
+
+
+
+Technical Documentation
+-----------------------
+
+The data are partitioned into disjoint groups.  The probability model
+for group i is:
+
+Y = X*beta + Z*gamma + epsilon
+
+where
+
+* n_i is the number of observations in group i
+* Y is a n_i dimensional response vector
+* X is a n_i x k_fe dimensional matrix of fixed effects
+  coefficients
+* beta is a k_fe-dimensional vector of fixed effects slopes
+* Z is a n_i x k_re dimensional matrix of random effects
+  coefficients
+* gamma is a k_re-dimensional random vector with mean 0
+  and covariance matrix Psi; note that each group
+  gets its own independent realization of gamma.
+* epsilon is a n_i dimensional vector of iid normal
+  errors with mean 0 and variance sigma^2; the epsilon
+  values are independent both within and between groups
+
+Y, X and Z must be entirely observed.  beta, Psi, and sigma^2 are
+estimated using ML or REML estimation, and gamma and epsilon are
+random so define the probability model.
+
+The mean structure is E[Y|X,Z] = X*beta.  If only the mean structure
+is of interest, GEE is a good alternative to mixed models.
+
+The primary reference for the implementation details is:
+
+MJ Lindstrom, DM Bates (1988).  "Newton Raphson and EM algorithms for
+linear mixed effects models for repeated measures data".  Journal of
+the American Statistical Association. Volume 83, Issue 404, pages
+1014-1022.
+
+See also this more recent document:
+
+http://econ.ucsb.edu/~doug/245a/Papers/Mixed%20Effects%20Implement.pdf
+
+All the likelihood, gradient, and Hessian calculations closely follow
+Lindstrom and Bates.
+
+The following two documents are written more from the perspective of
+users:
+
+http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
+
+http://lme4.r-forge.r-project.org/slides/2009-07-07-Rennes/3Longitudinal-4.pdf
+
+Notation:
+
+* `cov_re` is the random effects covariance matrix (referred to above
+  as Psi) and `scale` is the (scalar) error variance.  For a single
+  group, the marginal covariance matrix of endog given exog is scale*I
+  + Z * cov_re * Z', where Z is the design matrix for the random
+  effects in one group.
+
+Notes:
+
+1. Three different parameterizations are used here in different
+places.  The regression slopes (usually called `fe_params`) are
+identical in all three parameterizations, but the variance parameters
+differ.  The parameterizations are:
+
+* The "natural parameterization" in which cov(endog) = scale*I + Z *
+  cov_re * Z', as described above.  This is the main parameterization
+  visible to the user.
+
+* The "profile parameterization" in which cov(endog) = I +
+  Z * cov_re1 * Z'.  This is the parameterization of the profile
+  likelihood that is maximized to produce parameter estimates.
+  (see Lindstrom and Bates for details).  The "natural" cov_re is
+  equal to the "profile" cov_re1 times scale.
+
+* The "square root parameterization" in which we work with the
+  Cholesky factor of cov_re1 instead of cov_re1 directly.
+
+All three parameterizations can be "packed" by concatenating fe_params
+together with the lower triangle of the dependence structure.  Note
+that when unpacking, it is important to either square or reflect the
+dependence structure depending on which parameterization is being
+used.
+
+2. The situation where the random effects covariance matrix is
+singular is numerically challenging.  Small changes in the covariance
+parameters may lead to large changes in the likelihood and
+derivatives.
+
+3. The optimization strategy is to optionally perform a few EM steps,
+followed by optionally performing a few steepest descent steps,
+followed by conjugate gradient descent using one of the scipy gradient
+optimizers.  The EM and steepest descent steps are used to get
+adequate starting values for the conjugate gradient optimization,
+which is much faster.
+
+
+References
+^^^^^^^^^^
+
+General references for this class of models are::
+
+TODO
+
+Module Reference
+----------------
+
+The model class is:
+
+.. autosummary::
+   :toctree: generated/
+
+   MixedLM
+
+The result classe are:
+
+.. autosummary::
+   :toctree: generated/
+
+   MixedLMResults
diff --git a/docs/source/plots/bkf_plot.py b/docs/source/plots/bkf_plot.py
new file mode 100644
index 00000000000..944afda2a00
--- /dev/null
+++ b/docs/source/plots/bkf_plot.py
@@ -0,0 +1,9 @@
+import statsmodels.api as sm
+
+from load_macrodata import dta
+
+cycles = sm.tsa.filters.bkfilter(dta[['realinv']], 6, 24, 12)
+
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots()
+cycles.plot(ax=ax, style=['r--', 'b-'])
diff --git a/docs/source/plots/cff_plot.py b/docs/source/plots/cff_plot.py
new file mode 100644
index 00000000000..313b7e1fc0a
--- /dev/null
+++ b/docs/source/plots/cff_plot.py
@@ -0,0 +1,9 @@
+import statsmodels.api as sm
+
+from load_macrodata import dta
+
+cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]])
+
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots()
+cf_cycles.plot(ax=ax, style=['r--', 'b-'])
diff --git a/docs/source/plots/graphics_month_plot.py b/docs/source/plots/graphics_month_plot.py
new file mode 100644
index 00000000000..f947ae7059a
--- /dev/null
+++ b/docs/source/plots/graphics_month_plot.py
@@ -0,0 +1,12 @@
+import statsmodels.api as sm
+import pandas as pd
+
+dta = sm.datasets.elnino.load_pandas().data
+dta['YEAR'] = dta.YEAR.astype(int).astype(str)
+dta = dta.set_index('YEAR').T.unstack()
+dates = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
+                                       dta.index.values)
+
+dta.index = pd.DatetimeIndex(dates, freq='M')
+dta.name = 'temp'
+fig = sm.graphics.tsa.month_plot(dta)
diff --git a/docs/source/plots/hpf_plot.py b/docs/source/plots/hpf_plot.py
new file mode 100644
index 00000000000..3a819be01cd
--- /dev/null
+++ b/docs/source/plots/hpf_plot.py
@@ -0,0 +1,13 @@
+import statsmodels.api as sm
+import pandas as pd
+from load_macrodata import dta
+
+cycle, trend = sm.tsa.filters.hpfilter(dta.realgdp, 1600)
+gdp_decomp = dta[['realgdp']]
+gdp_decomp["cycle"] = cycle
+gdp_decomp["trend"] = trend
+
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots()
+gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax,
+                                                     fontsize=16)
diff --git a/docs/source/plots/load_macrodata.py b/docs/source/plots/load_macrodata.py
new file mode 100644
index 00000000000..624509ff42e
--- /dev/null
+++ b/docs/source/plots/load_macrodata.py
@@ -0,0 +1,6 @@
+import statsmodels.api as sm
+import pandas as pd
+dta = sm.datasets.macrodata.load_pandas().data
+dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')
+index = pd.DatetimeIndex(dates)
+dta.set_index(index, inplace=True)
diff --git a/docs/source/plots/var_plots.py b/docs/source/plots/var_plots.py
index 55d783b5072..db77a6b42ef 100644
--- a/docs/source/plots/var_plots.py
+++ b/docs/source/plots/var_plots.py
@@ -9,7 +9,7 @@
 mdata = ds.macrodata.load_pandas().data
 
 # prepare the dates index
-dates = mdata[['year', 'quarter']].astype(int).astype(str)
+dates = mdata[['year', 'quarter']].astype(int).astype('S4')
 quarterly = dates["year"] + "Q" + dates["quarter"]
 quarterly = dates_from_str(quarterly)
 
diff --git a/docs/source/regression.rst b/docs/source/regression.rst
index d31fc3e99d7..77a2648556d 100644
--- a/docs/source/regression.rst
+++ b/docs/source/regression.rst
@@ -35,9 +35,9 @@ Detailed examples can be found here:
 .. toctree::
    :maxdepth: 1
 
-   examples/generated/example_ols
-   examples/generated/example_wls
-   examples/generated/example_gls
+   examples/notebooks/generated/ols
+   examples/notebooks/generated/wls
+   examples/notebooks/generated/gls
 
 Technical Documentation
 -----------------------
diff --git a/docs/source/related.rst b/docs/source/related.rst
index c02acbcd09a..1234cc2db22 100644
--- a/docs/source/related.rst
+++ b/docs/source/related.rst
@@ -22,7 +22,7 @@ Data Handling
 Pandas
 ^^^^^^
 
-http://pypi.python.org/pypi/pandas
+https://pypi.python.org/pypi/pandas
 
 "This project aims to provide the following
  * A set of fast NumPy-based data structures optimized for panel, time series,
@@ -51,7 +51,7 @@ class.
 Tabular
 ^^^^^^^
 
-http://pypi.python.org/pypi/tabular
+https://pypi.python.org/pypi/tabular
 
 "Tabular data container and associated convenience routines in Python
 
@@ -78,7 +78,7 @@ arrays.
 La
 ^^
 
-http://pypi.python.org/pypi/la
+https://pypi.python.org/pypi/la
 
 "Label the rows, columns, any dimension, of your NumPy arrays.
 
@@ -106,7 +106,7 @@ Data Analysis
 Pymc
 ^^^^
 
-http://pypi.python.org/pypi/pymc
+https://pypi.python.org/pypi/pymc
 
 "Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is
 an increasingly relevant approach to statistical estimation.
@@ -126,7 +126,7 @@ far the most mature project in this group including statsmodels.
 Scikits.talkbox
 ^^^^^^^^^^^^^^^
 
-http://pypi.python.org/pypi/scikits.talkbox
+https://pypi.python.org/pypi/scikits.talkbox
 
 Talkbox is set of python modules for speech/signal processing. The goal of this
 toolbox is to be a sandbox for features which may end up in scipy at some
@@ -146,7 +146,7 @@ AR coefficients.
 
 Nitime
 ^^^^^^
-http://github.com/fperez/nitime
+https://github.com/fperez/nitime
 
 "Nitime is a library for time-series analysis of data from neuroscience experiments.
 
@@ -169,7 +169,7 @@ time series.
 KF - Kalman Filter
 ^^^^^^^^^^^^^^^^^^
 
-http://pypi.python.org/pypi/KF
+https://pypi.python.org/pypi/KF
 
 "This project was started to test different avaiable tools to track mutual
 funds and hedge fund using Capital Asset Pricing Model (CAPM thereafter)
@@ -188,6 +188,13 @@ and allows for equality and inequality constraints in the estimation.
 Includes its own time series class, and the estimation seems (?) to depend on
 it.
 
+scikit-learn
+^^^^^^^^^^^^
+Simple and efficient tools for data mining and data analysis. Accessible to 
+everybody, and reusable in various contexts. Built on NumPy, SciPy, and 
+matplotlib
+http://scikit-learn.org/stable/
+License: BSD
 
 
 Domain-specific Data Analysis
@@ -202,7 +209,7 @@ Pymvpa
 
 PyMVPA is a Python module intended to ease pattern classification analyses of
 large datasets
-http://pymvpa.org/
+http://www.pymvpa.org/
 License: MIT
 
 Nipy
@@ -242,13 +249,8 @@ Other packages
 There exists a large number of machine learning packages in python, many of
 them with a well established code base. Unfortunately, none of the packages
 with a wider coverage of algorithms has a scipy compatible license.
-A listing can be found at http://mloss.org/software/language/python/
-scikits.learn includes several machine learning algorithms and is currently
-undergoing a cleanup and enhancement http://pypi.python.org/pypi/scikits.learn/0.1 .
-
-Other packages are available that provide additional functionality,
-especially openopt which offers additional optimization routines compared to
-the ones in scipy.
-
-
+A listing can be found at http://mloss.org/software/language/python/. Other 
+packages are available that provide additional functionality, especially 
+openopt which offers additional optimization routines compared to the ones 
+in scipy.
 
diff --git a/docs/source/release/version0.5.rst b/docs/source/release/version0.5.rst
index c4831ebe5b5..45bc33cdf40 100644
--- a/docs/source/release/version0.5.rst
+++ b/docs/source/release/version0.5.rst
@@ -14,7 +14,7 @@ The following major new features appear in this version.
 Support for Model Formulas via Patsy
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Statsmodels now supports fitting models with a formula. This functionality is provided by `patsy <http://patsy.readthedocs.org/en/latest/>`_. Patsy is now a dependency for statsmodels. Models can be individually imported from the ``statsmodels.formula.api`` namespace or you can import them all as::
+Statsmodels now supports fitting models with a formula. This functionality is provided by `patsy <https://patsy.readthedocs.org/en/latest/>`_. Patsy is now a dependency for statsmodels. Models can be individually imported from the ``statsmodels.formula.api`` namespace or you can import them all as::
 
     import statsmodels.formula.api as smf
 
diff --git a/docs/source/release/version0.6.rst b/docs/source/release/version0.6.rst
new file mode 100644
index 00000000000..2e3d3fa095a
--- /dev/null
+++ b/docs/source/release/version0.6.rst
@@ -0,0 +1,192 @@
+:orphan:
+
+===========
+0.6 Release
+===========
+
+Release 0.6.0
+=============
+
+Release summary.
+
+Major changes:
+
+Addition of Generalized Estimating Equations GEE
+
+Generalized Estimating Equations
+--------------------------------
+
+Generalized Estimating Equations (GEE) provide an approach to handling
+dependent data in a regression analysis.  Dependent data arise
+commonly in practice, such as in a longitudinal study where repeated
+observations are collected on subjects. GEE can be viewed as an
+extension of the generalized linear modeling (GLM) framework to the
+dependent data setting.  The familiar GLM families such as the
+Gaussian, Poisson, and logistic families can be used to accommodate
+dependent variables with various distributions.
+
+Here is an example of GEE Poisson regression in a data set with four
+count-type repeated measures per subject, and three explanatory
+covariates.
+
+.. code-block:: python
+
+   import numpy as np
+   import pandas as pd
+   from statsmodels.genmod.generalized_estimating_equations import GEE
+   from statsmodels.genmod.dependence_structures import Independence
+   from statsmodels.genmod.families import Poisson
+
+   data_url = "http://vincentarelbundock.github.io/Rdatasets/csv/MASS/epil.csv"
+   data = pd.read_csv(data_url)
+
+   fam = Poisson()
+   ind = Independence()
+   md = GEE.from_formula("y ~ age + trt + base", "subject", data,
+                          covstruct=ind, family=fam)
+   mdf = md.fit()
+   print mdf.summary()
+
+
+The dependence structure in a GEE is treated as a nuisance parameter
+and is modeled in terms of a "working dependence structure".  The
+statsmodels GEE implementation currently includes five working
+dependence structures (independent, exchangeable, autoregressive,
+nested, and a global odds ratio for working with categorical data).
+Since the GEE estimates are not maximum likelihood estimates,
+alternative approaches to some common inference procedures have been
+developed.  The statsmodels GEE implementation currently provides
+standard errors, Wald tests, score tests for arbitrary parameter
+contrasts, and estimates and tests for marginal effects.  Several
+forms of standard errors are provided, including robust standard
+errors that are approximately correct even if the working dependence
+structure is misspecified.
+
+
+Seasonality Plots
+-----------------
+
+Adding functionality to look at seasonality in plots. Two new functions are :func:`sm.graphics.tsa.month_plot` and :func:`sm.graphics.tsa.quarter_plot`. Another function :func:`sm.graphics.tsa.seasonal_plot` is available for power users.
+
+.. code-block:: python
+
+    import statsmodels.api as sm
+    import pandas as pd
+
+    dta = sm.datasets.elnino.load_pandas().data
+    dta['YEAR'] = dta.YEAR.astype(int).astype(str)
+    dta = dta.set_index('YEAR').T.unstack()
+    dates = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
+                                           dta.index.values)
+
+    dta.index = pd.DatetimeIndex(dates, freq='M')
+    fig = sm.tsa.graphics.month_plot(dta)
+
+.. currentmodule:: statsmodels.tsa
+
+Seasonal Decomposition
+----------------------
+
+We added a naive seasonal decomposition tool in the same vein as R's ``decompose``. This function can be found as :func:`sm.tsa.seasonal_decompose <tsa.seasonal.seasonal_decompose>`.
+
+
+.. code-block:: python
+
+    import statsmodels.api as sm
+
+    dta = sm.datasets.co2.load_pandas().data
+    # deal with missing values. see issue
+    dta.co2.interpolate(inplace=True)
+
+    res = sm.tsa.seasonal_decompose(dta.co2)
+    res.plot()
+
+
+Addition of Linear Mixed Effects Models (MixedLM)
+
+Linear Mixed Effects Models
+---------------------------
+
+Linear Mixed Effects models are used for regression analyses involving
+dependent data.  Such data arise when working with longitudinal and
+other study designs in which multiple observations are made on each
+subject.  Two specific mixed effects models are "random intercepts
+models", where all responses in a single group are additively shifted
+by a value that is specific to the group, and "random slopes models",
+where the values follow a mean trajectory that is linear in observed
+covariates, with both the slopes and intercept being specific to the
+group.  The Statsmodels MixedLM implementation allows arbitrary random
+effects design matrices to be specified for the groups, so these and
+other types of random effects models can all be fit.
+
+Here is an example of fitting a random intercepts model to data from a
+longitudinal study:
+
+data = pd.read_csv("http://vincentarelbundock.github.io/Rdatasets/csv/geepack/dietox.csv")
+md = MixedLM.from_formula("Weight ~ Time", data, groups=data["Pig"])
+mdf = md.fit()
+print mdf.summary()
+
+To extend this to a random slopes model, we would add the statement
+`md.set_random("Time", data)` before calling the `fit` method.
+
+The Statsmodels LME framework currently supports post-estimation
+inference via Wald tests and confidence intervals on the coefficients,
+profile likelihood analysis, likelihood ratio testing, and AIC.  Some
+limitations of the current implementation are that it does not support
+structure more complex on the residual errors (they are always
+homoscedastic), and it does not support crossed random effects.  We
+hope to implement these features for the next release.
+
+
+Other important new features
+----------------------------
+
+* The Kalman filter Cython code underlying AR(I)MA estimation has been substantially optimized. You can expect speed-ups of one to two orders of magnitude.
+
+* Added :func:`sm.tsa.arma_order_select_ic`. A convenience function to quickly get the information criteria for use in tentative order selection of ARMA processes.
+
+* Plotting functions for timeseries is now imported under the ``sm.tsa.graphics`` namespace in addition to ``sm.graphics.tsa``.
+
+* **New datasets**: Added new :ref:`datasets <datasets>` for examples. ``sm.datasets.co2`` is a univariate time-series dataset of weekly co2 readings. It exhibits a trend and seasonality and has missing values.
+
+* Added robust skewness and kurtosis estimators in :func:`sm.stats.stattools.robust_skewness` and :func:`sm.stats.stattools.robust_kurtosis`, respectively.  An alternative robust measure of skewness has been added in :func:`sm.stats.stattools.medcouple`.
+
+* New functions added to correlation tools: `corr_nearest_factor`
+  finds the closest factor-structured correlation matrix to a given
+  square matrix in the Frobenius norm; `corr_thresholded` efficiently
+  constructs a hard-thresholded correlation matrix using sparse matrix
+  operations.
+
+* New `dot_plot` in graphics: A dotplot is a way to visualize a small dataset
+  in a way that immediately conveys the identity of every point in the plot.
+  Dotplots are commonly seen in meta-analyses, where they are known
+  as "forest plots", but can be used in many other settings as well.
+  Most tables that appear in research papers can be represented
+  graphically as a dotplot.
+
+
+Major Bugs fixed
+----------------
+
+* Bullet list of major bugs
+* With a link to its github issue.
+* Use the syntax ``:ghissue:`###```.
+
+.. currentmodule:: statsmodels.tsa
+
+Backwards incompatible changes and deprecations
+-----------------------------------------------
+
+* RegressionResults.norm_resid is now a readonly property, rather than a function.
+* The function ``statsmodels.tsa.filters.arfilter`` has been removed. This did not compute a recursive AR filter but was instead a convolution filter. Two new functions have been added with clearer names :func:`sm.tsa.filters.recursive_filter <tsa.filters.filtertools.recursive_filter>` and :func:`sm.tsa.filters.convolution_filter <tsa.filters.filtertools.convolution_filter>`.
+
+Development summary and credits
+-------------------------------
+
+A blurb about the number of changes and the contributors list.
+
+.. note::
+
+   Obtained by running ``git log v0.5.0..HEAD --format='* %aN <%aE>' | sed 's/@/\-at\-/' | sed 's/<>//' | sort -u``.
+
diff --git a/docs/source/rlm.rst b/docs/source/rlm.rst
index 401c5d0540c..f72d110f9a4 100644
--- a/docs/source/rlm.rst
+++ b/docs/source/rlm.rst
@@ -30,7 +30,8 @@ Detailed examples can be found here:
 .. toctree::
     :maxdepth: 1
 
-    examples/generated/example_rlm
+    examples/notebooks/generated/robust_models_0
+    examples/notebooks/generated/robust_models_1
 
 Technical Documentation
 -----------------------
diff --git a/docs/source/tsa.rst b/docs/source/tsa.rst
index fc44740ea78..be9153f57f1 100644
--- a/docs/source/tsa.rst
+++ b/docs/source/tsa.rst
@@ -74,6 +74,7 @@ Descriptive Statistics and Tests
    stattools.q_stat
    stattools.grangercausalitytests
    stattools.levinson_durbin
+   stattools.arma_order_select_ic
 
 Estimation
 """"""""""
@@ -117,7 +118,7 @@ Vector Autogressive Processes (VAR)
    vector_ar.var_model.VARResults
    vector_ar.dynamic.DynamicVAR
 
-.. seealso:: :ref:`VAR documentation <var>`
+.. seealso:: tutorial :ref:`VAR documentation <var>`
 
 .. currentmodule:: statsmodels.tsa
 
@@ -137,7 +138,7 @@ estimation are available for vector autoregressive processes.
    vector_ar.var_model.FEVD
    vector_ar.dynamic.DynamicVAR
 
-.. seealso:: :ref:`VAR documentation <var>`
+.. seealso:: tutorial :ref:`VAR documentation <var>`
 
 ARMA Process
 """"""""""""
@@ -174,17 +175,18 @@ process for given lag-polynomials.
 
 .. currentmodule:: statsmodels.tsa
 
-Other Time Series Filters
-"""""""""""""""""""""""""
+Time Series Filters
+"""""""""""""""""""
 
 .. autosummary::
    :toctree: generated/
 
-   filters.bkfilter
-   filters.hpfilter
-   filters.arfilter
-   filters.cffilter
-   filters.miso_lfilter
+   filters.bk_filter.bkfilter
+   filters.hp_filter.hpfilter
+   filters.cf_filter.cffilter
+   filters.filtertools.convolution_filter
+   filters.filtertools.recursive_filter
+   filters.filtertools.miso_lfilter
    filters.filtertools.fftconvolve3
    filters.filtertools.fftconvolveinv
 
diff --git a/docs/source/vector_ar.rst b/docs/source/vector_ar.rst
index de7b1be6a51..65dc1d25805 100644
--- a/docs/source/vector_ar.rst
+++ b/docs/source/vector_ar.rst
@@ -24,7 +24,7 @@ and their lagged values is the *vector autoregression process*:
 where :math:`A_i` is a :math:`K \times K` coefficient matrix.
 
 We follow in large part the methods and notation of `Lutkepohl (2005)
-<http://www.springer.com/economics/econometrics/book/978-3-540-26239-8>`__,
+<http://www.springer.com/economics/econometrics/book/978-3-540-40172-8?otherVersion=978-3-540-26239-8>`__,
 which we will not develop here.
 
 Model fitting
@@ -211,7 +211,7 @@ to specify the "initial value" for the forecast:
 ::
 
     >>> lag_order = results.k_ar
-    >>> results.forecast(data.values[-lagorder:], 5)
+    >>> results.forecast(data.values[-lag_order:], 5)
     array([[ 0.00616044,  0.00500006,  0.00916198],
            [ 0.00427559,  0.00344836, -0.00238478],
            [ 0.00416634,  0.0070728 , -0.01193629],
@@ -375,7 +375,7 @@ Dynamic Vector Autoregressions
 
 .. note::
 
-    To use this functionality, `pandas <http://pypi.python.org/pypi/pandas>`__
+    To use this functionality, `pandas <https://pypi.python.org/pypi/pandas>`__
     must be installed. See the `pandas documentation
     <http://pandas.pydata.org>`__ for more information on the below data
     structures.
diff --git a/docs/themes/statsmodels/indexsidebar.html b/docs/themes/statsmodels/indexsidebar.html
index 2d03f5bf63a..fd2ed37424b 100644
--- a/docs/themes/statsmodels/indexsidebar.html
+++ b/docs/themes/statsmodels/indexsidebar.html
@@ -21,7 +21,7 @@ <h3>Participate</h3>
   <input type="submit" name="sub" value="Subscribe" style="margin-left:15px"/>
 </form>
 <p style="font-size:1em">
-    Grab the souce from <a href="https://github.com/statsmodels/statsmodels/">Github</a>.
+    Grab the source from <a href="https://github.com/statsmodels/statsmodels/">Github</a>.
     Report bugs to the <a href="https://github.com/statsmodels/statsmodels/issues">Issue Tracker</a>.
     Have a look at our <a href="{{ pathto('dev/index') }}">Developer</a> and <a href = "{{ pathto('dev/roadmap_todo') }}">Get Involved</a> Pages.
 </p>
diff --git a/docs/themes/statsmodels/layout.html b/docs/themes/statsmodels/layout.html
index a53eb03a0cb..90069d37903 100644
--- a/docs/themes/statsmodels/layout.html
+++ b/docs/themes/statsmodels/layout.html
@@ -7,6 +7,7 @@
 {% extends "basic/layout.html" %}
 {% block extrahead %}
 <link rel="stylesheet" href="../../_static/facebox.css" type="text/css" />
+<link rel="stylesheet" href="../_static/examples.css" type="text/css" />
 <script type="text/javascript" src="{{pathto('_static/scripts.js', 1) }}">
 </script>
 <script type="text/javascript" src="{{pathto('_static/facebox.js', 1) }}">
@@ -27,6 +28,7 @@
 <li><a href="https://github.com/statsmodels/statsmodels/issues">Bugs</a></li> &nbsp;|&nbsp;
 <li><a href="{{ pathto('dev/index') }}">Develop</a></li> &nbsp;|&nbsp;
 <li><a href="{{ pathto('examples/index') }}">Examples</a></li> &nbsp;|&nbsp;
+<li><a href="{{ pathto('faq') }}">FAQ</a></li> &nbsp;|&nbsp;
 {% endblock %}
 
 {# Render the Header with Banner #}
diff --git a/docs/themes/statsmodels/page.html b/docs/themes/statsmodels/page.html
new file mode 100644
index 00000000000..e363835f1d4
--- /dev/null
+++ b/docs/themes/statsmodels/page.html
@@ -0,0 +1,51 @@
+{#
+    Overwrites what is displayed on the examples landing page.
+#}
+{%- extends "layout.html" %}
+{% block body %}
+
+
+{% if pagename == 'examples/index' %}
+<div class="examples-page">
+    <h1>Statsmodels Examples</h1>
+    <div class="padleft">
+This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the <a href="https://github.com/statsmodels/statsmodels/tree/master/examples">statsmodels github repository</a>.<br /><br />
+
+If you are not comfortable with git, we also encourage users to submit their own examples, tutorials or cool statsmodels tricks to the <a href="https://github.com/statsmodels/statsmodels/wiki/Examples">Examples wiki page</a>.
+</div>
+
+
+    <h2 style="padding-left:-30px;">Topics</h2>
+    <div class="padleft">
+    {% for section in examples %}
+    <ul class="toclist">
+        <li><a href="#{{section.id}}">{{section.header}}</a></li>
+    </ul>
+    {% endfor %}
+
+    </div> <!-- end padleft -->
+
+    {% for section in examples %}
+    <h2 id="{{ section.id }}">{{ section.header }}</h2>
+
+    <div class="marketing">
+      <ul class="thumbnails example-sites">
+        {% for link in section.links %}
+        <li class="span4">
+        <p class="marketing-byline">{{link.text}}</p>
+        <a class="thumbnail" href="{{link.target}}" target="_blank">
+            <img src="{{link.img}}" >
+          </a>
+        </li>
+        {% endfor %}
+      </ul>
+  </div>
+    {% endfor %}
+</div>
+{% else %}
+
+  {{ body }}
+
+{% endif %}
+
+{% endblock %}
diff --git a/docs/themes/statsmodels/static/nature.css_t b/docs/themes/statsmodels/static/nature.css_t
index 270ee8798a7..8130bb25014 100644
--- a/docs/themes/statsmodels/static/nature.css_t
+++ b/docs/themes/statsmodels/static/nature.css_t
@@ -266,3 +266,26 @@ div.viewcode-block:target {
     border-top: 1px solid #ac9;
     border-bottom: 1px solid #ac9;
 }
+
+/* For docstrings */
+
+.field-name {
+    white-space: nowrap;
+}
+
+.field-body {
+    margin-top: 2px;
+    margin-bottom: 2px;
+    max-width: 600px;
+}
+
+/* For notebooks */
+div.input_area>div.highlight>pre {
+    -webkit-box-shadow: none;
+    -moz-box-shadow: none;
+}
+
+div.output_area pre {
+    -webkit-box-shadow: none;
+    -moz-box-shadow: none;
+}
diff --git a/examples/example_discrete.py b/examples/example_discrete.py
deleted file mode 100644
index 4bbcab52148..00000000000
--- a/examples/example_discrete.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""Discrete Data Models
-"""
-
-import numpy as np
-import statsmodels.api as sm
-
-# Load data from Spector and Mazzeo (1980). Examples follow Greene's
-# Econometric Analysis Ch. 21 (5th Edition).
-spector_data = sm.datasets.spector.load()
-spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
-
-# Inspect the data:
-spector_data.exog[:5, :]
-spector_data.endog[:5]
-
-# Linear Probability Model (OLS)
-#-------------------------------
-lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)
-lpm_res = lpm_mod.fit()
-print lpm_res.params[:-1]
-
-#Logit Model
-#-----------
-logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
-logit_res = logit_mod.fit()
-print logit_res.params
-logit_margeff = logit_res.get_margeff(method='dydx', at='overall')
-print logit_margeff.summary()
-
-#l1 regularized logit
-#--------------------
-# The regularization parameter alpha should be a scalar or have the same shape
-# as results.params
-alpha = 0.1 * len(spector_data.endog) * np.ones(spector_data.exog.shape[1])
-# Choose not to regularize the constant
-alpha[-1] = 0
-logit_l1_res = logit_mod.fit_regularized(method='l1', alpha=alpha)
-print logit_l1_res.summary()
-
-
-# As in all the discrete data models presented below, we can print a nice
-# summary of results:
-print logit_res.summary()
-
-#Probit Model
-#------------
-probit_mod = sm.Probit(spector_data.endog, spector_data.exog)
-probit_res = probit_mod.fit()
-print probit_res.params
-probit_margeff = probit_res.get_margeff()
-print probit_margeff.summary()
-
-#Multinomial Logit
-#-----------------
-
-# Load data from the American National Election Studies:
-anes_data = sm.datasets.anes96.load()
-anes_exog = anes_data.exog
-anes_exog = sm.add_constant(anes_exog, prepend=False)
-
-# Inspect the data:
-print anes_data.exog[:5, :]
-print anes_data.endog[:5]
-
-# Fit MNL model
-mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
-mlogit_res = mlogit_mod.fit()
-print mlogit_res.params
-mlogit_margeff = mlogit_res.get_margeff()
-print mlogit_margeff.summary()
-
-#l1 regularized Multinomial Logit
-#--------------------------------
-# The regularization parameter alpha should be a scalar or have the same shape as
-# as results.params
-alpha = 10 * np.ones((mlogit_mod.K, mlogit_mod.J - 1))
-# Choose not to regularize the constant
-alpha[-1, :] = 0
-mlogit_mod2 = sm.MNLogit(anes_data.endog, anes_exog)
-mlogit_l1_res = mlogit_mod2.fit_regularized(method='l1', alpha=alpha)
-print mlogit_l1_res.summary()
-
-#Poisson model
-#-------------
-
-# Load the Rand data. Note that this example is similar to Cameron and
-# Trivedi's `Microeconometrics` Table 20.5, but it is slightly different
-# because of minor changes in the data.
-rand_data = sm.datasets.randhie.load()
-rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)
-rand_exog = sm.add_constant(rand_exog, prepend=False)
-
-# Fit Poisson model:
-poisson_mod = sm.Poisson(rand_data.endog, rand_exog)
-poisson_res = poisson_mod.fit(method="newton")
-print poisson_res.summary()
-poisson_margeff = poisson_res.get_margeff()
-print poisson_margeff.summary()
-
-# l1 regularized Poisson model
-poisson_mod2 = sm.Poisson(rand_data.endog, rand_exog)
-alpha = 0.1 * len(rand_data.endog) * np.ones(rand_exog.shape[1])
-alpha[-1] = 0
-poisson_l1_res = poisson_mod2.fit_regularized(method='l1', alpha=alpha)
-
-# Negative binomial model
-#------------------------
-
-# The negative binomial model gives slightly different results:
-mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)
-res_nbin = mod_nbin.fit(disp=False)
-print res_nbin.summary()
-
-#Alternative solvers
-#-------------------
-
-# The default method for fitting discrete data MLE models is Newton-Raphson.
-# You can use other solvers by using the ``method`` argument:
-mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=500)
-
-#.. The below needs a lot of iterations to get it right?
-#.. TODO: Add a technical note on algorithms
-#.. mlogit_res = mlogit_mod.fit(method='ncg') # this takes forever
diff --git a/examples/example_formula_glm.py b/examples/example_formula_glm.py
deleted file mode 100644
index a1720fe7b2b..00000000000
--- a/examples/example_formula_glm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""GLM Formula Example
-"""
-
-import statsmodels.api as sm
-
-star98 = sm.datasets.star98.load_pandas().data
-
-formula = ('SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT '
-           '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF')
-
-dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',
-              'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',
-              'PERSPENK', 'PTRATIO', 'PCTAF']]
-
-endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))
-del dta['NABOVE']
-dta['SUCCESS'] = endog
-
-mod = sm.GLM.from_formula(formula=formula, data=dta,
-                          family=sm.families.Binomial()).fit()
-
-# try passing a formula object, using arbitrary user-injected code
-
-
-def double_it(x):
-    return 2 * x
-
-formula = ('SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + '
-           'PCTCHRT + PCTYRRND + PERMINTE*AVYRSEXP*AVSALK'
-           '+ PERSPENK*PTRATIO*PCTAF')
-mod2 = sm.GLM.from_formula(formula=formula, data=dta,
-                           family=sm.families.Binomial()).fit()
diff --git a/examples/example_glm.py b/examples/example_glm.py
deleted file mode 100644
index 784f42d43df..00000000000
--- a/examples/example_glm.py
+++ /dev/null
@@ -1,141 +0,0 @@
-"""Generalized Linear Models
-"""
-import numpy as np
-import statsmodels.api as sm
-from scipy import stats
-from matplotlib import pyplot as plt
-
-#GLM: Binomial response data
-#---------------------------
-
-#Load data
-#^^^^^^^^^
-# In this example, we use the Star98 dataset which was taken with permission
-# from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook
-# information can be obtained by typing:
-print sm.datasets.star98.NOTE
-
-# Load the data and add a constant to the exogenous (independent) variables:
-data = sm.datasets.star98.load()
-data.exog = sm.add_constant(data.exog, prepend=False)
-
-# The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW):
-print data.endog[:5, :]
-
-# The independent variables include all the other variables described above, as
-# well as the interaction terms:
-print data.exog[:2, :]
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())
-res = glm_binom.fit()
-print res.summary()
-
-#Quantities of interest
-#^^^^^^^^^^^^^^^^^^^^^^
-# Total number of trials:
-print data.endog[0].sum()
-
-# Parameter estimates:
-print res.params
-
-# The corresponding t-values:
-print res.tvalues
-
-# First differences: We hold all explanatory variables constant at their means
-# and manipulate the percentage of low income households to assess its impact
-# on the response variables:
-means = data.exog.mean(axis=0)
-means25 = means.copy()
-means25[0] = stats.scoreatpercentile(data.exog[:, 0], 25)
-means75 = means.copy()
-means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:, 0], 75)
-resp_25 = res.predict(means25)
-resp_75 = res.predict(means75)
-diff = resp_75 - resp_25
-
-# The interquartile first difference for the percentage of low income
-# households in a school district is:
-print '%2.4f%%' % (diff * 100)
-
-#Plots
-#^^^^^
-
-# We extract information that will be used to draw some interesting plots:
-nobs = res.nobs
-y = data.endog[:, 0] / data.endog.sum(1)
-yhat = res.mu
-
-# Plot yhat vs y:
-plt.figure();
-plt.scatter(yhat, y);
-
-line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=False)).fit().params
-fit = lambda x: line_fit[1] + line_fit[0] * x  # better way in scipy?
-
-plt.plot(np.linspace(0, 1, nobs), fit(np.linspace(0, 1, nobs)));
-plt.title('Model Fit Plot');
-plt.ylabel('Observed values');
-#@savefig glm_fitted.png
-plt.xlabel('Fitted values');
-
-# Plot yhat vs. Pearson residuals:
-plt.figure();
-plt.scatter(yhat, res.resid_pearson);
-plt.plot([0.0, 1.0], [0.0, 0.0], 'k-');
-plt.title('Residual Dependence Plot');
-plt.ylabel('Pearson Residuals');
-#@savefig glm_resids.png
-plt.xlabel('Fitted values');
-
-#Histogram of standardized deviance residuals
-plt.figure();
-resid = res.resid_deviance.copy()
-resid_std = (resid - resid.mean()) / resid.std()
-plt.hist(resid_std, bins=25);
-#@savefig glm_hist_res.png
-plt.title('Histogram of standardized deviance residuals');
-
-#QQ Plot of Deviance Residuals
-from statsmodels import graphics
-#@savefig glm_qqplot.png
-graphics.gofplots.qqplot(resid, line='r');
-
-#GLM: Gamma for proportional count response
-#------------------------------------------
-#Load data
-#^^^^^^^^^
-# In the example above, we printed the ``NOTE`` attribute to learn about the
-# Star98 dataset. Statsmodels datasets ships with other useful information. For
-# example:
-print sm.datasets.scotland.DESCRLONG
-
-# Load the data and add a constant to the exogenous variables:
-data2 = sm.datasets.scotland.load()
-data2.exog = sm.add_constant(data2.exog, prepend=False)
-print data2.exog[:5, :]
-print data2.endog[:5]
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())
-glm_results = glm_gamma.fit()
-print glm_results.summary()
-
-#GLM: Gaussian distribution with a noncanonical link
-#---------------------------------------------------
-#Artificial data
-#^^^^^^^^^^^^^^^
-nobs2 = 100
-x = np.arange(nobs2)
-np.random.seed(54321)
-X = np.column_stack((x, x**2))
-X = sm.add_constant(X, prepend=False)
-lny = np.exp(-(.03 * x + .0001 * x**2 - 1.0)) + .001 * np.random.rand(nobs2)
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))
-gauss_log_results = gauss_log.fit()
-print gauss_log_results.summary()
diff --git a/examples/example_gls.py b/examples/example_gls.py
deleted file mode 100644
index 888388e2077..00000000000
--- a/examples/example_gls.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""Generalized Least Squares
-"""
-
-import statsmodels.api as sm
-data = sm.datasets.longley.load()
-data.exog = sm.add_constant(data.exog)
-
-# The Longley dataset is a time series dataset
-# Let's assume that the data is heteroskedastic and that we know
-# the nature of the heteroskedasticity.  We can then define
-# `sigma` and use it to give us a GLS model
-
-# First we will obtain the residuals from an OLS fit
-
-ols_resid = sm.OLS(data.endog, data.exog).fit().resid
-
-# Assume that the error terms follow an AR(1) process with a trend
-# resid[i] = beta_0 + rho*resid[i-1] + e[i]
-# where e ~ N(0,some_sigma**2)
-# and that rho is simply the correlation of the residuals
-# a consistent estimator for rho is to regress the residuals
-# on the lagged residuals
-
-resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()
-print resid_fit.tvalues[1]
-print resid_fit.pvalues[1]
-# While we don't have strong evidence that the errors follow an AR(1)
-# process we continue
-
-rho = resid_fit.params[1]
-
-# As we know, an AR(1) process means that near-neighbors have a stronger
-# relation so we can give this structure by using a toeplitz matrix
-
-from scipy.linalg import toeplitz
-
-toeplitz(range(5))
-#.. array([[0, 1, 2, 3, 4],
-#..       [1, 0, 1, 2, 3],
-#..       [2, 1, 0, 1, 2],
-#..       [3, 2, 1, 0, 1],
-#..       [4, 3, 2, 1, 0]])
-
-order = toeplitz(range(len(ols_resid)))
-
-# so that our error covariance structure is actually rho**order
-# which defines an autocorrelation structure
-
-sigma = rho**order
-
-gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)
-gls_results = gls_model.fit()
-
-# of course, the exact rho in this instance is not known so it
-# it might make more sense to use feasible gls, which currently only
-# has experimental support
-
-# We can use the GLSAR model with one lag, to get to a similar result
-
-glsar_model = sm.GLSAR(data.endog, data.exog, 1)
-glsar_results = glsar_model.iterative_fit(1)
-
-# comparing gls and glsar results, we see that there are some small
-# differences in the parameter estimates and the resulting standard
-# errors of the parameter estimate. This might be do to the numerical
-# differences in the algorithm, e.g. the treatment of initial conditions,
-# because of the small number of observations in the longley dataset.
-print gls_results.params
-print glsar_results.params
-print gls_results.bse
-print glsar_results.bse
diff --git a/examples/example_gmle.py b/examples/example_gmle.py
deleted file mode 100644
index 17f84494c29..00000000000
--- a/examples/example_gmle.py
+++ /dev/null
@@ -1,151 +0,0 @@
-'''Generic Maximum Likelihood Models'''
-
-#This tutorial explains how to quickly implement new maximum likelihood models
-#in ``statsmodels``. The `GenericLikelihoodModel
-#<../../dev/generated/statsmodels.base.model.GenericLikelihoodModel.html#statsmodels.base.model.GenericLikelihoodModel>`_
-#class eases the process by providing tools such as automatic numeric
-#differentiation and a unified interface to ``scipy`` optimization functions.
-#Using ``statsmodels``, users can fit new MLE models simply by "plugging-in" a
-#log-likelihood function.
-
-#
-#Negative Binomial Regression for Count Data
-#-------------------------------------------
-
-#Consider a negative binomial regression model for count data with
-#log-likelihood (type NB-2) function expressed as:
-
-#.. math::
-#
-#    \mathcal{L}(\beta_j; y, \alpha) = \sum_{i=1}^n y_i ln
-#    \left ( \frac{\alpha exp(X_i'\beta)}{1+\alpha exp(X_i'\beta)} \right ) -
-#    \frac{1}{\alpha} ln(1+\alpha exp(X_i'\beta)) \\
-#    + ln \Gamma (y_i + 1/\alpha) - ln \Gamma (y_i+1) - ln \Gamma (1/\alpha)
-
-#with a matrix of regressors :math:`X`, a vector of coefficients :math:`\beta`,
-#and the negative binomial heterogeneity parameter :math:`\alpha`.
-
-#Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood
-#simply as:
-
-import numpy as np
-from scipy.stats import nbinom
-
-
-def _ll_nb2(y, X, beta, alph):
-    mu = np.exp(np.dot(X, beta))
-    size = 1 / alph
-    prob = size / (size + mu)
-    ll = nbinom.logpmf(y, size, prob)
-    return ll
-
-
-#New Model Class
-#---------------
-
-#We create a new model class which inherits from ``GenericLikelihoodModel``:
-
-from statsmodels.base.model import GenericLikelihoodModel
-
-
-class NBin(GenericLikelihoodModel):
-    def __init__(self, endog, exog, **kwds):
-        super(NBin, self).__init__(endog, exog, **kwds)
-
-    def nloglikeobs(self, params):
-        alph = params[-1]
-        beta = params[:-1]
-        ll = _ll_nb2(self.endog, self.exog, beta, alph)
-        return -ll
-
-    def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):
-        if start_params == None:
-            # Reasonable starting values
-            start_params = np.append(np.zeros(self.exog.shape[1]), .5)
-            start_params[0] = np.log(self.endog.mean())
-        return super(NBin, self).fit(start_params=start_params,
-                                     maxiter=maxiter, maxfun=maxfun,
-                                     **kwds)
-
-#Two important things to notice:
-
-#+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix).
-#+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.
-
-#That's it! You're done!
-
-#Usage Example
-#-------------
-
-#The `Medpar <http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html>`_
-#dataset is hosted in CSV format at the `Rdatasets repository
-#<http://vincentarelbundock.github.com/Rdatasets>`_. We use the ``read_csv``
-#function from the `Pandas library <http://pandas.pydata.org>`_ to load the data
-#in memory. We then print the first few columns:
-
-import pandas as pd
-url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'
-medpar = pd.read_csv(url)
-medpar.head()
-
-#The model we are interested in has a vector of non-negative integers as
-#dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,
-#``type3``, ``hmo``, ``white``.
-
-#For estimation, we need to create 2 numpy arrays (pandas DataFrame should also
-#work): a 1d array of length *N* to hold ``los`` values, and a *N* by 5
-#array to hold our 5 regressors. These arrays can be constructed manually or
-#using any number of helper functions; the details matter little for our current
-#purposes.  Here, we build the arrays we need using the `Patsy
-#<http://patsy.readthedocs.org>`_ package:
-
-import patsy
-y, X = patsy.dmatrices('los~type2+type3+hmo+white', medpar)
-print y[:5]
-print X[:5]
-
-#Then, we fit the model and extract some information:
-
-mod = NBin(y, X)
-res = mod.fit()
-
-# Extract parameter estimates, standard errors, p-values, AIC, etc.:
-res.params
-res.bse
-res.pvalues
-res.aic
-
-#As usual, you can obtain a full list of available information by typing
-#``dir(res)``.
-#
-#To ensure that the above results are sound, we compare them to results
-# obtained using the MASS implementation for R::
-#
-#    url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'
-#    medpar = read.csv(url)
-#    f = los~factor(type)+hmo+white
-#
-#    library(MASS)
-#    mod = glm.nb(f, medpar)
-#    coef(summary(mod))
-#                     Estimate Std. Error   z value      Pr(>|z|)
-#    (Intercept)    2.31027893 0.06744676 34.253370 3.885556e-257
-#    factor(type)2  0.22124898 0.05045746  4.384861  1.160597e-05
-#    factor(type)3  0.70615882 0.07599849  9.291748  1.517751e-20
-#    hmo           -0.06795522 0.05321375 -1.277024  2.015939e-01
-#    white         -0.12906544 0.06836272 -1.887951  5.903257e-02
-
-#Numerical precision
-#^^^^^^^^^^^^^^^^^^^
-
-#The ``statsmodels`` and ``R`` parameter estimates agree up to the fourth
-#decimal. The standard errors, however, agree only up to the second decimal.
-#This discrepancy may be the result of imprecision in our Hessian numerical
-#estimates. In the current context, the difference between ``MASS`` and
-#``statsmodels`` standard error estimates is substantively irrelevant, but it
-#highlights the fact that users who need very precise estimates may not always
-#want to rely on default settings when using numerical derivatives. In such
-#cases, it may be better to use analytical derivatives with the `LikelihoodModel
-#<../../dev/generated/statsmodels.base.model.GenericLikelihoodModel.html#statsmodels.base.model.GenericLikelihoodModel>`_
-#class.
-
diff --git a/examples/example_interactions.py b/examples/example_interactions.py
deleted file mode 100644
index 4dc499a8616..00000000000
--- a/examples/example_interactions.py
+++ /dev/null
@@ -1,373 +0,0 @@
-"""Interactions and ANOVA
-"""
-#.. note:: This script is based heavily on Jonathan Taylor's class notes
-#          http://www.stanford.edu/class/stats191/interactions.html
-
-from urllib2 import urlopen
-
-import numpy as np
-#@suppress
-np.set_printoptions(precision=4, suppress=True)
-import pandas
-import matplotlib.pyplot as plt
-
-from statsmodels.formula.api import ols
-from statsmodels.graphics.api import interaction_plot, abline_plot
-from statsmodels.stats.anova import anova_lm
-
-
-# load data
-
-try:
-    salary_table = pandas.read_csv('./salary.table')
-except:
-    print "fetching from website"
-    url = 'http://stats191.stanford.edu/data/salary.table'
-    #the next line is not necessary with recent version of pandas
-    url = urlopen(url)
-    salary_table = pandas.read_table(url)
-    salary_table.to_csv('salary.table', index=False)
-
-E = salary_table.E
-M = salary_table.M
-X = salary_table.X
-S = salary_table.S
-
-# Take a look at the data
-plt.figure(figsize=(6, 6));
-symbols = ['D', '^']
-colors = ['r', 'g', 'blue']
-factor_groups = salary_table.groupby(['E', 'M'])
-for values, group in factor_groups:
-    i, j = values
-    plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i - 1],
-                s=144)
-plt.xlabel('Experience');
-#@savefig raw_data_interactions.png align=center
-plt.ylabel('Salary');
-
-# Fit a linear model
-formula = 'S ~ C(E) + C(M) + X'
-lm = ols(formula, salary_table).fit()
-print lm.summary()
-
-# Have a look at the created design matrix
-lm.model.exog[:20]
-
-# Or since we initially passed in a DataFrame, we have a DataFrame available in
-lm.model.data.orig_exog
-
-# We keep a reference to the original untouched data in
-lm.model.data.frame
-
-# Get influence statistics
-infl = lm.get_influence()
-
-print infl.summary_table()
-
-# or get a dataframe
-df_infl = infl.summary_frame()
-
-#Now plot the reiduals within the groups separately
-resid = lm.resid
-
-plt.figure(figsize=(6, 6));
-for values, group in factor_groups:
-    i, j = values
-    group_num = i * 2 + j - 1  # for plotting purposes
-    x = [group_num] * len(group)
-    plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i - 1],
-                s=144, edgecolors='black')
-plt.xlabel('Group');
-#@savefig residual_groups.png align=center
-plt.ylabel('Residuals');
-
-# now we will test some interactions using anova or f_test
-
-interX_lm = ols("S ~ C(E) * X + C(M)", salary_table).fit()
-print interX_lm.summary()
-
-# Do an ANOVA check
-table1 = anova_lm(lm, interX_lm)
-print table1
-
-interM_lm = ols("S ~ X + C(E)*C(M)", data=salary_table).fit()
-print interM_lm.summary()
-
-table2 = anova_lm(lm, interM_lm)
-print table2
-
-# The design matrix as a DataFrame
-interM_lm.model.data.orig_exog
-# The design matrix as an ndarray
-interM_lm.model.exog
-interM_lm.model.exog_names
-
-infl = interM_lm.get_influence()
-resid = infl.resid_studentized_internal
-
-plt.figure(figsize=(6, 6));
-for values, group in factor_groups:
-    i, j = values
-    idx = group.index
-    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i - 1],
-            s=144, edgecolors='black')
-
-plt.xlabel('X');
-#@savefig standardized_resids.png align=center
-plt.ylabel('standardized resids');
-
-# Looks like one observation is an outlier.
-#TODO: do we have Bonferonni outlier test?
-
-drop_idx = abs(resid).argmax()
-print drop_idx  # zero-based index
-idx = salary_table.index.drop([drop_idx])
-
-lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()
-
-print lm32.summary()
-
-interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()
-
-print interX_lm32.summary()
-
-table3 = anova_lm(lm32, interX_lm32)
-print table3
-
-interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()
-
-table4 = anova_lm(lm32, interM_lm32)
-print table4
-
-# Replot the residuals
-try:
-    resid = interM_lm32.get_influence().summary_frame()['standard_resid']
-except:
-    resid = interM_lm32.get_influence().summary_frame()['standard_resid']
-
-plt.figure(figsize=(6, 6));
-for values, group in factor_groups:
-    i, j = values
-    idx = group.index
-    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i - 1],
-            s=144, edgecolors='black')
-plt.xlabel('X[~[32]]');
-#@savefig standardized_drop32.png align=center
-plt.ylabel('standardized resids');
-
-# Plot the fitted values
-
-lm_final = ols('S ~ X + C(E)*C(M)',
-               data=salary_table.drop([drop_idx])).fit()
-mf = lm_final.model.data.orig_exog
-lstyle = ['-', '--']
-
-plt.figure(figsize=(6, 6));
-for values, group in factor_groups:
-    i, j = values
-    idx = group.index
-    plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i - 1],
-            s=144, edgecolors='black')
-    # drop NA because there is no idx 32 in the final model
-    plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),
-            ls=lstyle[j], color=colors[i - 1])
-plt.xlabel('Experience');
-#@savefig fitted_drop32.png align=center
-plt.ylabel('Salary');
-
-# From our first look at the data, the difference between Master's and PhD in
-# the management group is different than in the non-management group.
-# This is an interaction between the two qualitative variables management, M
-# and education,E. We can visualize this by first removing the effect of
-# experience, then plotting the means within each of the 6 groups
-# using interaction.plot.
-
-U = S - X * interX_lm32.params['X']
-
-plt.figure(figsize=(6, 6));
-#@savefig interaction_plot.png align=center
-interaction_plot(E, M, U, colors=['red', 'blue'], markers=['^', 'D'],
-        markersize=10, ax=plt.gca())
-
-# Minority Employment Data
-# ------------------------
-
-try:
-    minority_table = pandas.read_table('minority.table')
-except:  # don't have data already
-    url = 'http://stats191.stanford.edu/data/minority.table'
-    #the next line is not necessary with recent version of pandas
-    url = urlopen(url)
-    minority_table = pandas.read_table(url)
-
-factor_group = minority_table.groupby(['ETHN'])
-
-plt.figure(figsize=(6, 6));
-colors = ['purple', 'green']
-markers = ['o', 'v']
-for factor, group in factor_group:
-    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
-                marker=markers[factor], s=12**2)
-plt.xlabel('TEST');
-#@savefig group_test.png align=center
-plt.ylabel('JPERF');
-
-min_lm = ols('JPERF ~ TEST', data=minority_table).fit()
-print min_lm.summary()
-
-plt.figure(figsize=(6, 6));
-for factor, group in factor_group:
-    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
-                marker=markers[factor], s=12**2)
-
-plt.xlabel('TEST')
-plt.ylabel('JPERF')
-#@savefig abline.png align=center
-abline_plot(model_results=min_lm, ax=plt.gca())
-
-min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',
-        data=minority_table).fit()
-
-print min_lm2.summary()
-
-plt.figure(figsize=(6, 6));
-for factor, group in factor_group:
-    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
-                marker=markers[factor], s=12**2)
-
-abline_plot(intercept=min_lm2.params['Intercept'],
-            slope=min_lm2.params['TEST'], ax=plt.gca(), color='purple')
-#@savefig abline2.png align=center
-abline_plot(intercept=min_lm2.params['Intercept'],
-            slope=min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],
-            ax=plt.gca(), color='green')
-
-
-min_lm3 = ols('JPERF ~ TEST + ETHN', data=minority_table).fit()
-print min_lm3.summary()
-
-plt.figure(figsize=(6, 6));
-for factor, group in factor_group:
-    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
-                marker=markers[factor], s=12**2)
-
-abline_plot(intercept=min_lm3.params['Intercept'],
-            slope=min_lm3.params['TEST'], ax=plt.gca(), color='purple')
-
-#@savefig abline3.png align=center
-abline_plot(intercept=min_lm3.params['Intercept'] + min_lm3.params['ETHN'],
-            slope=min_lm3.params['TEST'], ax=plt.gca(), color='green')
-
-
-min_lm4 = ols('JPERF ~ TEST * ETHN', data=minority_table).fit()
-print min_lm4.summary()
-
-plt.figure(figsize=(6, 6));
-for factor, group in factor_group:
-    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
-                marker=markers[factor], s=12**2)
-
-abline_plot(intercept=min_lm4.params['Intercept'],
-            slope=min_lm4.params['TEST'], ax=plt.gca(), color='purple')
-#@savefig abline4.png align=center
-abline_plot(intercept=min_lm4.params['Intercept'] + min_lm4.params['ETHN'],
-        slope=min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],
-        ax=plt.gca(), color='green')
-
-# is there any effect of ETHN on slope or intercept
-table5 = anova_lm(min_lm, min_lm4)
-print table5
-# is there any effect of ETHN on intercept
-table6 = anova_lm(min_lm, min_lm3)
-print table6
-# is there any effect of ETHN on slope
-table7 = anova_lm(min_lm, min_lm2)
-print table7
-# is it just the slope or both?
-table8 = anova_lm(min_lm2, min_lm4)
-print table8
-
-
-# One-way ANOVA
-# -------------
-try:
-    rehab_table = pandas.read_csv('rehab.table')
-except:
-    url = 'http://stats191.stanford.edu/data/rehab.csv'
-    #the next line is not necessary with recent version of pandas
-    url = urlopen(url)
-    rehab_table = pandas.read_table(url, delimiter=",")
-    rehab_table.to_csv('rehab.table')
-
-plt.figure(figsize=(6, 6));
-#@savefig plot_boxplot.png align=center
-rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())
-
-rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit()
-table9 = anova_lm(rehab_lm)
-
-print table9
-print rehab_lm.model.data.orig_exog
-print rehab_lm.summary()
-
-# Two-way ANOVA
-# -------------
-
-try:
-    kidney_table = pandas.read_table('./kidney.table')
-except:
-    url = 'http://stats191.stanford.edu/data/kidney.table'
-    #the next line is not necessary with recent version of pandas
-    url = urlopen(url)
-    kidney_table = pandas.read_table(url, delimiter=" *")
-
-# Explore the dataset
-print kidney_table.groupby(['Weight', 'Duration']).size()
-# balanced panel
-
-kt = kidney_table
-plt.figure(figsize=(6, 6));
-#@savefig kidney_interactiong.png align=center
-interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days'] + 1),
-                 colors=['red', 'blue'], markers=['D', '^'], ms=10,
-                 ax=plt.gca())
-
-# You have things available in the calling namespace available
-# in the formula evaluation namespace
-kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()
-
-table10 = anova_lm(kidney_lm)
-
-print anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',
-                data=kt).fit(), kidney_lm)
-print anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(),
-               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',
-                   data=kt).fit())
-print anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(),
-               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',
-                   data=kt).fit())
-
-# Sum of squares
-# --------------
-#
-# Illustrates the use of different types of sums of squares (I,II,II)
-# and how the Sum contrast can be used to produce the same output between
-# the 3.
-
-# Types I and II are equivalent under a balanced design.
-
-# Don't use Type III with non-orthogonal contrast - ie., Treatment
-
-sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',
-             data=kt).fit()
-
-print anova_lm(sum_lm)
-print anova_lm(sum_lm, typ=2)
-print anova_lm(sum_lm, typ=3)
-
-nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',
-            data=kt).fit()
-print anova_lm(nosum_lm)
-print anova_lm(nosum_lm, typ=2)
-print anova_lm(nosum_lm, typ=3)
diff --git a/examples/example_ols.py b/examples/example_ols.py
deleted file mode 100644
index ecda9e627ad..00000000000
--- a/examples/example_ols.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""Ordinary Least Squares
-"""
-import numpy as np
-import statsmodels.api as sm
-import matplotlib.pyplot as plt
-from statsmodels.sandbox.regression.predstd import wls_prediction_std
-np.random.seed(9876789)
-
-#OLS Estimation
-#--------------
-
-#Artificial data
-#^^^^^^^^^^^^^^^^
-nsample = 100
-x = np.linspace(0, 10, 100)
-X = np.column_stack((x, x**2))
-beta = np.array([1, 0.1, 10])
-e = np.random.normal(size=nsample)
-
-# Our model needs an intercept so we add a column of 1s:
-X = sm.add_constant(X, prepend=False)
-y = np.dot(X, beta) + e
-
-# Inspect data
-print X[:5, :]
-print y[:5]
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-
-model = sm.OLS(y, X)
-results = model.fit()
-print results.summary()
-
-# Quantities of interest can be extracted directly from the fitted model. Type
-# ``dir(results)`` for a full list. Here are some examples:
-print results.params
-print results.rsquared
-
-#OLS non-linear curve but linear in parameters
-#---------------------------------------------
-
-#Artificial data
-#^^^^^^^^^^^^^^^
-# Non-linear relationship between x and y
-nsample = 50
-sig = 0.5
-x = np.linspace(0, 20, nsample)
-X = np.c_[x, np.sin(x), (x - 5)**2, np.ones(nsample)]
-beta = [0.5, 0.5, -0.02, 5.]
-y_true = np.dot(X, beta)
-y = y_true + sig * np.random.normal(size=nsample)
-
-# Fit and summary
-#^^^^^^^^^^^^^^^^
-res = sm.OLS(y, X).fit()
-print res.summary()
-
-# Extract other quantities of interest
-print res.params
-print res.bse
-print res.predict()
-
-# Draw a plot to compare the true relationship to OLS predictions. Confidence
-# intervals around the predictions are built using the ``wls_prediction_std``
-# command.
-plt.figure();
-plt.plot(x, y, 'o', x, y_true, 'b-');
-prstd, iv_l, iv_u = wls_prediction_std(res);
-plt.plot(x, res.fittedvalues, 'r--.');
-plt.plot(x, iv_u, 'r--');
-plt.plot(x, iv_l, 'r--');
-#@savefig ols_predict_0.png
-plt.title('blue: true,   red: OLS');
-
-#OLS with dummy variables
-#------------------------
-
-#Artificial data
-#^^^^^^^^^^^^^^^^
-# We create 3 groups which will be modelled using dummy variables. Group 0 is
-# the omitted/benchmark category.
-nsample = 50
-groups = np.zeros(nsample, int)
-groups[20:40] = 1
-groups[40:] = 2
-dummy = (groups[:, None] == np.unique(groups)).astype(float)
-x = np.linspace(0, 20, nsample)
-X = np.c_[x, dummy[:, 1:], np.ones(nsample)]
-beta = [1., 3, -3, 10]
-y_true = np.dot(X, beta)
-e = np.random.normal(size=nsample)
-y = y_true + e
-
-# Inspect the data
-print X[:5, :]
-print y[:5]
-print groups
-print dummy[:5, :]
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-res2 = sm.OLS(y, X).fit()
-print res.summary()
-print res2.params
-print res2.bse
-print res.predict()
-
-# Draw a plot to compare the true relationship to OLS predictions.
-prstd, iv_l, iv_u = wls_prediction_std(res2);
-plt.figure();
-plt.plot(x, y, 'o', x, y_true, 'b-');
-plt.plot(x, res2.fittedvalues, 'r--.');
-plt.plot(x, iv_u, 'r--');
-plt.plot(x, iv_l, 'r--');
-#@savefig ols_predict_1.png
-plt.title('blue: true,   red: OLS');
-
-#Joint hypothesis tests
-#----------------------
-
-#F test
-#^^^^^^
-# We want to test the hypothesis that both coefficients on the dummy variables
-# are equal to zero, that is, :math:`R \times \beta = 0`. An F test leads us to
-# strongly reject the null hypothesis of identical constant in the 3 groups:
-
-R = [[0, 1, 0, 0], [0, 0, 1, 0]]
-print np.array(R)
-print res2.f_test(R)
-
-#T test
-#^^^^^^
-# We want to test the null hypothesis that the effects of the 2nd and 3rd
-# groups add to zero. The T-test allows us to reject the Null (but note the
-# one-sided p-value):
-R = [0, 1, -1, 0]
-print res2.t_test(R)
-
-#Small group effects
-#^^^^^^^^^^^^^^^^^^^
-# If we generate artificial data with smaller group effects, the T test can no
-# longer reject the Null hypothesis:
-beta = [1., 0.3, -0.0, 10]
-y_true = np.dot(X, beta)
-y = y_true + np.random.normal(size=nsample)
-res3 = sm.OLS(y, X).fit()
-print res3.f_test(R)
-
-#Multicollinearity
-#-----------------
-
-#Data
-#^^^^
-# The Longley dataset is well known to have high multicollinearity, that is,
-# the exogenous predictors are highly correlated. This is problematic because
-# it can affect the stability of our coefficient estimates as we make minor
-# changes to model specification.
-from statsmodels.datasets.longley import load
-y = load().endog
-X = load().exog
-X = sm.tools.add_constant(X, prepend=False)
-
-#Fit and summary
-#^^^^^^^^^^^^^^^
-ols_model = sm.OLS(y, X)
-ols_results = ols_model.fit()
-print ols_results.summary()
-
-#Condition number
-#^^^^^^^^^^^^^^^^
-# One way to assess multicollinearity is to compute the condition number.
-# Values over 20 are worrisome (see Greene 4.9). The first step is to normalize
-# the independent variables to have unit length:
-norm_x = np.ones_like(X)
-for i in range(int(ols_model.df_model)):
-    norm_x[:, i] = X[:, i] / np.linalg.norm(X[:, i])
-norm_xtx = np.dot(norm_x.T, norm_x)
-
-# Then, we take the square root of the ratio of the biggest to the smallest
-# eigen values.
-eigs = np.linalg.eigvals(norm_xtx)
-condition_number = np.sqrt(eigs.max() / eigs.min())
-print condition_number
-
-#Dropping an observation
-#^^^^^^^^^^^^^^^^^^^^^^^
-# Greene also points out that dropping a single observation can have a dramatic
-# effect on the coefficient estimates:
-ols_results2 = sm.OLS(y[:-1], X[:-1, :]).fit()
-res_dropped = ols_results.params / ols_results2.params * 100 - 100
-print 'Percentage change %4.2f%%\n' * 7 % tuple(i for i in res_dropped)
diff --git a/examples/example_predict.py b/examples/example_predict.py
deleted file mode 100644
index b41504aaa78..00000000000
--- a/examples/example_predict.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Out of sample prediction
-"""
-
-import numpy as np
-import statsmodels.api as sm
-
-#Create some data
-
-nsample = 50
-sig = 0.25
-x1 = np.linspace(0, 20, nsample)
-X = np.c_[x1, np.sin(x1), (x1 - 5)**2, np.ones(nsample)]
-beta = [0.5, 0.5, -0.02, 5.]
-y_true = np.dot(X, beta)
-y = y_true + sig * np.random.normal(size=nsample)
-
-#Setup and estimate the model
-
-olsmod = sm.OLS(y, X)
-olsres = olsmod.fit()
-print olsres.params
-print olsres.bse
-
-#In-sample prediction
-
-ypred = olsres.predict(X)
-
-#Create a new sample of explanatory variables Xnew, predict and plot
-
-x1n = np.linspace(20.5, 25, 10)
-Xnew = np.c_[x1n, np.sin(x1n), (x1n - 5)**2, np.ones(10)]
-ynewpred = olsres.predict(Xnew)  # predict out of sample
-print ypred
-
-import matplotlib.pyplot as plt
-plt.figure();
-plt.plot(x1, y, 'o', x1, y_true, 'b-');
-plt.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r');
-#@savefig ols_predict.png
-plt.title('OLS prediction, blue: true and data, fitted/predicted values:red');
diff --git a/examples/example_rlm.py b/examples/example_rlm.py
deleted file mode 100644
index f7fb03e162e..00000000000
--- a/examples/example_rlm.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""
-Robust Linear Models
-
-Notes
------
-The syntax for the arguments will be shortened to accept string arguments
-in the future.
-"""
-
-import numpy as np
-import statsmodels.api as sm
-import matplotlib.pyplot as plt
-from statsmodels.sandbox.regression.predstd import wls_prediction_std
-
-#Estimating RLM
-#--------------
-
-# Load data
-data = sm.datasets.stackloss.load()
-data.exog = sm.add_constant(data.exog)
-
-# Huber's T norm with the (default) median absolute deviation scaling
-huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())
-hub_results = huber_t.fit()
-print hub_results.params
-print hub_results.bse
-varnames = ['var_%d' % i for i in range(len(hub_results.params))]
-print hub_results.summary(yname='y', xname=varnames)
-
-# Huber's T norm with 'H2' covariance matrix
-hub_results2 = huber_t.fit(cov="H2")
-print hub_results2.params
-print hub_results2.bse
-
-# Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix
-andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())
-andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(),
-                                cov='H3')
-print andrew_results.params
-
-# See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for
-# scale options
-
-#Comparing OLS and RLM
-#---------------------
-
-#Artificial data
-#^^^^^^^^^^^^^^^
-nsample = 50
-x1 = np.linspace(0, 20, nsample)
-X = np.c_[x1, (x1 - 5)**2, np.ones(nsample)]
-sig = 0.3   # smaller error variance makes OLS<->RLM contrast bigger
-beta = [0.5, -0.0, 5.]
-y_true2 = np.dot(X, beta)
-y2 = y_true2 + sig * 1. * np.random.normal(size=nsample)
-y2[[39, 41, 43, 45, 48]] -= 5   # add some outliers (10% of nsample)
-
-#Example: quadratic function with linear truth
-#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-# Note that the quadratic term in OLS regression will capture outlier effects.
-res = sm.OLS(y2, X).fit()
-print res.params
-print res.bse
-print res.predict
-
-# Estimate RLM
-resrlm = sm.RLM(y2, X).fit()
-print resrlm.params
-print resrlm.bse
-
-# Draw a plot to compare OLS estimates to the robust estimates
-plt.figure();
-plt.plot(x1, y2, 'o', x1, y_true2, 'b-');
-prstd, iv_l, iv_u = wls_prediction_std(res);
-plt.plot(x1, res.fittedvalues, 'r-');
-plt.plot(x1, iv_u, 'r--');
-plt.plot(x1, iv_l, 'r--');
-plt.plot(x1, resrlm.fittedvalues, 'g.-');
-#@savefig rlm_ols_0.png
-plt.title('blue: true,   red: OLS,   green: RLM');
-
-#Example: linear function with linear truth
-#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-# Fit a new OLS model using only the linear term and the constant
-X2 = X[:, [0, 2]]
-res2 = sm.OLS(y2, X2).fit()
-print res2.params
-print res2.bse
-
-# Estimate RLM
-resrlm2 = sm.RLM(y2, X2).fit()
-print resrlm2.params
-print resrlm2.bse
-
-# Draw a plot to compare OLS estimates to the robust estimates
-prstd, iv_l, iv_u = wls_prediction_std(res2)
-plt.figure();
-plt.plot(x1, y2, 'o', x1, y_true2, 'b-');
-plt.plot(x1, res2.fittedvalues, 'r-');
-plt.plot(x1, iv_u, 'r--');
-plt.plot(x1, iv_l, 'r--');
-plt.plot(x1, resrlm2.fittedvalues, 'g.-');
-#@savefig rlm_ols_1.png
-plt.title('blue: true,   red: OLS,   green: RLM');
diff --git a/examples/example_wls.py b/examples/example_wls.py
deleted file mode 100644
index 6fac1c5fe3b..00000000000
--- a/examples/example_wls.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""Weighted Least Squares
-"""
-
-import numpy as np
-from scipy import stats
-import statsmodels.api as sm
-import matplotlib.pyplot as plt
-from statsmodels.sandbox.regression.predstd import wls_prediction_std
-from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)
-np.random.seed(1024)
-
-# WLS Estimation
-# --------------
-
-# Artificial data: Heteroscedasticity 2 groups
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-# Model assumptions:
-#
-# * Misspecificaion: true model is quadratic, estimate only linear
-# * Independent noise/error term
-# * Two groups for error variance, low and high variance groups
-
-nsample = 50
-x = np.linspace(0, 20, nsample)
-X = np.c_[x, (x - 5)**2, np.ones(nsample)]
-beta = [0.5, -0.01, 5.]
-sig = 0.5
-w = np.ones(nsample)
-w[nsample * 6 / 10:] = 3
-y_true = np.dot(X, beta)
-e = np.random.normal(size=nsample)
-y = y_true + sig * w * e
-X = X[:, [0, 2]]
-
-#WLS knowing the true variance ratio of heteroscedasticity
-#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-mod_wls = sm.WLS(y, X, weights=1. / w)
-res_wls = mod_wls.fit()
-print res_wls.summary()
-
-#OLS vs. WLS
-#-----------
-
-# Estimate an OLS model for comparison
-res_ols = sm.OLS(y, X).fit()
-
-# Compare the estimated parameters in WLS and OLS
-print res_ols.params
-print res_wls.params
-
-# Compare the WLS standard errors to  heteroscedasticity corrected OLS standard
-# errors:
-se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se],
-                [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]])
-se = np.round(se, 4)
-colnames = 'x1', 'const'
-rownames = 'WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3'
-tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)
-print tabl
-
-# Calculate OLS prediction interval
-covb = res_ols.cov_params()
-prediction_var = res_ols.mse_resid + (X * np.dot(covb, X.T).T).sum(1)
-prediction_std = np.sqrt(prediction_var)
-tppf = stats.t.ppf(0.975, res_ols.df_resid)
-
-# Draw a plot to compare predicted values in WLS and OLS:
-prstd, iv_l, iv_u = wls_prediction_std(res_wls)
-plt.figure();
-plt.plot(x, y, 'o', x, y_true, 'b-');
-plt.plot(x, res_ols.fittedvalues, 'r--');
-plt.plot(x, res_ols.fittedvalues + tppf * prediction_std, 'r--');
-plt.plot(x, res_ols.fittedvalues - tppf * prediction_std, 'r--');
-plt.plot(x, res_wls.fittedvalues, 'g--.');
-plt.plot(x, iv_u, 'g--');
-plt.plot(x, iv_l, 'g--');
-#@savefig wls_ols_0.png
-plt.title('blue: true, red: OLS, green: WLS');
-
-# Feasible Weighted Least Squares (2-stage FWLS)
-# ----------------------------------------------
-
-resid1 = res_ols.resid[w == 1.]
-var1 = resid1.var(ddof=int(res_ols.df_model) + 1)
-resid2 = res_ols.resid[w != 1.]
-var2 = resid2.var(ddof=int(res_ols.df_model) + 1)
-w_est = w.copy()
-w_est[w != 1.] = np.sqrt(var2) / np.sqrt(var1)
-res_fwls = sm.WLS(y, X, 1. / w_est).fit()
-print res_fwls.summary()
diff --git a/examples/tsa/example_arima.py b/examples/incomplete/arima.py
similarity index 96%
rename from examples/tsa/example_arima.py
rename to examples/incomplete/arima.py
index 817c3938a75..9be32bb0632 100644
--- a/examples/tsa/example_arima.py
+++ b/examples/incomplete/arima.py
@@ -1,3 +1,4 @@
+from __future__ import print_function
 from statsmodels.datasets.macrodata import load_pandas
 from statsmodels.tsa.base.datetools import dates_from_range
 from statsmodels.tsa.arima_model import ARIMA
@@ -13,7 +14,7 @@
 cpi.index = dates
 
 res = ARIMA(cpi, (1, 1, 1), freq='Q').fit()
-print res.summary()
+print(res.summary())
 
 # we can look at the series
 cpi.diff().plot()
diff --git a/examples/tsa/ex_arma2.py b/examples/incomplete/arma2.py
similarity index 93%
rename from examples/tsa/ex_arma2.py
rename to examples/incomplete/arma2.py
index 8dc85b44d88..cc4fbefe446 100644
--- a/examples/tsa/ex_arma2.py
+++ b/examples/incomplete/arma2.py
@@ -21,8 +21,8 @@
 
 # Now, optionally, we can add some dates information. For this example,
 # we'll use a pandas time series.
-import pandas
+import pandas as pd
 dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
-y = pandas.TimeSeries(y, index=dates)
+y = pd.TimeSeries(y, index=dates)
 arma_mod = sm.tsa.ARMA(y, order=(2, 2))
 arma_res = arma_mod.fit(trend='nc', disp=-1)
diff --git a/examples/tsa/ex_dates.py b/examples/incomplete/dates.py
similarity index 90%
rename from examples/tsa/ex_dates.py
rename to examples/incomplete/dates.py
index e578868672e..9ea779a1f90 100644
--- a/examples/tsa/ex_dates.py
+++ b/examples/incomplete/dates.py
@@ -2,7 +2,7 @@
 Using dates with timeseries models
 """
 import statsmodels.api as sm
-import pandas
+import pandas as pd
 
 # Getting started
 # ---------------
@@ -17,7 +17,7 @@
 # ------------
 
 # Make a pandas TimeSeries or DataFrame
-endog = pandas.TimeSeries(data.endog, index=dates)
+endog = pd.TimeSeries(data.endog, index=dates)
 
 # and instantiate the model
 ar_model = sm.tsa.AR(endog, freq='A')
@@ -25,7 +25,7 @@
 
 # Let's do some out-of-sample prediction
 pred = pandas_ar_res.predict(start='2005', end='2015')
-print pred
+print(pred)
 
 # Using explicit dates
 # --------------------
@@ -33,12 +33,12 @@
 ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')
 ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
 pred = ar_res.predict(start='2005', end='2015')
-print pred
+print(pred)
 
 # This just returns a regular array, but since the model has date information
 # attached, you can get the prediction dates in a roundabout way.
 
-print ar_res.data.predict_dates
+print(ar_res.data.predict_dates)
 
 # This attribute only exists if predict has been called. It holds the dates
 # associated with the last call to predict.
diff --git a/examples/example_glsar.py b/examples/incomplete/glsar.py
similarity index 69%
rename from examples/example_glsar.py
rename to examples/incomplete/glsar.py
index ca97c032058..52938e14c69 100644
--- a/examples/example_glsar.py
+++ b/examples/incomplete/glsar.py
@@ -19,7 +19,7 @@
 examples = examples_all  # [5]
 
 if 0 in examples:
-    print '\n Example 0'
+    print('\n Example 0')
     X = np.arange(1, 8)
     X = sm.add_constant(X)
     Y = np.array((1, 3, 4, 5, 8, 10, 9))
@@ -27,19 +27,19 @@
     model = GLSAR(Y, X, 2)
     for i in range(6):
         results = model.fit()
-        print 'AR coefficients:', model.rho
+        print('AR coefficients:', model.rho)
         rho, sigma = yule_walker(results.resid, order=model.order)
         model = GLSAR(Y, X, rho)
 
     par0 = results.params
-    print 'params fit', par0
+    print('params fit', par0)
     model0if = GLSAR(Y, X, 2)
     res = model0if.iterative_fit(6)
-    print 'iterativefit beta', res.params
+    print('iterativefit beta', res.params)
     results.tvalues   # XXX is this correct? it does equal params/bse
     # but isn't the same as the AR example (which was wrong in the first place..)
-    print results.t_test([0, 1])  # are sd and t correct? vs
-    print results.f_test(np.eye(2))
+    print(results.t_test([0, 1]))  # are sd and t correct? vs
+    print(results.f_test(np.eye(2)))
 
 
 rhotrue = np.array([0.5, 0.2])
@@ -62,7 +62,7 @@
         arnoise[i] = rhotrue * arnoise[i - 1] + wnoise[i]
     noise = arnoise[1:]
     an = signal.lfilter([1], np.hstack((1, -rhotrue)), wnoise[1:])
-    print 'simulate AR(1) difference', np.max(np.abs(noise - an))
+    print('simulate AR(1) difference', np.max(np.abs(noise - an)))
 else:
     noise = signal.lfilter([1], np.hstack((1, -rhotrue)), wnoise)[nlags:]
 
@@ -70,31 +70,31 @@
 y1 = np.dot(X1, beta) + noise
 
 if 1 in examples:
-    print '\nExample 1: iterative_fit and repeated calls'
+    print('\nExample 1: iterative_fit and repeated calls')
     mod1 = GLSAR(y1, X1, 1)
     res = mod1.iterative_fit()
-    print res.params
-    print mod1.rho
+    print(res.params)
+    print(mod1.rho)
     mod1 = GLSAR(y1, X1, 2)
     for i in range(5):
         res1 = mod1.iterative_fit(2)
 #        mod1.fit()
-        print mod1.rho
-        print res1.params
+        print(mod1.rho)
+        print(res1.params)
 
 if 2 in examples:
-    print '\nExample 2: iterative fitting of first model'
-    print 'with AR(0)', par0
+    print('\nExample 2: iterative fitting of first model')
+    print('with AR(0)', par0)
     parold = par0
     mod0 = GLSAR(Y, X, 1)
     for i in range(5):
-        #print mod0.wexog.sum()
-        #print mod0.pinv_wexog.sum()
+        #print(mod0.wexog.sum())
+        #print(mod0.pinv_wexog.sum())
         res0 = mod0.iterative_fit(1)
-        print 'rho', mod0.rho,
+        print('rho', mod0.rho)
         parnew = res0.params
-        print 'params', parnew,
-        print 'params change in iteration', parnew - parold
+        print('params', parnew,)
+        print('params change in iteration', parnew - parold)
         parold = parnew
 
 # generate pure AR(p) process
@@ -104,49 +104,49 @@
 #results now have same estimated rho as yule-walker directly
 
 if 3 in examples:
-    print '\nExample 3: pure AR(2), GLSAR versus Yule_Walker'
+    print('\nExample 3: pure AR(2), GLSAR versus Yule_Walker')
     model3 = GLSAR(Y, rho=2)
     for i in range(5):
         results = model3.fit()
-        print "AR coefficients:", model3.rho, results.params
+        print("AR coefficients:", model3.rho, results.params)
         rho, sigma = yule_walker(results.resid, order=model3.order)
         model3 = GLSAR(Y, rho=rho)
 
 if 'test_copy' in examples:
     xx = X.copy()
     rhoyw, sigmayw = yule_walker(xx[:, 0], order=2)
-    print rhoyw, sigmayw
-    print (xx == X).all()  # test for unchanged array (fixed)
+    print(rhoyw, sigmayw)
+    print((xx == X).all())  # test for unchanged array (fixed)
 
     yy = Y.copy()
     rhoyw, sigmayw = yule_walker(yy, order=2)
-    print rhoyw, sigmayw
-    print (yy == Y).all()  # test for unchanged array (fixed)
+    print(rhoyw, sigmayw)
+    print((yy == Y).all())  # test for unchanged array (fixed)
 
 
 if 4 in examples:
-    print '\nExample 4: demeaned pure AR(2), GLSAR versus Yule_Walker'
+    print('\nExample 4: demeaned pure AR(2), GLSAR versus Yule_Walker')
     Ydemeaned = Y - Y.mean()
     model4 = GLSAR(Ydemeaned, rho=2)
     for i in range(5):
         results = model4.fit()
-        print "AR coefficients:", model3.rho, results.params
+        print("AR coefficients:", model3.rho, results.params)
         rho, sigma = yule_walker(results.resid, order=model4.order)
         model4 = GLSAR(Ydemeaned, rho=rho)
 
 if 5 in examples:
-    print '\nExample 5: pure AR(2), GLSAR iterative_fit versus Yule_Walker'
+    print('\nExample 5: pure AR(2), GLSAR iterative_fit versus Yule_Walker')
     model3a = GLSAR(Y, rho=1)
     res3a = model3a.iterative_fit(5)
-    print res3a.params
-    print model3a.rho
+    print(res3a.params)
+    print(model3a.rho)
     rhoyw, sigmayw = yule_walker(Y, order=1)
-    print rhoyw, sigmayw
+    print(rhoyw, sigmayw)
     npt.assert_array_almost_equal(model3a.rho, rhoyw, 15)
     for i in range(6):
         model3b = GLSAR(Y, rho=0.1)
-        print i, model3b.iterative_fit(i).params, model3b.rho
+        print(i, model3b.iterative_fit(i).params, model3b.rho)
 
     model3b = GLSAR(Y, rho=0.1)
     for i in range(6):
-        print i, model3b.iterative_fit(2).params, model3b.rho
+        print(i, model3b.iterative_fit(2).params, model3b.rho)
diff --git a/examples/example_ols_table.py b/examples/incomplete/ols_table.py
similarity index 94%
rename from examples/example_ols_table.py
rename to examples/incomplete/ols_table.py
index d16074ef349..381bbd50317 100644
--- a/examples/example_ols_table.py
+++ b/examples/incomplete/ols_table.py
@@ -65,5 +65,5 @@
 nanrep = ' ' * (nn - 1)
 nanrep = nanrep[:nn // 2] + '-' + nanrep[nn // 2:]
 
-print 'Longley data - sensitivity to dropping an explanatory variable'
-print str(tabl).replace(nanstring, nanrep)
+print('Longley data - sensitivity to dropping an explanatory variable')
+print(str(tabl).replace(nanstring, nanrep))
diff --git a/examples/example_ols_tftest.py b/examples/incomplete/ols_tftest.py
similarity index 71%
rename from examples/example_ols_tftest.py
rename to examples/incomplete/ols_tftest.py
index 37b01a9dbec..badca40c93b 100644
--- a/examples/example_ols_tftest.py
+++ b/examples/incomplete/ols_tftest.py
@@ -15,7 +15,7 @@
 import numpy.testing as npt
 import statsmodels.api as sm
 
-print '\n\n Example 1: Longley Data, high multicollinearity'
+print('\n\n Example 1: Longley Data, high multicollinearity')
 
 data = sm.datasets.longley.load()
 data.exog = sm.add_constant(data.exog, prepend=False)
@@ -24,7 +24,7 @@
 # test pairwise equality of some coefficients
 R2 = [[0, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 1, -1, 0]]
 Ftest = res.f_test(R2)
-print repr((Ftest.fvalue, Ftest.pvalue))  # use repr to get more digits
+print(repr((Ftest.fvalue, Ftest.pvalue)))  # use repr to get more digits
 # 9.740461873303655 0.0056052885317360301
 
 ##Compare to R (after running R_lm.s in the longley folder)
@@ -44,19 +44,19 @@
 ##1      9   836424
 ##2     11  2646903 -2  -1810479 9.7405 0.005605 **
 
-print 'Regression Results Summary'
-print res.summary()
+print('Regression Results Summary')
+print(res.summary())
 
 
-print '\n F-test whether all variables have zero effect'
+print('\n F-test whether all variables have zero effect')
 R = np.eye(7)[:-1, :]
 Ftest0 = res.f_test(R)
-print repr((Ftest0.fvalue, Ftest0.pvalue))
-print '%r' % res.fvalue
+print(repr((Ftest0.fvalue, Ftest0.pvalue)))
+print('%r' % res.fvalue)
 npt.assert_almost_equal(res.fvalue, Ftest0.fvalue, decimal=9)
 
 ttest0 = res.t_test(R[0, :])
-print repr((ttest0.tvalue, ttest0.pvalue))
+print(repr((ttest0.tvalue, ttest0.pvalue)))
 
 betatval = res.tvalues
 betatval[0]
@@ -66,7 +66,7 @@
 # several ttests at the same time
 # currently not checked for this, but it (kind of) works
 >>> ttest0 = res.t_test(R[:2,:])
->>> print repr((ttest0.t, ttest0.pvalue))
+>>> print(repr((ttest0.t, ttest0.pvalue))
 (array([[ 0.17737603,         NaN],
        [        NaN, -1.06951632]]), array([[ 0.43157042,  1.        ],
        [ 1.        ,  0.84365947]]))
@@ -93,36 +93,36 @@
         4.01588981])
 """
 
-print '\nsimultaneous t-tests'
+print('\nsimultaneous t-tests')
 ttest0 = res.t_test(R2)
 
 t2 = ttest0.tvalue
-print ttest0.tvalue
-print t2
+print(ttest0.tvalue)
+print(t2)
 t2a = np.r_[res.t_test(np.array(R2)[0, :]).tvalue,
             res.t_test(np.array(R2)[1, :]).tvalue]
-print t2 - t2a
+print(t2 - t2a)
 t2pval = ttest0.pvalue
-print '%r' % t2pval  # reject
+print('%r' % t2pval)  # reject
 # array([  9.33832896e-04,   9.98483623e-01])
-print 'reject'
-print '%r' % (t2pval < 0.05)
+print('reject')
+print('%r' % (t2pval < 0.05))
 
 # f_test needs 2-d currently
 Ftest2a = res.f_test(np.asarray(R2)[:1, :])
-print repr((Ftest2a.fvalue, Ftest2a.pvalue))
+print(repr((Ftest2a.fvalue, Ftest2a.pvalue)))
 Ftest2b = res.f_test(np.asarray(R2)[1:2, :])
-print repr((Ftest2b.fvalue, Ftest2b.pvalue))
+print(repr((Ftest2b.fvalue, Ftest2b.pvalue)))
 
-print '\nequality of t-test and F-test'
-print t2a**2 - np.array((Ftest2a.fvalue, Ftest2b.fvalue))
+print('\nequality of t-test and F-test')
+print(t2a**2 - np.array((Ftest2a.fvalue, Ftest2b.fvalue)))
 npt.assert_almost_equal(t2a**2, np.vstack((Ftest2a.fvalue, Ftest2b.fvalue)))
 #npt.assert_almost_equal(t2pval, np.array((Ftest2a.pvalue, Ftest2b.pvalue)))
 npt.assert_almost_equal(t2pval * 2, np.c_[Ftest2a.pvalue,
     Ftest2b.pvalue].squeeze())
 
 
-print '\n\n Example 2: Artificial Data'
+print('\n\n Example 2: Artificial Data')
 
 nsample = 100
 ncat = 4
@@ -137,47 +137,47 @@
 mod2 = sm.OLS(y[:, 0], X)
 res2 = mod2.fit()
 
-print res2.summary()
+print(res2.summary())
 
 R3 = np.eye(ncat)[:-1, :]
 Ftest = res2.f_test(R3)
-print repr((Ftest.fvalue, Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
 R3 = np.atleast_2d([0, 1, -1, 2])
 Ftest = res2.f_test(R3)
-print repr((Ftest.fvalue, Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
 
-print 'simultaneous t-test for zero effects'
+print('simultaneous t-test for zero effects')
 R4 = np.eye(ncat)[:-1, :]
 ttest = res2.t_test(R4)
-print repr((ttest.tvalue, ttest.pvalue))
+print(repr((ttest.tvalue, ttest.pvalue)))
 
 
 R5 = np.atleast_2d([0, 1, 1, 2])
 np.dot(R5, res2.params)
 Ftest = res2.f_test(R5)
-print repr((Ftest.fvalue, Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
 ttest = res2.t_test(R5)
-#print repr((ttest.t, ttest.pvalue))
-print repr((ttest.tvalue, ttest.pvalue))
+#print(repr((ttest.t, ttest.pvalue))
+print(repr((ttest.tvalue, ttest.pvalue)))
 
 R6 = np.atleast_2d([1, -1, 0, 0])
 np.dot(R6, res2.params)
 Ftest = res2.f_test(R6)
-print repr((Ftest.fvalue, Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
 ttest = res2.t_test(R6)
-#print repr((ttest.t, ttest.pvalue))
-print repr((ttest.tvalue, ttest.pvalue))
+#print(repr((ttest.t, ttest.pvalue))
+print(repr((ttest.tvalue, ttest.pvalue)))
 
 R7 = np.atleast_2d([1, 0, 0, 0])
 np.dot(R7, res2.params)
 Ftest = res2.f_test(R7)
-print repr((Ftest.fvalue, Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
 ttest = res2.t_test(R7)
-#print repr((ttest.t, ttest.pvalue))
-print repr((ttest.tvalue, ttest.pvalue))
+#print(repr((ttest.t, ttest.pvalue))
+print(repr((ttest.tvalue, ttest.pvalue)))
 
 
-print '\nExample: 2 categories: replicate stats.glm and stats.ttest_ind'
+print('\nExample: 2 categories: replicate stats.glm and stats.ttest_ind')
 
 mod2 = sm.OLS(y[xcat.flat < 2][:, 0], X[xcat.flat < 2, :][:, (0, -1)])
 res2 = mod2.fit()
@@ -185,15 +185,15 @@
 R8 = np.atleast_2d([1, 0])
 np.dot(R8, res2.params)
 Ftest = res2.f_test(R8)
-print repr((Ftest.fvalue, Ftest.pvalue))
-print repr((np.sqrt(Ftest.fvalue), Ftest.pvalue))
+print(repr((Ftest.fvalue, Ftest.pvalue)))
+print(repr((np.sqrt(Ftest.fvalue), Ftest.pvalue)))
 ttest = res2.t_test(R8)
-#print repr(ttest.t), ttest.pvalue))
-print repr((ttest.tvalue, ttest.pvalue))
+#print(repr(ttest.t), ttest.pvalue)))
+print(repr((ttest.tvalue, ttest.pvalue)))
 
 
 from scipy import stats
-print stats.glm(y[xcat < 2].ravel(), xcat[xcat < 2].ravel())
-print stats.ttest_ind(y[xcat == 0], y[xcat == 1])
+print(stats.glm(y[xcat < 2].ravel(), xcat[xcat < 2].ravel()))
+print(stats.ttest_ind(y[xcat == 0], y[xcat == 1]))
 
 #TODO: compare with f_oneway
diff --git a/examples/try_wls.py b/examples/incomplete/wls_extended.py
similarity index 82%
rename from examples/try_wls.py
rename to examples/incomplete/wls_extended.py
index d7249df78de..4f8777cb495 100644
--- a/examples/try_wls.py
+++ b/examples/incomplete/wls_extended.py
@@ -5,6 +5,7 @@
 at outliers, compares with RLM and a short bootstrap
 
 """
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
@@ -55,19 +56,19 @@
 # stays small. Below also compared using squared or sqrt of weight variable.
 # TODO: need to add 45 degree line to graphs
 wls_fit3 = sm.WLS(data.endog, data.exog[:,(0,1,3,4)], weights=1/incomesq).fit()
-print wls_fit3.summary()
-print 'corrected rsquared',
-print (wls_fit3.uncentered_tss - wls_fit3.ssr)/wls_fit3.uncentered_tss
+print(wls_fit3.summary())
+print('corrected rsquared')
+print((wls_fit3.uncentered_tss - wls_fit3.ssr)/wls_fit3.uncentered_tss)
 plt.figure();
 plt.title('WLS dropping heteroscedasticity variable from regressors');
 plt.plot(data.endog, wls_fit3.fittedvalues, 'o');
 plt.xlim([0,2000]);
 #@savefig wls_drop_het.png
 plt.ylim([0,2000]);
-print 'raw correlation of endog and fittedvalues'
-print np.corrcoef(data.endog, wls_fit.fittedvalues)
-print 'raw correlation coefficient of endog and fittedvalues squared'
-print np.corrcoef(data.endog, wls_fit.fittedvalues)[0,1]**2
+print('raw correlation of endog and fittedvalues')
+print(np.corrcoef(data.endog, wls_fit.fittedvalues))
+print('raw correlation coefficient of endog and fittedvalues squared')
+print(np.corrcoef(data.endog, wls_fit.fittedvalues)[0,1]**2)
 
 # compare with robust regression,
 # heteroscedasticity correction downweights the outliers
@@ -150,7 +151,7 @@ def index_trim_outlier(resid, k):
 
 #ols_test_fit = sm.OLS(data.endog, data.exog).fit()
 olskeep, olsoutl = index_trim_outlier(ols_fit.resid, 2)
-print 'ols outliers', olsoutl, ols_fit.resid[olsoutl]
+print('ols outliers', olsoutl, ols_fit.resid[olsoutl])
 ols_fit_rm2 = sm.OLS(data.endog[olskeep], data.exog[olskeep,:]).fit()
 rlm_fit_rm2 = sm.RLM(data.endog[olskeep], data.exog[olskeep,:]).fit()
 #weights = 1/incomesq
@@ -158,46 +159,46 @@ def index_trim_outlier(resid, k):
 results = [ols_fit, ols_fit_rm2, rlm_fit, rlm_fit_rm2]
 #Note: I think incomesq is already square
 for weights in [1/incomesq, 1/incomesq**2, np.sqrt(incomesq)]:
-    print '\nComparison OLS and WLS with and without outliers'
+    print('\nComparison OLS and WLS with and without outliers')
     wls_fit0 = sm.WLS(data.endog, data.exog, weights=weights).fit()
     wls_fit_rm2 = sm.WLS(data.endog[olskeep], data.exog[olskeep,:],
                          weights=weights[olskeep]).fit()
     wlskeep, wlsoutl = index_trim_outlier(ols_fit.resid, 2)
-    print '2 outliers candidates and residuals'
-    print wlsoutl, wls_fit.resid[olsoutl]
+    print('2 outliers candidates and residuals')
+    print(wlsoutl, wls_fit.resid[olsoutl])
     # redundant because ols and wls outliers are the same:
     ##wls_fit_rm2_ = sm.WLS(data.endog[wlskeep], data.exog[wlskeep,:],
     ##                     weights=1/incomesq[wlskeep]).fit()
 
-    print 'outliers ols, wls:', olsoutl, wlsoutl
+    print('outliers ols, wls:', olsoutl, wlsoutl)
 
-    print 'rsquared'
-    print 'ols vs ols rm2', ols_fit.rsquared, ols_fit_rm2.rsquared
-    print 'wls vs wls rm2', wls_fit0.rsquared, wls_fit_rm2.rsquared #, wls_fit_rm2_.rsquared
-    print 'compare R2_resid  versus  R2_wresid'
-    print 'ols minus 2', getrsq(ols_fit_rm2)[0],
-    print getrsq((ols_fit_rm2.wresid, ols_fit_rm2.model.wendog))[0]
-    print 'wls        ', getrsq(wls_fit)[0],
-    print getrsq((wls_fit.wresid, wls_fit.model.wendog))[0]
+    print('rsquared')
+    print('ols vs ols rm2', ols_fit.rsquared, ols_fit_rm2.rsquared)
+    print('wls vs wls rm2', wls_fit0.rsquared, wls_fit_rm2.rsquared) #, wls_fit_rm2_.rsquared
+    print('compare R2_resid  versus  R2_wresid')
+    print('ols minus 2', getrsq(ols_fit_rm2)[0],)
+    print(getrsq((ols_fit_rm2.wresid, ols_fit_rm2.model.wendog))[0])
+    print('wls        ', getrsq(wls_fit)[0],)
+    print(getrsq((wls_fit.wresid, wls_fit.model.wendog))[0])
 
-    print 'wls minus 2', getrsq(wls_fit_rm2)[0],
+    print('wls minus 2', getrsq(wls_fit_rm2)[0])
     # next is same as wls_fit_rm2.rsquared for cross checking
-    print getrsq((wls_fit_rm2.wresid, wls_fit_rm2.model.wendog))[0]
-    #print getrsq(wls_fit_rm2_)[0],
-    #print getrsq((wls_fit_rm2_.wresid, wls_fit_rm2_.model.wendog))[0]
+    print(getrsq((wls_fit_rm2.wresid, wls_fit_rm2.model.wendog))[0])
+    #print(getrsq(wls_fit_rm2_)[0],
+    #print(getrsq((wls_fit_rm2_.wresid, wls_fit_rm2_.model.wendog))[0]
     results.extend([wls_fit0, wls_fit_rm2])
 
-print '     ols             ols_rm2       rlm           rlm_rm2     wls (lin)    wls_rm2 (lin)   wls (squ)   wls_rm2 (squ)  wls (sqrt)   wls_rm2 (sqrt)'
-print 'Parameter estimates'
-print np.column_stack([r.params for r in results])
-print 'R2 original data, next line R2 weighted data'
-print np.column_stack([getattr(r, 'rsquared', None) for r in results])
+print('     ols             ols_rm2       rlm           rlm_rm2     wls (lin)    wls_rm2 (lin)   wls (squ)   wls_rm2 (squ)  wls (sqrt)   wls_rm2 (sqrt)')
+print('Parameter estimates')
+print(np.column_stack([r.params for r in results]))
+print('R2 original data, next line R2 weighted data')
+print(np.column_stack([getattr(r, 'rsquared', None) for r in results]))
 
-print 'Standard errors'
-print np.column_stack([getattr(r, 'bse', None) for r in results])
-print 'Heteroscedasticity robust standard errors (with ols)'
-print 'with outliers'
-print np.column_stack([getattr(ols_fit, se, None) for se in ['HC0_se', 'HC1_se', 'HC2_se', 'HC3_se']])
+print('Standard errors')
+print(np.column_stack([getattr(r, 'bse', None) for r in results]))
+print('Heteroscedasticity robust standard errors (with ols)')
+print('with outliers')
+print(np.column_stack([getattr(ols_fit, se, None) for se in ['HC0_se', 'HC1_se', 'HC2_se', 'HC3_se']]))
 
 #..'''
 #..
@@ -260,17 +261,17 @@ def index_trim_outlier(resid, k):
     bootres[it, :nvar] = res.params
     bootres[it, nvar:] = res.bse
 
-np.set_printoptions(linewidth=200)
-print 'Bootstrap Results of parameters and parameter standard deviation  OLS'
-print 'Parameter estimates'
-print 'median', np.median(bootres[:,:5], 0)
-print 'mean  ', np.mean(bootres[:,:5], 0)
-print 'std   ', np.std(bootres[:,:5], 0)
+np.set_print(options(linewidth=200))
+print('Bootstrap Results of parameters and parameter standard deviation  OLS')
+print('Parameter estimates')
+print('median', np.median(bootres[:,:5], 0))
+print('mean  ', np.mean(bootres[:,:5], 0))
+print('std   ', np.std(bootres[:,:5], 0))
 
-print 'Standard deviation of parameter estimates'
-print 'median', np.median(bootres[:,5:], 0)
-print 'mean  ', np.mean(bootres[:,5:], 0)
-print 'std   ', np.std(bootres[:,5:], 0)
+print('Standard deviation of parameter estimates')
+print('median', np.median(bootres[:,5:], 0))
+print('mean  ', np.mean(bootres[:,5:], 0))
+print('std   ', np.std(bootres[:,5:], 0))
 
 plt.figure()
 for i in range(4):
@@ -299,17 +300,17 @@ def index_trim_outlier(resid, k):
     bootreswls[it, :nvar] = res.params
     bootreswls[it, nvar:] = res.bse
 
-print 'Bootstrap Results of parameters and parameter standard deviation',
-print 'WLS removed 2 outliers from sample'
-print 'Parameter estimates'
-print 'median', np.median(bootreswls[:,:5], 0)
-print 'mean  ', np.mean(bootreswls[:,:5], 0)
-print 'std   ', np.std(bootreswls[:,:5], 0)
-
-print 'Standard deviation of parameter estimates'
-print 'median', np.median(bootreswls[:,5:], 0)
-print 'mean  ', np.mean(bootreswls[:,5:], 0)
-print 'std   ', np.std(bootreswls[:,5:], 0)
+print('Bootstrap Results of parameters and parameter standard deviation',)
+print('WLS removed 2 outliers from sample')
+print('Parameter estimates')
+print('median', np.median(bootreswls[:,:5], 0))
+print('mean  ', np.mean(bootreswls[:,:5], 0))
+print('std   ', np.std(bootreswls[:,:5], 0))
+
+print('Standard deviation of parameter estimates')
+print('median', np.median(bootreswls[:,5:], 0))
+print('mean  ', np.mean(bootreswls[:,5:], 0))
+print('std   ', np.std(bootreswls[:,5:], 0))
 
 plt.figure()
 for i in range(4):
diff --git a/examples/notebooks/categorical_interaction_plot.ipynb b/examples/notebooks/categorical_interaction_plot.ipynb
new file mode 100644
index 00000000000..1d02c9542da
--- /dev/null
+++ b/examples/notebooks/categorical_interaction_plot.ipynb
@@ -0,0 +1,67 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Plot Interaction of Categorical Factors"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "import pandas as pd\n",
+      "from statsmodels.graphics.factorplots import interaction_plot"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.random.seed(12345)\n",
+      "weight = pd.Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')\n",
+      "nutrition = pd.Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')\n",
+      "days = np.log(np.random.randint(1, 30, size=60))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = plt.subplots(figsize=(6, 6))\n",
+      "fig = interaction_plot(x=weight, trace=nutrition, response=days, \n",
+      "                       colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=ax)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/examples/notebooks/contrasts.ipynb b/examples/notebooks/contrasts.ipynb
index 3bf14f15bc8..6f1ee3457cf 100644
--- a/examples/notebooks/contrasts.ipynb
+++ b/examples/notebooks/contrasts.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "contrasts"
+  "name": "",
+  "signature": "sha256:449860e3d233f6adbb5b4dc1e92baeac7ffac56aa94ba803446b40cd5e6b69a6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
+      "import numpy as np\n",
       "import statsmodels.api as sm"
      ],
      "language": "python",
@@ -33,7 +36,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.\n",
@@ -55,9 +58,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pandas\n",
+      "import pandas as pd\n",
       "url = 'http://www.ats.ucla.edu/stat/data/hsb2.csv'\n",
-      "hsb2 = pandas.read_table(url, delimiter=\",\")"
+      "hsb2 = pd.read_table(url, delimiter=\",\")"
      ],
      "language": "python",
      "metadata": {},
@@ -74,7 +77,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian))."
@@ -99,7 +102,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be"
@@ -112,14 +115,14 @@
       "from patsy.contrasts import Treatment\n",
       "levels = [1,2,3,4]\n",
       "contrast = Treatment(reference=0).code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Here we used `reference=0`, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let's look at how this would encode the `race` variable."
@@ -139,7 +142,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print contrast.matrix[hsb2.race-1, :][:20]"
+      "print(contrast.matrix[hsb2.race-1, :][:20])"
      ],
      "language": "python",
      "metadata": {},
@@ -156,7 +159,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "This is a bit of a trick, as the `race` category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won't work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above"
@@ -169,14 +172,14 @@
       "from statsmodels.formula.api import ols\n",
       "mod = ols(\"write ~ C(race, Treatment)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this."
@@ -191,7 +194,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. Patsy doesn't have the Simple contrast included, but you can easily define your own contrasts. To do so, write a class that contains a code_with_intercept and a code_without_intercept method that returns a patsy.contrast.ContrastMatrix instance"
@@ -241,7 +244,7 @@
      "collapsed": false,
      "input": [
       "contrast = Simple().code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
@@ -253,7 +256,7 @@
      "input": [
       "mod = ols(\"write ~ C(race, Simple)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -268,7 +271,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others."
@@ -280,7 +283,7 @@
      "input": [
       "from patsy.contrasts import Sum\n",
       "contrast = Sum().code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
@@ -292,14 +295,14 @@
      "input": [
       "mod = ols(\"write ~ C(race, Sum)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "This corresponds to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level."
@@ -324,7 +327,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable."
@@ -336,7 +339,7 @@
      "input": [
       "from patsy.contrasts import Diff\n",
       "contrast = Diff().code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
@@ -348,14 +351,14 @@
      "input": [
       "mod = ols(\"write ~ C(race, Diff)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "For example, here the coefficient on level 1 is the mean of `write` at level 2 compared with the mean at level 1. Ie.,"
@@ -382,7 +385,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name 'reverse' being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so:"
@@ -394,7 +397,7 @@
      "input": [
       "from patsy.contrasts import Helmert\n",
       "contrast = Helmert().code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
@@ -406,14 +409,14 @@
      "input": [
       "mod = ols(\"write ~ C(race, Helmert)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4"
@@ -431,7 +434,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same."
@@ -459,7 +462,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The coefficients taken on by polynomial coding for `k=4` levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order `k-1`. Since `race` is not an ordered factor variable let's use `read` as an example. First we need to create an ordered categorical from `read`."
@@ -469,7 +472,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "hsb2['readcat'] = pandas.cut(hsb2.read, bins=3)\n",
+      "hsb2['readcat'] = pd.cut(hsb2.read, bins=3)\n",
       "hsb2.groupby('readcat').mean()['write']"
      ],
      "language": "python",
@@ -483,7 +486,7 @@
       "from patsy.contrasts import Poly\n",
       "levels = hsb2.readcat.unique().tolist()\n",
       "contrast = Poly().code_without_intercept(levels)\n",
-      "print contrast.matrix"
+      "print(contrast.matrix)"
      ],
      "language": "python",
      "metadata": {},
@@ -495,14 +498,14 @@
      "input": [
       "mod = ols(\"write ~ C(readcat, Poly)\", data=hsb2)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "As you can see, readcat has a significant linear effect on the dependent variable `write` but not a significant quadratic or cubic effect."
diff --git a/examples/notebooks/discrete_choice.ipynb b/examples/notebooks/discrete_choice_example.ipynb
similarity index 91%
rename from examples/notebooks/discrete_choice.ipynb
rename to examples/notebooks/discrete_choice_example.ipynb
index 336b0d1435b..07e387275ce 100644
--- a/examples/notebooks/discrete_choice.ipynb
+++ b/examples/notebooks/discrete_choice_example.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "discrete_choice"
+  "name": "",
+  "signature": "sha256:c66a2fe54673206e51268c5ed0eea9da14548d5e438543a304cdf4de74e141da"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -34,6 +35,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "from scipy import stats\n",
       "import matplotlib.pyplot as plt\n",
@@ -48,7 +50,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.fair.SOURCE"
+      "print(sm.datasets.fair.SOURCE)"
      ],
      "language": "python",
      "metadata": {},
@@ -58,7 +60,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.fair.NOTE"
+      "print( sm.datasets.fair.NOTE)"
      ],
      "language": "python",
      "metadata": {},
@@ -79,7 +81,7 @@
      "collapsed": false,
      "input": [
       "dta['affair'] = (dta['affairs'] > 0).astype(float)\n",
-      "print dta.head(10)"
+      "print(dta.head(10))"
      ],
      "language": "python",
      "metadata": {},
@@ -89,7 +91,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print dta.describe()"
+      "print(dta.describe())"
      ],
      "language": "python",
      "metadata": {},
@@ -111,14 +113,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print affair_mod.summary()"
+      "print(affair_mod.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "How well are we predicting?"
@@ -135,7 +137,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects."
@@ -146,7 +148,7 @@
      "collapsed": false,
      "input": [
       "mfx = affair_mod.get_margeff()\n",
-      "print mfx.summary()"
+      "print(mfx.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -157,7 +159,7 @@
      "collapsed": false,
      "input": [
       "respondent1000 = dta.ix[1000]\n",
-      "print respondent1000"
+      "print(respondent1000)"
      ],
      "language": "python",
      "metadata": {},
@@ -172,7 +174,7 @@
       "                                            \"age\", \"yrs_married\", \"children\", \n",
       "                                            \"religious\"]].tolist()))\n",
       "resp.update({0 : 1})\n",
-      "print resp"
+      "print(resp)"
      ],
      "language": "python",
      "metadata": {},
@@ -183,7 +185,7 @@
      "collapsed": false,
      "input": [
       "mfx = affair_mod.get_margeff(atexog=resp)\n",
-      "print mfx.summary()"
+      "print(mfx.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -220,7 +222,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The \"correct\" model here is likely the Tobit model. We have an work in progress branch \"tobit-model\" on github, if anyone is interested in censored regression models."
@@ -265,7 +267,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?"
@@ -283,7 +285,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.star98.SOURCE"
+      "print(sm.datasets.star98.SOURCE)"
      ],
      "language": "python",
      "metadata": {},
@@ -293,7 +295,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.star98.DESCRLONG"
+      "print(sm.datasets.star98.DESCRLONG)"
      ],
      "language": "python",
      "metadata": {},
@@ -303,7 +305,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.star98.NOTE"
+      "print(sm.datasets.star98.NOTE)"
      ],
      "language": "python",
      "metadata": {},
@@ -314,7 +316,7 @@
      "collapsed": false,
      "input": [
       "dta = sm.datasets.star98.load_pandas().data\n",
-      "print dta.columns"
+      "print(dta.columns)"
      ],
      "language": "python",
      "metadata": {},
@@ -324,7 +326,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10)"
+      "print(dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10))"
      ],
      "language": "python",
      "metadata": {},
@@ -334,7 +336,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10)"
+      "print(dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10))"
      ],
      "language": "python",
      "metadata": {},
@@ -360,7 +362,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Toss a six-sided die 5 times, what's the probability of exactly 2 fours?"
@@ -402,14 +404,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print glm_mod.summary()"
+      "print(glm_mod.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The number of trials "
@@ -436,7 +438,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact\n",
@@ -458,7 +460,7 @@
      "collapsed": false,
      "input": [
       "means25 = exog.mean()\n",
-      "print means25"
+      "print(means25)"
      ],
      "language": "python",
      "metadata": {},
@@ -469,7 +471,7 @@
      "collapsed": false,
      "input": [
       "means25['LOWINC'] = exog['LOWINC'].quantile(.25)\n",
-      "print means25"
+      "print(means25)"
      ],
      "language": "python",
      "metadata": {},
@@ -481,7 +483,7 @@
      "input": [
       "means75 = exog.mean()\n",
       "means75['LOWINC'] = exog['LOWINC'].quantile(.75)\n",
-      "print means75"
+      "print(means75)"
      ],
      "language": "python",
      "metadata": {},
@@ -500,7 +502,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The interquartile first difference for the percentage of low income households in a school district is:"
@@ -510,7 +512,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print \"%2.4f%%\" % (diff[0]*100)"
+      "print(\"%2.4f%%\" % (diff[0]*100))"
      ],
      "language": "python",
      "metadata": {},
@@ -591,7 +593,7 @@
      "source": [
       "The definition of the deviance residuals depends on the family. For the Binomial distribution this is \n",
       "\n",
-      "$$r_{dev} = sign\\(Y-\\mu\\)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n",
+      "$$r_{dev} = sign\\left(Y-\\mu\\right)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n",
       "\n",
       "They can be used to detect ill-fitting covariates"
      ]
diff --git a/examples/notebooks/example_discrete.ipynb b/examples/notebooks/discrete_choice_overview.ipynb
similarity index 89%
rename from examples/notebooks/example_discrete.ipynb
rename to examples/notebooks/discrete_choice_overview.ipynb
index 66cee9da43d..cf7965db451 100644
--- a/examples/notebooks/example_discrete.ipynb
+++ b/examples/notebooks/discrete_choice_overview.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_discrete"
+  "name": "",
+  "signature": "sha256:db4cb100feb256e662d3fa7f3ff2347df004518535f3116bea8e9207c6b0d378"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm"
      ],
@@ -57,8 +59,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print spector_data.exog[:5,:]\n",
-      "print spector_data.endog[:5]"
+      "print(spector_data.exog[:5,:])\n",
+      "print(spector_data.endog[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -77,7 +79,7 @@
      "input": [
       "lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)\n",
       "lpm_res = lpm_mod.fit()\n",
-      "print 'Parameters: ', lpm_res.params[:-1]"
+      "print('Parameters: ', lpm_res.params[:-1])"
      ],
      "language": "python",
      "metadata": {},
@@ -96,7 +98,7 @@
      "input": [
       "logit_mod = sm.Logit(spector_data.endog, spector_data.exog)\n",
       "logit_res = logit_mod.fit(disp=0)\n",
-      "print 'Parameters: ', logit_res.params"
+      "print('Parameters: ', logit_res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -114,7 +116,7 @@
      "collapsed": false,
      "input": [
       "margeff = logit_res.get_margeff()\n",
-      "print margeff.summary()"
+      "print(margeff.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -131,7 +133,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print logit_res.summary()"
+      "print(logit_res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -151,9 +153,9 @@
       "probit_mod = sm.Probit(spector_data.endog, spector_data.exog)\n",
       "probit_res = probit_mod.fit()\n",
       "probit_margeff = probit_res.get_margeff()\n",
-      "print 'Parameters: ', probit_res.params\n",
-      "print 'Marginal effects: '\n",
-      "print probit_margeff.summary()"
+      "print('Parameters: ', probit_res.params)\n",
+      "print('Marginal effects: ')\n",
+      "print(probit_margeff.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -196,8 +198,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print anes_data.exog[:5,:]\n",
-      "print anes_data.endog[:5]"
+      "print(anes_data.exog[:5,:])\n",
+      "print(anes_data.endog[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -216,7 +218,7 @@
      "input": [
       "mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)\n",
       "mlogit_res = mlogit_mod.fit()\n",
-      "print mlogit_res.params"
+      "print(mlogit_res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -256,7 +258,7 @@
      "input": [
       "poisson_mod = sm.Poisson(rand_data.endog, rand_exog)\n",
       "poisson_res = poisson_mod.fit(method=\"newton\")\n",
-      "print poisson_res.summary()"
+      "print(poisson_res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -277,7 +279,7 @@
      "input": [
       "mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)\n",
       "res_nbin = mod_nbin.fit(disp=False)\n",
-      "print res_nbin.summary()"
+      "print(res_nbin.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -297,7 +299,7 @@
      "collapsed": false,
      "input": [
       "mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)\n",
-      "print mlogit_res.summary()"
+      "print(mlogit_res.summary())"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/example_gmle.ipynb b/examples/notebooks/example_gmle.ipynb
deleted file mode 100644
index 156f3575f10..00000000000
--- a/examples/notebooks/example_gmle.ipynb
+++ /dev/null
@@ -1,314 +0,0 @@
-{
- "metadata": {
-  "name": "example_gmle"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Generic Maximum Likelihood Model"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. \n",
-      "\n",
-      "## Example: Negative Binomial Regression for Count Data\n",
-      "\n",
-      "Consider a negative binomial regression model for count data with\n",
-      "log-likelihood (type NB-2) function expressed as:\n",
-      "\n",
-      "$$\n",
-      "    \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n",
-      "    \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n",
-      "    \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n",
-      "$$\n",
-      "\n",
-      "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n",
-      "and the negative binomial heterogeneity parameter $\\alpha$. \n",
-      "\n",
-      "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n",
-      "simply as:\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "from scipy.stats import nbinom"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def _ll_nb2(y, X, beta, alph):\n",
-      "    mu = np.exp(np.dot(X, beta))\n",
-      "    size = 1/alph\n",
-      "    prob = size/(size+mu)\n",
-      "    ll = nbinom.logpmf(y, size, prob)\n",
-      "    return ll"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## New Model Class\n",
-      "\n",
-      "We create a new model class which inherits from ``GenericLikelihoodModel``:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from statsmodels.base.model import GenericLikelihoodModel"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class NBin(GenericLikelihoodModel):\n",
-      "    def __init__(self, endog, exog, **kwds):\n",
-      "        super(NBin, self).__init__(endog, exog, **kwds)\n",
-      "        \n",
-      "    def nloglikeobs(self, params):\n",
-      "        alph = params[-1]\n",
-      "        beta = params[:-1]\n",
-      "        ll = _ll_nb2(self.endog, self.exog, beta, alph)\n",
-      "        return -ll \n",
-      "    \n",
-      "    def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n",
-      "        # we have one additional parameter and we need to add it for summary\n",
-      "        self.exog_names.append('alpha')\n",
-      "        if start_params == None:\n",
-      "            # Reasonable starting values\n",
-      "            start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n",
-      "            # intercept\n",
-      "            start_params[-2] = np.log(self.endog.mean())\n",
-      "        return super(NBin, self).fit(start_params=start_params, \n",
-      "                                     maxiter=maxiter, maxfun=maxfun, \n",
-      "                                     **kwds) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Two important things to notice: \n",
-      "\n",
-      "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n",
-      "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n",
-      "   \n",
-      "That's it! You're done!\n",
-      "\n",
-      "Usage Example\n",
-      "-------------\n",
-      "\n",
-      "The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)\n",
-      "dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``\n",
-      "function from the [Pandas library](http://pandas.pydata.org) to load the data\n",
-      "in memory. We then print the first few columns: \n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import statsmodels.api as sm"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n",
-      "\n",
-      "medpar.head()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The model we are interested in has a vector of non-negative integers as\n",
-      "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n",
-      "``type3``, ``hmo``, ``white``.\n",
-      "\n",
-      "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "y = medpar.los\n",
-      "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]]\n",
-      "X[\"constant\"] = 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Then, we fit the model and extract some information: "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mod = NBin(y, X)\n",
-      "res = mod.fit()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      " Extract parameter estimates, standard errors, p-values, AIC, etc.:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Parameters: ', res.params\n",
-      "print 'Standard errors: ', res.bse\n",
-      "print 'P-values: ', res.pvalues\n",
-      "print 'AIC: ', res.aic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As usual, you can obtain a full list of available information by typing\n",
-      "``dir(res)``.\n",
-      "We can also look at the summary of the estimation results."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print res.summary()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Testing"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n",
-      "print res_nbin.summary()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print res_nbin.params"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print res_nbin.bse"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Or we could compare them to results obtained using the MASS implementation for R:\n",
-      "\n",
-      "    url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'\n",
-      "    medpar = read.csv(url)\n",
-      "    f = los~factor(type)+hmo+white\n",
-      "    \n",
-      "    library(MASS)\n",
-      "    mod = glm.nb(f, medpar)\n",
-      "    coef(summary(mod))\n",
-      "                     Estimate Std. Error   z value      Pr(>|z|)\n",
-      "    (Intercept)    2.31027893 0.06744676 34.253370 3.885556e-257\n",
-      "    factor(type)2  0.22124898 0.05045746  4.384861  1.160597e-05\n",
-      "    factor(type)3  0.70615882 0.07599849  9.291748  1.517751e-20\n",
-      "    hmo           -0.06795522 0.05321375 -1.277024  2.015939e-01\n",
-      "    white         -0.12906544 0.06836272 -1.887951  5.903257e-02\n",
-      "\n",
-      "## Numerical precision \n",
-      "\n",
-      "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class.\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/examples/notebooks/example_formulas.ipynb b/examples/notebooks/formulas.ipynb
similarity index 92%
rename from examples/notebooks/example_formulas.ipynb
rename to examples/notebooks/formulas.ipynb
index 21baf356d11..e5b0cae437d 100644
--- a/examples/notebooks/example_formulas.ipynb
+++ b/examples/notebooks/formulas.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_formulas"
+  "name": "",
+  "signature": "sha256:d48ca7165bce5adbf6b30594f316a588aeadb01fd81e0b38b6374f24583b8e45"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,7 +13,7 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Fitting models using R-style formulas"
+      "Formulas: Fitting models using R-style formulas"
      ]
     },
     {
@@ -30,8 +31,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
-      "import pandas\n",
       "import statsmodels.api as sm"
      ],
      "language": "python",
@@ -91,7 +92,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import statsmodels.formula as smf"
+      "import statsmodels.formula.api as smf"
      ],
      "language": "python",
      "metadata": {},
@@ -169,7 +170,7 @@
      "input": [
       "mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n",
       "res = mod.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -191,7 +192,7 @@
      "collapsed": false,
      "input": [
       "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()\n",
-      "print res.params"
+      "print(res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -222,7 +223,7 @@
      "collapsed": false,
      "input": [
       "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()\n",
-      "print res.params"
+      "print(res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -243,8 +244,8 @@
      "input": [
       "res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()\n",
       "res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()\n",
-      "print res1.params, '\\n'\n",
-      "print res2.params"
+      "print(res1.params, '\\n')\n",
+      "print(res2.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -270,8 +271,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "res = sm.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n",
-      "print res.params"
+      "res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n",
+      "print(res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -290,8 +291,8 @@
      "input": [
       "def log_plus_1(x):\n",
       "    return np.log(x) + 1.\n",
-      "res = sm.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n",
-      "print res.params"
+      "res = smf.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n",
+      "print(res.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -323,8 +324,8 @@
       "import patsy\n",
       "f = 'Lottery ~ Literacy * Wealth'\n",
       "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n",
-      "print y[:5]\n",
-      "print X[:5]"
+      "print(y[:5])\n",
+      "print(X[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -343,8 +344,8 @@
      "input": [
       "f = 'Lottery ~ Literacy * Wealth'\n",
       "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n",
-      "print y[:5]\n",
-      "print X[:5]"
+      "print(y[:5])\n",
+      "print(X[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -354,7 +355,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.OLS(y, X).fit().summary()"
+      "print(sm.OLS(y, X).fit().summary())"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/generic_mle.ipynb b/examples/notebooks/generic_mle.ipynb
index 09d8d86ae47..07d94ca9c8c 100644
--- a/examples/notebooks/generic_mle.ipynb
+++ b/examples/notebooks/generic_mle.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "generic_mle"
+  "name": "",
+  "signature": "sha256:d3903edb7fb7da675fc25f6f8753fc1ad37719bd9ff21a0bda90d9048db2f1f0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,13 +13,33 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Generic Maximum Likelihood Estimator"
+      "Maximum Likelihood Estimation (Generic models)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n",
+      "\n",
+      "1. Probit model for binary dependent variables\n",
+      "2. Negative binomial model for count data\n",
+      "\n",
+      "The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Example 1: Probit model"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "from scipy import stats\n",
       "import statsmodels.api as sm\n",
@@ -29,42 +50,48 @@
      "outputs": []
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print sm.datasets.spector.NOTE"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": []
+     "source": [
+      "The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:"
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "data = sm.datasets.spector.load_pandas()\n",
-      "exog = sm.add_constant(data.exog, prepend=True)\n",
-      "endog = data.endog"
+      "exog = data.exog\n",
+      "endog = data.endog\n",
+      "print(sm.datasets.spector.NOTE)\n",
+      "print(data.exog.head())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Them, we add a constant to the matrix of regressors:"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "sm_probit = sm.Probit(endog, exog).fit()"
+      "exog = sm.add_constant(exog, prepend=True)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "* To create your own Likelihood Model, you just need to overwrite the loglike method."
+      "To create your own Likelihood Model, you simply need to overwrite the loglike method."
      ]
     },
     {
@@ -82,21 +109,36 @@
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Estimate the model and print a summary:"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "my_probit = MyProbit(endog, exog).fit()"
+      "sm_probit_manual = MyProbit(endog, exog).fit()\n",
+      "print(sm_probit_manual.summary())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Compare your Probit implementation to ``statsmodels``' \"canned\" implementation:"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm_probit.params"
+      "sm_probit_canned = sm.Probit(endog, exog).fit()"
      ],
      "language": "python",
      "metadata": {},
@@ -106,7 +148,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm_probit.cov_params()"
+      "print(sm_probit_canned.params)\n",
+      "print(sm_probit_manual.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -116,28 +159,309 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print my_probit.params"
+      "print(sm_probit_canned.cov_params())\n",
+      "print(sm_probit_manual.cov_params())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates."
+     ]
+    },
+    {
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "You can get the variance-covariance of the parameters. Notice that we didn't have to provide Hessian or Score functions."
+      "\n",
+      "\n",
+      "## Example 2: Negative Binomial Regression for Count Data\n",
+      "\n",
+      "Consider a negative binomial regression model for count data with\n",
+      "log-likelihood (type NB-2) function expressed as:\n",
+      "\n",
+      "$$\n",
+      "    \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n",
+      "    \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n",
+      "    \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n",
+      "$$\n",
+      "\n",
+      "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n",
+      "and the negative binomial heterogeneity parameter $\\alpha$. \n",
+      "\n",
+      "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n",
+      "simply as:\n"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print my_probit.cov_params()"
+      "import numpy as np\n",
+      "from scipy.stats import nbinom"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def _ll_nb2(y, X, beta, alph):\n",
+      "    mu = np.exp(np.dot(X, beta))\n",
+      "    size = 1/alph\n",
+      "    prob = size/(size+mu)\n",
+      "    ll = nbinom.logpmf(y, size, prob)\n",
+      "    return ll"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### New Model Class\n",
+      "\n",
+      "We create a new model class which inherits from ``GenericLikelihoodModel``:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from statsmodels.base.model import GenericLikelihoodModel"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class NBin(GenericLikelihoodModel):\n",
+      "    def __init__(self, endog, exog, **kwds):\n",
+      "        super(NBin, self).__init__(endog, exog, **kwds)\n",
+      "        \n",
+      "    def nloglikeobs(self, params):\n",
+      "        alph = params[-1]\n",
+      "        beta = params[:-1]\n",
+      "        ll = _ll_nb2(self.endog, self.exog, beta, alph)\n",
+      "        return -ll \n",
+      "    \n",
+      "    def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n",
+      "        # we have one additional parameter and we need to add it for summary\n",
+      "        self.exog_names.append('alpha')\n",
+      "        if start_params == None:\n",
+      "            # Reasonable starting values\n",
+      "            start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n",
+      "            # intercept\n",
+      "            start_params[-2] = np.log(self.endog.mean())\n",
+      "        return super(NBin, self).fit(start_params=start_params, \n",
+      "                                     maxiter=maxiter, maxfun=maxfun, \n",
+      "                                     **kwds) "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Two important things to notice: \n",
+      "\n",
+      "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n",
+      "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n",
+      "   \n",
+      "That's it! You're done!\n",
+      "\n",
+      "### Usage Example\n",
+      "\n",
+      "The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)\n",
+      "dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``\n",
+      "function from the [Pandas library](http://pandas.pydata.org) to load the data\n",
+      "in memory. We then print the first few columns: \n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import statsmodels.api as sm"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n",
+      "\n",
+      "medpar.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The model we are interested in has a vector of non-negative integers as\n",
+      "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n",
+      "``type3``, ``hmo``, ``white``.\n",
+      "\n",
+      "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y = medpar.los\n",
+      "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]]\n",
+      "X[\"constant\"] = 1"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Then, we fit the model and extract some information: "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mod = NBin(y, X)\n",
+      "res = mod.fit()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " Extract parameter estimates, standard errors, p-values, AIC, etc.:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Parameters: ', res.params)\n",
+      "print('Standard errors: ', res.bse)\n",
+      "print('P-values: ', res.pvalues)\n",
+      "print('AIC: ', res.aic)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As usual, you can obtain a full list of available information by typing\n",
+      "``dir(res)``.\n",
+      "We can also look at the summary of the estimation results."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(res.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Testing"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n",
+      "print(res_nbin.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(res_nbin.params)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(res_nbin.bse)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Or we could compare them to results obtained using the MASS implementation for R:\n",
+      "\n",
+      "    url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'\n",
+      "    medpar = read.csv(url)\n",
+      "    f = los~factor(type)+hmo+white\n",
+      "    \n",
+      "    library(MASS)\n",
+      "    mod = glm.nb(f, medpar)\n",
+      "    coef(summary(mod))\n",
+      "                     Estimate Std. Error   z value      Pr(>|z|)\n",
+      "    (Intercept)    2.31027893 0.06744676 34.253370 3.885556e-257\n",
+      "    factor(type)2  0.22124898 0.05045746  4.384861  1.160597e-05\n",
+      "    factor(type)3  0.70615882 0.07599849  9.291748  1.517751e-20\n",
+      "    hmo           -0.06795522 0.05321375 -1.277024  2.015939e-01\n",
+      "    white         -0.12906544 0.06836272 -1.887951  5.903257e-02\n",
+      "\n",
+      "### Numerical precision \n",
+      "\n",
+      "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class."
+     ]
     }
    ],
    "metadata": {}
diff --git a/examples/notebooks/example_glm.ipynb b/examples/notebooks/glm.ipynb
similarity index 93%
rename from examples/notebooks/example_glm.ipynb
rename to examples/notebooks/glm.ipynb
index dcea3984a2e..36958006aed 100644
--- a/examples/notebooks/example_glm.ipynb
+++ b/examples/notebooks/glm.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_glm"
+  "name": "",
+  "signature": "sha256:683e32a0ed1a159e5647f23a44b0b5bc839f9a791ad9f300aa744e7243bf7c2f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm\n",
       "from scipy import stats\n",
@@ -45,7 +47,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.star98.NOTE"
+      "print(sm.datasets.star98.NOTE)"
      ],
      "language": "python",
      "metadata": {},
@@ -80,7 +82,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print data.endog[:5,:]"
+      "print(data.endog[:5,:])"
      ],
      "language": "python",
      "metadata": {},
@@ -98,7 +100,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print data.exog[:2,:]"
+      "print(data.exog[:2,:])"
      ],
      "language": "python",
      "metadata": {},
@@ -117,7 +119,7 @@
      "input": [
       "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n",
       "res = glm_binom.fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -134,9 +136,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print 'Total number of trials:',  data.endog[0].sum()\n",
-      "print 'Parameters: ', res.params\n",
-      "print 'T-values: ', res.tvalues"
+      "print('Total number of trials:',  data.endog[0].sum())\n",
+      "print('Parameters: ', res.params)\n",
+      "print('T-values: ', res.tvalues)"
      ],
      "language": "python",
      "metadata": {},
@@ -177,7 +179,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print \"%2.4f%%\" % (diff*100)"
+      "print(\"%2.4f%%\" % (diff*100))"
      ],
      "language": "python",
      "metadata": {},
@@ -322,7 +324,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.scotland.DESCRLONG"
+      "print(sm.datasets.scotland.DESCRLONG)"
      ],
      "language": "python",
      "metadata": {},
@@ -341,8 +343,8 @@
      "input": [
       "data2 = sm.datasets.scotland.load()\n",
       "data2.exog = sm.add_constant(data2.exog, prepend=False)\n",
-      "print data2.exog[:5,:]\n",
-      "print data2.endog[:5]"
+      "print(data2.exog[:5,:])\n",
+      "print(data2.endog[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -361,7 +363,7 @@
      "input": [
       "glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())\n",
       "glm_results = glm_gamma.fit()\n",
-      "print glm_results.summary()"
+      "print(glm_results.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -404,7 +406,7 @@
      "input": [
       "gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))\n",
       "gauss_log_results = gauss_log.fit()\n",
-      "print gauss_log_results.summary()"
+      "print(gauss_log_results.summary())"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/glm_formula.ipynb b/examples/notebooks/glm_formula.ipynb
new file mode 100644
index 00000000000..99b2eb9180b
--- /dev/null
+++ b/examples/notebooks/glm_formula.ipynb
@@ -0,0 +1,111 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:4d0a48ce24362e292246573d1784aae3c74f74e611ae1ea9570c003e60083096"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Generalized Linear Models (Formula)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.\n",
+      "\n",
+      "To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import print_function\n",
+      "import statsmodels.api as sm\n",
+      "import statsmodels.formula.api as smf\n",
+      "star98 = sm.datasets.star98.load_pandas().data\n",
+      "formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
+      "           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
+      "dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n",
+      "              'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',\n",
+      "              'PERSPENK', 'PTRATIO', 'PCTAF']]\n",
+      "endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))\n",
+      "del dta['NABOVE']\n",
+      "dta['SUCCESS'] = endog"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Then, we fit the GLM model:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
+      "mod1.summary()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Finally, we define a function to operate customized data transformation using the formula framework:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def double_it(x):\n",
+      "    return 2 * x\n",
+      "formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
+      "           PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
+      "mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
+      "mod2.summary()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(mod1.params[1])\n",
+      "print(mod2.params[1] * 2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/examples/notebooks/example_gls.ipynb b/examples/notebooks/gls.ipynb
similarity index 92%
rename from examples/notebooks/example_gls.ipynb
rename to examples/notebooks/gls.ipynb
index bf18fd76d7e..69cb5e827fa 100644
--- a/examples/notebooks/example_gls.ipynb
+++ b/examples/notebooks/gls.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_gls"
+  "name": "",
+  "signature": "sha256:a71297440c00135de29e296d1bacbc38ddcd90ca149d1c2042d1ef2e222bfb96"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import statsmodels.api as sm\n",
       "import numpy as np\n",
       "from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)"
@@ -40,7 +42,7 @@
      "input": [
       "data = sm.datasets.longley.load()\n",
       "data.exog = sm.add_constant(data.exog)\n",
-      "print data.exog[:5]"
+      "print(data.exog[:5])"
      ],
      "language": "python",
      "metadata": {},
@@ -86,8 +88,8 @@
      "collapsed": false,
      "input": [
       "resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()\n",
-      "print resid_fit.tvalues[1]\n",
-      "print resid_fit.pvalues[1]"
+      "print(resid_fit.tvalues[1])\n",
+      "print(resid_fit.pvalues[1])"
      ],
      "language": "python",
      "metadata": {},
@@ -176,7 +178,7 @@
      "input": [
       "glsar_model = sm.GLSAR(data.endog, data.exog, 1)\n",
       "glsar_results = glsar_model.iterative_fit(1)\n",
-      "print glsar_results.summary()"
+      "print(glsar_results.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -197,10 +199,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print gls_results.params\n",
-      "print glsar_results.params\n",
-      "print gls_results.bse\n",
-      "print glsar_results.bse"
+      "print(gls_results.params)\n",
+      "print(glsar_results.params)\n",
+      "print(gls_results.bse)\n",
+      "print(glsar_results.bse)"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/example_interactions.ipynb b/examples/notebooks/interactions_anova.ipynb
similarity index 68%
rename from examples/notebooks/example_interactions.ipynb
rename to examples/notebooks/interactions_anova.ipynb
index ea24335324f..77c7c91c683 100644
--- a/examples/notebooks/example_interactions.ipynb
+++ b/examples/notebooks/interactions_anova.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_interactions"
+  "name": "",
+  "signature": "sha256:9ef8012a6a994c0fe838bff4a0334f1d703a756d7dc3d82b9ad34418b92e21cc"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -28,22 +29,24 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
+      "from statsmodels.compat import urlopen\n",
       "import numpy as np\n",
       "np.set_printoptions(precision=4, suppress=True)\n",
       "import statsmodels.api as sm\n",
-      "import pandas\n",
+      "import pandas as pd\n",
+      "pd.set_option(\"display.width\", 100)\n",
       "import matplotlib.pyplot as plt\n",
       "from statsmodels.formula.api import ols\n",
       "from statsmodels.graphics.api import interaction_plot, abline_plot\n",
       "from statsmodels.stats.anova import anova_lm\n",
       "\n",
       "try:\n",
-      "    salary_table = pandas.read_csv('salary.table')\n",
+      "    salary_table = pd.read_csv('salary.table')\n",
       "except:  # recent pandas can read URL without urlopen\n",
-      "    from urllib2 import urlopen\n",
       "    url = 'http://stats191.stanford.edu/data/salary.table'\n",
       "    fh = urlopen(url)\n",
-      "    salary_table = pandas.read_table(fh)\n",
+      "    salary_table = pd.read_table(fh)\n",
       "    salary_table.to_csv('salary.table')\n",
       "\n",
       "E = salary_table.E\n",
@@ -94,7 +97,7 @@
      "input": [
       "formula = 'S ~ C(E) + C(M) + X'\n",
       "lm = ols(formula, salary_table).fit()\n",
-      "print lm.summary()"
+      "print(lm.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -163,7 +166,7 @@
      "collapsed": false,
      "input": [
       "infl = lm.get_influence()\n",
-      "print infl.summary_table()"
+      "print(infl.summary_table())"
      ],
      "language": "python",
      "metadata": {},
@@ -234,7 +237,7 @@
      "collapsed": false,
      "input": [
       "interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n",
-      "print interX_lm.summary()"
+      "print(interX_lm.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -254,13 +257,13 @@
       "from statsmodels.stats.api import anova_lm\n",
       "\n",
       "table1 = anova_lm(lm, interX_lm)\n",
-      "print table1\n",
+      "print(table1)\n",
       "\n",
-      "interM_lm = ols(\"S ~ X + C(E)*C(M)\", df=salary_table).fit()\n",
-      "print interM_lm.summary()\n",
+      "interM_lm = ols(\"S ~ X + C(E)*C(M)\", data=salary_table).fit()\n",
+      "print(interM_lm.summary())\n",
       "\n",
       "table2 = anova_lm(lm, interM_lm)\n",
-      "print table2"
+      "print(table2)"
      ],
      "language": "python",
      "metadata": {},
@@ -332,24 +335,30 @@
      "collapsed": false,
      "input": [
       "drop_idx = abs(resid).argmax()\n",
-      "print drop_idx  # zero-based index\n",
+      "print(drop_idx)  # zero-based index\n",
       "idx = salary_table.index.drop(drop_idx)\n",
       "\n",
-      "lm32 = ols('S ~ C(E) + X + C(M)', df=salary_table, subset=idx).fit()\n",
+      "lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()\n",
       "\n",
-      "print lm32.summary()\n",
+      "print(lm32.summary())\n",
+      "print('\\n')\n",
       "\n",
-      "interX_lm32 = ols('S ~ C(E) * X + C(M)', df=salary_table, subset=idx).fit()\n",
+      "interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()\n",
+      "\n",
+      "print(interX_lm32.summary())\n",
+      "print('\\n')\n",
       "\n",
-      "print interX_lm32.summary()\n",
       "\n",
       "table3 = anova_lm(lm32, interX_lm32)\n",
-      "print table3\n",
+      "print(table3)\n",
+      "print('\\n')\n",
+      "\n",
       "\n",
-      "interM_lm32 = ols('S ~ X + C(E) * C(M)', df=salary_table, subset=idx).fit()\n",
+      "interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()\n",
       "\n",
       "table4 = anova_lm(lm32, interM_lm32)\n",
-      "print table4"
+      "print(table4)\n",
+      "print('\\n')"
      ],
      "language": "python",
      "metadata": {},
@@ -395,7 +404,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "lm_final = ols('S ~ X + C(E)*C(M)', df = salary_table.drop([drop_idx])).fit()\n",
+      "lm_final = ols('S ~ X + C(E)*C(M)', data = salary_table.drop([drop_idx])).fit()\n",
       "mf = lm_final.model.data.orig_exog\n",
       "lstyle = ['-','--']\n",
       "\n",
@@ -448,78 +457,140 @@
      "collapsed": false,
      "input": [
       "try:\n",
-      "    minority_table = pandas.read_table('minority.table')\n",
+      "    minority_table = pd.read_table('minority.table')\n",
       "except:  # don't have data already\n",
       "    url = 'http://stats191.stanford.edu/data/minority.table'\n",
-      "    minority_table = pandas.read_table(url)\n",
+      "    minority_table = pd.read_table(url)\n",
       "\n",
       "factor_group = minority_table.groupby(['ETHN'])\n",
       "\n",
-      "plt.figure(figsize=(6,6))\n",
+      "fig, ax = plt.subplots(figsize=(6,6))\n",
       "colors = ['purple', 'green']\n",
       "markers = ['o', 'v']\n",
       "for factor, group in factor_group:\n",
-      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
+      "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
       "                marker=markers[factor], s=12**2)\n",
-      "plt.xlabel('TEST');\n",
-      "plt.ylabel('JPERF');\n",
-      "\n",
-      "min_lm = ols('JPERF ~ TEST', df=minority_table).fit()\n",
-      "print min_lm.summary()\n",
-      "\n",
-      "plt.figure(figsize=(6,6));\n",
+      "ax.set_xlabel('TEST');\n",
+      "ax.set_ylabel('JPERF');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "min_lm = ols('JPERF ~ TEST', data=minority_table).fit()\n",
+      "print(min_lm.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = plt.subplots(figsize=(6,6));\n",
       "for factor, group in factor_group:\n",
-      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
+      "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
       "                marker=markers[factor], s=12**2)\n",
       "\n",
-      "plt.xlabel('TEST')\n",
-      "plt.ylabel('JPERF')\n",
-      "abline_plot(model_results = min_lm, ax=plt.gca());\n",
-      "\n",
+      "ax.set_xlabel('TEST')\n",
+      "ax.set_ylabel('JPERF')\n",
+      "fig = abline_plot(model_results = min_lm, ax=ax)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',\n",
-      "        df=minority_table).fit()\n",
+      "        data=minority_table).fit()\n",
       "\n",
-      "print min_lm2.summary()\n",
-      "\n",
-      "plt.figure(figsize=(6,6));\n",
+      "print(min_lm2.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = plt.subplots(figsize=(6,6));\n",
       "for factor, group in factor_group:\n",
-      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
+      "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
       "                marker=markers[factor], s=12**2)\n",
       "\n",
-      "abline_plot(intercept = min_lm2.params['Intercept'],\n",
-      "                 slope = min_lm2.params['TEST'], ax=plt.gca(), color='purple');\n",
-      "abline_plot(intercept = min_lm2.params['Intercept'],\n",
+      "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
+      "                 slope = min_lm2.params['TEST'], ax=ax, color='purple');\n",
+      "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
       "        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n",
-      "        ax=plt.gca(), color='green');\n",
-      "\n",
-      "\n",
-      "min_lm3 = ols('JPERF ~ TEST + ETHN', df = minority_table).fit()\n",
-      "print min_lm3.summary()\n",
-      "\n",
-      "plt.figure(figsize=(6,6));\n",
+      "        ax=ax, color='green');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "min_lm3 = ols('JPERF ~ TEST + ETHN', data = minority_table).fit()\n",
+      "print(min_lm3.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = plt.subplots(figsize=(6,6));\n",
       "for factor, group in factor_group:\n",
-      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
+      "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
       "                marker=markers[factor], s=12**2)\n",
       "\n",
-      "abline_plot(intercept = min_lm3.params['Intercept'],\n",
-      "                 slope = min_lm3.params['TEST'], ax=plt.gca(), color='purple');\n",
-      "abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
-      "        slope = min_lm3.params['TEST'], ax=plt.gca(), color='green');\n",
-      "\n",
-      "\n",
-      "min_lm4 = ols('JPERF ~ TEST * ETHN', df = minority_table).fit()\n",
-      "print min_lm4.summary()\n",
-      "\n",
-      "plt.figure(figsize=(6,6));\n",
+      "fig = abline_plot(intercept = min_lm3.params['Intercept'],\n",
+      "                 slope = min_lm3.params['TEST'], ax=ax, color='purple');\n",
+      "fig = abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
+      "        slope = min_lm3.params['TEST'], ax=ax, color='green');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "min_lm4 = ols('JPERF ~ TEST * ETHN', data = minority_table).fit()\n",
+      "print(min_lm4.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig, ax = plt.subplots(figsize=(8,6));\n",
       "for factor, group in factor_group:\n",
-      "    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
+      "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
       "                marker=markers[factor], s=12**2)\n",
       "\n",
-      "abline_plot(intercept = min_lm4.params['Intercept'],\n",
-      "                 slope = min_lm4.params['TEST'], ax=plt.gca(), color='purple');\n",
-      "abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
+      "fig = abline_plot(intercept = min_lm4.params['Intercept'],\n",
+      "                 slope = min_lm4.params['TEST'], ax=ax, color='purple');\n",
+      "fig = abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
       "        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n",
-      "        ax=plt.gca(), color='green');\n"
+      "        ax=ax, color='green');"
      ],
      "language": "python",
      "metadata": {},
@@ -531,19 +602,43 @@
      "input": [
       "# is there any effect of ETHN on slope or intercept?\n",
       "table5 = anova_lm(min_lm, min_lm4)\n",
-      "print table5\n",
-      "\n",
+      "print(table5)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "# is there any effect of ETHN on intercept\n",
       "table6 = anova_lm(min_lm, min_lm3)\n",
-      "print table6\n",
-      "\n",
+      "print(table6)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "# is there any effect of ETHN on slope\n",
       "table7 = anova_lm(min_lm, min_lm2)\n",
-      "print table7\n",
-      "\n",
+      "print(table7)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "# is it just the slope or both?\n",
       "table8 = anova_lm(min_lm2, min_lm4)\n",
-      "print table8"
+      "print(table8)"
      ],
      "language": "python",
      "metadata": {},
@@ -561,22 +656,38 @@
      "collapsed": false,
      "input": [
       "try:\n",
-      "    rehab_table = pandas.read_csv('rehab.table')\n",
+      "    rehab_table = pd.read_csv('rehab.table')\n",
       "except:\n",
       "    url = 'http://stats191.stanford.edu/data/rehab.csv'\n",
-      "    rehab_table = pandas.read_table(url, delimiter=\",\")\n",
+      "    rehab_table = pd.read_table(url, delimiter=\",\")\n",
       "    rehab_table.to_csv('rehab.table')\n",
       "\n",
-      "plt.figure(figsize=(6,6))\n",
-      "rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())\n",
-      "\n",
-      "rehab_lm = ols('Time ~ C(Fitness)', df=rehab_table).fit()\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
+      "fig = rehab_table.boxplot('Time', 'Fitness', ax=ax, grid=False)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit()\n",
       "table9 = anova_lm(rehab_lm)\n",
-      "print table9\n",
-      "\n",
-      "print rehab_lm.model.data.orig_exog\n",
+      "print(table9)\n",
       "\n",
-      "print rehab_lm.summary()\n"
+      "print(rehab_lm.model.data.orig_exog)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(rehab_lm.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -594,10 +705,10 @@
      "collapsed": false,
      "input": [
       "try:\n",
-      "    kidney_table = pandas.read_table('./kidney.table')\n",
+      "    kidney_table = pd.read_table('./kidney.table')\n",
       "except:\n",
       "    url = 'http://stats191.stanford.edu/data/kidney.table'\n",
-      "    kidney_table = pandas.read_table(url, delimiter=\" *\")"
+      "    kidney_table = pd.read_table(url, delimiter=\" *\")"
      ],
      "language": "python",
      "metadata": {},
@@ -632,8 +743,8 @@
      "collapsed": false,
      "input": [
       "kt = kidney_table\n",
-      "plt.figure(figsize=(6,6))\n",
-      "interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
+      "plt.figure(figsize=(8,6))\n",
+      "fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
       "        colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())"
      ],
      "language": "python",
@@ -651,18 +762,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', df=kt).fit()\n",
+      "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()\n",
       "\n",
       "table10 = anova_lm(kidney_lm)\n",
       "\n",
-      "print anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n",
-      "                df=kt).fit(), kidney_lm)\n",
-      "print anova_lm(ols('np.log(Days+1) ~ C(Duration)', df=kt).fit(),\n",
+      "print(anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',\n",
+      "                data=kt).fit(), kidney_lm))\n",
+      "print(anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(),\n",
       "               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
-      "                   df=kt).fit())\n",
-      "print anova_lm(ols('np.log(Days+1) ~ C(Weight)', df=kt).fit(),\n",
+      "                   data=kt).fit()))\n",
+      "print(anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(),\n",
       "               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',\n",
-      "                   df=kt).fit())"
+      "                   data=kt).fit()))"
      ],
      "language": "python",
      "metadata": {},
@@ -688,17 +799,25 @@
      "collapsed": false,
      "input": [
       "sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',\n",
-      "            df=kt).fit()\n",
-      "\n",
-      "print anova_lm(sum_lm)\n",
-      "print anova_lm(sum_lm, typ=2)\n",
-      "print anova_lm(sum_lm, typ=3)\n",
+      "            data=kt).fit()\n",
       "\n",
+      "print(anova_lm(sum_lm))\n",
+      "print(anova_lm(sum_lm, typ=2))\n",
+      "print(anova_lm(sum_lm, typ=3))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',\n",
-      "            df=kt).fit()\n",
-      "print anova_lm(nosum_lm)\n",
-      "print anova_lm(nosum_lm, typ=2)\n",
-      "print anova_lm(nosum_lm, typ=3)"
+      "            data=kt).fit()\n",
+      "print(anova_lm(nosum_lm))\n",
+      "print(anova_lm(nosum_lm, typ=2))\n",
+      "print(anova_lm(nosum_lm, typ=3))"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/kernel_density.ipynb b/examples/notebooks/kernel_density.ipynb
index 87e3957937f..0474712c18c 100644
--- a/examples/notebooks/kernel_density.ipynb
+++ b/examples/notebooks/kernel_density.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "kernel_density"
+  "name": "",
+  "signature": "sha256:72c9666bd25c4484a0eadf7c38539ed7a8a8ef5cd37d857a9553e8523ee4740c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -110,7 +111,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The fitted KDE object is a full non-parametric distribution."
diff --git a/examples/notebooks/example_ols.ipynb b/examples/notebooks/ols.ipynb
similarity index 90%
rename from examples/notebooks/example_ols.ipynb
rename to examples/notebooks/ols.ipynb
index ca1253274ad..879b29ad4ee 100644
--- a/examples/notebooks/example_ols.ipynb
+++ b/examples/notebooks/ols.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_ols"
+  "name": "",
+  "signature": "sha256:5db438f961adadf8fe26821d384aca07c66313bbe5deae4565558e80a8ab58ad"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm\n",
       "import matplotlib.pyplot as plt\n",
@@ -71,24 +73,6 @@
      "metadata": {},
      "outputs": []
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Inspect data:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "X = sm.add_constant(X)\n",
-      "y = np.dot(X, beta) + e"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -102,7 +86,7 @@
      "input": [
       "model = sm.OLS(y, X)\n",
       "results = model.fit()\n",
-      "print results.summary()"
+      "print(results.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -119,8 +103,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print 'Parameters: ', results.params\n",
-      "print 'R2: ', results.rsquared"
+      "print('Parameters: ', results.params)\n",
+      "print('R2: ', results.rsquared)"
      ],
      "language": "python",
      "metadata": {},
@@ -164,7 +148,7 @@
      "collapsed": false,
      "input": [
       "res = sm.OLS(y, X).fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -181,9 +165,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print 'Parameters: ', res.params\n",
-      "print 'Standard errors: ', res.bse\n",
-      "print 'Predicted values: ', res.predict()"
+      "print('Parameters: ', res.params)\n",
+      "print('Standard errors: ', res.bse)\n",
+      "print('Predicted values: ', res.predict())"
      ],
      "language": "python",
      "metadata": {},
@@ -202,7 +186,7 @@
      "input": [
       "prstd, iv_l, iv_u = wls_prediction_std(res)\n",
       "\n",
-      "fig, ax = plt.subplots()\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
       "\n",
       "ax.plot(x, y, 'o', label=\"data\")\n",
       "ax.plot(x, y_true, 'b-', label=\"True\")\n",
@@ -260,10 +244,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print X[:5,:]\n",
-      "print y[:5]\n",
-      "print groups\n",
-      "print dummy[:5,:]"
+      "print(X[:5,:])\n",
+      "print(y[:5])\n",
+      "print(groups)\n",
+      "print(dummy[:5,:])"
      ],
      "language": "python",
      "metadata": {},
@@ -281,7 +265,7 @@
      "collapsed": false,
      "input": [
       "res2 = sm.OLS(y, X).fit()\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -300,14 +284,14 @@
      "input": [
       "prstd, iv_l, iv_u = wls_prediction_std(res2)\n",
       "\n",
-      "fig, ax = plt.subplots()\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
       "\n",
       "ax.plot(x, y, 'o', label=\"Data\")\n",
       "ax.plot(x, y_true, 'b-', label=\"True\")\n",
       "ax.plot(x, res2.fittedvalues, 'r--.', label=\"Predicted\")\n",
       "ax.plot(x, iv_u, 'r--')\n",
       "ax.plot(x, iv_l, 'r--')\n",
-      "ax.legend(loc=\"best\")"
+      "legend = ax.legend(loc=\"best\")"
      ],
      "language": "python",
      "metadata": {},
@@ -329,8 +313,8 @@
      "collapsed": false,
      "input": [
       "R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n",
-      "print np.array(R)\n",
-      "print res2.f_test(R)"
+      "print(np.array(R))\n",
+      "print(res2.f_test(R))"
      ],
      "language": "python",
      "metadata": {},
@@ -347,7 +331,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print res2.f_test(\"x2 = x3 = 0\")"
+      "print(res2.f_test(\"x2 = x3 = 0\"))"
      ],
      "language": "python",
      "metadata": {},
@@ -380,7 +364,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print res3.f_test(R)"
+      "print(res3.f_test(R))"
      ],
      "language": "python",
      "metadata": {},
@@ -390,7 +374,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print res3.f_test(\"x2 = x3 = 0\")"
+      "print(res3.f_test(\"x2 = x3 = 0\"))"
      ],
      "language": "python",
      "metadata": {},
@@ -431,7 +415,7 @@
      "input": [
       "ols_model = sm.OLS(y, X)\n",
       "ols_results = ols_model.fit()\n",
-      "print ols_results.summary()"
+      "print(ols_results.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -450,6 +434,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "norm_x = X.values\n",
       "for i, name in enumerate(X):\n",
       "    if name == \"const\":\n",
       "        continue\n",
@@ -473,7 +458,7 @@
      "input": [
       "eigs = np.linalg.eigvals(norm_xtx)\n",
       "condition_number = np.sqrt(eigs.max() / eigs.min())\n",
-      "print condition_number"
+      "print(condition_number)"
      ],
      "language": "python",
      "metadata": {},
@@ -493,7 +478,7 @@
      "collapsed": false,
      "input": [
       "ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()\n",
-      "print \"Percentage change %4.2f%%\\n\"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100])"
+      "print(\"Percentage change %4.2f%%\\n\"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100]))"
      ],
      "language": "python",
      "metadata": {},
@@ -537,7 +522,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print infl.summary_frame().filter(regex=\"dfb\")"
+      "print(infl.summary_frame().filter(regex=\"dfb\"))"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/example_predict.ipynb b/examples/notebooks/predict.ipynb
similarity index 94%
rename from examples/notebooks/example_predict.ipynb
rename to examples/notebooks/predict.ipynb
index 763b18720f0..6e2633d2d5f 100644
--- a/examples/notebooks/example_predict.ipynb
+++ b/examples/notebooks/predict.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_predict"
+  "name": "",
+  "signature": "sha256:3d56dcbfbf79a5d9735e5240f044566b3157072f90f085a06c84b6ee3e94ae70"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,13 +13,14 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Out of sample prediction"
+      "Prediction (out of sample)"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm"
      ],
@@ -63,7 +65,7 @@
      "input": [
       "olsmod = sm.OLS(y, X)\n",
       "olsres = olsmod.fit()\n",
-      "print olsres.summary()"
+      "print(olsres.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -81,7 +83,7 @@
      "collapsed": false,
      "input": [
       "ypred = olsres.predict(X)\n",
-      "print ypred"
+      "print(ypred)"
      ],
      "language": "python",
      "metadata": {},
@@ -102,7 +104,7 @@
       "Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2))\n",
       "Xnew = sm.add_constant(Xnew)\n",
       "ynewpred =  olsres.predict(Xnew) # predict out of sample\n",
-      "print ynewpred"
+      "print(ynewpred)"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/ipynb/example_quantreg.ipynb b/examples/notebooks/quantile_regression.ipynb
similarity index 87%
rename from examples/ipynb/example_quantreg.ipynb
rename to examples/notebooks/quantile_regression.ipynb
index 32b0bdbe1f9..c11d635b19d 100644
--- a/examples/ipynb/example_quantreg.ipynb
+++ b/examples/notebooks/quantile_regression.ipynb
@@ -1,17 +1,25 @@
 {
  "metadata": {
-  "name": "example_quantreg"
+  "name": "",
+  "signature": "sha256:838d662c4a388aff01e7b6abd67e882909456f79f93c394dcc15b0a07d06aa5c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Quantile regression"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# Quantile regression\n",
       "\n",
       "This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n",
       "\n",
@@ -28,6 +36,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import patsy\n",
       "import numpy as np\n",
       "import pandas as pd\n",
@@ -58,7 +67,7 @@
      "input": [
       "mod = smf.quantreg('foodexp ~ income', data)\n",
       "res = mod.fit(q=.5)\n",
-      "print res.summary()"
+      "print(res.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -102,8 +111,8 @@
       "           lb = ols_ci[0],\n",
       "           ub = ols_ci[1])\n",
       "\n",
-      "print models\n",
-      "print ols"
+      "print(models)\n",
+      "print(ols)"
      ],
      "language": "python",
      "metadata": {},
@@ -129,20 +138,21 @@
       "x = np.arange(data.income.min(), data.income.max(), 50)\n",
       "get_y = lambda a, b: a + b * x\n",
       "\n",
+      "fig, ax = plt.subplots(figsize=(8, 6))\n",
+      "\n",
       "for i in range(models.shape[0]):\n",
       "    y = get_y(models.a[i], models.b[i])\n",
-      "    plt.plot(x, y, linestyle='dotted', color='grey')\n",
+      "    ax.plot(x, y, linestyle='dotted', color='grey')\n",
       "    \n",
       "y = get_y(ols['a'], ols['b'])\n",
-      "plt.plot(x, y, color='red', label='OLS')\n",
       "\n",
-      "plt.scatter(data.income, data.foodexp, alpha=.2)\n",
-      "plt.xlim((240, 3000))\n",
-      "plt.ylim((240, 2000))\n",
-      "plt.legend()\n",
-      "plt.xlabel('Income')\n",
-      "plt.ylabel('Food expenditure')\n",
-      "plt.show()"
+      "ax.plot(x, y, color='red', label='OLS')\n",
+      "ax.scatter(data.income, data.foodexp, alpha=.2)\n",
+      "ax.set_xlim((240, 3000))\n",
+      "ax.set_ylim((240, 2000))\n",
+      "legend = ax.legend()\n",
+      "ax.set_xlabel('Income', fontsize=16)\n",
+      "ax.set_ylabel('Food expenditure', fontsize=16);"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/regression_diagnostics.ipynb b/examples/notebooks/regression_diagnostics.ipynb
new file mode 100644
index 00000000000..497771557df
--- /dev/null
+++ b/examples/notebooks/regression_diagnostics.ipynb
@@ -0,0 +1,241 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:5b33d116834aaf77cd394c7959d16b3a68926a99ff8ead6d821e47b75c2e0836"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Regression diagnostics"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the [Regression Diagnostics page.](http://statsmodels.sourceforge.net/stable/diagnostic.html) \n",
+      "\n",
+      "Note that most of the tests described here only return a tuple of numbers, without any annotation. A full description of outputs is always included in the docstring and in the online ``statsmodels`` documentation. For presentation purposes, we use the ``zip(name,test)`` construct to pretty-print short descriptions in the examples below."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Estimate a regression model"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import print_function\n",
+      "from statsmodels.compat import lzip\n",
+      "import statsmodels\n",
+      "import numpy as np\n",
+      "import pandas as pd\n",
+      "import statsmodels.formula.api as smf\n",
+      "import statsmodels.stats.api as sms\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "# Load data\n",
+      "url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'\n",
+      "dat = pd.read_csv(url)\n",
+      "\n",
+      "# Fit regression model (using the natural log of one of the regressaors)\n",
+      "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()\n",
+      "\n",
+      "# Inspect the results\n",
+      "print(results.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Normality of the residuals"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Jarque-Bera test:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = ['Jarque-Bera', 'Chi^2 two-tail prob.', 'Skew', 'Kurtosis']\n",
+      "test = sms.jarque_bera(results.resid)\n",
+      "lzip(name, test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Omni test:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = ['Chi^2', 'Two-tail probability']\n",
+      "test = sms.omni_normtest(results.resid)\n",
+      "lzip(name, test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Influence tests\n",
+      "\n",
+      "Once created, an object of class ``OLSInfluence`` holds attributes and methods that allow users to assess the influence of each observation. For example, we can compute and extract the first few rows of DFbetas by:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from statsmodels.stats.outliers_influence import OLSInfluence\n",
+      "test_class = OLSInfluence(results)\n",
+      "test_class.dfbetas[:5,:]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Explore other options by typing ``dir(influence_test)``\n",
+      "\n",
+      "Useful information on leverage can also be plotted:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from statsmodels.graphics.regressionplots import plot_leverage_resid2\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
+      "fig = plot_leverage_resid2(results, ax = ax)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Other plotting options can be found on the [Graphics page.](http://statsmodels.sourceforge.net/stable/graphics.html)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Multicollinearity\n",
+      "\n",
+      "Condition number:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.linalg.cond(results.model.exog)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Heteroskedasticity tests\n",
+      "\n",
+      "Breush-Pagan test:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = ['Lagrange multiplier statistic', 'p-value', \n",
+      "        'f-value', 'f p-value']\n",
+      "test = sms.het_breushpagan(results.resid, results.model.exog)\n",
+      "lzip(name, test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Goldfeld-Quandt test"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = ['F statistic', 'p-value']\n",
+      "test = sms.het_goldfeldquandt(results.resid, results.model.exog)\n",
+      "lzip(name, test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Linearity\n",
+      "\n",
+      "Harvey-Collier multiplier test for Null hypothesis that the linear specification is correct:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "name = ['t value', 'p value']\n",
+      "test = sms.linear_harvey_collier(results)\n",
+      "lzip(name, test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/examples/notebooks/example_regression_plots.ipynb b/examples/notebooks/regression_plots.ipynb
similarity index 94%
rename from examples/notebooks/example_regression_plots.ipynb
rename to examples/notebooks/regression_plots.ipynb
index 16db0f0e8bf..54842f252db 100644
--- a/examples/notebooks/example_regression_plots.ipynb
+++ b/examples/notebooks/regression_plots.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_regression_plots"
+  "name": "",
+  "signature": "sha256:bb2a1556e6467b17fee3cdbdc7d81b52b24506277e11efb42bd983f42ec7998f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,8 +20,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
+      "from statsmodels.compat import lzip\n",
       "import numpy as np\n",
-      "import pandas\n",
+      "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "import statsmodels.api as sm\n",
       "from statsmodels.formula.api import ols"
@@ -86,7 +89,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print prestige_model.summary()"
+      "print(prestige_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -174,11 +177,7 @@
      "collapsed": false,
      "input": [
       "fig, ax = plt.subplots(figsize=(12,8))\n",
-      "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"income\", \"education\"], data=prestige, ax=ax)\n",
-      "ax = fig.axes[0]\n",
-      "\n",
-      "ax.set_xlim(-2e-15, 1e-14)\n",
-      "ax.set_ylim(-25, 30);"
+      "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"income\", \"education\"], data=prestige, ax=ax)"
      ],
      "language": "python",
      "metadata": {},
@@ -208,7 +207,7 @@
      "input": [
       "subset = ~prestige.index.isin([\"conductor\", \"RR.engineer\", \"minister\"])\n",
       "prestige_model2 = ols(\"prestige ~ income + education\", data=prestige, subset=subset).fit()\n",
-      "print prestige_model2.summary()"
+      "print(prestige_model2.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -357,7 +356,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#dta = pandas.read_csv(\"http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv\")\n",
+      "#dta = pd.read_csv(\"http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv\")\n",
       "#dta = dta.set_index(\"State\", inplace=True).dropna()\n",
       "#dta.rename(columns={\"VR\" : \"crime\",\n",
       "#                    \"MR\" : \"murder\",\n",
@@ -389,7 +388,7 @@
      "collapsed": false,
      "input": [
       "crime_model = ols(\"murder ~ urban + poverty + hs_grad + single\", data=dta).fit()\n",
-      "print crime_model.summary()"
+      "print(crime_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -501,7 +500,7 @@
      "input": [
       "rob_crime_model = rlm(\"murder ~ urban + poverty + hs_grad + single\", data=dta, \n",
       "                      M=sm.robust.norms.TukeyBiweight(3)).fit(conv=\"weights\")\n",
-      "print rob_crime_model.summary()"
+      "print(rob_crime_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -512,7 +511,7 @@
      "collapsed": false,
      "input": [
       "#rob_crime_model = rlm(\"murder ~ pctmetro + poverty + pcths + single\", data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv=\"weights\")\n",
-      "#print rob_crime_model.summary()"
+      "#print(rob_crime_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -522,7 +521,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "There aren't yet an influence diagnostics as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))"
+      "There isn't yet an influence diagnostics method as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))"
      ]
     },
     {
@@ -531,7 +530,7 @@
      "input": [
       "weights = rob_crime_model.weights\n",
       "idx = weights > 0\n",
-      "X = rob_crime_model.model.exog[idx]\n",
+      "X = rob_crime_model.model.exog[idx.values]\n",
       "ww = weights[idx] / weights[idx].mean()\n",
       "hat_matrix_diag = ww*(X*np.linalg.pinv(X).T).sum(1)\n",
       "resid = rob_crime_model.resid\n",
@@ -553,7 +552,7 @@
       "fig, ax = plt.subplots(figsize=(12,8))\n",
       "ax.plot(resid2[idx], hat_matrix_diag, 'o')\n",
       "ax = utils.annotate_axes(range(nobs), labels=rob_crime_model.model.data.row_labels[idx], \n",
-      "                    points=zip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,\n",
+      "                    points=lzip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,\n",
       "                    size=\"large\", ax=ax)\n",
       "ax.set_xlabel(\"resid2\")\n",
       "ax.set_ylabel(\"leverage\")\n",
diff --git a/examples/notebooks/example_rlm.ipynb b/examples/notebooks/robust_models_0.ipynb
similarity index 89%
rename from examples/notebooks/example_rlm.ipynb
rename to examples/notebooks/robust_models_0.ipynb
index bea361d8aeb..63a19f459b8 100644
--- a/examples/notebooks/example_rlm.ipynb
+++ b/examples/notebooks/robust_models_0.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_rlm"
+  "name": "",
+  "signature": "sha256:4e49430d14da775f781b66225695e4ec1273fd081b7cf8d18a00bf840a60842c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm\n",
       "import matplotlib.pyplot as plt\n",
@@ -61,10 +63,10 @@
      "input": [
       "huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())\n",
       "hub_results = huber_t.fit()\n",
-      "print hub_results.params\n",
-      "print hub_results.bse\n",
-      "print hub_results.summary(yname='y',\n",
-      "            xname=['var_%d' % i for i in range(len(hub_results.params))])"
+      "print(hub_results.params)\n",
+      "print(hub_results.bse)\n",
+      "print(hub_results.summary(yname='y',\n",
+      "            xname=['var_%d' % i for i in range(len(hub_results.params))]))"
      ],
      "language": "python",
      "metadata": {},
@@ -82,8 +84,8 @@
      "collapsed": false,
      "input": [
       "hub_results2 = huber_t.fit(cov=\"H2\")\n",
-      "print hub_results2.params\n",
-      "print hub_results2.bse"
+      "print(hub_results2.params)\n",
+      "print(hub_results2.bse)"
      ],
      "language": "python",
      "metadata": {},
@@ -102,7 +104,7 @@
      "input": [
       "andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())\n",
       "andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov=\"H3\")\n",
-      "print 'Parameters: ', andrew_results.params"
+      "print('Parameters: ', andrew_results.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -151,9 +153,9 @@
      "collapsed": false,
      "input": [
       "res = sm.OLS(y2, X).fit()\n",
-      "print res.params\n",
-      "print res.bse\n",
-      "print res.predict()"
+      "print(res.params)\n",
+      "print(res.bse)\n",
+      "print(res.predict())"
      ],
      "language": "python",
      "metadata": {},
@@ -171,8 +173,8 @@
      "collapsed": false,
      "input": [
       "resrlm = sm.RLM(y2, X).fit()\n",
-      "print resrlm.params\n",
-      "print resrlm.bse"
+      "print(resrlm.params)\n",
+      "print(resrlm.bse)"
      ],
      "language": "python",
      "metadata": {},
@@ -219,8 +221,8 @@
      "input": [
       "X2 = X[:,[0,1]] \n",
       "res2 = sm.OLS(y2, X2).fit()\n",
-      "print res2.params\n",
-      "print res2.bse"
+      "print(res2.params)\n",
+      "print(res2.bse)"
      ],
      "language": "python",
      "metadata": {},
@@ -238,8 +240,8 @@
      "collapsed": false,
      "input": [
       "resrlm2 = sm.RLM(y2, X2).fit()\n",
-      "print resrlm2.params\n",
-      "print resrlm2.bse"
+      "print(resrlm2.params)\n",
+      "print(resrlm2.bse)"
      ],
      "language": "python",
      "metadata": {},
@@ -258,14 +260,14 @@
      "input": [
       "prstd, iv_l, iv_u = wls_prediction_std(res2)\n",
       "\n",
-      "fig, ax = plt.subplots()\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
       "ax.plot(x1, y2, 'o', label=\"data\")\n",
       "ax.plot(x1, y_true2, 'b-', label=\"True\")\n",
       "ax.plot(x1, res2.fittedvalues, 'r-', label=\"OLS\")\n",
       "ax.plot(x1, iv_u, 'r--')\n",
       "ax.plot(x1, iv_l, 'r--')\n",
       "ax.plot(x1, resrlm2.fittedvalues, 'g.-', label=\"RLM\")\n",
-      "ax.legend(loc=\"best\")"
+      "legend = ax.legend(loc=\"best\")"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/robust_models.ipynb b/examples/notebooks/robust_models_1.ipynb
similarity index 95%
rename from examples/notebooks/robust_models.ipynb
rename to examples/notebooks/robust_models_1.ipynb
index 631dd747322..8cba649d19b 100644
--- a/examples/notebooks/robust_models.ipynb
+++ b/examples/notebooks/robust_models_1.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "robust_models"
+  "name": "",
+  "signature": "sha256:a992c3ee53b62d7c984ded70673dee2cb0d547a694dcf174a874b3a61052df9c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
+      "from statsmodels.compat import lmap\n",
       "import numpy as np\n",
       "from scipy import stats\n",
       "import matplotlib.pyplot as plt\n",
@@ -35,7 +38,7 @@
      "source": [
       "* An M-estimator minimizes the function \n",
       "\n",
-      "$$Q(e_i, \\rho) = \\sum_i~\\rho(\\frac{e_i}{s})$$\n",
+      "$$Q(e_i, \\rho) = \\sum_i~\\rho \\left (\\frac{e_i}{s}\\right )$$\n",
       "\n",
       "where $\\rho$ is a symmetric function of the residuals \n",
       "\n",
@@ -45,7 +48,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* We have several choices available for the weighting functions to be used"
@@ -303,7 +306,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* Robust estimates of the location"
@@ -354,7 +357,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* Analagously for the scale\n",
@@ -407,7 +410,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print x"
+      "print(x)"
      ],
      "language": "python",
      "metadata": {},
@@ -434,7 +437,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* The default for Robust Linear Models is MAD\n",
@@ -470,7 +473,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print fat_tails.mean(), fat_tails.std()"
+      "print(fat_tails.mean(), fat_tails.std())"
      ],
      "language": "python",
      "metadata": {},
@@ -480,7 +483,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print stats.norm.fit(fat_tails)"
+      "print(stats.norm.fit(fat_tails))"
      ],
      "language": "python",
      "metadata": {},
@@ -490,7 +493,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print stats.t.fit(fat_tails, f0=6)"
+      "print(stats.t.fit(fat_tails, f0=6))"
      ],
      "language": "python",
      "metadata": {},
@@ -502,7 +505,7 @@
      "input": [
       "huber = sm.robust.scale.Huber()\n",
       "loc, scale = huber(fat_tails)\n",
-      "print loc, scale"
+      "print(loc, scale)"
      ],
      "language": "python",
      "metadata": {},
@@ -571,7 +574,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print prestige.head(10)"
+      "print(prestige.head(10))"
      ],
      "language": "python",
      "metadata": {},
@@ -599,7 +602,7 @@
      "collapsed": false,
      "input": [
       "ols_model = ols('prestige ~ income + education', prestige).fit()\n",
-      "print ols_model.summary()"
+      "print(ols_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -611,7 +614,7 @@
      "input": [
       "infl = ols_model.get_influence()\n",
       "student = infl.summary_frame()['student_resid']\n",
-      "print student"
+      "print(student)"
      ],
      "language": "python",
      "metadata": {},
@@ -621,7 +624,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print student.ix[np.abs(student) > 2]"
+      "print(student.ix[np.abs(student) > 2])"
      ],
      "language": "python",
      "metadata": {},
@@ -631,7 +634,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print infl.summary_frame().ix['minister']"
+      "print(infl.summary_frame().ix['minister'])"
      ],
      "language": "python",
      "metadata": {},
@@ -643,7 +646,7 @@
      "input": [
       "sidak = ols_model.outlier_test('sidak')\n",
       "sidak.sort('unadj_p', inplace=True)\n",
-      "print sidak"
+      "print(sidak)"
      ],
      "language": "python",
      "metadata": {},
@@ -655,7 +658,7 @@
      "input": [
       "fdr = ols_model.outlier_test('fdr_bh')\n",
       "fdr.sort('unadj_p', inplace=True)\n",
-      "print fdr"
+      "print(fdr)"
      ],
      "language": "python",
      "metadata": {},
@@ -666,7 +669,7 @@
      "collapsed": false,
      "input": [
       "rlm_model = rlm('prestige ~ income + education', prestige).fit()\n",
-      "print rlm_model.summary()"
+      "print(rlm_model.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -676,7 +679,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print rlm_model.weights"
+      "print(rlm_model.weights)"
      ],
      "language": "python",
      "metadata": {},
@@ -803,7 +806,7 @@
      "input": [
       "sidak2 = ols_model.outlier_test('sidak')\n",
       "sidak2.sort('unadj_p', inplace=True)\n",
-      "print sidak2"
+      "print(sidak2)"
      ],
      "language": "python",
      "metadata": {},
@@ -815,7 +818,7 @@
      "input": [
       "fdr2 = ols_model.outlier_test('fdr_bh')\n",
       "fdr2.sort('unadj_p', inplace=True)\n",
-      "print fdr2"
+      "print(fdr2)"
      ],
      "language": "python",
      "metadata": {},
@@ -900,7 +903,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print params"
+      "print(params)"
      ],
      "language": "python",
      "metadata": {},
@@ -963,7 +966,7 @@
      "input": [
       "all_betas = np.asarray(all_betas)\n",
       "se_loss = lambda x : np.linalg.norm(x, ord=2)**2\n",
-      "se_beta = map(se_loss, all_betas - beta_true)"
+      "se_beta = lmap(se_loss, all_betas - beta_true)"
      ],
      "language": "python",
      "metadata": {},
@@ -1021,4 +1024,4 @@
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file
diff --git a/examples/notebooks/tsa_arma.ipynb b/examples/notebooks/tsa_arma_0.ipynb
similarity index 90%
rename from examples/notebooks/tsa_arma.ipynb
rename to examples/notebooks/tsa_arma_0.ipynb
index 0ed1751e350..3e1d4152e82 100644
--- a/examples/notebooks/tsa_arma.ipynb
+++ b/examples/notebooks/tsa_arma_0.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "tsa_arma"
+  "name": "",
+  "signature": "sha256:3cbe6ba3b2194a314ffa7247abcdb7d7acbdbc850b8cf241a4682a995c15fe98"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,16 +13,17 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "ARMA Example"
+      "Autoregressive Moving Average (ARMA): Sunspots data"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "from scipy import stats\n",
-      "import pandas\n",
+      "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "import statsmodels.api as sm"
@@ -52,7 +54,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.sunspots.NOTE"
+      "print(sm.datasets.sunspots.NOTE)"
      ],
      "language": "python",
      "metadata": {},
@@ -72,7 +74,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "dta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n",
+      "dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n",
       "del dta[\"YEAR\"]"
      ],
      "language": "python",
@@ -108,7 +110,7 @@
      "collapsed": false,
      "input": [
       "arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()\n",
-      "print arma_mod20.params"
+      "print(arma_mod20.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -128,7 +130,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic"
+      "print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)"
      ],
      "language": "python",
      "metadata": {},
@@ -138,7 +140,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print arma_mod30.params"
+      "print(arma_mod30.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -148,7 +150,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic"
+      "print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)"
      ],
      "language": "python",
      "metadata": {},
@@ -235,8 +237,8 @@
      "input": [
       "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n",
       "data = np.c_[range(1,41), r[1:], q, p]\n",
-      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
-      "print table.set_index('lag')"
+      "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
+      "print(table.set_index('lag'))"
      ],
      "language": "python",
      "metadata": {},
@@ -261,7 +263,7 @@
      "collapsed": false,
      "input": [
       "predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)\n",
-      "print predict_sunspots"
+      "print(predict_sunspots)"
      ],
      "language": "python",
      "metadata": {},
@@ -344,7 +346,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "* Let's make sure this models is estimable."
+      "Let's make sure this model is estimable."
      ]
     },
     {
@@ -378,7 +380,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* What does this mean?"
@@ -434,7 +436,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags. \n",
@@ -449,8 +451,8 @@
       "resid = arma11.resid\n",
       "r,q,p = sm.tsa.acf(resid, qstat=True)\n",
       "data = np.c_[range(1,41), r[1:], q, p]\n",
-      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
-      "print table.set_index('lag')"
+      "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
+      "print(table.set_index('lag'))"
      ],
      "language": "python",
      "metadata": {},
@@ -464,8 +466,8 @@
       "resid = arma41.resid\n",
       "r,q,p = sm.tsa.acf(resid, qstat=True)\n",
       "data = np.c_[range(1,41), r[1:], q, p]\n",
-      "table = pandas.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
-      "print table.set_index('lag')"
+      "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
+      "print(table.set_index('lag'))"
      ],
      "language": "python",
      "metadata": {},
@@ -484,7 +486,7 @@
      "collapsed": false,
      "input": [
       "macrodta = sm.datasets.macrodata.load_pandas().data\n",
-      "macrodta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
+      "macrodta.index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
       "cpi = macrodta[\"cpi\"]"
      ],
      "language": "python",
@@ -513,7 +515,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "P-value of the unit-root test, resoundly rejects the null of no unit-root."
@@ -523,7 +525,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.tsa.adfuller(cpi)[1]"
+      "print(sm.tsa.adfuller(cpi)[1])"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/example_arma.ipynb b/examples/notebooks/tsa_arma_1.ipynb
similarity index 80%
rename from examples/notebooks/example_arma.ipynb
rename to examples/notebooks/tsa_arma_1.ipynb
index 62c445e89d9..83f6caa45c9 100644
--- a/examples/notebooks/example_arma.ipynb
+++ b/examples/notebooks/tsa_arma_1.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "ex_arma2"
+  "name": "",
+  "signature": "sha256:7b49505639a0d75e364e2a67988129e3eaaab83b05b1aff4fa0df12dd3d5d3da"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,15 +13,17 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Autoregressive Moving Average (ARMA) Model"
+      "Autoregressive Moving Average (ARMA): Artificial data"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "import statsmodels.api as sm\n",
+      "import pandas as pd\n",
       "from statsmodels.tsa.arima_process import arma_generate_sample\n",
       "np.random.seed(12345)"
      ],
@@ -77,15 +80,24 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pandas\n",
       "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n",
-      "y = pandas.TimeSeries(y, index=dates)\n",
+      "y = pd.TimeSeries(y, index=dates)\n",
       "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n",
       "arma_res = arma_mod.fit(trend='nc', disp=-1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(arma_res.summary())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/examples/notebooks/example_tsa_dates.ipynb b/examples/notebooks/tsa_dates.ipynb
similarity index 90%
rename from examples/notebooks/example_tsa_dates.ipynb
rename to examples/notebooks/tsa_dates.ipynb
index ebc92de5575..a94763e5377 100644
--- a/examples/notebooks/example_tsa_dates.ipynb
+++ b/examples/notebooks/tsa_dates.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "ex_dates"
+  "name": "",
+  "signature": "sha256:fdc85b7fc5fa0dbe79fab572ae79c45c49bf2d964d3dae03dbb5e8bb85143785"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,16 +13,17 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Using dates with timeseries models"
+      "Dates in timeseries models"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import statsmodels.api as sm\n",
       "import numpy as np\n",
-      "import pandas"
+      "import pandas as pd"
      ],
      "language": "python",
      "metadata": {},
@@ -75,7 +77,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "endog = pandas.TimeSeries(data.endog, index=dates)"
+      "endog = pd.TimeSeries(data.endog, index=dates)"
      ],
      "language": "python",
      "metadata": {},
@@ -111,7 +113,7 @@
      "collapsed": false,
      "input": [
       "pred = pandas_ar_res.predict(start='2005', end='2015')\n",
-      "print pred"
+      "print(pred)"
      ],
      "language": "python",
      "metadata": {},
@@ -131,7 +133,7 @@
       "ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')\n",
       "ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)\n",
       "pred = ar_res.predict(start='2005', end='2015')\n",
-      "print pred"
+      "print(pred)"
      ],
      "language": "python",
      "metadata": {},
@@ -148,7 +150,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print ar_res.data.predict_dates"
+      "print(ar_res.data.predict_dates)"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/tsa_filters.ipynb b/examples/notebooks/tsa_filters.ipynb
index 08a91287e31..1a5753eef56 100644
--- a/examples/notebooks/tsa_filters.ipynb
+++ b/examples/notebooks/tsa_filters.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "tsa_filters"
+  "name": "",
+  "signature": "sha256:6dbdecf9269083ae578ec9a81ffe3b9d5076fd2650c3c7964bb71f7e991e07e6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,7 +20,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import pandas\n",
+      "from __future__ import print_function\n",
+      "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "import statsmodels.api as sm"
@@ -42,8 +44,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
-      "print index"
+      "index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n",
+      "print(index)"
      ],
      "language": "python",
      "metadata": {},
@@ -65,7 +67,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.datasets.macrodata.NOTE"
+      "print(sm.datasets.macrodata.NOTE)"
      ],
      "language": "python",
      "metadata": {},
@@ -75,7 +77,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print dta.head(10)"
+      "print(dta.head(10))"
      ],
      "language": "python",
      "metadata": {},
@@ -207,7 +209,7 @@
      "outputs": []
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "* We lose K observations on both ends. It is suggested to use K=12 for quarterly data."
@@ -217,7 +219,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "fig = plt.figure(figsize=(14,10))\n",
+      "fig = plt.figure(figsize=(12,10))\n",
       "ax = fig.add_subplot(111)\n",
       "bk_cycles.plot(ax=ax, style=['r--', 'b-']);"
      ],
@@ -252,7 +254,7 @@
      ]
     },
     {
-     "cell_type": "raw",
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The CF filter is appropriate for series that may follow a random walk."
@@ -262,7 +264,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.tsa.stattools.adfuller(dta['unemp'])[:3]"
+      "print(sm.tsa.stattools.adfuller(dta['unemp'])[:3])"
      ],
      "language": "python",
      "metadata": {},
@@ -272,7 +274,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sm.tsa.stattools.adfuller(dta['infl'])[:3]"
+      "print(sm.tsa.stattools.adfuller(dta['infl'])[:3])"
      ],
      "language": "python",
      "metadata": {},
@@ -283,7 +285,7 @@
      "collapsed": false,
      "input": [
       "cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[[\"infl\",\"unemp\"]])\n",
-      "print cf_cycles.head(10)"
+      "print(cf_cycles.head(10))"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/notebooks/example_wls.ipynb b/examples/notebooks/wls.ipynb
similarity index 92%
rename from examples/notebooks/example_wls.ipynb
rename to examples/notebooks/wls.ipynb
index 135aa42fea4..6338176b04b 100644
--- a/examples/notebooks/example_wls.ipynb
+++ b/examples/notebooks/wls.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "example_wls"
+  "name": "",
+  "signature": "sha256:5b05396fa56ec58d503b5038f250184417df9fafd2972494b8f3ec4729eb07c0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -19,6 +20,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import numpy as np\n",
       "from scipy import stats\n",
       "import statsmodels.api as sm\n",
@@ -41,7 +43,7 @@
       "\n",
       "Model assumptions:\n",
       "\n",
-      " * Misspecificaion: true model is quadratic, estimate only linear\n",
+      " * Misspecification: true model is quadratic, estimate only linear\n",
       " * Independent noise/error term\n",
       " * Two groups for error variance, low and high variance groups"
      ]
@@ -80,7 +82,7 @@
      "input": [
       "mod_wls = sm.WLS(y, X, weights=1./w)\n",
       "res_wls = mod_wls.fit()\n",
-      "print res_wls.summary()"
+      "print(res_wls.summary())"
      ],
      "language": "python",
      "metadata": {},
@@ -100,8 +102,8 @@
      "collapsed": false,
      "input": [
       "res_ols = sm.OLS(y, X).fit()\n",
-      "print res_ols.params\n",
-      "print res_wls.params"
+      "print(res_ols.params)\n",
+      "print(res_wls.params)"
      ],
      "language": "python",
      "metadata": {},
@@ -124,7 +126,7 @@
       "colnames = ['x1', 'const']\n",
       "rownames = ['WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3']\n",
       "tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)\n",
-      "print tabl"
+      "print(tabl)"
      ],
      "language": "python",
      "metadata": {},
@@ -173,7 +175,7 @@
      "input": [
       "prstd, iv_l, iv_u = wls_prediction_std(res_wls)\n",
       "\n",
-      "fig, ax = plt.subplots()\n",
+      "fig, ax = plt.subplots(figsize=(8,6))\n",
       "ax.plot(x, y, 'o', label=\"Data\")\n",
       "ax.plot(x, y_true, 'b-', label=\"True\")\n",
       "# OLS\n",
@@ -208,7 +210,7 @@
       "w_est = w.copy()\n",
       "w_est[w!=1.] = np.sqrt(var2) / np.sqrt(var1)\n",
       "res_fwls = sm.WLS(y, X, 1./w_est).fit()\n",
-      "print res_fwls.summary()"
+      "print(res_fwls.summary())"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/example_interaction_categorial.py b/examples/python/categorical_interaction_plot.py
similarity index 56%
rename from examples/example_interaction_categorial.py
rename to examples/python/categorical_interaction_plot.py
index 84886cf5a84..cb46e71c5cf 100644
--- a/examples/example_interaction_categorial.py
+++ b/examples/python/categorical_interaction_plot.py
@@ -1,25 +1,23 @@
-# -*- coding: utf-8 -*-
-"""Plot Interaction of Categorical Factors
-"""
 
-# In this example, we will vizualize the interaction between
-# categorical factors. First, we will create some categorical
-# data are initialized. Then plotted using the interaction_plot
-# function which internally recodes the x-factor categories to
-# ingegers.
+## Plot Interaction of Categorical Factors
+
+# In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers.
 
 import numpy as np
 import matplotlib.pyplot as plt
 from statsmodels.graphics.factorplots import interaction_plot
 from pandas import Series
-
 np.random.seed(12345)
 weight = Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')
 nutrition = Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')
 days = np.log(np.random.randint(1, 30, size=60))
-
 plt.figure(figsize=(6, 6));
-
-#@savefig interaction_plot_categorial.png align=center
 interaction_plot(x=weight, trace=nutrition, response=days,
-                 colors=['red', 'blue'], markers=['D', '^'], ms=10);
+                 colors=['red', 'blue'], markers=['D', '^'], ms=10)
+
+
+#     <matplotlib.figure.Figure at 0x106dd2a10>
+
+# image file:
+
+# image file:
diff --git a/examples/python/contrasts.py b/examples/python/contrasts.py
new file mode 100644
index 00000000000..2f80b016056
--- /dev/null
+++ b/examples/python/contrasts.py
@@ -0,0 +1,186 @@
+
+## Contrasts Overview
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+
+
+# This document is based heavily on this excellent resource from UCLA http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm
+
+# A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy coding is called Treatment coding in R parlance, and we will follow this convention. There are, however, different coding methods that amount to different sets of linear hypotheses.
+#
+# In fact, the dummy coding is not technically a contrast coding. This is because the dummy variables add to one and are not functionally independent of the model's intercept. On the other hand, a set of *contrasts* for a categorical variable with `k` levels is a set of `k-1` functionally independent linear combinations of the factor level means that are also independent of the sum of the dummy variables. The dummy coding isn't wrong *per se*. It captures all of the coefficients, but it complicates matters when the model assumes independence of the coefficients such as in ANOVA. Linear regression models do not assume independence of the coefficients and thus dummy coding is often the only coding that is taught in this context.
+#
+# To have a look at the contrast matrices in Patsy, we will use data from UCLA ATS. First let's load the data.
+
+##### Example Data
+
+import pandas as pd
+url = 'http://www.ats.ucla.edu/stat/data/hsb2.csv'
+hsb2 = pd.read_table(url, delimiter=",")
+
+
+hsb2.head(10)
+
+
+# It will be instructive to look at the mean of the dependent variable, write, for each level of race ((1 = Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian)).
+
+hsb2.groupby('race')['write'].mean()
+
+
+##### Treatment (Dummy) Coding
+
+# Dummy coding is likely the most well known coding scheme. It compares each level of the categorical variable to a base reference level. The base reference level is the value of the intercept. It is the default contrast in Patsy for unordered categorical factors. The Treatment contrast matrix for race would be
+
+from patsy.contrasts import Treatment
+levels = [1,2,3,4]
+contrast = Treatment(reference=0).code_without_intercept(levels)
+print(contrast.matrix)
+
+
+# Here we used `reference=0`, which implies that the first level, Hispanic, is the reference category against which the other level effects are measured. As mentioned above, the columns do not sum to zero and are thus not independent of the intercept. To be explicit, let's look at how this would encode the `race` variable.
+
+hsb2.race.head(10)
+
+
+print(contrast.matrix[hsb2.race-1, :][:20])
+
+
+sm.categorical(hsb2.race.values)
+
+
+# This is a bit of a trick, as the `race` category conveniently maps to zero-based indices. If it does not, this conversion happens under the hood, so this won't work in general but nonetheless is a useful exercise to fix ideas. The below illustrates the output using the three contrasts above
+
+from statsmodels.formula.api import ols
+mod = ols("write ~ C(race, Treatment)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+# We explicitly gave the contrast for race; however, since Treatment is the default, we could have omitted this.
+
+#### Simple Coding
+
+# Like Treatment Coding, Simple Coding compares each level to a fixed reference level. However, with simple coding, the intercept is the grand mean of all the levels of the factors. Patsy doesn't have the Simple contrast included, but you can easily define your own contrasts. To do so, write a class that contains a code_with_intercept and a code_without_intercept method that returns a patsy.contrast.ContrastMatrix instance
+
+from patsy.contrasts import ContrastMatrix
+
+def _name_levels(prefix, levels):
+    return ["[%s%s]" % (prefix, level) for level in levels]
+
+class Simple(object):
+    def _simple_contrast(self, levels):
+        nlevels = len(levels)
+        contr = -1./nlevels * np.ones((nlevels, nlevels-1))
+        contr[1:][np.diag_indices(nlevels-1)] = (nlevels-1.)/nlevels
+        return contr
+
+    def code_with_intercept(self, levels):
+        contrast = np.column_stack((np.ones(len(levels)),
+                                    self._simple_contrast(levels)))
+        return ContrastMatrix(contrast, _name_levels("Simp.", levels))
+
+    def code_without_intercept(self, levels):
+        contrast = self._simple_contrast(levels)
+        return ContrastMatrix(contrast, _name_levels("Simp.", levels[:-1]))
+
+
+hsb2.groupby('race')['write'].mean().mean()
+
+
+contrast = Simple().code_without_intercept(levels)
+print(contrast.matrix)
+
+
+mod = ols("write ~ C(race, Simple)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+#### Sum (Deviation) Coding
+
+# Sum coding compares the mean of the dependent variable for a given level to the overall mean of the dependent variable over all the levels. That is, it uses contrasts between each of the first k-1 levels and level k In this example, level 1 is compared to all the others, level 2 to all the others, and level 3 to all the others.
+
+from patsy.contrasts import Sum
+contrast = Sum().code_without_intercept(levels)
+print(contrast.matrix)
+
+
+mod = ols("write ~ C(race, Sum)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+# This corresponds to a parameterization that forces all the coefficients to sum to zero. Notice that the intercept here is the grand mean where the grand mean is the mean of means of the dependent variable by each level.
+
+hsb2.groupby('race')['write'].mean().mean()
+
+
+#### Backward Difference Coding
+
+# In backward difference coding, the mean of the dependent variable for a level is compared with the mean of the dependent variable for the prior level. This type of coding may be useful for a nominal or an ordinal variable.
+
+from patsy.contrasts import Diff
+contrast = Diff().code_without_intercept(levels)
+print(contrast.matrix)
+
+
+mod = ols("write ~ C(race, Diff)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+# For example, here the coefficient on level 1 is the mean of `write` at level 2 compared with the mean at level 1. Ie.,
+
+res.params["C(race, Diff)[D.1]"]
+hsb2.groupby('race').mean()["write"][2] -      hsb2.groupby('race').mean()["write"][1]
+
+
+#### Helmert Coding
+
+# Our version of Helmert coding is sometimes referred to as Reverse Helmert Coding. The mean of the dependent variable for a level is compared to the mean of the dependent variable over all previous levels. Hence, the name 'reverse' being sometimes applied to differentiate from forward Helmert coding. This comparison does not make much sense for a nominal variable such as race, but we would use the Helmert contrast like so:
+
+from patsy.contrasts import Helmert
+contrast = Helmert().code_without_intercept(levels)
+print(contrast.matrix)
+
+
+mod = ols("write ~ C(race, Helmert)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+# To illustrate, the comparison on level 4 is the mean of the dependent variable at the previous three levels taken from the mean at level 4
+
+grouped = hsb2.groupby('race')
+grouped.mean()["write"][4] - grouped.mean()["write"][:3].mean()
+
+
+# As you can see, these are only equal up to a constant. Other versions of the Helmert contrast give the actual difference in means. Regardless, the hypothesis tests are the same.
+
+k = 4
+1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean())
+k = 3
+1./k * (grouped.mean()["write"][k] - grouped.mean()["write"][:k-1].mean())
+
+
+#### Orthogonal Polynomial Coding
+
+# The coefficients taken on by polynomial coding for `k=4` levels are the linear, quadratic, and cubic trends in the categorical variable. The categorical variable here is assumed to be represented by an underlying, equally spaced numeric variable. Therefore, this type of encoding is used only for ordered categorical variables with equal spacing. In general, the polynomial contrast produces polynomials of order `k-1`. Since `race` is not an ordered factor variable let's use `read` as an example. First we need to create an ordered categorical from `read`.
+
+hsb2['readcat'] = pd.cut(hsb2.read, bins=3)
+hsb2.groupby('readcat').mean()['write']
+
+
+from patsy.contrasts import Poly
+levels = hsb2.readcat.unique().tolist()
+contrast = Poly().code_without_intercept(levels)
+print(contrast.matrix)
+
+
+mod = ols("write ~ C(readcat, Poly)", data=hsb2)
+res = mod.fit()
+print(res.summary())
+
+
+# As you can see, readcat has a significant linear effect on the dependent variable `write` but not a significant quadratic or cubic effect.
diff --git a/examples/python/discrete_choice_example.py b/examples/python/discrete_choice_example.py
new file mode 100644
index 00000000000..cbe4aaec447
--- /dev/null
+++ b/examples/python/discrete_choice_example.py
@@ -0,0 +1,231 @@
+
+## Discrete Choice Models
+
+### Fair's Affair data
+
+# A survey of women only was conducted in 1974 by *Redbook* asking about extramarital affairs.
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+from statsmodels.formula.api import logit, probit, poisson, ols
+
+
+print(sm.datasets.fair.SOURCE)
+
+
+print(sm.datasets.fair.NOTE)
+
+
+dta = sm.datasets.fair.load_pandas().data
+
+
+dta['affair'] = (dta['affairs'] > 0).astype(float)
+print(dta.head(10))
+
+
+print(dta.describe())
+
+
+affair_mod = logit("affair ~ occupation + educ + occupation_husb" 
+                   "+ rate_marriage + age + yrs_married + children"
+                   " + religious", dta).fit()
+
+
+print(affair_mod.summary())
+
+
+# How well are we predicting?
+
+affair_mod.pred_table()
+
+
+# The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects.
+
+mfx = affair_mod.get_margeff()
+print(mfx.summary())
+
+
+respondent1000 = dta.ix[1000]
+print(respondent1000)
+
+
+resp = dict(zip(range(1,9), respondent1000[["occupation", "educ", 
+                                            "occupation_husb", "rate_marriage", 
+                                            "age", "yrs_married", "children", 
+                                            "religious"]].tolist()))
+resp.update({0 : 1})
+print(resp)
+
+
+mfx = affair_mod.get_margeff(atexog=resp)
+print(mfx.summary())
+
+
+affair_mod.predict(respondent1000)
+
+
+affair_mod.fittedvalues[1000]
+
+
+affair_mod.model.cdf(affair_mod.fittedvalues[1000])
+
+
+# The "correct" model here is likely the Tobit model. We have an work in progress branch "tobit-model" on github, if anyone is interested in censored regression models.
+
+#### Exercise: Logit vs Probit
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+support = np.linspace(-6, 6, 1000)
+ax.plot(support, stats.logistic.cdf(support), 'r-', label='Logistic')
+ax.plot(support, stats.norm.cdf(support), label='Probit')
+ax.legend();
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+support = np.linspace(-6, 6, 1000)
+ax.plot(support, stats.logistic.pdf(support), 'r-', label='Logistic')
+ax.plot(support, stats.norm.pdf(support), label='Probit')
+ax.legend();
+
+
+# Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?
+
+#### Genarlized Linear Model Example
+
+print(sm.datasets.star98.SOURCE)
+
+
+print(sm.datasets.star98.DESCRLONG)
+
+
+print(sm.datasets.star98.NOTE)
+
+
+dta = sm.datasets.star98.load_pandas().data
+print(dta.columns)
+
+
+print(dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10))
+
+
+print(dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10))
+
+
+formula = 'NABOVE + NBELOW ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT '
+formula += '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'
+
+
+##### Aside: Binomial distribution
+
+# Toss a six-sided die 5 times, what's the probability of exactly 2 fours?
+
+stats.binom(5, 1./6).pmf(2)
+
+
+from scipy.misc import comb
+comb(5,2) * (1/6.)**2 * (5/6.)**3
+
+
+from statsmodels.formula.api import glm
+glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()
+
+
+print(glm_mod.summary())
+
+
+# The number of trials 
+
+glm_mod.model.data.orig_endog.sum(1)
+
+
+glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)
+
+
+# First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact
+# on the response variables:
+
+exog = glm_mod.model.data.orig_exog # get the dataframe
+
+
+means25 = exog.mean()
+print(means25)
+
+
+means25['LOWINC'] = exog['LOWINC'].quantile(.25)
+print(means25)
+
+
+means75 = exog.mean()
+means75['LOWINC'] = exog['LOWINC'].quantile(.75)
+print(means75)
+
+
+resp25 = glm_mod.predict(means25)
+resp75 = glm_mod.predict(means75)
+diff = resp75 - resp25
+
+
+# The interquartile first difference for the percentage of low income households in a school district is:
+
+print("%2.4f%%" % (diff[0]*100))
+
+
+nobs = glm_mod.nobs
+y = glm_mod.model.endog
+yhat = glm_mod.mu
+
+
+from statsmodels.graphics.api import abline_plot
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111, ylabel='Observed Values', xlabel='Fitted Values')
+ax.scatter(yhat, y)
+y_vs_yhat = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()
+fig = abline_plot(model_results=y_vs_yhat, ax=ax)
+
+
+##### Plot fitted values vs Pearson residuals
+
+# Pearson residuals are defined to be 
+# 
+# $$\frac{(y - \mu)}{\sqrt{(var(\mu))}}$$
+# 
+# where var is typically determined by the family. E.g., binomial variance is $np(1 - p)$
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111, title='Residual Dependence Plot', xlabel='Fitted Values',
+                          ylabel='Pearson Residuals')
+ax.scatter(yhat, stats.zscore(glm_mod.resid_pearson))
+ax.axis('tight')
+ax.plot([0.0, 1.0],[0.0, 0.0], 'k-');
+
+
+##### Histogram of standardized deviance residuals with Kernel Density Estimate overlayed
+
+# The definition of the deviance residuals depends on the family. For the Binomial distribution this is 
+# 
+# $$r_{dev} = sign\(Y-\mu\)*\sqrt{2n(Y\log\frac{Y}{\mu}+(1-Y)\log\frac{(1-Y)}{(1-\mu)}}$$
+# 
+# They can be used to detect ill-fitting covariates
+
+resid = glm_mod.resid_deviance
+resid_std = stats.zscore(resid) 
+kde_resid = sm.nonparametric.KDEUnivariate(resid_std)
+kde_resid.fit()
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111, title="Standardized Deviance Residuals")
+ax.hist(resid_std, bins=25, normed=True);
+ax.plot(kde_resid.support, kde_resid.density, 'r');
+
+
+##### QQ-plot of deviance residuals
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+fig = sm.graphics.qqplot(resid, line='r', ax=ax)
+
diff --git a/examples/python/discrete_choice_overview.py b/examples/python/discrete_choice_overview.py
new file mode 100644
index 00000000000..19547a8ebd9
--- /dev/null
+++ b/examples/python/discrete_choice_overview.py
@@ -0,0 +1,110 @@
+
+## Discrete Choice Models Overview
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+
+
+# ## Data
+# 
+# Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition).
+
+spector_data = sm.datasets.spector.load()
+spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
+
+
+# Inspect the data:
+
+print(spector_data.exog[:5,:])
+print(spector_data.endog[:5])
+
+
+# ## Linear Probability Model (OLS)
+
+lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)
+lpm_res = lpm_mod.fit()
+print('Parameters: ', lpm_res.params[:-1])
+
+
+# ## Logit Model
+
+logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
+logit_res = logit_mod.fit(disp=0)
+print('Parameters: ', logit_res.params)
+
+
+# Marginal Effects
+
+margeff = logit_res.get_margeff()
+print(margeff.summary())
+
+
+# As in all the discrete data models presented below, we can print(a nice summary of results:
+
+print(logit_res.summary())
+
+
+# ## Probit Model 
+
+probit_mod = sm.Probit(spector_data.endog, spector_data.exog)
+probit_res = probit_mod.fit()
+probit_margeff = probit_res.get_margeff()
+print('Parameters: ', probit_res.params)
+print('Marginal effects: ')
+print(probit_margeff.summary())
+
+
+# ## Multinomial Logit
+
+# Load data from the American National Election Studies:
+
+anes_data = sm.datasets.anes96.load()
+anes_exog = anes_data.exog
+anes_exog = sm.add_constant(anes_exog, prepend=False)
+
+
+# Inspect the data:
+
+print(anes_data.exog[:5,:])
+print(anes_data.endog[:5])
+
+
+# Fit MNL model:
+
+mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
+mlogit_res = mlogit_mod.fit()
+print(mlogit_res.params)
+
+
+# ## Poisson
+# 
+# Load the Rand data. Note that this example is similar to Cameron and Trivedi's `Microeconometrics` Table 20.5, but it is slightly different because of minor changes in the data. 
+
+rand_data = sm.datasets.randhie.load()
+rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)
+rand_exog = sm.add_constant(rand_exog, prepend=False)
+
+
+# Fit Poisson model: 
+
+poisson_mod = sm.Poisson(rand_data.endog, rand_exog)
+poisson_res = poisson_mod.fit(method="newton")
+print(poisson_res.summary())
+
+
+# ## Negative Binomial
+# 
+# The negative binomial model gives slightly different results. 
+
+mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)
+res_nbin = mod_nbin.fit(disp=False)
+print(res_nbin.summary())
+
+
+# ## Alternative solvers
+# 
+# The default method for fitting discrete data MLE models is Newton-Raphson. You can use other solvers by using the ``method`` argument: 
+
+mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)
+print(mlogit_res.summary())
+
diff --git a/examples/python/formulas.py b/examples/python/formulas.py
new file mode 100644
index 00000000000..ac80cdc6b48
--- /dev/null
+++ b/examples/python/formulas.py
@@ -0,0 +1,138 @@
+
+## Formulas: Fitting models using R-style formulas
+
+# Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs:
+#
+# * [Patsy formula language description](http://patsy.readthedocs.org/)
+#
+# ## Loading modules and functions
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+
+
+##### Import convention
+
+# You can import explicitly from statsmodels.formula.api
+
+from statsmodels.formula.api import ols
+
+
+# Alternatively, you can just use the `formula` namespace of the main `statsmodels.api`.
+
+sm.formula.ols
+
+
+# Or you can use the following conventioin
+
+import statsmodels.formula.api as smf
+
+
+# These names are just a convenient way to get access to each model's `from_formula` classmethod. See, for instance
+
+sm.OLS.from_formula
+
+
+# All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables.
+#
+# ``dir(sm.formula)`` will print(a list of available models.
+#
+# Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)``
+
+#
+# ## OLS regression using formulas
+#
+# To begin, we fit the linear model described on the [Getting Started](gettingstarted.html) page. Download the data, subset columns, and list-wise delete to remove missing observations:
+
+dta = sm.datasets.get_rdataset("Guerry", "HistData", cache=True)
+
+
+df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()
+df.head()
+
+
+# Fit the model:
+
+mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)
+res = mod.fit()
+print(res.summary())
+
+
+# ## Categorical variables
+#
+# Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. `patsy`'s default is also to include an intercept, so we automatically dropped one of the *Region* categories.
+#
+# If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator:
+
+res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()
+print(res.params)
+
+
+# Patsy's mode advanced features for categorical variables are discussed in: [Patsy: Contrast Coding Systems for categorical variables](contrasts.html)
+
+# ## Operators
+#
+# We have already seen that "~" separates the left-hand side of the model from the right-hand side, and that "+" adds new columns to the design matrix.
+#
+# ### Removing variables
+#
+# The "-" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by:
+
+res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()
+print(res.params)
+
+
+# ### Multiplicative interactions
+#
+# ":" adds a new column to the design matrix with the interaction of the other two columns. "*" will also include the individual columns that were multiplied together:
+
+res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()
+res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()
+print(res1.params, '\n')
+print(res2.params)
+
+
+# Many other things are possible with operators. Please consult the [patsy docs](https://patsy.readthedocs.org/en/latest/formulas.html) to learn more.
+
+# ## Functions
+#
+# You can apply vectorized functions to the variables in your model:
+
+res = sm.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()
+print(res.params)
+
+
+# Define a custom function:
+
+def log_plus_1(x):
+    return np.log(x) + 1.
+res = sm.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()
+print(res.params)
+
+
+# Any function that is in the calling namespace is available to the formula.
+
+# ## Using formulas with models that do not (yet) support them
+#
+# Even if a given `statsmodels` function does not support formulas, you can still use `patsy`'s formula language to produce design matrices. Those matrices
+# can then be fed to the fitting function as `endog` and `exog` arguments.
+#
+# To generate ``numpy`` arrays:
+
+import patsy
+f = 'Lottery ~ Literacy * Wealth'
+y,X = patsy.dmatrices(f, df, return_type='dataframe')
+print(y[:5])
+print(X[:5])
+
+
+# To generate pandas data frames:
+
+f = 'Lottery ~ Literacy * Wealth'
+y,X = patsy.dmatrices(f, df, return_type='dataframe')
+print(y[:5])
+print(X[:5])
+
+
+print(sm.OLS(y, X).fit().summary())
+
diff --git a/examples/python/generic_mle.py b/examples/python/generic_mle.py
new file mode 100644
index 00000000000..7f7b91e420a
--- /dev/null
+++ b/examples/python/generic_mle.py
@@ -0,0 +1,214 @@
+
+## Maximum Likelihood Estimation (Generic models)
+
+# This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: 
+# 
+# 1. Probit model for binary dependent variables
+# 2. Negative binomial model for count data
+# 
+# The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by "plugging-in" a log-likelihood function. 
+
+# ## Example 1: Probit model
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import statsmodels.api as sm
+from statsmodels.base.model import GenericLikelihoodModel
+
+
+# The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:
+
+data = sm.datasets.spector.load_pandas()
+exog = data.exog
+endog = data.endog
+print(sm.datasets.spector.NOTE)
+print(data.exog.head())
+
+
+# Them, we add a constant to the matrix of regressors:
+
+exog = sm.add_constant(exog, prepend=True)
+
+
+# To create your own Likelihood Model, you simply need to overwrite the loglike method.
+
+class MyProbit(GenericLikelihoodModel):
+    def loglike(self, params):
+        exog = self.exog
+        endog = self.endog
+        q = 2 * endog - 1
+        return stats.norm.logcdf(q*np.dot(exog, params)).sum()
+
+
+# Estimate the model and print(a summary:
+
+sm_probit_manual = MyProbit(endog, exog).fit()
+print(sm_probit_manual.summary())
+
+
+# Compare your Probit implementation to ``statsmodels``' "canned" implementation:
+
+sm_probit_canned = sm.Probit(endog, exog).fit()
+
+
+print(sm_probit_canned.params)
+print(sm_probit_manual.params)
+
+
+print(sm_probit_canned.cov_params())
+print(sm_probit_manual.cov_params())
+
+
+# Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates.
+
+# 
+# 
+# ## Example 2: Negative Binomial Regression for Count Data
+# 
+# Consider a negative binomial regression model for count data with
+# log-likelihood (type NB-2) function expressed as:
+# 
+# $$
+#     \mathcal{L}(\beta_j; y, \alpha) = \sum_{i=1}^n y_i ln 
+#     \left ( \frac{\alpha exp(X_i'\beta)}{1+\alpha exp(X_i'\beta)} \right ) -
+#     \frac{1}{\alpha} ln(1+\alpha exp(X_i'\beta)) + ln \Gamma (y_i + 1/\alpha) - ln \Gamma (y_i+1) - ln \Gamma (1/\alpha)
+# $$
+# 
+# with a matrix of regressors $X$, a vector of coefficients $\beta$,
+# and the negative binomial heterogeneity parameter $\alpha$. 
+# 
+# Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood
+# simply as:
+# 
+
+import numpy as np
+from scipy.stats import nbinom
+
+
+def _ll_nb2(y, X, beta, alph):
+    mu = np.exp(np.dot(X, beta))
+    size = 1/alph
+    prob = size/(size+mu)
+    ll = nbinom.logpmf(y, size, prob)
+    return ll
+
+
+# ### New Model Class
+# 
+# We create a new model class which inherits from ``GenericLikelihoodModel``:
+
+from statsmodels.base.model import GenericLikelihoodModel
+
+
+class NBin(GenericLikelihoodModel):
+    def __init__(self, endog, exog, **kwds):
+        super(NBin, self).__init__(endog, exog, **kwds)
+        
+    def nloglikeobs(self, params):
+        alph = params[-1]
+        beta = params[:-1]
+        ll = _ll_nb2(self.endog, self.exog, beta, alph)
+        return -ll 
+    
+    def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):
+        # we have one additional parameter and we need to add it for summary
+        self.exog_names.append('alpha')
+        if start_params == None:
+            # Reasonable starting values
+            start_params = np.append(np.zeros(self.exog.shape[1]), .5)
+            # intercept
+            start_params[-2] = np.log(self.endog.mean())
+        return super(NBin, self).fit(start_params=start_params, 
+                                     maxiter=maxiter, maxfun=maxfun, 
+                                     **kwds) 
+
+
+# Two important things to notice: 
+# 
+# + ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). 
+# + ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.
+#    
+# That's it! You're done!
+# 
+# ### Usage Example
+# 
+# The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)
+# dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``
+# function from the [Pandas library](http://pandas.pydata.org) to load the data
+# in memory. We then print(the first few columns: 
+# 
+
+import statsmodels.api as sm
+
+
+medpar = sm.datasets.get_rdataset("medpar", "COUNT", cache=True).data
+
+medpar.head()
+
+
+# The model we are interested in has a vector of non-negative integers as
+# dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,
+# ``type3``, ``hmo``, ``white``.
+# 
+# For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects.
+
+y = medpar.los
+X = medpar[["type2", "type3", "hmo", "white"]]
+X["constant"] = 1
+
+
+# Then, we fit the model and extract some information: 
+
+mod = NBin(y, X)
+res = mod.fit()
+
+
+#  Extract parameter estimates, standard errors, p-values, AIC, etc.:
+
+print('Parameters: ', res.params)
+print('Standard errors: ', res.bse)
+print('P-values: ', res.pvalues)
+print('AIC: ', res.aic)
+
+
+# As usual, you can obtain a full list of available information by typing
+# ``dir(res)``.
+# We can also look at the summary of the estimation results.
+
+print(res.summary())
+
+
+# ### Testing
+
+# We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian.
+
+res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)
+print(res_nbin.summary())
+
+
+print(res_nbin.params)
+
+
+print(res_nbin.bse)
+
+
+# Or we could compare them to results obtained using the MASS implementation for R:
+# 
+#     url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'
+#     medpar = read.csv(url)
+#     f = los~factor(type)+hmo+white
+#     
+#     library(MASS)
+#     mod = glm.nb(f, medpar)
+#     coef(summary(mod))
+#                      Estimate Std. Error   z value      Pr(>|z|)
+#     (Intercept)    2.31027893 0.06744676 34.253370 3.885556e-257
+#     factor(type)2  0.22124898 0.05045746  4.384861  1.160597e-05
+#     factor(type)3  0.70615882 0.07599849  9.291748  1.517751e-20
+#     hmo           -0.06795522 0.05321375 -1.277024  2.015939e-01
+#     white         -0.12906544 0.06836272 -1.887951  5.903257e-02
+# 
+# ### Numerical precision 
+# 
+# The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class.
+# 
diff --git a/examples/python/glm.py b/examples/python/glm.py
new file mode 100644
index 00000000000..f3bf9797cbd
--- /dev/null
+++ b/examples/python/glm.py
@@ -0,0 +1,167 @@
+
+## Generalized Linear Models
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+from scipy import stats
+from matplotlib import pyplot as plt
+
+
+# ## GLM: Binomial response data
+# 
+# ### Load data
+# 
+#  In this example, we use the Star98 dataset which was taken with permission
+#  from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook
+#  information can be obtained by typing: 
+
+print(sm.datasets.star98.NOTE)
+
+
+# Load the data and add a constant to the exogenous (independent) variables:
+
+data = sm.datasets.star98.load()
+data.exog = sm.add_constant(data.exog, prepend=False)
+
+
+#  The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): 
+
+print(data.endog[:5,:])
+
+
+#  The independent variables include all the other variables described above, as
+#  well as the interaction terms:
+
+print(data.exog[:2,:])
+
+
+# ### Fit and summary
+
+glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())
+res = glm_binom.fit()
+print(res.summary())
+
+
+# ### Quantities of interest
+
+print('Total number of trials:',  data.endog[0].sum())
+print('Parameters: ', res.params)
+print('T-values: ', res.tvalues)
+
+
+# First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: 
+
+means = data.exog.mean(axis=0)
+means25 = means.copy()
+means25[0] = stats.scoreatpercentile(data.exog[:,0], 25)
+means75 = means.copy()
+means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:,0], 75)
+resp_25 = res.predict(means25)
+resp_75 = res.predict(means75)
+diff = resp_75 - resp_25
+
+
+# The interquartile first difference for the percentage of low income households in a school district is:
+
+print("%2.4f%%" % (diff*100))
+
+
+# ### Plots
+# 
+#  We extract information that will be used to draw some interesting plots: 
+
+nobs = res.nobs
+y = data.endog[:,0]/data.endog.sum(1)
+yhat = res.mu
+
+
+# Plot yhat vs y:
+
+from statsmodels.graphics.api import abline_plot
+
+
+fig, ax = plt.subplots()
+ax.scatter(yhat, y)
+line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()
+abline_plot(model_results=line_fit, ax=ax)
+
+
+ax.set_title('Model Fit Plot')
+ax.set_ylabel('Observed values')
+ax.set_xlabel('Fitted values');
+
+
+# Plot yhat vs. Pearson residuals:
+
+fig, ax = plt.subplots()
+
+ax.scatter(yhat, res.resid_pearson)
+ax.hlines(0, 0, 1)
+ax.set_xlim(0, 1)
+ax.set_title('Residual Dependence Plot')
+ax.set_ylabel('Pearson Residuals')
+ax.set_xlabel('Fitted values')
+
+
+# Histogram of standardized deviance residuals:
+
+from scipy import stats
+
+fig, ax = plt.subplots()
+
+resid = res.resid_deviance.copy()
+resid_std = stats.zscore(resid)
+ax.hist(resid_std, bins=25)
+ax.set_title('Histogram of standardized deviance residuals');
+
+
+# QQ Plot of Deviance Residuals:
+
+from statsmodels import graphics
+graphics.gofplots.qqplot(resid, line='r')
+
+
+# ## GLM: Gamma for proportional count response
+# 
+# ### Load data
+# 
+#  In the example above, we printed the ``NOTE`` attribute to learn about the
+#  Star98 dataset. Statsmodels datasets ships with other useful information. For
+#  example: 
+
+print(sm.datasets.scotland.DESCRLONG)
+
+
+#  Load the data and add a constant to the exogenous variables:
+
+data2 = sm.datasets.scotland.load()
+data2.exog = sm.add_constant(data2.exog, prepend=False)
+print(data2.exog[:5,:])
+print(data2.endog[:5])
+
+
+# ### Fit and summary
+
+glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())
+glm_results = glm_gamma.fit()
+print(glm_results.summary())
+
+
+# ## GLM: Gaussian distribution with a noncanonical link
+# 
+# ### Artificial data
+
+nobs2 = 100
+x = np.arange(nobs2)
+np.random.seed(54321)
+X = np.column_stack((x,x**2))
+X = sm.add_constant(X, prepend=False)
+lny = np.exp(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)
+
+
+# ### Fit and summary
+
+gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))
+gauss_log_results = gauss_log.fit()
+print(gauss_log_results.summary())
+
diff --git a/examples/python/glm_formula.py b/examples/python/glm_formula.py
new file mode 100644
index 00000000000..3184fec7084
--- /dev/null
+++ b/examples/python/glm_formula.py
@@ -0,0 +1,40 @@
+
+## Generalized Linear Models (Formula)
+
+# This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.
+# 
+# To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:
+
+from __future__ import print_function
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+star98 = sm.datasets.star98.load_pandas().data
+formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT +            PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'
+dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',
+              'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',
+              'PERSPENK', 'PTRATIO', 'PCTAF']]
+endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))
+del dta['NABOVE']
+dta['SUCCESS'] = endog
+
+
+# Then, we fit the GLM model:
+
+mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()
+mod1.summary()
+
+
+# Finally, we define a function to operate customized data transformation using the formula framework:
+
+def double_it(x):
+    return 2 * x
+formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT +            PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'
+mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()
+mod2.summary()
+
+
+# As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:
+
+print(mod1.params[1])
+print(mod2.params[1] * 2)
+
diff --git a/examples/python/gls.py b/examples/python/gls.py
new file mode 100644
index 00000000000..b2c8afb7d34
--- /dev/null
+++ b/examples/python/gls.py
@@ -0,0 +1,84 @@
+
+## Generalized Least Squares
+
+from __future__ import print_function
+import statsmodels.api as sm
+import numpy as np
+from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)
+
+
+# The Longley dataset is a time series dataset: 
+
+data = sm.datasets.longley.load()
+data.exog = sm.add_constant(data.exog)
+print(data.exog[:5])
+
+
+# 
+#  Let's assume that the data is heteroskedastic and that we know
+#  the nature of the heteroskedasticity.  We can then define
+#  `sigma` and use it to give us a GLS model
+# 
+#  First we will obtain the residuals from an OLS fit
+
+ols_resid = sm.OLS(data.endog, data.exog).fit().resid
+
+
+# Assume that the error terms follow an AR(1) process with a trend:
+# 
+# $\epsilon_i = \beta_0 + \rho\epsilon_{i-1} + \eta_i$
+# 
+# where $\eta \sim N(0,\Sigma^2)$
+#  
+# and that $\rho$ is simply the correlation of the residual a consistent estimator for rho is to regress the residuals on the lagged residuals
+
+resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()
+print(resid_fit.tvalues[1])
+print(resid_fit.pvalues[1])
+
+
+#  While we don't have strong evidence that the errors follow an AR(1)
+#  process we continue
+
+rho = resid_fit.params[1]
+
+
+# As we know, an AR(1) process means that near-neighbors have a stronger
+#  relation so we can give this structure by using a toeplitz matrix
+
+from scipy.linalg import toeplitz
+
+toeplitz(range(5))
+
+
+order = toeplitz(range(len(ols_resid)))
+
+
+# so that our error covariance structure is actually rho**order
+#  which defines an autocorrelation structure
+
+sigma = rho**order
+gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)
+gls_results = gls_model.fit()
+
+
+# Of course, the exact rho in this instance is not known so it it might make more sense to use feasible gls, which currently only has experimental support. 
+# 
+# We can use the GLSAR model with one lag, to get to a similar result:
+
+glsar_model = sm.GLSAR(data.endog, data.exog, 1)
+glsar_results = glsar_model.iterative_fit(1)
+print(glsar_results.summary())
+
+
+# Comparing gls and glsar results, we see that there are some small
+#  differences in the parameter estimates and the resulting standard
+#  errors of the parameter estimate. This might be do to the numerical
+#  differences in the algorithm, e.g. the treatment of initial conditions,
+#  because of the small number of observations in the longley dataset.
+
+print(gls_results.params)
+print(glsar_results.params)
+print(gls_results.bse)
+print(glsar_results.bse)
+
diff --git a/examples/python/interactions_anova.py b/examples/python/interactions_anova.py
new file mode 100644
index 00000000000..98889d115de
--- /dev/null
+++ b/examples/python/interactions_anova.py
@@ -0,0 +1,392 @@
+
+## Interactions and ANOVA
+
+# Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html
+#
+# Download and format data:
+
+from __future__ import print_function
+from statsmodels.compat import urlopen
+import numpy as np
+np.set_printoptions(precision=4, suppress=True)
+import statsmodels.api as sm
+import pandas as pd
+import matplotlib.pyplot as plt
+from statsmodels.formula.api import ols
+from statsmodels.graphics.api import interaction_plot, abline_plot
+from statsmodels.stats.anova import anova_lm
+
+try:
+    salary_table = pd.read_csv('salary.table')
+except:  # recent pandas can read URL without urlopen
+    url = 'http://stats191.stanford.edu/data/salary.table'
+    fh = urlopen(url)
+    salary_table = pd.read_table(fh)
+    salary_table.to_csv('salary.table')
+
+E = salary_table.E
+M = salary_table.M
+X = salary_table.X
+S = salary_table.S
+
+
+# Take a look at the data:
+
+plt.figure(figsize=(6,6))
+symbols = ['D', '^']
+colors = ['r', 'g', 'blue']
+factor_groups = salary_table.groupby(['E','M'])
+for values, group in factor_groups:
+    i,j = values
+    plt.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],
+               s=144)
+plt.xlabel('Experience');
+plt.ylabel('Salary');
+
+
+# Fit a linear model:
+
+formula = 'S ~ C(E) + C(M) + X'
+lm = ols(formula, salary_table).fit()
+print(lm.summary())
+
+
+# Have a look at the created design matrix:
+
+lm.model.exog[:5]
+
+
+# Or since we initially passed in a DataFrame, we have a DataFrame available in
+
+lm.model.data.orig_exog[:5]
+
+
+# We keep a reference to the original untouched data in
+
+lm.model.data.frame[:5]
+
+
+# Influence statistics
+
+infl = lm.get_influence()
+print(infl.summary_table())
+
+
+# or get a dataframe
+
+df_infl = infl.summary_frame()
+
+
+df_infl[:5]
+
+
+# Now plot the reiduals within the groups separately:
+
+resid = lm.resid
+plt.figure(figsize=(6,6));
+for values, group in factor_groups:
+    i,j = values
+    group_num = i*2 + j - 1  # for plotting purposes
+    x = [group_num] * len(group)
+    plt.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],
+            s=144, edgecolors='black')
+plt.xlabel('Group')
+plt.ylabel('Residuals')
+
+
+# Now we will test some interactions using anova or f_test
+
+interX_lm = ols("S ~ C(E) * X + C(M)", salary_table).fit()
+print(interX_lm.summary())
+
+
+# Do an ANOVA check
+
+from statsmodels.stats.api import anova_lm
+
+table1 = anova_lm(lm, interX_lm)
+print(table1)
+
+interM_lm = ols("S ~ X + C(E)*C(M)", data=salary_table).fit()
+print(interM_lm.summary())
+
+table2 = anova_lm(lm, interM_lm)
+print(table2)
+
+
+# The design matrix as a DataFrame
+
+interM_lm.model.data.orig_exog[:5]
+
+
+# The design matrix as an ndarray
+
+interM_lm.model.exog
+interM_lm.model.exog_names
+
+
+infl = interM_lm.get_influence()
+resid = infl.resid_studentized_internal
+plt.figure(figsize=(6,6))
+for values, group in factor_groups:
+    i,j = values
+    idx = group.index
+    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],
+            s=144, edgecolors='black')
+plt.xlabel('X');
+plt.ylabel('standardized resids');
+
+
+# Looks like one observation is an outlier.
+
+drop_idx = abs(resid).argmax()
+print(drop_idx)  # zero-based index
+idx = salary_table.index.drop(drop_idx)
+
+lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()
+
+print(lm32.summary())
+print('\n')
+
+interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()
+
+print(interX_lm32.summary())
+print('\n')
+
+
+table3 = anova_lm(lm32, interX_lm32)
+print(table3)
+print('\n')
+
+
+interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()
+
+table4 = anova_lm(lm32, interM_lm32)
+print(table4)
+print('\n')
+
+
+#  Replot the residuals
+
+try:
+    resid = interM_lm32.get_influence().summary_frame()['standard_resid']
+except:
+    resid = interM_lm32.get_influence().summary_frame()['standard_resid']
+
+plt.figure(figsize=(6,6))
+for values, group in factor_groups:
+    i,j = values
+    idx = group.index
+    plt.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],
+            s=144, edgecolors='black')
+plt.xlabel('X[~[32]]');
+plt.ylabel('standardized resids');
+
+
+#  Plot the fitted values
+
+lm_final = ols('S ~ X + C(E)*C(M)', data = salary_table.drop([drop_idx])).fit()
+mf = lm_final.model.data.orig_exog
+lstyle = ['-','--']
+
+plt.figure(figsize=(6,6))
+for values, group in factor_groups:
+    i,j = values
+    idx = group.index
+    plt.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],
+                s=144, edgecolors='black')
+    # drop NA because there is no idx 32 in the final model
+    plt.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),
+            ls=lstyle[j], color=colors[i-1])
+plt.xlabel('Experience');
+plt.ylabel('Salary');
+
+
+# From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot.
+
+U = S - X * interX_lm32.params['X']
+
+plt.figure(figsize=(6,6))
+interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],
+        markersize=10, ax=plt.gca())
+
+
+# ## Minority Employment Data
+
+try:
+    minority_table = pd.read_table('minority.table')
+except:  # don't have data already
+    url = 'http://stats191.stanford.edu/data/minority.table'
+    minority_table = pd.read_table(url)
+
+factor_group = minority_table.groupby(['ETHN'])
+
+plt.figure(figsize=(6,6))
+colors = ['purple', 'green']
+markers = ['o', 'v']
+for factor, group in factor_group:
+    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
+                marker=markers[factor], s=12**2)
+plt.xlabel('TEST');
+plt.ylabel('JPERF');
+
+min_lm = ols('JPERF ~ TEST', data=minority_table).fit()
+print(min_lm.summary())
+
+plt.figure(figsize=(6,6));
+for factor, group in factor_group:
+    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
+                marker=markers[factor], s=12**2)
+
+plt.xlabel('TEST')
+plt.ylabel('JPERF')
+abline_plot(model_results = min_lm, ax=plt.gca());
+
+min_lm2 = ols('JPERF ~ TEST + TEST:ETHN',
+        data=minority_table).fit()
+
+print(min_lm2.summary())
+
+plt.figure(figsize=(6,6));
+for factor, group in factor_group:
+    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
+                marker=markers[factor], s=12**2)
+
+abline_plot(intercept = min_lm2.params['Intercept'],
+                 slope = min_lm2.params['TEST'], ax=plt.gca(), color='purple');
+abline_plot(intercept = min_lm2.params['Intercept'],
+        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],
+        ax=plt.gca(), color='green');
+
+
+min_lm3 = ols('JPERF ~ TEST + ETHN', data = minority_table).fit()
+print(min_lm3.summary())
+
+plt.figure(figsize=(6,6));
+for factor, group in factor_group:
+    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
+                marker=markers[factor], s=12**2)
+
+abline_plot(intercept = min_lm3.params['Intercept'],
+                 slope = min_lm3.params['TEST'], ax=plt.gca(), color='purple');
+abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],
+        slope = min_lm3.params['TEST'], ax=plt.gca(), color='green');
+
+
+min_lm4 = ols('JPERF ~ TEST * ETHN', data = minority_table).fit()
+print(min_lm4.summary())
+
+plt.figure(figsize=(6,6));
+for factor, group in factor_group:
+    plt.scatter(group['TEST'], group['JPERF'], color=colors[factor],
+                marker=markers[factor], s=12**2)
+
+abline_plot(intercept = min_lm4.params['Intercept'],
+                 slope = min_lm4.params['TEST'], ax=plt.gca(), color='purple');
+abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],
+        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],
+        ax=plt.gca(), color='green');
+
+
+# is there any effect of ETHN on slope or intercept?
+table5 = anova_lm(min_lm, min_lm4)
+print(table5)
+
+# is there any effect of ETHN on intercept
+table6 = anova_lm(min_lm, min_lm3)
+print(table6)
+
+# is there any effect of ETHN on slope
+table7 = anova_lm(min_lm, min_lm2)
+print(table7)
+
+# is it just the slope or both?
+table8 = anova_lm(min_lm2, min_lm4)
+print(table8)
+
+
+# ## One-way ANOVA
+
+try:
+    rehab_table = pd.read_csv('rehab.table')
+except:
+    url = 'http://stats191.stanford.edu/data/rehab.csv'
+    rehab_table = pd.read_table(url, delimiter=",")
+    rehab_table.to_csv('rehab.table')
+
+plt.figure(figsize=(6,6))
+rehab_table.boxplot('Time', 'Fitness', ax=plt.gca())
+
+rehab_lm = ols('Time ~ C(Fitness)', data=rehab_table).fit()
+table9 = anova_lm(rehab_lm)
+print(table9)
+
+print(rehab_lm.model.data.orig_exog)
+
+print(rehab_lm.summary())
+
+
+# ## Two-way ANOVA
+
+try:
+    kidney_table = pd.read_table('./kidney.table')
+except:
+    url = 'http://stats191.stanford.edu/data/kidney.table'
+    kidney_table = pd.read_table(url, delimiter=" *")
+
+
+# Explore the dataset
+
+kidney_table.groupby(['Weight', 'Duration']).size()
+
+
+# Balanced panel
+
+kt = kidney_table
+plt.figure(figsize=(6,6))
+interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),
+        colors=['red', 'blue'], markers=['D','^'], ms=10, ax=plt.gca())
+
+
+# You have things available in the calling namespace available in the formula evaluation namespace
+
+kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()
+
+table10 = anova_lm(kidney_lm)
+
+print(anova_lm(ols('np.log(Days+1) ~ C(Duration) + C(Weight)',
+                data=kt).fit(), kidney_lm))
+print(anova_lm(ols('np.log(Days+1) ~ C(Duration)', data=kt).fit(),
+               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',
+                   data=kt).fit()))
+print(anova_lm(ols('np.log(Days+1) ~ C(Weight)', data=kt).fit(),
+               ols('np.log(Days+1) ~ C(Duration) + C(Weight, Sum)',
+                   data=kt).fit()))
+
+
+# ## Sum of squares
+#
+#  Illustrates the use of different types of sums of squares (I,II,II)
+#  and how the Sum contrast can be used to produce the same output between
+#  the 3.
+#
+#  Types I and II are equivalent under a balanced design.
+#
+#  Don't use Type III with non-orthogonal contrast - ie., Treatment
+
+sum_lm = ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)',
+            data=kt).fit()
+
+print(anova_lm(sum_lm))
+print(anova_lm(sum_lm, typ=2))
+print(anova_lm(sum_lm, typ=3))
+
+nosum_lm = ols('np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)',
+            data=kt).fit()
+print(anova_lm(nosum_lm))
+print(anova_lm(nosum_lm, typ=2))
+print(anova_lm(nosum_lm, typ=3))
+
+
+
+
diff --git a/examples/python/kernel_density.py b/examples/python/kernel_density.py
new file mode 100644
index 00000000000..006118bd42c
--- /dev/null
+++ b/examples/python/kernel_density.py
@@ -0,0 +1,83 @@
+
+## Kernel Density Estimation
+
+import numpy as np
+from scipy import stats
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+from statsmodels.distributions.mixture_rvs import mixture_rvs
+
+
+##### A univariate example.
+
+np.random.seed(12345)
+
+
+obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.norm],
+                kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+
+
+kde = sm.nonparametric.KDEUnivariate(obs_dist1)
+kde.fit()
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.hist(obs_dist1, bins=50, normed=True, color='red')
+ax.plot(kde.support, kde.density, lw=2, color='black');
+
+
+obs_dist2 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta],
+            kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5))))
+
+kde2 = sm.nonparametric.KDEUnivariate(obs_dist2)
+kde2.fit()
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.hist(obs_dist2, bins=50, normed=True, color='red')
+ax.plot(kde2.support, kde2.density, lw=2, color='black');
+
+
+# The fitted KDE object is a full non-parametric distribution.
+
+obs_dist3 = mixture_rvs([.25,.75], size=1000, dist=[stats.norm, stats.norm],
+                kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+kde3 = sm.nonparametric.KDEUnivariate(obs_dist3)
+kde3.fit()
+
+
+kde3.entropy
+
+
+kde3.evaluate(-1)
+
+
+##### CDF
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(kde3.support, kde3.cdf);
+
+
+##### Cumulative Hazard Function
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(kde3.support, kde3.cumhazard);
+
+
+##### Inverse CDF
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(kde3.support, kde3.icdf);
+
+
+##### Survival Function
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(kde3.support, kde3.sf);
+
diff --git a/examples/python/ols.py b/examples/python/ols.py
new file mode 100644
index 00000000000..7c884dbdc82
--- /dev/null
+++ b/examples/python/ols.py
@@ -0,0 +1,227 @@
+
+## Ordinary Least Squares
+
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+from statsmodels.sandbox.regression.predstd import wls_prediction_std
+
+np.random.seed(9876789)
+
+
+# ## OLS estimation
+# 
+# Artificial data:
+
+nsample = 100
+x = np.linspace(0, 10, 100)
+X = np.column_stack((x, x**2))
+beta = np.array([1, 0.1, 10])
+e = np.random.normal(size=nsample)
+
+
+# Our model needs an intercept so we add a column of 1s:
+
+X = sm.add_constant(X)
+y = np.dot(X, beta) + e
+
+
+# Inspect data:
+
+X = sm.add_constant(X)
+y = np.dot(X, beta) + e
+
+
+# Fit and summary:
+
+model = sm.OLS(y, X)
+results = model.fit()
+print(results.summary())
+
+
+# Quantities of interest can be extracted directly from the fitted model. Type ``dir(results)`` for a full list. Here are some examples:  
+
+print('Parameters: ', results.params)
+print('R2: ', results.rsquared)
+
+
+# ## OLS non-linear curve but linear in parameters
+# 
+# We simulate artificial data with a non-linear relationship between x and y:
+
+nsample = 50
+sig = 0.5
+x = np.linspace(0, 20, nsample)
+X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))
+beta = [0.5, 0.5, -0.02, 5.]
+
+y_true = np.dot(X, beta)
+y = y_true + sig * np.random.normal(size=nsample)
+
+
+# Fit and summary:
+
+res = sm.OLS(y, X).fit()
+print(res.summary())
+
+
+# Extract other quantities of interest:
+
+print('Parameters: ', res.params)
+print('Standard errors: ', res.bse)
+print('Predicted values: ', res.predict())
+
+
+# Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the ``wls_prediction_std`` command.
+
+prstd, iv_l, iv_u = wls_prediction_std(res)
+
+fig, ax = plt.subplots()
+
+ax.plot(x, y, 'o', label="data")
+ax.plot(x, y_true, 'b-', label="True")
+ax.plot(x, res.fittedvalues, 'r--.', label="OLS")
+ax.plot(x, iv_u, 'r--')
+ax.plot(x, iv_l, 'r--')
+ax.legend(loc='best');
+
+
+# ## OLS with dummy variables
+# 
+# We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category.
+
+nsample = 50
+groups = np.zeros(nsample, int)
+groups[20:40] = 1
+groups[40:] = 2
+#dummy = (groups[:,None] == np.unique(groups)).astype(float)
+
+dummy = sm.categorical(groups, drop=True)
+x = np.linspace(0, 20, nsample)
+# drop reference category
+X = np.column_stack((x, dummy[:,1:]))
+X = sm.add_constant(X, prepend=False)
+
+beta = [1., 3, -3, 10]
+y_true = np.dot(X, beta)
+e = np.random.normal(size=nsample)
+y = y_true + e
+
+
+# Inspect the data:
+
+print(X[:5,:])
+print(y[:5])
+print(groups)
+print(dummy[:5,:])
+
+
+# Fit and summary:
+
+res2 = sm.OLS(y, X).fit()
+print(res.summary())
+
+
+# Draw a plot to compare the true relationship to OLS predictions:
+
+prstd, iv_l, iv_u = wls_prediction_std(res2)
+
+fig, ax = plt.subplots()
+
+ax.plot(x, y, 'o', label="Data")
+ax.plot(x, y_true, 'b-', label="True")
+ax.plot(x, res2.fittedvalues, 'r--.', label="Predicted")
+ax.plot(x, iv_u, 'r--')
+ax.plot(x, iv_l, 'r--')
+ax.legend(loc="best")
+
+
+# ## Joint hypothesis test
+# 
+# ### F test
+# 
+# We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \times \beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:
+
+R = [[0, 1, 0, 0], [0, 0, 1, 0]]
+print(np.array(R))
+print(res2.f_test(R))
+
+
+# You can also use formula-like syntax to test hypotheses
+
+print(res2.f_test("x2 = x3 = 0"))
+
+
+# ### Small group effects
+# 
+# If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis: 
+
+beta = [1., 0.3, -0.0, 10]
+y_true = np.dot(X, beta)
+y = y_true + np.random.normal(size=nsample)
+
+res3 = sm.OLS(y, X).fit()
+
+
+print(res3.f_test(R))
+
+
+print(res3.f_test("x2 = x3 = 0"))
+
+
+# ### Multicollinearity
+# 
+# The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. 
+
+from statsmodels.datasets.longley import load_pandas
+y = load_pandas().endog
+X = load_pandas().exog
+X = sm.add_constant(X)
+
+
+# Fit and summary:
+
+ols_model = sm.OLS(y, X)
+ols_results = ols_model.fit()
+print(ols_results.summary())
+
+
+# #### Condition number
+# 
+# One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: 
+
+for i, name in enumerate(X):
+    if name == "const":
+        continue
+    norm_x[:,i] = X[name]/np.linalg.norm(X[name])
+norm_xtx = np.dot(norm_x.T,norm_x)
+
+
+# Then, we take the square root of the ratio of the biggest to the smallest eigen values. 
+
+eigs = np.linalg.eigvals(norm_xtx)
+condition_number = np.sqrt(eigs.max() / eigs.min())
+print(condition_number)
+
+
+# #### Dropping an observation
+# 
+# Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates: 
+
+ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()
+print("Percentage change %4.2f%%\n"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100]))
+
+
+# We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out.
+
+infl = ols_results.get_influence()
+
+
+# In general we may consider DBETAS in absolute value greater than $2/\sqrt{N}$ to be influential observations
+
+2./len(X)**.5
+
+
+print(infl.summary_frame().filter(regex="dfb"))
+
diff --git a/examples/python/predict.py b/examples/python/predict.py
new file mode 100644
index 00000000000..ee884d2ba81
--- /dev/null
+++ b/examples/python/predict.py
@@ -0,0 +1,73 @@
+
+## Prediction (out of sample)
+
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+
+
+# ## Artificial data
+
+nsample = 50
+sig = 0.25
+x1 = np.linspace(0, 20, nsample)
+X = np.column_stack((x1, np.sin(x1), (x1-5)**2))
+X = sm.add_constant(X)
+beta = [5., 0.5, 0.5, -0.02]
+y_true = np.dot(X, beta)
+y = y_true + sig * np.random.normal(size=nsample)
+
+
+# ## Estimation 
+
+olsmod = sm.OLS(y, X)
+olsres = olsmod.fit()
+print(olsres.summary())
+
+
+# ## In-sample prediction
+
+ypred = olsres.predict(X)
+print(ypred)
+
+
+# ## Create a new sample of explanatory variables Xnew, predict and plot
+
+x1n = np.linspace(20.5,25, 10)
+Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2))
+Xnew = sm.add_constant(Xnew)
+ynewpred =  olsres.predict(Xnew) # predict out of sample
+print(ynewpred)
+
+
+# ## Plot comparison
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+ax.plot(x1, y, 'o', label="Data")
+ax.plot(x1, y_true, 'b-', label="True")
+ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label="OLS prediction")
+ax.legend(loc="best");
+
+
+### Predicting with Formulas
+
+# Using formulas can make both estimation and prediction a lot easier
+
+from statsmodels.formula.api import ols
+
+data = {"x1" : x1, "y" : y}
+
+res = ols("y ~ x1 + np.sin(x1) + I((x1-5)**2)", data=data).fit()
+
+
+# We use the `I` to indicate use of the Identity transform. Ie., we don't want any expansion magic from using `**2`
+
+res.params
+
+
+# Now we only have to pass the single variable and we get the transformed right-hand side variables automatically
+
+res.predict(exog=dict(x1=x1n))
+
diff --git a/examples/python/quantile_regression.py b/examples/python/quantile_regression.py
new file mode 100644
index 00000000000..d73f8de10c2
--- /dev/null
+++ b/examples/python/quantile_regression.py
@@ -0,0 +1,110 @@
+
+## Quantile regression
+
+# 
+# This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in 
+# 
+# * Koenker, Roger and Kevin F. Hallock. "Quantile Regressioin". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143–156
+# 
+# We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). 
+# 
+# ## Setup
+# 
+# We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``.
+
+from __future__ import print_function
+import patsy
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+import matplotlib.pyplot as plt
+from statsmodels.regression.quantile_regression import QuantReg
+
+data = sm.datasets.engel.load_pandas().data
+data.head()
+
+
+# ## Least Absolute Deviation
+# 
+# The LAD model is a special case of quantile regression where q=0.5
+
+mod = smf.quantreg('foodexp ~ income', data)
+res = mod.fit(q=.5)
+print(res.summary())
+
+
+# ## Visualizing the results
+# 
+# We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. 
+
+# ### Prepare data for plotting
+# 
+# For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary.
+
+quantiles = np.arange(.05, .96, .1)
+def fit_model(q):
+    res = mod.fit(q=q)
+    return [q, res.params['Intercept'], res.params['income']] +             res.conf_int().ix['income'].tolist()
+    
+models = [fit_model(x) for x in quantiles]
+models = pd.DataFrame(models, columns=['q', 'a', 'b','lb','ub'])
+
+ols = smf.ols('foodexp ~ income', data).fit()
+ols_ci = ols.conf_int().ix['income'].tolist()
+ols = dict(a = ols.params['Intercept'],
+           b = ols.params['income'],
+           lb = ols_ci[0],
+           ub = ols_ci[1])
+
+print(models)
+print(ols)
+
+
+# ### First plot
+# 
+# This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:
+# 
+# 1. Food expenditure increases with income
+# 2. The *dispersion* of food expenditure increases with income
+# 3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)
+
+x = np.arange(data.income.min(), data.income.max(), 50)
+get_y = lambda a, b: a + b * x
+
+for i in range(models.shape[0]):
+    y = get_y(models.a[i], models.b[i])
+    plt.plot(x, y, linestyle='dotted', color='grey')
+    
+y = get_y(ols['a'], ols['b'])
+plt.plot(x, y, color='red', label='OLS')
+
+plt.scatter(data.income, data.foodexp, alpha=.2)
+plt.xlim((240, 3000))
+plt.ylim((240, 2000))
+plt.legend()
+plt.xlabel('Income')
+plt.ylabel('Food expenditure')
+plt.show()
+
+
+# ### Second plot
+# 
+# The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confindence interval.
+# 
+# In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution.
+
+from matplotlib import rc
+rc('text', usetex=True)
+n = models.shape[0]
+p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')
+p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')
+p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')
+p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')
+p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')
+p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')
+plt.ylabel(r'\beta_\mbox{income}')
+plt.xlabel('Quantiles of the conditional food expenditure distribution')
+plt.legend()
+plt.show()
+
diff --git a/examples/python/regression_diagnostics.py b/examples/python/regression_diagnostics.py
new file mode 100644
index 00000000000..cb292274476
--- /dev/null
+++ b/examples/python/regression_diagnostics.py
@@ -0,0 +1,95 @@
+
+## Regression diagnostics
+
+# This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the [Regression Diagnostics page.](http://statsmodels.sourceforge.net/stable/diagnostic.html) 
+# 
+# Note that most of the tests described here only return a tuple of numbers, without any annotation. A full description of outputs is always included in the docstring and in the online ``statsmodels`` documentation. For presentation purposes, we use the ``zip(name,test)`` construct to pretty-print(short descriptions in the examples below.
+
+# ## Estimate a regression model
+
+from __future__ import print_function
+from statsmodels.compat import lzip
+import statsmodels
+import numpy as np
+import pandas as pd
+import statsmodels.formula.api as smf
+import statsmodels.stats.api as sms
+
+# Load data
+url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
+dat = pd.read_csv(url)
+
+# Fit regression model (using the natural log of one of the regressaors)
+results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()
+
+# Inspect the results
+print(results.summary())
+
+
+# ## Normality of the residuals
+
+# Jarque-Bera test:
+
+name = ['Jarque-Bera', 'Chi^2 two-tail prob.', 'Skew', 'Kurtosis']
+test = sms.jarque_bera(results.resid)
+lzip(name, test)
+
+
+# Omni test:
+
+name = ['Chi^2', 'Two-tail probability']
+test = sms.omni_normtest(results.resid)
+lzip(name, test)
+
+
+# ## Influence tests
+# 
+# Once created, an object of class ``OLSInfluence`` holds attributes and methods that allow users to assess the influence of each observation. For example, we can compute and extract the first few rows of DFbetas by:
+
+from statsmodels.stats.outliers_influence import OLSInfluence
+test_class = OLSInfluence(results)
+test_class.dfbetas[:5,:]
+
+
+# Explore other options by typing ``dir(influence_test)``
+# 
+# Useful information on leverage can also be plotted:
+
+from statsmodels.graphics.regressionplots import plot_leverage_resid2
+print(plot_leverage_resid2(results))
+
+
+# Other plotting options can be found on the [Graphics page.](http://statsmodels.sourceforge.net/stable/graphics.html)
+
+# ## Multicollinearity
+# 
+# Condition number:
+
+np.linalg.cond(results.model.exog)
+
+
+# ## Heteroskedasticity tests
+# 
+# Breush-Pagan test:
+
+name = ['Lagrange multiplier statistic', 'p-value', 
+        'f-value', 'f p-value']
+test = sms.het_breushpagan(results.resid, results.model.exog)
+lzip(name, test)
+
+
+# Goldfeld-Quandt test
+
+name = ['F statistic', 'p-value']
+test = sms.het_goldfeldquandt(results.resid, results.model.exog)
+lzip(name, test)
+
+
+# ## Linearity
+# 
+# Harvey-Collier multiplier test for Null hypothesis that the linear specification is correct:
+
+name = ['t value', 'p value']
+test = sms.linear_harvey_collier(results)
+lzip(name, test)
+
diff --git a/examples/python/regression_plots.py b/examples/python/regression_plots.py
new file mode 100644
index 00000000000..d02e00b0453
--- /dev/null
+++ b/examples/python/regression_plots.py
@@ -0,0 +1,232 @@
+
+## Regression Plots
+
+from __future__ import print_function
+from statsmodels.compat import lzip
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+from statsmodels.formula.api import ols
+
+
+### Duncan's Prestige Dataset
+
+#### Load the Data
+
+# We can use a utility function to load any R dataset available from the great <a href="http://vincentarelbundock.github.com/Rdatasets/">Rdatasets package</a>.
+
+prestige = sm.datasets.get_rdataset("Duncan", "car", cache=True).data
+
+
+prestige.head()
+
+
+prestige_model = ols("prestige ~ income + education", data=prestige).fit()
+
+
+print(prestige_model.summary())
+
+
+#### Influence plots
+
+# Influence plots show the (externally) studentized residuals vs. the leverage of each observation as measured by the hat matrix.
+#
+# Externally studentized residuals are residuals that are scaled by their standard deviation where
+#
+# $$var(\\hat{\epsilon}_i)=\hat{\sigma}^2_i(1-h_{ii})$$
+#
+# with
+#
+# $$\hat{\sigma}^2_i=\frac{1}{n - p - 1 \;\;}\sum_{j}^{n}\;\;\;\forall \;\;\; j \neq i$$
+#
+# $n$ is the number of observations and $p$ is the number of regressors. $h_{ii}$ is the $i$-th diagonal element of the hat matrix
+#
+# $$H=X(X^{\;\prime}X)^{-1}X^{\;\prime}$$
+#
+# The influence of each point can be visualized by the criterion keyword argument. Options are Cook's distance and DFFITS, two measures of influence.
+
+fig, ax = plt.subplots(figsize=(12,8))
+fig = sm.graphics.influence_plot(prestige_model, ax=ax, criterion="cooks")
+
+
+# As you can see there are a few worrisome observations. Both contractor and reporter have low leverage but a large residual. <br />
+# RR.engineer has small residual and large leverage. Conductor and minister have both high leverage and large residuals, and, <br />
+# therefore, large influence.
+
+#### Partial Regression Plots
+
+# Since we are doing multivariate regressions, we cannot just look at individual bivariate plots to discern relationships. <br />
+# Instead, we want to look at the relationship of the dependent variable and independent variables conditional on the other <br />
+# independent variables. We can do this through using partial regression plots, otherwise known as added variable plots. <br />
+#
+# In a partial regression plot, to discern the relationship between the response variable and the $k$-th variabe, we compute <br />
+# the residuals by regressing the response variable versus the independent variables excluding $X_k$. We can denote this by <br />
+# $X_{\sim k}$. We then compute the residuals by regressing $X_k$ on $X_{\sim k}$. The partial regression plot is the plot <br />
+# of the former versus the latter residuals. <br />
+#
+# The notable points of this plot are that the fitted line has slope $\beta_k$ and intercept zero. The residuals of this plot <br />
+# are the same as those of the least squares fit of the original model with full $X$. You can discern the effects of the <br />
+# individual data values on the estimation of a coefficient easily. If obs_labels is True, then these points are annotated <br />
+# with their observation label. You can also see the violation of underlying assumptions such as homooskedasticity and <br />
+# linearity.
+
+fig, ax = plt.subplots(figsize=(12,8))
+fig = sm.graphics.plot_partregress("prestige", "income", ["income", "education"], data=prestige, ax=ax)
+ax = fig.axes[0]
+
+ax.set_xlim(-2e-15, 1e-14)
+ax.set_ylim(-25, 30);
+
+
+fix, ax = plt.subplots(figsize=(12,14))
+fig = sm.graphics.plot_partregress("prestige", "income", ["education"], data=prestige, ax=ax)
+
+
+# As you can see the partial regression plot confirms the influence of conductor, minister, and RR.engineer on the partial relationship between income and prestige. The cases greatly decrease the effect of income on prestige. Dropping these cases confirms this.
+
+subset = ~prestige.index.isin(["conductor", "RR.engineer", "minister"])
+prestige_model2 = ols("prestige ~ income + education", data=prestige, subset=subset).fit()
+print(prestige_model2.summary())
+
+
+# For a quick check of all the regressors, you can use plot_partregress_grid. These plots will not label the <br />
+# points, but you can use them to identify problems and then use plot_partregress to get more information.
+
+fig = plt.figure(figsize=(12,8))
+fig = sm.graphics.plot_partregress_grid(prestige_model, fig=fig)
+
+
+#### Component-Component plus Residual (CCPR) Plots
+
+# The CCPR plot provides a way to judge the effect of one regressor on the <br />
+# response variable by taking into account the effects of the other  <br />
+# independent variables. The partial residuals plot is defined as  <br />
+# $\text{Residuals} + B_iX_i \text{ }\text{ }$   versus $X_i$. The component adds $B_iX_i$ versus  <br />
+# $X_i$ to show where the fitted line would lie. Care should be taken if $X_i$  <br />
+# is highly correlated with any of the other independent variables. If this  <br />
+# is the case, the variance evident in the plot will be an underestimate of  <br />
+# the true variance.
+
+fig, ax = plt.subplots(figsize=(12, 8))
+fig = sm.graphics.plot_ccpr(prestige_model, "education", ax=ax)
+
+
+# As you can see the relationship between the variation in prestige explained by education conditional on income seems to be linear, though you can see there are some observations that are exerting considerable influence on the relationship. We can quickly look at more than one variable by using plot_ccpr_grid.
+
+fig = plt.figure(figsize=(12, 8))
+fig = sm.graphics.plot_ccpr_grid(prestige_model, fig=fig)
+
+
+#### Regression Plots
+
+# The plot_regress_exog function is a convenience function that gives a 2x2 plot containing the dependent variable and fitted values with confidence intervals vs. the independent variable chosen, the residuals of the model vs. the chosen independent variable, a partial regression plot, and a CCPR plot. This function can be used for quickly checking modeling assumptions with respect to a single regressor.
+
+fig = plt.figure(figsize=(12,8))
+fig = sm.graphics.plot_regress_exog(prestige_model, "education", fig=fig)
+
+
+#### Fit Plot
+
+# The plot_fit function plots the fitted values versus a chosen independent variable. It includes prediction confidence intervals and optionally plots the true dependent variable.
+
+fig, ax = plt.subplots(figsize=(12, 8))
+fig = sm.graphics.plot_fit(prestige_model, "education", ax=ax)
+
+
+### Statewide Crime 2009 Dataset
+
+# Compare the following to http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg_self_assessment_answers4.htm
+#
+# Though the data here is not the same as in that example. You could run that example by uncommenting the necessary cells below.
+
+#dta = pd.read_csv("http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv")
+#dta = dta.set_index("State", inplace=True).dropna()
+#dta.rename(columns={"VR" : "crime",
+#                    "MR" : "murder",
+#                    "M"  : "pctmetro",
+#                    "W"  : "pctwhite",
+#                    "H"  : "pcths",
+#                    "P"  : "poverty",
+#                    "S"  : "single"
+#                    }, inplace=True)
+#
+#crime_model = ols("murder ~ pctmetro + poverty + pcths + single", data=dta).fit()
+
+
+dta = sm.datasets.statecrime.load_pandas().data
+
+
+crime_model = ols("murder ~ urban + poverty + hs_grad + single", data=dta).fit()
+print(crime_model.summary())
+
+
+#### Partial Regression Plots
+
+fig = plt.figure(figsize=(12,8))
+fig = sm.graphics.plot_partregress_grid(crime_model, fig=fig)
+
+
+fig, ax = plt.subplots(figsize=(12,8))
+fig = sm.graphics.plot_partregress("murder", "hs_grad", ["urban", "poverty", "single"],  ax=ax, data=dta)
+
+
+#### Leverage-Resid<sup>2</sup> Plot
+
+# Closely related to the influence_plot is the leverage-resid<sup>2</sup> plot.
+
+fig, ax = plt.subplots(figsize=(8,6))
+fig = sm.graphics.plot_leverage_resid2(crime_model, ax=ax)
+
+
+#### Influence Plot
+
+fig, ax = plt.subplots(figsize=(8,6))
+fig = sm.graphics.influence_plot(crime_model, ax=ax)
+
+
+#### Using robust regression to correct for outliers.
+
+# Part of the problem here in recreating the Stata results is that M-estimators are not robust to leverage points. MM-estimators should do better with this examples.
+
+from statsmodels.formula.api import rlm
+
+
+rob_crime_model = rlm("murder ~ urban + poverty + hs_grad + single", data=dta,
+                      M=sm.robust.norms.TukeyBiweight(3)).fit(conv="weights")
+print(rob_crime_model.summary())
+
+
+#rob_crime_model = rlm("murder ~ pctmetro + poverty + pcths + single", data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv="weights")
+#print(rob_crime_model.summary())
+
+
+# There aren't yet an influence diagnostics as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))
+
+weights = rob_crime_model.weights
+idx = weights > 0
+X = rob_crime_model.model.exog[idx]
+ww = weights[idx] / weights[idx].mean()
+hat_matrix_diag = ww*(X*np.linalg.pinv(X).T).sum(1)
+resid = rob_crime_model.resid
+resid2 = resid**2
+resid2 /= resid2.sum()
+nobs = int(idx.sum())
+hm = hat_matrix_diag.mean()
+rm = resid2.mean()
+
+
+from statsmodels.graphics import utils
+fig, ax = plt.subplots(figsize=(12,8))
+ax.plot(resid2[idx], hat_matrix_diag, 'o')
+ax = utils.annotate_axes(range(nobs), labels=rob_crime_model.model.data.row_labels[idx],
+                    points=lzip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,
+                    size="large", ax=ax)
+ax.set_xlabel("resid2")
+ax.set_ylabel("leverage")
+ylim = ax.get_ylim()
+ax.vlines(rm, *ylim)
+xlim = ax.get_xlim()
+ax.hlines(hm, *xlim)
+ax.margins(0,0)
+
diff --git a/examples/python/robust_models_0.py b/examples/python/robust_models_0.py
new file mode 100644
index 00000000000..ae3b74dabc3
--- /dev/null
+++ b/examples/python/robust_models_0.py
@@ -0,0 +1,120 @@
+
+## Robust Linear Models
+
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+from statsmodels.sandbox.regression.predstd import wls_prediction_std
+
+
+# ## Estimation
+# 
+# Load data:
+
+data = sm.datasets.stackloss.load()
+data.exog = sm.add_constant(data.exog)
+
+
+# Huber's T norm with the (default) median absolute deviation scaling
+
+huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())
+hub_results = huber_t.fit()
+print(hub_results.params)
+print(hub_results.bse)
+print(hub_results.summary(yname='y',
+            xname=['var_%d' % i for i in range(len(hub_results.params))]))
+
+
+# Huber's T norm with 'H2' covariance matrix
+
+hub_results2 = huber_t.fit(cov="H2")
+print(hub_results2.params)
+print(hub_results2.bse)
+
+
+# Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix
+
+andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())
+andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov="H3")
+print('Parameters: ', andrew_results.params)
+
+
+# See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for scale options
+# 
+# ## Comparing OLS and RLM
+# 
+# Artificial data with outliers:
+
+nsample = 50
+x1 = np.linspace(0, 20, nsample)
+X = np.column_stack((x1, (x1-5)**2))
+X = sm.add_constant(X)
+sig = 0.3   # smaller error variance makes OLS<->RLM contrast bigger
+beta = [5, 0.5, -0.0]
+y_true2 = np.dot(X, beta)
+y2 = y_true2 + sig*1. * np.random.normal(size=nsample)
+y2[[39,41,43,45,48]] -= 5   # add some outliers (10% of nsample)
+
+
+# ### Example 1: quadratic function with linear truth
+# 
+# Note that the quadratic term in OLS regression will capture outlier effects. 
+
+res = sm.OLS(y2, X).fit()
+print(res.params)
+print(res.bse)
+print(res.predict())
+
+
+# Estimate RLM:
+
+resrlm = sm.RLM(y2, X).fit()
+print(resrlm.params)
+print(resrlm.bse)
+
+
+# Draw a plot to compare OLS estimates to the robust estimates:
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(x1, y2, 'o',label="data")
+ax.plot(x1, y_true2, 'b-', label="True")
+prstd, iv_l, iv_u = wls_prediction_std(res)
+ax.plot(x1, res.fittedvalues, 'r-', label="OLS")
+ax.plot(x1, iv_u, 'r--')
+ax.plot(x1, iv_l, 'r--')
+ax.plot(x1, resrlm.fittedvalues, 'g.-', label="RLM")
+ax.legend(loc="best")
+
+
+# ### Example 2: linear function with linear truth
+# 
+# Fit a new OLS model using only the linear term and the constant:
+
+X2 = X[:,[0,1]] 
+res2 = sm.OLS(y2, X2).fit()
+print(res2.params)
+print(res2.bse)
+
+
+# Estimate RLM:
+
+resrlm2 = sm.RLM(y2, X2).fit()
+print(resrlm2.params)
+print(resrlm2.bse)
+
+
+# Draw a plot to compare OLS estimates to the robust estimates:
+
+prstd, iv_l, iv_u = wls_prediction_std(res2)
+
+fig, ax = plt.subplots()
+ax.plot(x1, y2, 'o', label="data")
+ax.plot(x1, y_true2, 'b-', label="True")
+ax.plot(x1, res2.fittedvalues, 'r-', label="OLS")
+ax.plot(x1, iv_u, 'r--')
+ax.plot(x1, iv_l, 'r--')
+ax.plot(x1, resrlm2.fittedvalues, 'g.-', label="RLM")
+ax.legend(loc="best")
+
diff --git a/examples/python/robust_models_1.py b/examples/python/robust_models_1.py
new file mode 100644
index 00000000000..e79ab1aa741
--- /dev/null
+++ b/examples/python/robust_models_1.py
@@ -0,0 +1,385 @@
+
+## M-Estimators for Robust Linear Modeling
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+
+import statsmodels.api as sm
+
+
+# * An M-estimator minimizes the function 
+# 
+# $$Q(e_i, \rho) = \sum_i~\rho \left (\frac{e_i}{s}\right )$$
+# 
+# where $\rho$ is a symmetric function of the residuals 
+# 
+# * The effect of $\rho$ is to reduce the influence of outliers
+# * $s$ is an estimate of scale. 
+# * The robust estimates $\hat{\beta}$ are computed by the iteratively re-weighted least squares algorithm
+
+# * We have several choices available for the weighting functions to be used
+
+norms = sm.robust.norms
+
+
+def plot_weights(support, weights_func, xlabels, xticks):
+    fig = plt.figure(figsize=(12,8))
+    ax = fig.add_subplot(111)
+    ax.plot(support, weights_func(support))
+    ax.set_xticks(xticks)
+    ax.set_xticklabels(xlabels, fontsize=16)
+    ax.set_ylim(-.1, 1.1)
+    return ax
+
+
+#### Andrew's Wave
+
+help(norms.AndrewWave.weights)
+
+
+a = 1.339
+support = np.linspace(-np.pi*a, np.pi*a, 100)
+andrew = norms.AndrewWave(a=a)
+plot_weights(support, andrew.weights, ['$-\pi*a$', '0', '$\pi*a$'], [-np.pi*a, 0, np.pi*a]);
+
+
+#### Hampel's 17A
+
+help(norms.Hampel.weights)
+
+
+c = 8
+support = np.linspace(-3*c, 3*c, 1000)
+hampel = norms.Hampel(a=2., b=4., c=c)
+plot_weights(support, hampel.weights, ['3*c', '0', '3*c'], [-3*c, 0, 3*c]);
+
+
+#### Huber's t
+
+help(norms.HuberT.weights)
+
+
+t = 1.345
+support = np.linspace(-3*t, 3*t, 1000)
+huber = norms.HuberT(t=t)
+plot_weights(support, huber.weights, ['-3*t', '0', '3*t'], [-3*t, 0, 3*t]);
+
+
+#### Least Squares
+
+help(norms.LeastSquares.weights)
+
+
+support = np.linspace(-3, 3, 1000)
+lst_sq = norms.LeastSquares()
+plot_weights(support, lst_sq.weights, ['-3', '0', '3'], [-3, 0, 3]);
+
+
+#### Ramsay's Ea
+
+help(norms.RamsayE.weights)
+
+
+a = .3
+support = np.linspace(-3*a, 3*a, 1000)
+ramsay = norms.RamsayE(a=a)
+plot_weights(support, ramsay.weights, ['-3*a', '0', '3*a'], [-3*a, 0, 3*a]);
+
+
+#### Trimmed Mean
+
+help(norms.TrimmedMean.weights)
+
+
+c = 2
+support = np.linspace(-3*c, 3*c, 1000)
+trimmed = norms.TrimmedMean(c=c)
+plot_weights(support, trimmed.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);
+
+
+#### Tukey's Biweight
+
+help(norms.TukeyBiweight.weights)
+
+
+c = 4.685
+support = np.linspace(-3*c, 3*c, 1000)
+tukey = norms.TukeyBiweight(c=c)
+plot_weights(support, tukey.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);
+
+
+#### Scale Estimators
+
+# * Robust estimates of the location
+
+x = np.array([1, 2, 3, 4, 500])
+
+
+# * The mean is not a robust estimator of location
+
+x.mean()
+
+
+# * The median, on the other hand, is a robust estimator with a breakdown point of 50%
+
+np.median(x)
+
+
+# * Analagously for the scale
+# * The standard deviation is not robust
+
+x.std()
+
+
+# Median Absolute Deviation
+# 
+# $$ median_i |X_i - median_j(X_j)|) $$
+
+# Standardized Median Absolute Deviation is a consistent estimator for $\hat{\sigma}$
+# 
+# $$\hat{\sigma}=K \cdot MAD$$
+# 
+# where $K$ depends on the distribution. For the normal distribution for example,
+# 
+# $$K = \Phi^{-1}(.75)$$
+
+stats.norm.ppf(.75)
+
+
+print(x)
+
+
+sm.robust.scale.stand_mad(x)
+
+
+np.array([1,2,3,4,5.]).std()
+
+
+# * The default for Robust Linear Models is MAD
+# * another popular choice is Huber's proposal 2
+
+np.random.seed(12345)
+fat_tails = stats.t(6).rvs(40)
+
+
+kde = sm.nonparametric.KDE(fat_tails)
+kde.fit()
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(kde.support, kde.density);
+
+
+print(fat_tails.mean(), fat_tails.std())
+
+
+print(stats.norm.fit(fat_tails))
+
+
+print(stats.t.fit(fat_tails, f0=6))
+
+
+huber = sm.robust.scale.Huber()
+loc, scale = huber(fat_tails)
+print(loc, scale)
+
+
+sm.robust.stand_mad(fat_tails)
+
+
+sm.robust.stand_mad(fat_tails, c=stats.t(6).ppf(.75))
+
+
+sm.robust.scale.mad(fat_tails)
+
+
+#### Duncan's Occupational Prestige data - M-estimation for outliers
+
+from statsmodels.graphics.api import abline_plot
+from statsmodels.formula.api import ols, rlm
+
+
+prestige = sm.datasets.get_rdataset("Duncan", "car", cache=True).data
+
+
+print(prestige.head(10))
+
+
+fig = plt.figure(figsize=(12,12))
+ax1 = fig.add_subplot(211, xlabel='Income', ylabel='Prestige')
+ax1.scatter(prestige.income, prestige.prestige)
+xy_outlier = prestige.ix['minister'][['income','prestige']]
+ax1.annotate('Minister', xy_outlier, xy_outlier+1, fontsize=16)
+ax2 = fig.add_subplot(212, xlabel='Education',
+                           ylabel='Prestige')
+ax2.scatter(prestige.education, prestige.prestige);
+
+
+ols_model = ols('prestige ~ income + education', prestige).fit()
+print(ols_model.summary())
+
+
+infl = ols_model.get_influence()
+student = infl.summary_frame()['student_resid']
+print(student)
+
+
+print(student.ix[np.abs(student) > 2])
+
+
+print(infl.summary_frame().ix['minister'])
+
+
+sidak = ols_model.outlier_test('sidak')
+sidak.sort('unadj_p', inplace=True)
+print(sidak)
+
+
+fdr = ols_model.outlier_test('fdr_bh')
+fdr.sort('unadj_p', inplace=True)
+print(fdr)
+
+
+rlm_model = rlm('prestige ~ income + education', prestige).fit()
+print(rlm_model.summary())
+
+
+print(rlm_model.weights)
+
+
+#### Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points
+
+# * Data is on the luminosity and temperature of 47 stars in the direction of Cygnus.
+
+dta = sm.datasets.get_rdataset("starsCYG", "robustbase", cache=True).data
+
+
+from matplotlib.patches import Ellipse
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111, xlabel='log(Temp)', ylabel='log(Light)', title='Hertzsprung-Russell Diagram of Star Cluster CYG OB1')
+ax.scatter(*dta.values.T)
+# highlight outliers
+e = Ellipse((3.5, 6), .2, 1, alpha=.25, color='r')
+ax.add_patch(e);
+ax.annotate('Red giants', xy=(3.6, 6), xytext=(3.8, 6),
+            arrowprops=dict(facecolor='black', shrink=0.05, width=2),
+            horizontalalignment='left', verticalalignment='bottom',
+            clip_on=True, # clip to the axes bounding box
+            fontsize=16,
+     )
+# annotate these with their index
+for i,row in dta.ix[dta['log.Te'] < 3.8].iterrows():
+    ax.annotate(i, row, row + .01, fontsize=14)
+xlim, ylim = ax.get_xlim(), ax.get_ylim()
+
+
+from IPython.display import Image
+Image(filename='star_diagram.png')
+
+
+y = dta['log.light']
+X = sm.add_constant(dta['log.Te'], prepend=True)
+ols_model = sm.OLS(y, X).fit()
+abline_plot(model_results=ols_model, ax=ax)
+
+
+rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()
+abline_plot(model_results=rlm_mod, ax=ax, color='red')
+
+
+# * Why? Because M-estimators are not robust to leverage points.
+
+infl = ols_model.get_influence()
+
+
+h_bar = 2*(ols_model.df_model + 1 )/ols_model.nobs
+hat_diag = infl.summary_frame()['hat_diag']
+hat_diag.ix[hat_diag > h_bar]
+
+
+sidak2 = ols_model.outlier_test('sidak')
+sidak2.sort('unadj_p', inplace=True)
+print(sidak2)
+
+
+fdr2 = ols_model.outlier_test('fdr_bh')
+fdr2.sort('unadj_p', inplace=True)
+print(fdr2)
+
+
+# * Let's delete that line
+
+del ax.lines[-1]
+
+
+weights = np.ones(len(X))
+weights[X[X['log.Te'] < 3.8].index.values - 1] = 0
+wls_model = sm.WLS(y, X, weights=weights).fit()
+abline_plot(model_results=wls_model, ax=ax, color='green')
+
+
+# * MM estimators are good for this type of problem, unfortunately, we don't yet have these yet. 
+# * It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook.
+
+yy = y.values[:,None]
+xx = X['log.Te'].values[:,None]
+
+
+get_ipython().magic(u'load_ext rmagic')
+
+get_ipython().magic(u'R library(robustbase)')
+get_ipython().magic(u'Rpush yy xx')
+get_ipython().magic(u'R mod <- lmrob(yy ~ xx);')
+get_ipython().magic(u'R params <- mod$coefficients;')
+get_ipython().magic(u'Rpull params')
+
+
+get_ipython().magic(u'R print(mod)')
+
+
+print(params)
+
+
+abline_plot(intercept=params[0], slope=params[1], ax=ax, color='green')
+
+
+#### Exercise: Breakdown points of M-estimator
+
+np.random.seed(12345)
+nobs = 200
+beta_true = np.array([3, 1, 2.5, 3, -4])
+X = np.random.uniform(-20,20, size=(nobs, len(beta_true)-1))
+# stack a constant in front
+X = sm.add_constant(X, prepend=True) # np.c_[np.ones(nobs), X]
+mc_iter = 500
+contaminate = .25 # percentage of response variables to contaminate
+
+
+all_betas = []
+for i in range(mc_iter):
+    y = np.dot(X, beta_true) + np.random.normal(size=200)
+    random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))
+    y[random_idx] = np.random.uniform(-750, 750)
+    beta_hat = sm.RLM(y, X).fit().params
+    all_betas.append(beta_hat)
+
+
+all_betas = np.asarray(all_betas)
+se_loss = lambda x : np.linalg.norm(x, ord=2)**2
+se_beta = map(se_loss, all_betas - beta_true)
+
+
+##### Squared error loss
+
+np.array(se_beta).mean()
+
+
+all_betas.mean(0)
+
+
+beta_true
+
+
+se_loss(all_betas.mean(0) - beta_true)
+
diff --git a/examples/python/tsa_arma_0.py b/examples/python/tsa_arma_0.py
new file mode 100644
index 00000000000..8cd268c165f
--- /dev/null
+++ b/examples/python/tsa_arma_0.py
@@ -0,0 +1,193 @@
+
+## Autoregressive Moving Average (ARMA): Sunspots data
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import statsmodels.api as sm
+
+
+from statsmodels.graphics.api import qqplot
+
+
+### Sunpots Data
+
+print(sm.datasets.sunspots.NOTE)
+
+
+dta = sm.datasets.sunspots.load_pandas().data
+
+
+dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
+del dta["YEAR"]
+
+
+dta.plot(figsize=(12,8));
+
+
+fig = plt.figure(figsize=(12,8))
+ax1 = fig.add_subplot(211)
+fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)
+ax2 = fig.add_subplot(212)
+fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)
+
+
+arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()
+print(arma_mod20.params)
+
+
+arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()
+
+
+print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)
+
+
+print(arma_mod30.params)
+
+
+print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)
+
+
+# * Does our model obey the theory?
+
+sm.stats.durbin_watson(arma_mod30.resid.values)
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax = arma_mod30.resid.plot(ax=ax);
+
+
+resid = arma_mod30.resid
+
+
+stats.normaltest(resid)
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+fig = qqplot(resid, line='q', ax=ax, fit=True)
+
+
+fig = plt.figure(figsize=(12,8))
+ax1 = fig.add_subplot(211)
+fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)
+ax2 = fig.add_subplot(212)
+fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)
+
+
+r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)
+data = np.c_[range(1,41), r[1:], q, p]
+table = pd.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
+print(table.set_index('lag'))
+
+
+# * This indicates a lack of fit.
+
+# * In-sample dynamic prediction. How good does our model do?
+
+predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)
+print(predict_sunspots)
+
+
+ax = dta.ix['1950':].plot(figsize=(12,8))
+ax = predict_sunspots.plot(ax=ax, style='r--', label='Dynamic Prediction')
+ax.legend()
+ax.axis((-20.0, 38.0, -4.0, 200.0))
+
+
+def mean_forecast_err(y, yhat):
+    return y.sub(yhat).mean()
+
+
+mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)
+
+
+#### Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)
+
+#### Simulated ARMA(4,1): Model Identification is Difficult
+
+from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess
+
+
+np.random.seed(1234)
+# include zero-th lag
+arparams = np.array([1, .75, -.65, -.55, .9])
+maparams = np.array([1, .65])
+
+
+# Let's make sure this model is estimable.
+
+arma_t = ArmaProcess(arparams, maparams)
+
+
+arma_t.isinvertible()
+
+
+arma_t.isstationary()
+
+
+# * What does this mean?
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax.plot(arma_t.generate_sample(size=50));
+
+
+arparams = np.array([1, .35, -.15, .55, .1])
+maparams = np.array([1, .65])
+arma_t = ArmaProcess(arparams, maparams)
+arma_t.isstationary()
+
+
+arma_rvs = arma_t.generate_sample(size=500, burnin=250, scale=2.5)
+
+
+fig = plt.figure(figsize=(12,8))
+ax1 = fig.add_subplot(211)
+fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)
+ax2 = fig.add_subplot(212)
+fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)
+
+
+# * For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags.
+# * The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags.
+
+arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()
+resid = arma11.resid
+r,q,p = sm.tsa.acf(resid, qstat=True)
+data = np.c_[range(1,41), r[1:], q, p]
+table = pd.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
+print(table.set_index('lag'))
+
+
+arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()
+resid = arma41.resid
+r,q,p = sm.tsa.acf(resid, qstat=True)
+data = np.c_[range(1,41), r[1:], q, p]
+table = pd.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
+print(table.set_index('lag'))
+
+
+#### Exercise: How good of in-sample prediction can you do for another series, say, CPI
+
+macrodta = sm.datasets.macrodata.load_pandas().data
+macrodta.index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))
+cpi = macrodta["cpi"]
+
+
+##### Hint:
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+ax = cpi.plot(ax=ax)
+ax.legend()
+
+
+# P-value of the unit-root test, resoundly rejects the null of no unit-root.
+
+print(sm.tsa.adfuller(cpi)[1])
+
diff --git a/examples/python/tsa_arma_1.py b/examples/python/tsa_arma_1.py
new file mode 100644
index 00000000000..8ac31612b52
--- /dev/null
+++ b/examples/python/tsa_arma_1.py
@@ -0,0 +1,32 @@
+
+## Autoregressive Moving Average (ARMA): Artificial data
+
+from __future__ import print_function
+import numpy as np
+import statsmodels.api as sm
+from statsmodels.tsa.arima_process import arma_generate_sample
+np.random.seed(12345)
+
+
+# Generate some data from an ARMA process:
+
+arparams = np.array([.75, -.25])
+maparams = np.array([.65, .35])
+
+
+# The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated.
+
+arparams = np.r_[1, -arparams]
+maparam = np.r_[1, maparams]
+nobs = 250
+y = arma_generate_sample(arparams, maparams, nobs)
+
+
+#  Now, optionally, we can add some dates information. For this example, we'll use a pandas time series.
+
+import pandas as pd
+dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
+y = pd.TimeSeries(y, index=dates)
+arma_mod = sm.tsa.ARMA(y, order=(2,2))
+arma_res = arma_mod.fit(trend='nc', disp=-1)
+
diff --git a/examples/python/tsa_dates.py b/examples/python/tsa_dates.py
new file mode 100644
index 00000000000..78d64c7469c
--- /dev/null
+++ b/examples/python/tsa_dates.py
@@ -0,0 +1,51 @@
+
+## Dates in timeseries models
+
+from __future__ import print_function
+import statsmodels.api as sm
+import pandas as pd
+
+
+# ## Getting started
+
+data = sm.datasets.sunspots.load()
+
+
+# Right now an annual date series must be datetimes at the end of the year.
+
+dates = sm.tsa.datetools.dates_from_range('1700', length=len(data.endog))
+
+
+# ## Using Pandas
+#
+# Make a pandas TimeSeries or DataFrame
+
+endog = pd.TimeSeries(data.endog, index=dates)
+
+
+# Instantiate the model
+
+ar_model = sm.tsa.AR(endog, freq='A')
+pandas_ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
+
+
+# Out-of-sample prediction
+
+pred = pandas_ar_res.predict(start='2005', end='2015')
+print(pred)
+
+
+# ## Using explicit dates
+
+ar_model = sm.tsa.AR(data.endog, dates=dates, freq='A')
+ar_res = ar_model.fit(maxlag=9, method='mle', disp=-1)
+pred = ar_res.predict(start='2005', end='2015')
+print(pred)
+
+
+# This just returns a regular array, but since the model has date information attached, you can get the prediction dates in a roundabout way.
+
+print(ar_res.data.predict_dates)
+
+
+# Note: This attribute only exists if predict has been called. It holds the dates associated with the last call to predict.
diff --git a/examples/python/tsa_filters.py b/examples/python/tsa_filters.py
new file mode 100644
index 00000000000..0300e7edc1d
--- /dev/null
+++ b/examples/python/tsa_filters.py
@@ -0,0 +1,130 @@
+
+## Time Series Filters
+
+from __future__ import print_function
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import statsmodels.api as sm
+
+
+dta = sm.datasets.macrodata.load_pandas().data
+
+
+index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))
+print(index)
+
+
+dta.index = index
+del dta['year']
+del dta['quarter']
+
+
+print(sm.datasets.macrodata.NOTE)
+
+
+print(dta.head(10))
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+dta.realgdp.plot(ax=ax);
+legend = ax.legend(loc = 'upper left');
+legend.prop.set_size(20);
+
+
+#### Hodrick-Prescott Filter
+
+# The Hodrick-Prescott filter separates a time-series $y_t$ into a trend $\tau_t$ and a cyclical component $\zeta_t$
+#
+# $$y_t = \tau_t + \zeta_t$$
+#
+# The components are determined by minimizing the following quadratic loss function
+#
+# $$\min_{\\{ \tau_{t}\\} }\sum_{t}^{T}\zeta_{t}^{2}+\lambda\sum_{t=1}^{T}\left[\left(\tau_{t}-\tau_{t-1}\right)-\left(\tau_{t-1}-\tau_{t-2}\right)\right]^{2}$$
+
+gdp_cycle, gdp_trend = sm.tsa.filters.hpfilter(dta.realgdp)
+
+
+gdp_decomp = dta[['realgdp']]
+gdp_decomp["cycle"] = gdp_cycle
+gdp_decomp["trend"] = gdp_trend
+
+
+fig = plt.figure(figsize=(12,8))
+ax = fig.add_subplot(111)
+gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax, fontsize=16);
+legend = ax.get_legend()
+legend.prop.set_size(20);
+
+
+#### Baxter-King approximate band-pass filter: Inflation and Unemployment
+
+##### Explore the hypothesis that inflation and unemployment are counter-cyclical.
+
+# The Baxter-King filter is intended to explictly deal with the periodicty of the business cycle. By applying their band-pass filter to a series, they produce a new series that does not contain fluctuations at higher or lower than those of the business cycle. Specifically, the BK filter takes the form of a symmetric moving average
+#
+# $$y_{t}^{*}=\sum_{k=-K}^{k=K}a_ky_{t-k}$$
+#
+# where $a_{-k}=a_k$ and $\sum_{k=-k}^{K}a_k=0$ to eliminate any trend in the series and render it stationary if the series is I(1) or I(2).
+#
+# For completeness, the filter weights are determined as follows
+#
+# $$a_{j} = B_{j}+\theta\text{ for }j=0,\pm1,\pm2,\dots,\pm K$$
+#
+# $$B_{0} = \frac{\left(\omega_{2}-\omega_{1}\right)}{\pi}$$
+# $$B_{j} = \frac{1}{\pi j}\left(\sin\left(\omega_{2}j\right)-\sin\left(\omega_{1}j\right)\right)\text{ for }j=0,\pm1,\pm2,\dots,\pm K$$
+#
+# where $\theta$ is a normalizing constant such that the weights sum to zero.
+#
+# $$\theta=\frac{-\sum_{j=-K^{K}b_{j}}}{2K+1}$$
+#
+# $$\omega_{1}=\frac{2\pi}{P_{H}}$$
+#
+# $$\omega_{2}=\frac{2\pi}{P_{L}}$$
+#
+# $P_L$ and $P_H$ are the periodicity of the low and high cut-off frequencies. Following Burns and Mitchell's work on US business cycles which suggests cycles last from 1.5 to 8 years, we use $P_L=6$ and $P_H=32$ by default.
+
+bk_cycles = sm.tsa.filters.bkfilter(dta[["infl","unemp"]])
+
+
+# * We lose K observations on both ends. It is suggested to use K=12 for quarterly data.
+
+fig = plt.figure(figsize=(14,10))
+ax = fig.add_subplot(111)
+bk_cycles.plot(ax=ax, style=['r--', 'b-']);
+
+
+#### Christiano-Fitzgerald approximate band-pass filter: Inflation and Unemployment
+
+# The Christiano-Fitzgerald filter is a generalization of BK and can thus also be seen as weighted moving average. However, the CF filter is asymmetric about $t$ as well as using the entire series. The implementation of their filter involves the
+# calculations of the weights in
+#
+# $$y_{t}^{*}=B_{0}y_{t}+B_{1}y_{t+1}+\dots+B_{T-1-t}y_{T-1}+\tilde B_{T-t}y_{T}+B_{1}y_{t-1}+\dots+B_{t-2}y_{2}+\tilde B_{t-1}y_{1}$$
+#
+# for $t=3,4,...,T-2$, where
+#
+# $$B_{j} = \frac{\sin(jb)-\sin(ja)}{\pi j},j\geq1$$
+#
+# $$B_{0} = \frac{b-a}{\pi},a=\frac{2\pi}{P_{u}},b=\frac{2\pi}{P_{L}}$$
+#
+# $\tilde B_{T-t}$ and $\tilde B_{t-1}$ are linear functions of the $B_{j}$'s, and the values for $t=1,2,T-1,$ and $T$ are also calculated in much the same way. $P_{U}$ and $P_{L}$ are as described above with the same interpretation.
+
+# The CF filter is appropriate for series that may follow a random walk.
+
+print(sm.tsa.stattools.adfuller(dta['unemp'])[:3])
+
+
+print(sm.tsa.stattools.adfuller(dta['infl'])[:3])
+
+
+cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl","unemp"]])
+print(cf_cycles.head(10))
+
+
+fig = plt.figure(figsize=(14,10))
+ax = fig.add_subplot(111)
+cf_cycles.plot(ax=ax, style=['r--','b-']);
+
+
+# Filtering assumes *a priori* that business cycles exist. Due to this assumption, many macroeconomic models seek to create models that match the shape of impulse response functions rather than replicating properties of filtered series. See VAR notebook.
diff --git a/examples/python/wls.py b/examples/python/wls.py
new file mode 100644
index 00000000000..ce74a9d20bb
--- /dev/null
+++ b/examples/python/wls.py
@@ -0,0 +1,103 @@
+
+## Weighted Least Squares
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+from statsmodels.sandbox.regression.predstd import wls_prediction_std
+from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)
+np.random.seed(1024)
+
+
+# ## WLS Estimation
+# 
+# ### Artificial data: Heteroscedasticity 2 groups 
+# 
+# Model assumptions:
+# 
+#  * Misspecification: true model is quadratic, estimate only linear
+#  * Independent noise/error term
+#  * Two groups for error variance, low and high variance groups
+
+nsample = 50
+x = np.linspace(0, 20, nsample)
+X = np.column_stack((x, (x - 5)**2))
+X = sm.add_constant(X)
+beta = [5., 0.5, -0.01]
+sig = 0.5
+w = np.ones(nsample)
+w[nsample * 6/10:] = 3
+y_true = np.dot(X, beta)
+e = np.random.normal(size=nsample)
+y = y_true + sig * w * e 
+X = X[:,[0,1]]
+
+
+# ### WLS knowing the true variance ratio of heteroscedasticity
+
+mod_wls = sm.WLS(y, X, weights=1./w)
+res_wls = mod_wls.fit()
+print(res_wls.summary())
+
+
+# ## OLS vs. WLS
+# 
+# Estimate an OLS model for comparison: 
+
+res_ols = sm.OLS(y, X).fit()
+print(res_ols.params)
+print(res_wls.params)
+
+
+# Compare the WLS standard errors to  heteroscedasticity corrected OLS standard errors:
+
+se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se], 
+                [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]])
+se = np.round(se,4)
+colnames = ['x1', 'const']
+rownames = ['WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3']
+tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)
+print(tabl)
+
+
+# Calculate OLS prediction interval:
+
+covb = res_ols.cov_params()
+prediction_var = res_ols.mse_resid + (X * np.dot(covb,X.T).T).sum(1)
+prediction_std = np.sqrt(prediction_var)
+tppf = stats.t.ppf(0.975, res_ols.df_resid)
+
+
+prstd_ols, iv_l_ols, iv_u_ols = wls_prediction_std(res_ols)
+
+
+# Draw a plot to compare predicted values in WLS and OLS:
+
+prstd, iv_l, iv_u = wls_prediction_std(res_wls)
+
+fig, ax = plt.subplots()
+ax.plot(x, y, 'o', label="Data")
+ax.plot(x, y_true, 'b-', label="True")
+# OLS
+ax.plot(x, res_ols.fittedvalues, 'r--')
+ax.plot(x, iv_u_ols, 'r--', label="OLS")
+ax.plot(x, iv_l_ols, 'r--')
+# WLS
+ax.plot(x, res_wls.fittedvalues, 'g--.')
+ax.plot(x, iv_u, 'g--', label="WLS")
+ax.plot(x, iv_l, 'g--')
+ax.legend(loc="best");
+
+
+# ## Feasible Weighted Least Squares (2-stage FWLS)
+
+resid1 = res_ols.resid[w==1.]
+var1 = resid1.var(ddof=int(res_ols.df_model)+1)
+resid2 = res_ols.resid[w!=1.]
+var2 = resid2.var(ddof=int(res_ols.df_model)+1)
+w_est = w.copy()
+w_est[w!=1.] = np.sqrt(var2) / np.sqrt(var1)
+res_fwls = sm.WLS(y, X, 1./w_est).fit()
+print(res_fwls.summary())
+
diff --git a/examples/run_all.py b/examples/run_all.py
index 5bd559f60eb..0c87bfa0ec5 100644
--- a/examples/run_all.py
+++ b/examples/run_all.py
@@ -7,7 +7,8 @@
 uncomment plt.show() to show all plot windows
 
 """
-
+from __future__ import print_function
+from statsmodels.compat import input
 stop_on_error = True
 
 
@@ -33,18 +34,18 @@ def noop(*args):
           This is done mainly to check that they are up to date.
           (y/n) >>> """
 
-    cont = raw_input(msg)
+    cont = input(msg)
     if 'y' in cont.lower():
         for run_all_f in filelist:
             try:
-                print '\n\nExecuting example file', run_all_f
-                print '-----------------------' + '-' * len(run_all_f)
-                execfile(run_all_f)
+                print('\n\nExecuting example file', run_all_f)
+                print('-----------------------' + '-' * len(run_all_f))
+                exec(open(run_all_f).read())
             except:
                 # f might be overwritten in the executed file
-                print '**********************' + '*' * len(run_all_f)
-                print 'ERROR in example file', run_all_f
-                print '**********************' + '*' * len(run_all_f)
+                print('**********************' + '*' * len(run_all_f))
+                print('ERROR in example file', run_all_f)
+                print('**********************' + '*' * len(run_all_f))
                 if stop_on_error:
                     raise
 
diff --git a/setup.py b/setup.py
index 03a616312c8..8565b037b26 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
 """
 
 import os
-from os.path import splitext, basename, join as pjoin
+from os.path import relpath, join as pjoin
 import sys
 import subprocess
 import re
@@ -18,12 +18,6 @@
 # certain easy_install versions
 os.environ["MPLCONFIGDIR"] = "."
 
-# may need to work around setuptools bug by providing a fake Pyrex
-try:
-    import Cython
-    sys.path.insert(0, pjoin(os.path.dirname(__file__), "fake_pyrex"))
-except ImportError:
-    pass
 
 # try bootstrapping setuptools if it doesn't exist
 try:
@@ -42,22 +36,19 @@
 
 setuptools_kwargs = {}
 if sys.version_info[0] >= 3:
-    setuptools_kwargs = {'use_2to3': True,
-                         'zip_safe': False,
-                         #'use_2to3_exclude_fixers': [],
-                         }
+    setuptools_kwargs = {'zip_safe': False}
+
     if not _have_setuptools:
         sys.exit("need setuptools/distribute for Py3k"
                  "\n$ pip install distribute")
-
 else:
     setuptools_kwargs = {
         'install_requires': [],
         'zip_safe': False,
     }
 
-    if not _have_setuptools:
-        setuptools_kwargs = {}
+if not _have_setuptools:
+    setuptools_kwargs = {}
 
 curdir = os.path.abspath(os.path.dirname(__file__))
 README = open(pjoin(curdir, "README.txt")).read()
@@ -76,13 +67,6 @@
 from distutils.command.sdist import sdist
 from distutils.command.build_ext import build_ext as _build_ext
 
-try:
-    from Cython.Distutils import build_ext as _build_ext
-    # from Cython.Distutils import Extension # to get pyrex debugging symbols
-    cython = True
-except ImportError:
-    cython = False
-
 
 class build_ext(_build_ext):
     def build_extensions(self):
@@ -95,9 +79,21 @@ def build_extensions(self):
         _build_ext.build_extensions(self)
 
 
+def generate_cython():
+    cwd = os.path.abspath(os.path.dirname(__file__))
+    print("Cythonizing sources")
+    p = subprocess.call([sys.executable,
+                          os.path.join(cwd, 'tools', 'cythonize.py'),
+                          'statsmodels'],
+                         cwd=cwd)
+    if p != 0:
+        raise RuntimeError("Running cythonize failed!")
+
+
 def strip_rc(version):
     return re.sub(r"rc\d+$", "", version)
 
+
 def check_dependency_versions(min_versions):
     """
     Don't let setuptools do this. It's rude.
@@ -118,7 +114,7 @@ def check_dependency_versions(min_versions):
         except ImportError:
             raise ImportError("statsmodels requires scipy")
     try:
-        from pandas.version import version as pversion
+        from pandas.version import short_version as pversion
     except ImportError:
         raise ImportError("statsmodels requires pandas")
     try:
@@ -137,10 +133,7 @@ def check_dependency_versions(min_versions):
         raise ImportError("Scipy version is %s. Requires >= %s" %
                 (spversion, min_versions['scipy']))
     try:
-        #NOTE: not sure how robust this regex is but it at least allows
-        # double digit version numbering
-        pversion = re.match("\d*\.\d*\.\d*", pversion).group()
-        assert StrictVersion(pversion) >= min_versions['pandas']
+        assert StrictVersion(strip_rc(pversion)) >= min_versions['pandas']
     except AssertionError:
         raise ImportError("Pandas version is %s. Requires >= %s" %
                 (pversion, min_versions['pandas']))
@@ -154,9 +147,9 @@ def check_dependency_versions(min_versions):
 
 
 MAJ = 0
-MIN = 5
+MIN = 6
 REV = 0
-ISRELEASED = True
+ISRELEASED = False
 VERSION = '%d.%d.%d' % (MAJ,MIN,REV)
 
 classifiers = [ 'Development Status :: 4 - Beta',
@@ -234,12 +227,6 @@ def write_version_py(filename=pjoin(curdir, 'statsmodels/version.py')):
         finally:
             a.close()
 
-try:
-    from distutils.command.build_py import build_py_2to3 as build_py
-except ImportError:
-    # 2.x
-    from distutils.command.build_py import build_py
-
 
 class CleanCommand(Command):
     """Custom distutils command to clean the .so and .pyc files."""
@@ -286,36 +273,6 @@ def run(self):
                 pass
 
 
-class CheckSDist(sdist):
-    """Custom sdist that ensures Cython has compiled all pyx files to c."""
-
-    _pyxfiles = ['statsmodels/nonparametric/linbin.pyx',
-                 'statsmodels/nonparametric/_smoothers_lowess.pyx',
-                 'statsmodels/tsa/kalmanf/kalman_loglike.pyx']
-
-    def initialize_options(self):
-        sdist.initialize_options(self)
-
-        '''
-        self._pyxfiles = []
-        for root, dirs, files in os.walk('statsmodels'):
-            for f in files:
-                if f.endswith('.pyx'):
-                    self._pyxfiles.append(pjoin(root, f))
-        '''
-
-    def run(self):
-        if 'cython' in cmdclass:
-            self.run_command('cython')
-        else:
-            for pyxfile in self._pyxfiles:
-                cfile = pyxfile[:-3] + 'c'
-                msg = "C-source file '%s' not found." % (cfile) +\
-                    " Run 'setup.py cython' before sdist."
-                assert os.path.isfile(cfile), msg
-        sdist.run(self)
-
-
 class CheckingBuildExt(build_ext):
     """Subclass build_ext to get clearer report if Cython is necessary."""
 
@@ -333,14 +290,6 @@ def build_extensions(self):
         build_ext.build_extensions(self)
 
 
-class CythonCommand(build_ext):
-    """Custom distutils command subclassed from Cython.Distutils.build_ext
-    to compile pyx->c, and stop there. All this does is override the
-    C-compile method build_extension() with a no-op."""
-    def build_extension(self, ext):
-        pass
-
-
 class DummyBuildSrc(Command):
     """ numpy's build_src command interferes with Cython's build_ext.
     """
@@ -357,79 +306,45 @@ def run(self):
 
 
 cmdclass = {'clean': CleanCommand,
-            'build': build,
-            'sdist': CheckSDist}
+            'build': build}
 
-if cython:
-    suffix = ".pyx"
-    cmdclass["build_ext"] = CheckingBuildExt
-    cmdclass["cython"] = CythonCommand
-else:
-    suffix = ".c"
-    cmdclass["build_src"] = DummyBuildSrc
-    cmdclass["build_ext"] = CheckingBuildExt
+cmdclass["build_src"] = DummyBuildSrc
+cmdclass["build_ext"] = CheckingBuildExt
 
-lib_depends = []
-
-def srcpath(name=None, suffix='.pyx', subdir='src'):
-    return pjoin('statsmodels', subdir, name + suffix)
-
-if suffix == ".pyx":
-    lib_depends = [srcpath(f, suffix=".pyx") for f in lib_depends]
-else:
-    lib_depends = []
-
-common_include = []
 
 # some linux distros require it
-libraries = ['m'] if 'win32' not in sys.platform else []
+#NOTE: we are not currently using this but add it to Extension, if needed.
+# libraries = ['m'] if 'win32' not in sys.platform else []
 
 ext_data = dict(
-        kalman_loglike = {"pyxfile" : "tsa/kalmanf/kalman_loglike",
-                  "depends" : [],
+        kalman_loglike = {"name" : "statsmodels/tsa/kalmanf/kalman_loglike.c",
+                  "depends" : ["statsmodels/tsa/kalmanf/capsule.h"],
                   "sources" : []},
 
-        linbin = {"pyxfile" : "nonparametric/linbin",
+        linbin = {"name" : "statsmodels/nonparametric/linbin.c",
                  "depends" : [],
                  "sources" : []},
-        _smoothers_lowess = {"pyxfile" : "nonparametric/_smoothers_lowess",
+        _smoothers_lowess = {"name" : "statsmodels/nonparametric/_smoothers_lowess.c",
                  "depends" : [],
                  "sources" : []}
         )
 
-def pxd(name):
-    return os.path.abspath(pjoin('pandas', name + '.pxd'))
 
 extensions = []
 for name, data in ext_data.items():
-    sources = [srcpath(data['pyxfile'], suffix=suffix, subdir='')]
-    pxds = [pxd(x) for x in data.get('pxdfiles', [])]
-    destdir = ".".join(os.path.dirname(data["pyxfile"]).split("/"))
-    if suffix == '.pyx' and pxds:
-        sources.extend(pxds)
+    data['sources'] = data.get('sources', []) + [data['name']]
 
-    sources.extend(data.get('sources', []))
+    destdir = ".".join(os.path.dirname(data["name"]).split("/"))
+    data.pop('name')
 
-    include = data.get('include', common_include)
-
-    obj = Extension('statsmodels.%s.%s' % (destdir, name),
-                    sources=sources,
-                    depends=data.get('depends', []),
-                    include_dirs=include)
+    obj = Extension('%s.%s' % (destdir, name), **data)
 
     extensions.append(obj)
 
-if suffix == '.pyx' and 'setuptools' in sys.modules:
-    # undo dumb setuptools bug clobbering .pyx sources back to .c
-    for ext in extensions:
-        if ext.sources[0].endswith('.c'):
-            root, _ = os.path.splitext(ext.sources[0])
-            ext.sources[0] = root + suffix
 
 if _have_setuptools:
     setuptools_kwargs["test_suite"] = "nose.collector"
 
-from os.path import relpath
 
 def get_data_files():
     sep = os.path.sep
@@ -445,12 +360,14 @@ def get_data_files():
                                                                   "*.dta"]})
     # add all the tests and results files
     for r, ds, fs in os.walk(pjoin(curdir, "statsmodels")):
-        if r.endswith('results') and 'sandbox' not in r:
-            data_files.update({relpath(r, start=curdir).replace(sep, ".") : ["*.csv",
-                                                               "*.txt"]})
+        r_ = relpath(r, start=curdir)
+        if r_.endswith('results'):
+            data_files.update({r_.replace(sep, ".") : ["*.csv",
+                                                       "*.txt"]})
 
     return data_files
 
+
 if __name__ == "__main__":
     if os.path.exists('MANIFEST'):
         os.unlink('MANIFEST')
@@ -465,8 +382,11 @@ def get_data_files():
         # 3.3 needs numpy 1.7+
         min_versions.update({"numpy" : "1.7.0b2"})
 
-    check_dependency_versions(min_versions)
-    write_version_py()
+    if not (len(sys.argv) >= 2 and ('--help' in sys.argv[1:] or
+            sys.argv[1] in ('--help-commands', 'egg_info', '--version',
+                            'clean'))):
+        check_dependency_versions(min_versions)
+        write_version_py()
 
     # this adds *.csv and *.dta files in datasets folders
     # and *.csv and *.txt files in test/results folders
@@ -480,19 +400,26 @@ def get_data_files():
     package_data["statsmodels.tsa.vector_ar.tests.results"].append("*.npz")
     # data files that don't follow the tests/results pattern. should fix.
     package_data.update({"statsmodels.stats.tests" : ["*.txt"]})
-    # the next two are in the sdist, but I don't manage to get them installed
-    package_data.update({"statsmodels.stats.libqstrung" :
+
+    package_data.update({"statsmodels.stats.libqsturng" :
                          ["*.r", "*.txt", "*.dat"]})
-    package_data.update({"statsmodels.stats.libqstrung.tests" :
+    package_data.update({"statsmodels.stats.libqsturng.tests" :
                          ["*.csv", "*.dat"]})
     package_data.update({"statsmodels.tsa.vector_ar.data" : ["*.dat"]})
     package_data.update({"statsmodels.tsa.vector_ar.data" : ["*.dat"]})
-    # Why are we installing this stuff?
+    # temporary, until moved:
+    package_data.update({"statsmodels.sandbox.regression.tests" :
+                         ["*.dta", "*.csv"]})
 
     #TODO: deal with this. Not sure if it ever worked for bdists
     #('docs/build/htmlhelp/statsmodelsdoc.chm',
     # 'statsmodels/statsmodelsdoc.chm')
 
+    cwd = os.path.abspath(os.path.dirname(__file__))
+    if not os.path.exists(os.path.join(cwd, 'PKG-INFO')):
+        # Generate Cython sources, unless building from source release
+        generate_cython()
+
     setup(name = DISTNAME,
           version = VERSION,
           maintainer = MAINTAINER,
@@ -508,5 +435,5 @@ def get_data_files():
           cmdclass = cmdclass,
           packages = packages,
           package_data = package_data,
-          include_package_data=True,
+          include_package_data=False,  # True will install all files in repo
           **setuptools_kwargs)
diff --git a/statsmodels/LICENSE.txt b/statsmodels/LICENSE.txt
index 3da6edd043a..96e0269f997 100644
--- a/statsmodels/LICENSE.txt
+++ b/statsmodels/LICENSE.txt
@@ -4,7 +4,7 @@ All rights reserved.
 Copyright (c) 2006-2008 Scipy Developers.
 All rights reserved.
 
-Copyright (c) 2009 Statsmodels Developers.
+Copyright (c) 2009-2013 Statsmodels Developers.
 All rights reserved.
 
 
diff --git a/statsmodels/__init__.py b/statsmodels/__init__.py
index 2974415477a..bbd38c057d4 100644
--- a/statsmodels/__init__.py
+++ b/statsmodels/__init__.py
@@ -1,8 +1,7 @@
 #
 # models - Statistical Models
 #
-from __future__ import with_statement
-
+from __future__ import print_function
 __docformat__ = 'restructuredtext'
 
 #from version import __version__
diff --git a/statsmodels/api.py b/statsmodels/api.py
index fb262689995..c26f166705f 100644
--- a/statsmodels/api.py
+++ b/statsmodels/api.py
@@ -1,21 +1,26 @@
-import iolib, datasets, tools
-from tools.tools import add_constant, categorical
-import regression
+from . import iolib
+from . import datasets
+from . import tools
+from .tools.tools import add_constant, categorical
+from . import regression
 from .regression.linear_model import OLS, GLS, WLS, GLSAR
 from .regression.quantile_regression import QuantReg
+from .regression.mixed_linear_model import MixedLM
 from .genmod.generalized_linear_model import GLM
+from .genmod.generalized_estimating_equations import GEE
 from .genmod import families
-import robust
+from . import robust
 from .robust.robust_linear_model import RLM
-from .discrete.discrete_model import (Poisson, Logit, Probit, MNLogit,
-                                      NegativeBinomial)
+from .discrete.discrete_model import (Poisson, Logit, Probit,
+                                      MNLogit, NegativeBinomial)
 from .tsa import api as tsa
+from .duration.hazard_regression import PHReg
 from .nonparametric import api as nonparametric
-import distributions
-from __init__ import test
+from . import distributions
+from .__init__ import test
 from . import version
-from info import __doc__
-from graphics.gofplots import qqplot, qqplot_2samples, qqline, ProbPlot
+from .info import __doc__
+from .graphics.gofplots import qqplot, qqplot_2samples, qqline, ProbPlot
 from .graphics import api as graphics
 from .stats import api as stats
 from .emplike import api as emplike
@@ -32,7 +37,8 @@
 if os.path.exists(chmpath):
     def open_help(chmpath=chmpath):
         from subprocess import Popen
+
         p = Popen(chmpath, shell=True)
 
 del os
-del chmpath
+del chmpath
\ No newline at end of file
diff --git a/statsmodels/base/_constraints.py b/statsmodels/base/_constraints.py
new file mode 100644
index 00000000000..59b0f508729
--- /dev/null
+++ b/statsmodels/base/_constraints.py
@@ -0,0 +1,308 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu May 15 16:36:05 2014
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+
+
+class TransformRestriction(object):
+    """Transformation for linear constraints `R params = q`
+
+    Note, the transformation from the reduced to the full parameters is an
+    affine and not a linear transformation if q is not zero.
+
+
+    Parameters
+    ----------
+    R : array_like
+        Linear restriction matrix
+    q : arraylike or None
+        values of the linear restrictions
+
+
+    Notes
+    -----
+    The reduced parameters are not sorted with respect to constraints.
+
+    TODO: error checking, eg. inconsistent constraints, how?
+
+    Inconsistent constraints will raise an exception in the calculation of
+    the constant or offset. However, homogeneous constraints, where q=0, will
+    can have a solution where the relevant parameters are constraint to be
+    zero, as in the following example::
+
+        b1 + b2 = 0 and b1 + 2*b2 = 0, implies that b2 = 0.
+
+    The transformation applied from full to reduced parameter space does not
+    raise and exception if the constraint doesn't hold.
+    TODO: maybe change this, what's the behavior in this case?
+
+
+    The `reduce` transform is applied to the array of explanatory variables,
+    `exog`, when transforming a linear model to impose the constraints.
+
+    """
+
+    def __init__(self, R, q=None):
+
+        # The calculations are based on Stata manual for makecns
+        R = self.R = np.atleast_2d(R)
+        if q is not None:
+            q = self.q = np.asarray(q)
+
+        k_constr, k_vars = R.shape
+        self.k_constr, self.k_vars = k_constr, k_vars
+        self.k_unconstr = k_vars - k_constr
+
+        m = np.eye(k_vars) - R.T.dot(np.linalg.pinv(R).T)
+        evals, evecs = np.linalg.eigh(m)
+
+        # This normalizes the transformation so the larges element is 1.
+        # It makes it easier to interpret simple restrictions, e.g. b1 + b2 = 0
+        # TODO: make this work, there is something wrong, does not round-trip
+        #       need to adjust constant
+        #evecs_maxabs = np.max(np.abs(evecs), 0)
+        #evecs = evecs / evecs_maxabs
+
+        self.evals = evals
+        self.evecs = evecs # temporarily attach as attribute
+        L = self.L = evecs[:, :k_constr]
+        self.transf_mat = evecs[:, k_constr:]
+
+        if q is not None:
+            # use solve instead of inv
+            #self.constant = q.T.dot(np.linalg.inv(L.T.dot(R.T)).dot(L.T))
+            try:
+                self.constant = q.T.dot(np.linalg.solve(L.T.dot(R.T), L.T))
+            except np.linalg.linalg.LinAlgError as e:
+                raise ValueError('possibly inconsistent constraints. error '
+                                 'generated by\n%r' % (e, ))
+        else:
+            self.constant = 0
+
+    def expand(self, params_reduced):
+        """transform from the reduced to the full parameter space
+
+        Parameters
+        ----------
+        params_reduced : array_like
+            parameters in the transformed space
+
+        Returns
+        -------
+        params : array_like
+            parameters in the original space
+
+        Notes
+        -----
+        If the restriction is not homogeneous, i.e. q is not equal to zero,
+        then this is an affine transform.
+
+        """
+        params_reduced = np.asarray(params_reduced)
+        return self.transf_mat.dot(params_reduced.T).T + self.constant
+
+    def reduce(self, params):
+        """transform from the full to the reduced parameter space
+
+        Parameters
+        ----------
+        params : array_like
+            parameters or data in the original space
+
+        Returns
+        -------
+        params_reduced : array_like
+            parameters in the transformed space
+
+        This transform can be applied to the original parameters as well
+        as to the data. If params is 2-d, then each row is transformed.
+
+        """
+        params = np.asarray(params)
+        return params.dot(self.transf_mat)
+
+
+
+def transform_params_constraint(params, Sinv, R, q):
+    """find the parameters that statisfy linear constraint from unconstraint
+
+    The linear constraint R params = q is imposed.
+
+    Parameters
+    ----------
+    params : array_like
+        unconstraint parameters
+    Sinv : ndarray, 2d, symmetric
+        covariance matrix of the parameter estimate
+    R : ndarray, 2d
+        constraint matrix
+    q : ndarray, 1d
+        values of the constraint
+
+    Returns
+    -------
+    params_constraint : ndarray
+        parameters of the same length as params satisfying the constraint
+
+    Notes
+    -----
+    This is the exact formula for OLS and other linear models. It will be
+    a local approximation for nonlinear models.
+
+    TODO: Is Sinv always the covariance matrix?
+    In the linear case it can be (X'X)^{-1} or sigmahat^2 (X'X)^{-1}.
+
+    My guess is that this is the point in the subspace that satisfies
+    the constraint that has minimum Mahalanobis distance. Proof ?
+
+    """
+
+    rsr = R.dot(Sinv).dot(R.T)
+
+    reduction = Sinv.dot(R.T).dot(np.linalg.solve(rsr, R.dot(params) - q))
+    return params - reduction
+
+
+def fit_constrained(model, constraint_matrix, constraint_values,
+                    start_params=None, fit_kwds=None):
+    # note: self is model instance
+    """fit model subject to linear equality constraints
+
+    The constraints are of the form   `R params = q`
+    where R is the constraint_matrix and q is the vector of constraint_values.
+
+    The estimation creates a new model with transformed design matrix,
+    exog, and converts the results back to the original parameterization.
+
+
+    Parameters
+    ----------
+    model: model instance
+        An instance of a model, see limitations in Notes section
+    constraint_matrix : array_like, 2D
+        This is R in the linear equality constraint `R params = q`.
+        The number of columns needs to be the same as the number of columns
+        in exog.
+    constraint_values :
+        This is `q` in the linear equality constraint `R params = q`
+        If it is a tuple, then the constraint needs to be given by two
+        arrays (constraint_matrix, constraint_value), i.e. (R, q).
+        Otherwise, the constraints can be given as strings or list of
+        strings.
+        see t_test for details
+    start_params : None or array_like
+        starting values for the optimization. `start_params` needs to be
+        given in the original parameter space and are internally
+        transformed.
+    **fit_kwds : keyword arguments
+        fit_kwds are used in the optimization of the transformed model.
+
+    Returns
+    -------
+    params : ndarray ?
+        estimated parameters (in the original parameterization
+    cov_params : ndarray
+        covariance matrix of the parameter estimates. This is a reverse
+        transformation of the covariance matrix of the transformed model given
+        by `cov_params()`
+        Note: `fit_kwds` can affect the choice of covariance, e.g. by
+        specifying `cov_type`, which will be reflected in the returned
+        covariance.
+    res_constr : results instance
+        This is the results instance for the created transformed model.
+
+
+    Notes
+    -----
+    Limitations:
+
+    Models where the number of parameters is different from the number of
+    columns of exog are not yet supported.
+
+    Requires a model that implement an offset option.
+
+
+    """
+    self = model   # internal alias, used for methods
+    if fit_kwds is None:
+        fit_kwds = {}
+
+    R, q = constraint_matrix, constraint_values
+    endog, exog = self.endog, self.exog
+
+    transf = TransformRestriction(R, q)
+
+    exogp_st = transf.reduce(exog)
+
+    offset = exog.dot(transf.constant.squeeze())
+    if hasattr(self, 'offset'):
+        offset += self.offset
+
+    if start_params is not None:
+        start_params =  transf.reduce(start_params)
+
+    #need copy, because we don't want to change it, we don't need deepcopy
+    import copy
+    init_kwds = copy.copy(self._get_init_kwds())
+    del init_kwds['offset']  # TODO: refactor to combine with above or offset_all
+
+    # using offset as keywords is not supported in all modules
+    mod_constr = self.__class__(endog, exogp_st, offset=offset, **init_kwds)
+    res_constr = mod_constr.fit(start_params=start_params, **fit_kwds)
+    params_orig = transf.expand(res_constr.params).squeeze()
+    cov_params = transf.transf_mat.dot(res_constr.cov_params()).dot(transf.transf_mat.T)
+
+    return params_orig, cov_params, res_constr
+
+
+def fit_constrained_wrap(model, constraints, start_params=None, **fit_kwds):
+    """fit_constraint that returns a results instance
+
+    This is a development version for fit_constrained methods or
+    fit_constrained as standalone function.
+
+    It will not work correctly for all models because creating a new
+    results instance is not standardized for use outside the `fit` methods,
+    and might need adjustements for this.
+
+    This is the prototype for the fit_constrained method that has been added
+    to Poisson and GLM.
+
+    """
+
+    self = model  # alias for use as method
+
+    #constraints = (R, q)
+    # TODO: temporary trailing underscore to not overwrite the monkey
+    #       patched version
+    # TODO: decide whether to move the imports
+    from patsy import DesignInfo
+    # we need this import if we copy it to a different module
+    #from statsmodels.base._constraints import fit_constrained
+
+    # same pattern as in base.LikelihoodModel.t_test
+    lc = DesignInfo(self.exog_names).linear_constraint(constraints)
+    R, q = lc.coefs, lc.constants
+
+    # TODO: add start_params option, need access to tranformation
+    #       fit_constrained needs to do the transformation
+    params, cov, res_constr = fit_constrained(self, R, q,
+                                              start_params=start_params,
+                                              fit_kwds=fit_kwds)
+    #create dummy results Instance, TODO: wire up properly
+    res = self.fit(start_params=params, maxiter=0) # we get a wrapper back
+    res._results.params = params
+    res._results.normalized_cov_params = cov
+    k_constr = len(q)
+    res._results.df_resid += k_constr
+    res._results.df_model -= k_constr
+    res._results.constraints = lc
+    res._results.k_constr = k_constr
+    res._results.results_constrained = res_constr
+    return res
diff --git a/statsmodels/base/_penalties.py b/statsmodels/base/_penalties.py
new file mode 100644
index 00000000000..1bea49fb61f
--- /dev/null
+++ b/statsmodels/base/_penalties.py
@@ -0,0 +1,176 @@
+"""
+A collection of smooth penalty functions.
+
+Penalties on vectors take a vector argument and return a scalar
+penalty.  The gradient of the penalty is a vector with the same shape
+as the input value.
+
+Penalties on covariance matrices take two arguments: the matrix and
+its inverse, both in unpacked (square) form.  The returned penalty is
+a scalar, and the gradient is returned as a vector that contains the
+gradient with respect to the free elements in the lower triangle of
+the covariance matrix.
+
+All penalties are subtracted from the log-likelihood, so greater
+penalty values correspond to a greater degree of penalization.
+
+The penaties should be smooth so that they can be subtracted from log
+likelihood functions and optimized using standard methods (i.e. L1
+penalties do not belong here).
+"""
+
+import numpy as np
+
+class Penalty(object):
+    """
+    A class for representing a scalar-value penalty.
+
+    Parameters:
+    -----------
+    wts : array-like
+        A vector of weights that determines the weight of the penalty
+        for each parameter.
+
+
+    Notes:
+    ------
+    The class has a member called `alpha` that scales the weights.
+    """
+
+    def __init__(self, wts):
+        self.wts = wts
+        self.alpha = 1.
+
+    def func(self, params):
+        """
+        A penalty function on a vector of parameters.
+
+        Parameters:
+        -----------
+        params : array-like
+            A vector of parameters.
+
+        Returns:
+        --------
+        A scalar penaty value; greater values imply greater
+        penalization.
+        """
+        raise NotImplementedError
+
+    def grad(self, params):
+        """
+        The gradient of a penalty function.
+
+        Parameters:
+        -----------
+        params : array-like
+            A vector of parameters
+
+        Returns:
+        --------
+        The gradient of the penalty with respect to each element in
+        `params`.
+        """
+        raise NotImplementedError
+
+
+class L2(Penalty):
+    """
+    The L2 (ridge) penalty.
+    """
+
+    def __init__(self, wts=None):
+        if wts is None:
+            self.wts = 1.
+        else:
+            self.wts = wts
+        self.alpha = 1.
+
+    def func(self, params):
+        return np.sum(self.wts * self.alpha * params**2)
+
+    def grad(self, params):
+        return 2 * self.wts * self.alpha * params
+
+
+class PseudoHuber(Penalty):
+    """
+    The pseudo-Huber penalty.
+    """
+
+    def __init__(self, dlt, wts=None):
+        self.dlt = dlt
+        if wts is None:
+            self.wts = 1.
+        else:
+            self.wts = wts
+        self.alpha = 1.
+
+    def func(self, params):
+        v = np.sqrt(1 + (params / self.dlt)**2)
+        v -= 1
+        v *= self.dlt**2
+        return np.sum(self.wts * self.alpha * v)
+
+    def grad(self, params):
+        v = np.sqrt(1 + (params / self.dlt)**2)
+        return params * self.wts * self.alpha / v
+
+
+class CovariancePenalty(object):
+
+    def __init__(self, wt):
+        self.wt = wt
+
+    def func(self, mat, mat_inv):
+        """
+        Parameters:
+        -----------
+        mat : square matrix
+            The matrix to be penalized.
+        mat_inv : square matrix
+            The inverse of `mat`.
+
+        Returns:
+        --------
+        A scalar penalty value
+        """
+        raise NotImplementedError
+
+    def grad(self, mat, mat_inv):
+        """
+        Parameters:
+        -----------
+        mat : square matrix
+            The matrix to be penalized.
+        mat_inv : square matrix
+            The inverse of `mat`.
+
+        Returns:
+        --------
+        A vector containing the gradient of the penalty
+        with respect to each element in the lower triangle
+        of `mat`.
+        """
+        raise NotImplementedError
+
+
+class PSD(CovariancePenalty):
+    """
+    A penalty that converges to +infinity as the argument matrix
+    approaches the boundary of the domain of symmetric, positive
+    definite matrices.
+    """
+
+    def func(self, mat, mat_inv):
+        try:
+            cy = np.linalg.cholesky(mat)
+        except np.linalg.LinAlgError:
+            return np.inf
+        return -2 * self.wt * np.sum(np.log(np.diag(cy)))
+
+    def grad(self, mat, mat_inv):
+        cy = mat_inv.copy()
+        cy = 2*cy - np.diag(np.diag(cy))
+        i,j = np.tril_indices(mat.shape[0])
+        return -self.wt * cy[i,j]
diff --git a/statsmodels/base/data.py b/statsmodels/base/data.py
index 6becf371326..729fe49bd8e 100644
--- a/statsmodels/base/data.py
+++ b/statsmodels/base/data.py
@@ -2,27 +2,21 @@
 Base tools for handling various kinds of data structures, attaching metadata to
 results, and doing data cleaning
 """
-
+from statsmodels.compat.python import reduce, iteritems, lmap, zip, range
+from statsmodels.compat.numpy import np_matrix_rank
 import numpy as np
 from pandas import DataFrame, Series, TimeSeries, isnull
-from statsmodels.tools.decorators import (resettable_cache,
-                cache_readonly, cache_writable)
+from statsmodels.tools.decorators import (resettable_cache, cache_readonly,
+                                          cache_writable)
 import statsmodels.tools.data as data_util
+from statsmodels.tools.sm_exceptions import MissingDataError
 
-try:
-    reduce
-    pass
-except NameError:
-    #python 3.2
-    from functools import reduce
-
-class MissingDataError(Exception):
-    pass
 
 def _asarray_2dcolumns(x):
     if np.asarray(x).ndim > 1 and np.asarray(x).squeeze().ndim == 1:
         return
 
+
 def _asarray_2d_null_rows(x):
     """
     Makes sure input is an array and is 2d. Makes sure output is 2d. True
@@ -32,8 +26,8 @@ def _asarray_2d_null_rows(x):
     #input
     x = np.asarray(x)
     if x.ndim == 1:
-        x = x[:,None]
-    return np.any(isnull(x), axis=1)[:,None]
+        x = x[:, None]
+    return np.any(isnull(x), axis=1)[:, None]
 
 
 def _nan_rows(*arrs):
@@ -44,6 +38,7 @@ def _nan_rows(*arrs):
     """
     if len(arrs) == 1:
         arrs += ([[False]],)
+
     def _nan_row_maybe_two_inputs(x, y):
         # check for dtype bc dataframe has dtypes
         x_is_boolean_array = hasattr(x, 'dtype') and x.dtype == bool and x
@@ -51,24 +46,25 @@ def _nan_row_maybe_two_inputs(x, y):
                              (x_is_boolean_array | _asarray_2d_null_rows(y)))
     return reduce(_nan_row_maybe_two_inputs, arrs).squeeze()
 
+
 class ModelData(object):
     """
     Class responsible for handling input data and extracting metadata into the
     appropriate form
     """
     def __init__(self, endog, exog=None, missing='none', hasconst=None,
-                       **kwargs):
+                 **kwargs):
         if missing != 'none':
-            arrays, nan_idx = self._handle_missing(endog, exog, missing,
-                                                       **kwargs)
+            arrays, nan_idx = self.handle_missing(endog, exog, missing,
+                                                  **kwargs)
             self.missing_row_idx = nan_idx
-            self.__dict__.update(arrays) # attach all the data arrays
+            self.__dict__.update(arrays)  # attach all the data arrays
             self.orig_endog = self.endog
             self.orig_exog = self.exog
             self.endog, self.exog = self._convert_endog_exog(self.endog,
-                    self.exog)
+                                                             self.exog)
         else:
-            self.__dict__.update(kwargs) # attach the extra arrays anyway
+            self.__dict__.update(kwargs)  # attach the extra arrays anyway
             self.orig_endog = endog
             self.orig_exog = exog
             self.endog, self.exog = self._convert_endog_exog(endog, exog)
@@ -86,25 +82,69 @@ def _handle_constant(self, hasconst):
             else:
                 self.k_constant = 0
                 self.const_idx = None
+        elif self.exog is None:
+            self.const_idx = None
+            self.k_constant = 0
         else:
-            try: # to detect where the constant is
-                const_idx = np.where(self.exog.var(axis = 0) == 0)[0].squeeze()
-                self.k_constant = const_idx.size
-                if self.k_constant > 1:
-                    raise ValueError("More than one constant detected.")
-                else:
+            # detect where the constant is
+            check_implicit = False
+            const_idx = np.where(self.exog.ptp(axis=0) == 0)[0].squeeze()
+            self.k_constant = const_idx.size
+
+            if self.k_constant == 1:
+                if self.exog[:, const_idx].mean() != 0:
                     self.const_idx = const_idx
-            except: # should be an index error but who knows, means no const
+                else:
+                    # we only have a zero column and no other constant
+                    check_implicit = True
+            elif self.k_constant > 1:
+                # we have more than one constant column
+                # look for ones
+                values = []  # keep values if we need != 0
+                for idx in const_idx:
+                    value = self.exog[:, idx].mean()
+                    if value == 1:
+                        self.k_constant = 1
+                        self.const_idx = idx
+                        break
+                    values.append(value)
+                else:
+                    # we didn't break, no column of ones
+                    pos = (np.array(values) != 0)
+                    if pos.any():
+                        # take the first nonzero column
+                        self.k_constant = 1
+                        self.const_idx = const_idx[pos.argmax()]
+                    else:
+                        # only zero columns
+                        check_implicit = True
+            elif self.k_constant == 0:
+                check_implicit = True
+            else:
+                # shouldn't be here
+                pass
+
+            if check_implicit:
+                # look for implicit constant
+                # Compute rank of augmented matrix
+                augmented_exog = np.column_stack(
+                            (np.ones(self.exog.shape[0]), self.exog))
+                rank_augm = np_matrix_rank(augmented_exog)
+                rank_orig = np_matrix_rank(self.exog)
+                self.k_constant = int(rank_orig == rank_augm)
                 self.const_idx = None
-                self.k_constant = 0
 
-    def _drop_nans(self, x, nan_mask):
+
+    @classmethod
+    def _drop_nans(cls, x, nan_mask):
         return x[nan_mask]
 
-    def _drop_nans_2d(self, x, nan_mask):
+    @classmethod
+    def _drop_nans_2d(cls, x, nan_mask):
         return x[nan_mask][:, nan_mask]
 
-    def _handle_missing(self, endog, exog, missing, **kwargs):
+    @classmethod
+    def handle_missing(cls, endog, exog, missing, **kwargs):
         """
         This returns a dictionary with keys endog, exog and the keys of
         kwargs. It preserves Nones.
@@ -123,7 +163,7 @@ def _handle_missing(self, endog, exog, missing, **kwargs):
         combined_2d = ()
         combined_2d_names = []
         if len(kwargs):
-            for key, value_array in kwargs.iteritems():
+            for key, value_array in iteritems(kwargs):
                 if value_array is None or value_array.ndim == 0:
                     none_array_names += [key]
                     continue
@@ -141,27 +181,36 @@ def _handle_missing(self, endog, exog, missing, **kwargs):
                     combined_2d_names += [key]
                 else:
                     raise ValueError("Arrays with more than 2 dimensions "
-                            "aren't yet handled")
+                                     "aren't yet handled")
 
         nan_mask = _nan_rows(*combined)
         if combined_2d:
-            nan_mask = _nan_rows(*(nan_mask[:,None],) + combined_2d)
+            nan_mask = _nan_rows(*(nan_mask[:, None],) + combined_2d)
 
-        if missing == 'raise' and np.any(nan_mask):
+        if not np.any(nan_mask):  # no missing don't do anything
+            combined = dict(zip(combined_names, combined))
+            if combined_2d:
+                combined.update(dict(zip(combined_2d_names, combined_2d)))
+            if none_array_names:
+                combined.update(dict(zip(none_array_names,
+                                         [None] * len(none_array_names))))
+
+            return combined, []
+
+        elif missing == 'raise':
             raise MissingDataError("NaNs were encountered in the data")
 
         elif missing == 'drop':
             nan_mask = ~nan_mask
-            drop_nans = lambda x : self._drop_nans(x, nan_mask)
-            drop_nans_2d = lambda x : self._drop_nans_2d(x, nan_mask)
-            combined = dict(zip(combined_names, map(drop_nans, combined)))
+            drop_nans = lambda x: cls._drop_nans(x, nan_mask)
+            drop_nans_2d = lambda x: cls._drop_nans_2d(x, nan_mask)
+            combined = dict(zip(combined_names, lmap(drop_nans, combined)))
             if combined_2d:
                 combined.update(dict(zip(combined_2d_names,
-                                         map(drop_nans_2d, combined_2d))))
+                                         lmap(drop_nans_2d, combined_2d))))
             if none_array_names:
                 combined.update(dict(zip(none_array_names,
-                                         [None]*len(none_array_names)
-                                         )))
+                                         [None] * len(none_array_names))))
             return combined, np.where(~nan_mask)[0].tolist()
         else:
             raise ValueError("missing option %s not understood" % missing)
@@ -235,7 +284,7 @@ def _get_yarr(self, endog):
         if data_util._is_structured_ndarray(endog):
             endog = data_util.struct_to_ndarray(endog)
         endog = np.asarray(endog)
-        if len(endog) == 1: # never squeeze to a scalar
+        if len(endog) == 1:  # never squeeze to a scalar
             if endog.ndim == 1:
                 return endog
             elif endog.ndim > 1:
@@ -287,35 +336,37 @@ def attach_rows(self, result):
     def attach_dates(self, result):
         return result
 
+
 class PatsyData(ModelData):
     def _get_names(self, arr):
         return arr.design_info.column_names
 
+
 class PandasData(ModelData):
     """
     Data handling class which knows how to reattach pandas metadata to model
     results
     """
-    def _drop_nans(self, x, nan_mask):
+    @classmethod
+    def _drop_nans(cls, x, nan_mask):
         if hasattr(x, 'ix'):
             return x.ix[nan_mask]
-        else: # extra arguments could be plain ndarrays
-            return super(PandasData, self)._drop_nans(x, nan_mask)
+        else:  # extra arguments could be plain ndarrays
+            return super(PandasData, cls)._drop_nans(x, nan_mask)
 
-    def _drop_nans_2d(self, x, nan_mask):
+    @classmethod
+    def _drop_nans_2d(cls, x, nan_mask):
         if hasattr(x, 'ix'):
             return x.ix[nan_mask].ix[:, nan_mask]
         else:  # extra arguments could be plain ndarrays
-            return super(PandasData, self)._drop_nans_2d(x, nan_mask)
+            return super(PandasData, cls)._drop_nans_2d(x, nan_mask)
 
     def _check_integrity(self):
-        try:
-            endog, exog = self.orig_endog, self.orig_exog
-            # exog can be None and we could be upcasting one or the other
-            if exog is not None and (hasattr(endog, 'index') and
-                    hasattr(exog, 'index')):
-                assert self.orig_endog.index.equals(self.orig_exog.index)
-        except AssertionError:
+        endog, exog = self.orig_endog, self.orig_exog
+        # exog can be None and we could be upcasting one or the other
+        if (exog is not None and
+                (hasattr(endog, 'index') and hasattr(exog, 'index')) and
+                not self.orig_endog.index.equals(self.orig_exog.index)):
             raise ValueError("The indices for endog and exog are not aligned")
         super(PandasData, self)._check_integrity()
 
@@ -333,7 +384,7 @@ def attach_columns(self, result):
         # if it needs a squeeze, the bug is elsewhere
         if result.ndim <= 1:
             return Series(result, index=self.xnames)
-        else: # for e.g., confidence intervals
+        else:  # for e.g., confidence intervals
             return DataFrame(result, index=self.xnames)
 
     def attach_columns_eq(self, result):
@@ -350,44 +401,50 @@ def attach_rows(self, result):
         # at the front, for AR lags, for example
         if result.squeeze().ndim == 1:
             return Series(result, index=self.row_labels[-len(result):])
-        else: # this is for VAR results, may not be general enough
+        else:  # this is for VAR results, may not be general enough
             return DataFrame(result, index=self.row_labels[-len(result):],
-                                columns=self.ynames)
+                             columns=self.ynames)
 
     def attach_dates(self, result):
         return TimeSeries(result, index=self.predict_dates)
 
+
 def _make_endog_names(endog):
     if endog.ndim == 1 or endog.shape[1] == 1:
         ynames = ['y']
-    else: # for VAR
+    else:  # for VAR
         ynames = ['y%d' % (i+1) for i in range(endog.shape[1])]
 
     return ynames
 
+
 def _make_exog_names(exog):
     exog_var = exog.var(0)
     if (exog_var == 0).any():
         # assumes one constant in first or last position
         # avoid exception if more than one constant
         const_idx = exog_var.argmin()
-        exog_names = ['x%d' % i for i in range(1,exog.shape[1])]
+        exog_names = ['x%d' % i for i in range(1, exog.shape[1])]
         exog_names.insert(const_idx, 'const')
     else:
-        exog_names = ['x%d' % i for i in range(1,exog.shape[1]+1)]
+        exog_names = ['x%d' % i for i in range(1, exog.shape[1]+1)]
 
     return exog_names
 
-def handle_data(endog, exog, missing='none', hasconst=None, **kwargs):
+
+def handle_missing(endog, exog=None, missing='none', **kwargs):
+    klass = handle_data_class_factory(endog, exog)
+    if missing == 'none':
+        ret_dict = dict(endog=endog, exog=exog)
+        ret_dict.update(kwargs)
+        return ret_dict, None
+    return klass.handle_missing(endog, exog, missing=missing, **kwargs)
+
+
+def handle_data_class_factory(endog, exog):
     """
     Given inputs
     """
-    # deal with lists and tuples up-front
-    if isinstance(endog, (list, tuple)):
-        endog = np.asarray(endog)
-    if isinstance(exog, (list, tuple)):
-        exog = np.asarray(exog)
-
     if data_util._is_using_ndarray_type(endog, exog):
         klass = ModelData
     elif data_util._is_using_pandas(endog, exog):
@@ -400,5 +457,16 @@ def handle_data(endog, exog, missing='none', hasconst=None, **kwargs):
     else:
         raise ValueError('unrecognized data structures: %s / %s' %
                          (type(endog), type(exog)))
+    return klass
+
+
+def handle_data(endog, exog, missing='none', hasconst=None, **kwargs):
+    # deal with lists and tuples up-front
+    if isinstance(endog, (list, tuple)):
+        endog = np.asarray(endog)
+    if isinstance(exog, (list, tuple)):
+        exog = np.asarray(exog)
 
-    return klass(endog, exog=exog, missing=missing, hasconst=hasconst, **kwargs)
+    klass = handle_data_class_factory(endog, exog)
+    return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
+                 **kwargs)
diff --git a/statsmodels/base/l1_solvers_common.py b/statsmodels/base/l1_solvers_common.py
index c9295601155..2d70eabcf87 100644
--- a/statsmodels/base/l1_solvers_common.py
+++ b/statsmodels/base/l1_solvers_common.py
@@ -1,8 +1,9 @@
 """
 Holds common functions for l1 solvers.
 """
+from __future__ import print_function
 import numpy as np
-
+from statsmodels.compat.python import range
 
 def qc_results(params, alpha, score, qc_tol, qc_verbose=False):
     """
@@ -47,7 +48,7 @@ def qc_results(params, alpha, score, qc_tol, qc_verbose=False):
     k_params = len(params)
 
     passed_array = np.array([True] * k_params)
-    for i in xrange(k_params):
+    for i in range(k_params):
         if alpha[i] > 0:
             # If |fprime| is too big, then something went wrong
             if (abs(fprime[i]) - alpha[i]) / alpha[i] > qc_tol:
@@ -63,7 +64,7 @@ def qc_results(params, alpha, score, qc_tol, qc_verbose=False):
             ', decreasing alpha, or switch solvers'
         if qc_verbose:
             message += _get_verbose_addon(qc_dict)
-        print message
+        print(message)
 
     return passed
 
@@ -79,7 +80,7 @@ def _get_verbose_addon(qc_dict):
     addon += '\n|%-10s|%-10s|%-10s|%-10s|' % (
         'passed', 'alpha', 'fprime', 'param')
     addon += '\n--------------------------------------------'
-    for i in xrange(len(alpha)):
+    for i in range(len(alpha)):
         addon += '\n|%-10s|%-10.3e|%-10.3e|%-10.3e|' % (
                 passed_array[i], alpha[i], fprime[i], params[i])
     return addon
@@ -133,18 +134,18 @@ def do_trim_params(params, k_params, alpha, score, passed, trim_mode,
     if trim_mode == 'off':
         trimmed = np.array([False] * k_params)
     elif trim_mode == 'auto' and not passed:
-        print "Could not trim params automatically due to failed QC "\
-            "check.  Trimming using trim_mode == 'size' will still work."
+        print("Could not trim params automatically due to failed QC "
+              "check.  Trimming using trim_mode == 'size' will still work.")
         trimmed = np.array([False] * k_params)
     elif trim_mode == 'auto' and passed:
         fprime = score(params)
-        for i in xrange(k_params):
+        for i in range(k_params):
             if alpha[i] != 0:
                 if (alpha[i] - abs(fprime[i])) / alpha[i] > auto_trim_tol:
                     params[i] = 0.0
                     trimmed[i] = True
     elif trim_mode == 'size':
-        for i in xrange(k_params):
+        for i in range(k_params):
             if alpha[i] != 0:
                 if abs(params[i]) < size_trim_tol:
                     params[i] = 0.0
diff --git a/statsmodels/base/model.py b/statsmodels/base/model.py
index 9a8ae599e49..c3609bef077 100644
--- a/statsmodels/base/model.py
+++ b/statsmodels/base/model.py
@@ -1,36 +1,42 @@
+from __future__ import print_function
+from statsmodels.compat.python import iterkeys, lzip, range, reduce
 import numpy as np
-from scipy import optimize, stats
+from scipy import stats
 from statsmodels.base.data import handle_data
 from statsmodels.tools.tools import recipr, nan_dot
 from statsmodels.stats.contrast import ContrastResults
-from statsmodels.tools.decorators import (resettable_cache,
-                                                  cache_readonly)
+from statsmodels.tools.decorators import resettable_cache, cache_readonly
 import statsmodels.base.wrapper as wrap
 from statsmodels.tools.numdiff import approx_fprime
 from statsmodels.formula import handle_formula_data
+from statsmodels.compat.numpy import np_matrix_rank
+from statsmodels.base.optimizer import Optimizer
 
 
-_model_params_doc = """\
+_model_params_doc = """
     Parameters
     ----------
     endog : array-like
         1-d endogenous response variable. The dependent variable.
     exog : array-like
         A nobs x k array where `nobs` is the number of observations and `k`
-        is the number of regressors. An interecept is not included by default
-        and should be added by the user. See `statsmodels.tools.add_constant`."""
+        is the number of regressors. An intercept is not included by default
+        and should be added by the user. See
+        :func:`statsmodels.tools.add_constant`."""
 
-_missing_param_doc = """missing : str
+_missing_param_doc = """\
+missing : str
         Available options are 'none', 'drop', and 'raise'. If 'none', no nan
         checking is done. If 'drop', any observations with nans are dropped.
-        If 'raise', an error is raised. Default is 'none.'
-        """
-_extra_param_doc = """hasconst : None or bool
+        If 'raise', an error is raised. Default is 'none.'"""
+_extra_param_doc = """
+    hasconst : None or bool
         Indicates whether the RHS includes a user-supplied constant. If True,
         a constant is not checked for and k_constant is set to 1 and all
         result statistics are calculated as if a constant is present. If
         False, a constant is not checked for and k_constant is set to 0.
-        """
+"""
+
 
 class Model(object):
     __doc__ = """
@@ -45,21 +51,45 @@ class Model(object):
     already stored in numpy arrays and it is changed then `endog` and `exog`
     will change as well.
     """ % {'params_doc' : _model_params_doc,
-            'extra_params_doc' : _missing_param_doc + _extra_param_doc}
+           'extra_params_doc' : _missing_param_doc + _extra_param_doc}
+
     def __init__(self, endog, exog=None, **kwargs):
         missing = kwargs.pop('missing', 'none')
         hasconst = kwargs.pop('hasconst', None)
-        self.data = handle_data(endog, exog, missing, hasconst, **kwargs)
+        self.data = self._handle_data(endog, exog, missing, hasconst,
+                                      **kwargs)
         self.k_constant = self.data.k_constant
         self.exog = self.data.exog
         self.endog = self.data.endog
-        # kwargs arrays could have changed, easier to just attach here
-        for key in kwargs:
-            # pop so we don't start keeping all these twice or references
-            setattr(self, key, self.data.__dict__.pop(key))
         self._data_attr = []
         self._data_attr.extend(['exog', 'endog', 'data.exog', 'data.endog',
                                 'data.orig_endog', 'data.orig_exog'])
+        # store keys for extras if we need to recreate model instance
+        # we don't need 'missing', maybe we need 'hasconst'
+        self._init_keys = list(kwargs.keys())
+        if hasconst is not None:
+            self._init_keys.append('hasconst')
+
+
+    def _get_init_kwds(self):
+        """return dictionary with extra keys used in model.__init__
+        """
+        kwds = dict(((key, getattr(self, key, None))
+                         for key in self._init_keys))
+
+        return kwds
+
+
+    def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
+        data = handle_data(endog, exog, missing, hasconst, **kwargs)
+        # kwargs arrays could have changed, easier to just attach here
+        for key in kwargs:
+            # pop so we don't start keeping all these twice or references
+            try:
+                setattr(self, key, data.__dict__.pop(key))
+            except KeyError:  # panel already pops keys in data handling
+                pass
+        return data
 
     @classmethod
     def from_formula(cls, formula, data, subset=None, *args, **kwargs):
@@ -94,7 +124,7 @@ def from_formula(cls, formula, data, subset=None, *args, **kwargs):
         #TODO: provide a docs template for args/kwargs from child models
         #TODO: subset could use syntax. issue #469.
         if subset is not None:
-            data= data.ix[subset]
+            data = data.ix[subset]
         endog, exog = handle_formula_data(data, None, formula)
         mod = cls(endog, exog, *args, **kwargs)
         mod.formula = formula
@@ -103,7 +133,6 @@ def from_formula(cls, formula, data, subset=None, *args, **kwargs):
         mod.data.frame = data
         return mod
 
-
     @property
     def endog_names(self):
         return self.data.ynames
@@ -186,33 +215,39 @@ def fit(self, start_params=None, method='newton', maxiter=100,
         start_params : array-like, optional
             Initial guess of the solution for the loglikelihood maximization.
             The default is an array of zeros.
-        method : str {'newton','nm','bfgs','powell','cg','ncg','basinhopping'}
-            Method can be 'newton' for Newton-Raphson, 'nm' for Nelder-Mead,
-            'bfgs' for Broyden-Fletcher-Goldfarb-Shanno, 'powell' for modified
-            Powell's method, 'cg' for conjugate gradient, 'ncg' for Newton-
-            conjugate gradient or 'basinhopping' for global basin-hopping
-            solver, if available. `method` determines which solver from
-            scipy.optimize is used. The explicit arguments in `fit` are passed
-            to the solver, with the exception of the basin-hopping solver. Each
+        method : str, optional
+            The `method` determines which solver from `scipy.optimize`
+            is used, and it can be chosen from among the following strings:
+
+            - 'newton' for Newton-Raphson, 'nm' for Nelder-Mead
+            - 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
+            - 'lbfgs' for limited-memory BFGS with optional box constraints
+            - 'powell' for modified Powell's method
+            - 'cg' for conjugate gradient
+            - 'ncg' for Newton-conjugate gradient
+            - 'basinhopping' for global basin-hopping solver
+
+            The explicit arguments in `fit` are passed to the solver,
+            with the exception of the basin-hopping solver. Each
             solver has several optional arguments that are not the same across
             solvers. See the notes section below (or scipy.optimize) for the
             available arguments and for the list of explicit arguments that the
-            basin-hopping solver supports..
-        maxiter : int
+            basin-hopping solver supports.
+        maxiter : int, optional
             The maximum number of iterations to perform.
-        full_output : bool
+        full_output : bool, optional
             Set to True to have all available output in the Results object's
             mle_retvals attribute. The output is dependent on the solver.
             See LikelihoodModelResults notes section for more information.
-        disp : bool
+        disp : bool, optional
             Set to True to print convergence messages.
-        fargs : tuple
+        fargs : tuple, optional
             Extra arguments passed to the likelihood function, i.e.,
             loglike(x,*args)
-        callback : callable callback(xk)
+        callback : callable callback(xk), optional
             Called after each iteration, as callback(xk), where xk is the
             current parameter vector.
-        retall : bool
+        retall : bool, optional
             Set to True to return list of solutions at each iteration.
             Available in Results object's mle_retvals attribute.
 
@@ -221,7 +256,7 @@ def fit(self, start_params=None, method='newton', maxiter=100,
         The 'basinhopping' solver ignores `maxiter`, `retall`, `full_output`
         explicit arguments.
 
-        Optional arguments for the solvers (available in Results.mle_settings)::
+        Optional arguments for solvers (see returned Results.mle_settings)::
 
             'newton'
                 tol : float
@@ -242,6 +277,23 @@ def fit(self, start_params=None, method='newton', maxiter=100,
                 epsilon
                     If fprime is approximated, use this value for the step
                     size. Only relevant if LikelihoodModel.score is None.
+            'lbfgs'
+                m : int
+                    This many terms are used for the Hessian approximation.
+                factr : float
+                    A stop condition that is a variant of relative error.
+                pgtol : float
+                    A stop condition that uses the projected gradient.
+                epsilon
+                    If fprime is approximated, use this value for the step
+                    size. Only relevant if LikelihoodModel.score is None.
+                maxfun : int
+                    Maximum number of function evaluations to make.
+                bounds : sequence
+                    (min, max) pairs for each element in x,
+                    defining the bounds on that parameter.
+                    Use None for one of min or max when there is no bound
+                    in that direction.
             'cg'
                 gtol : float
                     Stop when norm of gradient is less than gtol.
@@ -298,14 +350,8 @@ def fit(self, start_params=None, method='newton', maxiter=100,
                       - `jac` <- `score`
                       - `hess` <- `hess`
         """
-        # Extract kwargs specific to fit_regularized calling fit
-        extra_fit_funcs = kwargs.setdefault('extra_fit_funcs', dict())
-        cov_params_func = kwargs.setdefault('cov_params_func', None)
-
         Hinv = None  # JP error if full_output=0, Hinv not defined
-        methods = ['newton', 'nm', 'bfgs', 'powell', 'cg', 'ncg',
-                   'basinhopping']
-        methods += extra_fit_funcs.keys()
+
         if start_params is None:
             if hasattr(self, 'start_params'):
                 start_params = self.start_params
@@ -316,11 +362,6 @@ def fit(self, start_params=None, method='newton', maxiter=100,
                 raise ValueError("If exog is None, then start_params should "
                                  "be specified")
 
-        if method.lower() not in methods:
-            message = "Unknown fit method %s" % method
-            raise ValueError(message)
-        method = method.lower()
-
         # TODO: separate args from nonarg taking score and hessian, ie.,
         # user-supplied and numerically evaluated estimate frprime doesn't take
         # args in most (any?) of the optimize function
@@ -333,32 +374,26 @@ def fit(self, start_params=None, method='newton', maxiter=100,
         except:
             hess = None
 
-        fit_funcs = {
-            'newton': _fit_mle_newton,
-            'nm': _fit_mle_nm,  # Nelder-Mead
-            'bfgs': _fit_mle_bfgs,
-            'cg': _fit_mle_cg,
-            'ncg': _fit_mle_ncg,
-            'powell': _fit_mle_powell,
-            'basinhopping': _fit_mle_basinhopping,
-        }
-        if extra_fit_funcs:
-            fit_funcs.update(extra_fit_funcs)
-
         if method == 'newton':
             score = lambda params: self.score(params) / nobs
             hess = lambda params: self.hessian(params) / nobs
             #TODO: why are score and hess positive?
 
-        func = fit_funcs[method]
-        xopt, retvals = func(f, score, start_params, fargs, kwargs,
-                             disp=disp, maxiter=maxiter, callback=callback,
-                             retall=retall, full_output=full_output,
-                             hess=hess)
-
+        optimizer = Optimizer()
+        xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
+                                                       fargs, kwargs,
+                                                       hessian=hess,
+                                                       method=method,
+                                                       disp=disp,
+                                                       maxiter=maxiter,
+                                                       callback=callback,
+                                                       retall=retall,
+                                                       full_output=full_output)
+
+        #NOTE: this is for fit_regularized and should be generalized
+        cov_params_func = kwargs.setdefault('cov_params_func', None)
         if not full_output: # xopt should be None and retvals is argmin
             xopt = retvals
-
         elif cov_params_func:
             Hinv = cov_params_func(self, xopt, retvals)
         elif method == 'newton' and full_output:
@@ -371,7 +406,7 @@ def fit(self, start_params=None, method='newton', maxiter=100,
                 from warnings import warn
                 warndoc = ('Inverting hessian failed, no bse or '
                            'cov_params available')
-                warn(warndoc, Warning)
+                warn(warndoc, RuntimeWarning)
                 Hinv = None
 
         #TODO: add Hessian approximation and change the above if needed
@@ -380,244 +415,11 @@ def fit(self, start_params=None, method='newton', maxiter=100,
         #TODO: hardcode scale?
         if isinstance(retvals, dict):
             mlefit.mle_retvals = retvals
-        optim_settings = {'optimizer': method, 'start_params': start_params,
-                          'maxiter': maxiter, 'full_output': full_output,
-                          'disp': disp, 'fargs': fargs, 'callback': callback,
-                          'retall': retall}
-        optim_settings.update(kwargs)
+
         mlefit.mle_settings = optim_settings
         return mlefit
 
 
-def _fit_mle_newton(f, score, start_params, fargs, kwargs, disp=True,
-                    maxiter=100, callback=None, retall=False,
-                    full_output=True, hess=None):
-    tol = kwargs.setdefault('tol', 1e-8)
-    iterations = 0
-    oldparams = np.inf
-    newparams = np.asarray(start_params)
-    if retall:
-        history = [oldparams, newparams]
-    while (iterations < maxiter and np.any(np.abs(newparams -
-            oldparams) > tol)):
-        H = hess(newparams)
-        oldparams = newparams
-        newparams = oldparams - np.dot(np.linalg.inv(H),
-                score(oldparams))
-        if retall:
-            history.append(newparams)
-        if callback is not None:
-            callback(newparams)
-        iterations += 1
-    fval = f(newparams, *fargs)  # this is the negative likelihood
-    if iterations == maxiter:
-        warnflag = 1
-        if disp:
-            print ("Warning: Maximum number of iterations has been "
-                   "exceeded.")
-            print "         Current function value: %f" % fval
-            print "         Iterations: %d" % iterations
-    else:
-        warnflag = 0
-        if disp:
-            print "Optimization terminated successfully."
-            print "         Current function value: %f" % fval
-            print "         Iterations %d" % iterations
-    if full_output:
-        (xopt, fopt, niter,
-         gopt, hopt) = (newparams, f(newparams, *fargs),
-                        iterations, score(newparams),
-                        hess(newparams))
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'iterations': niter, 'score': gopt,
-                   'Hessian': hopt, 'warnflag': warnflag,
-                   'converged': converged}
-        if retall:
-            retvals.update({'allvecs': history})
-
-    else:
-        retvals = newparams
-        xopt = None
-
-    return xopt, retvals
-
-
-
-def _fit_mle_bfgs(f, score, start_params, fargs, kwargs, disp=True,
-                    maxiter=100, callback=None, retall=False,
-                    full_output=True, hess=None):
-    gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05)
-    norm = kwargs.setdefault('norm', np.Inf)
-    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
-    retvals = optimize.fmin_bfgs(f, start_params, score, args=fargs,
-                                 gtol=gtol, norm=norm, epsilon=epsilon,
-                                 maxiter=maxiter, full_output=full_output,
-                                 disp=disp, retall=retall, callback=callback)
-    if full_output:
-        if not retall:
-            xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag = retvals
-        else:
-            (xopt, fopt, gopt, Hinv, fcalls,
-             gcalls, warnflag, allvecs) = retvals
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'gopt': gopt, 'Hinv': Hinv,
-                'fcalls': fcalls, 'gcalls': gcalls, 'warnflag':
-                warnflag, 'converged': converged}
-        if retall:
-            retvals.update({'allvecs': allvecs})
-    else:
-        xopt = None
-
-    return xopt, retvals
-
-
-def _fit_mle_nm(f, score, start_params, fargs, kwargs, disp=True,
-                maxiter=100, callback=None, retall=False,
-                full_output=True, hess=None):
-    xtol = kwargs.setdefault('xtol', 0.0001)
-    ftol = kwargs.setdefault('ftol', 0.0001)
-    maxfun = kwargs.setdefault('maxfun', None)
-    retvals = optimize.fmin(f, start_params, args=fargs, xtol=xtol,
-                            ftol=ftol, maxiter=maxiter, maxfun=maxfun,
-                            full_output=full_output, disp=disp, retall=retall,
-                            callback=callback)
-    if full_output:
-        if not retall:
-            xopt, fopt, niter, fcalls, warnflag = retvals
-        else:
-            xopt, fopt, niter, fcalls, warnflag, allvecs = retvals
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'iterations': niter,
-                   'fcalls': fcalls, 'warnflag': warnflag,
-                   'converged': converged}
-        if retall:
-            retvals.update({'allvecs': allvecs})
-    else:
-        xopt = None
-
-    return xopt, retvals
-
-
-def _fit_mle_cg(f, score, start_params, fargs, kwargs, disp=True,
-                maxiter=100, callback=None, retall=False,
-                full_output=True, hess=None):
-    gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05)
-    norm = kwargs.setdefault('norm', np.Inf)
-    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
-    retvals = optimize.fmin_cg(f, start_params, score, gtol=gtol, norm=norm,
-                               epsilon=epsilon, maxiter=maxiter,
-                               full_output=full_output, disp=disp,
-                               retall=retall, callback=callback)
-    if full_output:
-        if not retall:
-            xopt, fopt, fcalls, gcalls, warnflag = retvals
-        else:
-            xopt, fopt, fcalls, gcalls, warnflag, allvecs = retvals
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
-                   'warnflag': warnflag, 'converged': converged}
-        if retall:
-            retvals.update({'allvecs': allvecs})
-
-    else:
-        xopt = None
-
-    return xopt, retvals
-
-
-def _fit_mle_ncg(f, score, start_params, fargs, kwargs, disp=True,
-                 maxiter=100, callback=None, retall=False,
-                 full_output=True, hess=None):
-    fhess_p = kwargs.setdefault('fhess_p', None)
-    avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05)
-    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
-    retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p,
-                                fhess=hess, args=fargs, avextol=avextol,
-                                epsilon=epsilon, maxiter=maxiter,
-                                full_output=full_output, disp=disp,
-                                retall=retall, callback=callback)
-    if full_output:
-        if not retall:
-            xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals
-        else:
-            xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\
-                retvals
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
-                   'hcalls': hcalls, 'warnflag': warnflag,
-                   'converged': converged}
-        if retall:
-            retvals.update({'allvecs': allvecs})
-    else:
-        xopt = None
-
-    return xopt, retvals
-
-
-def _fit_mle_powell(f, score, start_params, fargs, kwargs, disp=True,
-                    maxiter=100, callback=None, retall=False,
-                    full_output=True, hess=None):
-    xtol = kwargs.setdefault('xtol', 0.0001)
-    ftol = kwargs.setdefault('ftol', 0.0001)
-    maxfun = kwargs.setdefault('maxfun', None)
-    start_direc = kwargs.setdefault('start_direc', None)
-    retvals = optimize.fmin_powell(f, start_params, args=fargs, xtol=xtol,
-                                   ftol=ftol, maxiter=maxiter, maxfun=maxfun,
-                                   full_output=full_output, disp=disp,
-                                   retall=retall, callback=callback,
-                                   direc=start_direc)
-    if full_output:
-        if not retall:
-            xopt, fopt, direc, niter, fcalls, warnflag = retvals
-        else:
-            xopt, fopt, direc, niter, fcalls, warnflag, allvecs =\
-                retvals
-        converged = not warnflag
-        retvals = {'fopt': fopt, 'direc': direc, 'iterations': niter,
-                   'fcalls': fcalls, 'warnflag': warnflag,
-                   'converged': converged}
-        if retall:
-            retvals.update({'allvecs': allvecs})
-    else:
-        xopt = None
-
-    return xopt, retvals
-
-def _fit_mle_basinhopping(f, score, start_params, fargs, kwargs, disp=True,
-                          maxiter=100, callback=None, retall=False,
-                          full_output=True, hess=None):
-    if not 'basinhopping' in vars(optimize):
-        msg = 'basinhopping solver is not available, use e.g. bfgs instead!'
-        raise ValueError(msg)
-
-    from copy import copy
-    kwargs = copy(kwargs)
-    niter = kwargs.setdefault('niter', 100)
-    niter_success = kwargs.setdefault('niter_success', None)
-    T = kwargs.setdefault('T', 1.0)
-    stepsize = kwargs.setdefault('stepsize', 0.5)
-    interval = kwargs.setdefault('interval', 50)
-    minimizer_kwargs = kwargs.get('minimizer', {})
-    minimizer_kwargs['args'] = fargs
-    minimizer_kwargs['jac'] = score
-    minimizer_kwargs['hess'] = hess
-
-    res = optimize.basinhopping(f, start_params,
-                                minimizer_kwargs=minimizer_kwargs,
-                                niter=niter, niter_success=niter_success,
-                                T=T, stepsize=stepsize, disp=disp,
-                                callback=callback, interval=interval)
-    if full_output:
-        xopt, fopt, niter, fcalls = res.x, res.fun, res.nit, res.nfev
-        converged = 'completed successfully' in res.message[0]
-        retvals = {'fopt': fopt, 'iterations': niter,
-                   'fcalls': fcalls, 'converged': converged}
-
-    else:
-        xopt = None
-
-    return xopt, retvals
-
 #TODO: the below is unfinished
 class GenericLikelihoodModel(LikelihoodModel):
     """
@@ -667,7 +469,8 @@ class GenericLikelihoodModel(LikelihoodModel):
 
     """
     def __init__(self, endog, exog=None, loglike=None, score=None,
-                 hessian=None, missing='none', extra_params_names=None, **kwds):
+                 hessian=None, missing='none', extra_params_names=None,
+                 **kwds):
     # let them be none in case user wants to use inheritance
         if not loglike is None:
             self.loglike = loglike
@@ -675,7 +478,6 @@ def __init__(self, endog, exog=None, loglike=None, score=None,
             self.score = score
         if not hessian is None:
             self.hessian = hessian
-        self.confint_dist = stats.norm
 
         self.__dict__.update(kwds)
 
@@ -683,8 +485,9 @@ def __init__(self, endog, exog=None, loglike=None, score=None,
 
         #TODO temporary solution, force approx normal
         #self.df_model = 9999
-        #somewhere: CacheWriteWarning: The attribute 'df_model' cannot be overwritten
-        super(GenericLikelihoodModel, self).__init__(endog, exog, missing=missing)
+        #somewhere: CacheWriteWarning: 'df_model' cannot be overwritten
+        super(GenericLikelihoodModel, self).__init__(endog, exog,
+                                                     missing=missing)
 
         # this won't work for ru2nmnl, maybe np.ndim of a dict?
         if exog is not None:
@@ -704,7 +507,6 @@ def _set_extra_params_names(self, extra_params_names):
 
         self.nparams = len(self.exog_names)
 
-
     #this is redundant and not used when subclassing
     def initialize(self):
         if not self.score:  # right now score is not optional
@@ -716,11 +518,13 @@ def initialize(self):
                 pass
         #Initialize is called by
         #statsmodels.model.LikelihoodModel.__init__
-        #and should contain any preprocessing that needs to be done for a model.
+        #and should contain any preprocessing that needs to be done for a model
         from statsmodels.tools import tools
         if self.exog is not None:
-            self.df_model = float(tools.rank(self.exog) - 1)  # assumes constant
-            self.df_resid = float(self.exog.shape[0] - tools.rank(self.exog))
+            # assume constant
+            self.df_model = float(np_matrix_rank(self.exog) - 1)
+            self.df_resid = (float(self.exog.shape[0] -
+                             np_matrix_rank(self.exog)))
         else:
             self.df_model = np.nan
             self.df_resid = np.nan
@@ -868,15 +672,28 @@ def predict(self, exog=None, transform=True, *args, **kwargs):
             you can pass a data structure that contains x1 and x2 in
             their original form. Otherwise, you'd need to log the data
             first.
+        args, kwargs :
+            Some models can take additional arguments or keywords, see the
+            predict method of the model for the details.
 
         Returns
         -------
-        See self.model.predict
+        prediction : ndarray or pandas.Series
+            See self.model.predict
+
         """
         if transform and hasattr(self.model, 'formula') and exog is not None:
             from patsy import dmatrix
             exog = dmatrix(self.model.data.orig_exog.design_info.builder,
-                    exog)
+                           exog)
+
+        if exog is not None:
+            exog = np.asarray(exog)
+            if exog.ndim == 1 and (self.model.exog.ndim == 1 or
+                                   self.model.exog.shape[1] == 1):
+                exog = exog[:, None]
+            exog = np.atleast_2d(exog)  # needed in count model shape[1]
+
         return self.model.predict(self.params, exog, *args, **kwargs)
 
 
@@ -920,10 +737,10 @@ class LikelihoodModelResults(Results):
 
 
     Notes
-    --------
+    -----
     The covariance of params is given by scale times normalized_cov_params.
 
-    Return values by solver if full_ouput is True during fit:
+    Return values by solver if full_output is True during fit:
 
         'newton'
             fopt : float
@@ -974,6 +791,22 @@ class LikelihoodModelResults(Results):
                 True: converged.  False: did not converge.
             allvecs : list
                 Results at each iteration.
+        'lbfgs'
+            fopt : float
+                Value of the (negative) loglikelihood at its minimum.
+            gopt : float
+                Value of gradient at minimum, which should be near 0.
+            fcalls : int
+                Number of calls to loglike.
+            warnflag : int
+                Warning flag:
+
+                - 0 if converged
+                - 1 if too many function evaluations or too many iterations
+                - 2 if stopped for another reason
+
+            converged : bool
+                True: converged.  False: did not converge.
         'powell'
             fopt : float
                 Value of the (negative) loglikelihood at its minimum.
@@ -1020,11 +853,17 @@ class LikelihoodModelResults(Results):
             allvecs : list
                 Results at each iteration.
         """
+
+    # by default we use normal distribution
+    # can be overwritten by instances or subclasses
+    use_t = False
+
     def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         super(LikelihoodModelResults, self).__init__(model, params)
         self.normalized_cov_params = normalized_cov_params
         self.scale = scale
 
+
     def normalized_cov_params(self):
         raise NotImplementedError
 
@@ -1045,7 +884,11 @@ def tvalues(self):
 
     @cache_readonly
     def pvalues(self):
-        return stats.norm.sf(np.abs(self.tvalues)) * 2
+        if self.use_t:
+            return stats.t.sf(np.abs(self.tvalues), self.df_resid)*2
+        else:
+            return stats.norm.sf(np.abs(self.tvalues))*2
+
 
     def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
             other=None):
@@ -1071,34 +914,39 @@ def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
 
         Returns
         -------
-        (The below are assumed to be in matrix notation.)
-
         cov : ndarray
+            covariance matrix of the parameter estimates or of linear
+            combination of parameter estimates. See Notes.
+
+        Notes
+        -----
+        (The below are assumed to be in matrix notation.)
 
         If no argument is specified returns the covariance matrix of a model
-        (scale)*(X.T X)^(-1)
+        ``(scale)*(X.T X)^(-1)``
 
         If contrast is specified it pre and post-multiplies as follows
-        (scale) * r_matrix (X.T X)^(-1) r_matrix.T
+        ``(scale) * r_matrix (X.T X)^(-1) r_matrix.T``
 
         If contrast and other are specified returns
-        (scale) * r_matrix (X.T X)^(-1) other.T
+        ``(scale) * r_matrix (X.T X)^(-1) other.T``
 
         If column is specified returns
-        (scale) * (X.T X)^(-1)[column,column] if column is 0d
+        ``(scale) * (X.T X)^(-1)[column,column]`` if column is 0d
 
         OR
 
-        (scale) * (X.T X)^(-1)[column][:,column] if column is 1d
+        ``(scale) * (X.T X)^(-1)[column][:,column]`` if column is 1d
 
         """
         if (hasattr(self, 'mle_settings') and
-            self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
             dot_fun = nan_dot
         else:
             dot_fun = np.dot
 
-        if cov_p is None and self.normalized_cov_params is None:
+        if (cov_p is None and self.normalized_cov_params is None and
+            not hasattr(self, 'cov_params_default')):
             raise ValueError('need covariance of parameters for computing '
                              '(unnormalized) covariances')
         if column is not None and (r_matrix is not None or other is not None):
@@ -1108,9 +956,12 @@ def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
             raise ValueError('other can only be specified with r_matrix')
 
         if cov_p is None:
-            if scale is None:
-                scale = self.scale
-            cov_p = self.normalized_cov_params * scale
+            if hasattr(self, 'cov_params_default'):
+                cov_p = self.cov_params_default
+            else:
+                if scale is None:
+                    scale = self.scale
+                cov_p = self.normalized_cov_params * scale
 
         if column is not None:
             column = np.asarray(column)
@@ -1129,13 +980,14 @@ def cov_params(self, r_matrix=None, column=None, scale=None, cov_p=None,
                 other = np.asarray(other)
             tmp = dot_fun(r_matrix, dot_fun(cov_p, np.transpose(other)))
             return tmp
-        else:  #if r_matrix is None and column is None:
+        else:  # if r_matrix is None and column is None:
             return cov_p
 
     #TODO: make sure this works as needed for GLMs
-    def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
+    def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None,
+               use_t=None):
         """
-        Compute a t-test for a joint linear hypothesis of the form Rb = q
+        Compute a t-test for a each linear hypothesis of the form Rb = q
 
         Parameters
         ----------
@@ -1157,6 +1009,19 @@ def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
         scale : float, optional
             An optional `scale` to use.  Default is the scale specified
             by the model fit.
+        use_t : bool, optional
+            If use_t is None, then the default of the model is used.
+            If use_t is True, then the p-values are based on the t
+            distribution.
+            If use_t is False, then the p-values are based on the normal
+            distribution.
+
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+            The available results have the same elements as the parameter table
+            in `summary()`.
 
         Examples
         --------
@@ -1167,14 +1032,14 @@ def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
         >>> results = sm.OLS(data.endog, data.exog).fit()
         >>> r = np.zeros_like(results.params)
         >>> r[5:] = [1,-1]
-        >>> print r
+        >>> print(r)
         [ 0.  0.  0.  0.  0.  1. -1.]
 
         r tests that the coefficients on the 5th and 6th independent
         variable are the same.
 
-        >>>T_Test = results.t_test(r)
-        >>>print T_test
+        >>> T_test = results.t_test(r)
+        >>> print(T_test)
         <T contrast: effect=-1829.2025687192481, sd=455.39079425193762,
         t=-4.0167754636411717, p=0.0015163772380899498, df_denom=9>
         >>> T_test.effect
@@ -1188,14 +1053,15 @@ def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
 
         Alternatively, you can specify the hypothesis tests using a string
 
+        >>> from statsmodels.formula.api import ols
         >>> dta = sm.datasets.longley.load_pandas().data
         >>> formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
         >>> results = ols(formula, dta).fit()
         >>> hypotheses = 'GNPDEFL = GNP, UNEMP = 2, YEAR/1829 = 1'
         >>> t_test = results.t_test(hypotheses)
-        >>> print t_test
+        >>> print(t_test)
 
-        See also
+        See Also
         ---------
         tvalues : individual t statistics
         f_test : for F tests
@@ -1228,6 +1094,10 @@ def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
                 raise ValueError("r_matrix and q_matrix must have the same "
                                  "number of rows")
 
+        if use_t is None:
+            #switch to use_t false if undefined
+            use_t = (hasattr(self, 'use_t') and self.use_t)
+
         _t = _sd = None
 
         _effect = np.dot(r_matrix, self.params)
@@ -1240,15 +1110,22 @@ def t_test(self, r_matrix, q_matrix=None, cov_p=None, scale=None):
         else:
             _sd = np.sqrt(self.cov_params(r_matrix=r_matrix, cov_p=cov_p))
         _t = (_effect - q_matrix) * recipr(_sd)
-        return ContrastResults(effect=_effect, t=_t, sd=_sd,
-                               df_denom=self.model.df_resid)
 
+        if use_t:
+            return ContrastResults(effect=_effect, t=_t, sd=_sd,
+                                   df_denom=self.df_resid)
+        else:
+            return ContrastResults(effect=_effect, statistic=_t, sd=_sd,
+                                   df_denom=self.df_resid,
+                                   distribution='norm')
 
-    #TODO: untested for GLMs?
     def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
                invcov=None):
         """
-        Compute an F-test for a joint linear hypothesis.
+        Compute the F-test for a joint linear hypothesis.
+
+        This is a special case of `wald_test` that always uses the F
+        distribution.
 
         Parameters
         ----------
@@ -1273,6 +1150,11 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
             A q x q array to specify an inverse covariance matrix based on a
             restrictions matrix.
 
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
+
         Examples
         --------
         >>> import numpy as np
@@ -1286,15 +1168,15 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
         This tests that each coefficient is jointly statistically
         significantly different from zero.
 
-        >>> print results.f_test(A)
+        >>> print(results.f_test(A))
         <F contrast: F=330.28533923463488, p=4.98403052872e-10,
          df_denom=9, df_num=6>
 
         Compare this to
 
-        >>> results.F
+        >>> results.fvalue
         330.2853392346658
-        >>> results.F_p
+        >>> results.f_pvalue
         4.98403096572e-10
 
         >>> B = np.array(([0,0,1,-1,0,0,0],[0,0,0,0,0,1,-1]))
@@ -1303,7 +1185,7 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
         equal and jointly that the coefficient on the 5th and 6th regressors
         are equal.
 
-        >>> print results.f_test(B)
+        >>> print(results.f_test(B))
         <F contrast: F=9.740461873303655, p=0.00560528853174, df_denom=9,
          df_num=2>
 
@@ -1316,12 +1198,74 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
         >>> results = ols(formula, dta).fit()
         >>> hypotheses = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
         >>> f_test = results.f_test(hypotheses)
-        >>> print f_test
+        >>> print(f_test)
+
+        See Also
+        --------
+        statsmodels.stats.contrast.ContrastResults
+        wald_test
+        t_test
+        patsy.DesignInfo.linear_constraint
+
+        Notes
+        -----
+        The matrix `r_matrix` is assumed to be non-singular. More precisely,
+
+        r_matrix (pX pX.T) r_matrix.T
+
+        is assumed invertible. Here, pX is the generalized inverse of the
+        design matrix of the model. There can be problems in non-OLS models
+        where the rank of the covariance of the noise is not full.
+        """
+        res = self.wald_test(r_matrix, q_matrix=q_matrix, cov_p=cov_p,
+                             scale=scale, invcov=invcov, use_f=True)
+        return res
+
+    #TODO: untested for GLMs?
+    def wald_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
+                  invcov=None, use_f=None):
+        """
+        Compute a Wald-test for a joint linear hypothesis.
+
+        Parameters
+        ----------
+        r_matrix : array-like, str, or tuple
+            - array : An r x k array where r is the number of restrictions to
+              test and k is the number of regressors.
+            - str : The full hypotheses to test can be given as a string.
+              See the examples.
+            - tuple : A tuple of arrays in the form (R, q), since q_matrix is
+              deprecated.
+        q_matrix : array-like
+            This is deprecated. See `r_matrix` and the examples for more
+            information on new usage. Can be either a scalar or a length p
+            row vector. If omitted and r_matrix is an array, `q_matrix` is
+            assumed to be a conformable array of zeros.
+        cov_p : array-like, optional
+            An alternative estimate for the parameter covariance matrix.
+            If None is given, self.normalized_cov_params is used.
+        scale : float, optional
+            Default is 1.0 for no scaling.
+        invcov : array-like, optional
+            A q x q array to specify an inverse covariance matrix based on a
+            restrictions matrix.
+        use_f : bool
+            If True, then the F-distribution is used. If False, then the
+            asymptotic distribution, chisquare is used. If use_f is None, then
+            the F distribution is used if the model specifies that use_t is True.
+            The test statistic is proportionally adjusted for the distribution
+            by the number of constraints in the hypothesis.
+
+        Returns
+        -------
+        res : ContrastResults instance
+            The results for the test are attributes of this results instance.
 
         See also
         --------
-        statsmodels.contrasts
-        statsmodels.model.t_test
+        statsmodels.stats.contrast.ContrastResults
+        f_test
+        t_test
         patsy.DesignInfo.linear_constraint
 
         Notes
@@ -1334,6 +1278,10 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
         design matrix of the model. There can be problems in non-OLS models
         where the rank of the covariance of the noise is not full.
         """
+        if use_f is None:
+            #switch to use_t false if undefined
+            use_f = (hasattr(self, 'use_t') and self.use_t)
+
         from patsy import DesignInfo
         if q_matrix is not None:
             from warnings import warn
@@ -1345,7 +1293,7 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
         r_matrix, q_matrix = LC.coefs, LC.constants
 
         if (self.normalized_cov_params is None and cov_p is None and
-            invcov is None):
+                invcov is None):
             raise ValueError('need covariance of parameters for computing '
                              'F statistics')
 
@@ -1365,16 +1313,23 @@ def f_test(self, r_matrix, q_matrix=None, cov_p=None, scale=1.0,
             cov_p = self.cov_params(r_matrix=r_matrix, cov_p=cov_p)
             if np.isnan(cov_p).max():
                 raise ValueError("r_matrix performs f_test for using "
-                    "dimensions that are asymptotically non-normal")
+                                 "dimensions that are asymptotically "
+                                 "non-normal")
             invcov = np.linalg.inv(cov_p)
 
         if (hasattr(self, 'mle_settings') and
-            self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
-            F = nan_dot(nan_dot(Rbq.T, invcov), Rbq) / J
+                self.mle_settings['optimizer'] in ['l1', 'l1_cvxopt_cp']):
+            F = nan_dot(nan_dot(Rbq.T, invcov), Rbq)
+        else:
+            F = np.dot(np.dot(Rbq.T, invcov), Rbq)
+
+        if use_f:
+            F /= J
+            return ContrastResults(F=F, df_denom=self.df_resid,
+                                   df_num=invcov.shape[0])
         else:
-            F = np.dot(np.dot(Rbq.T, invcov), Rbq) / J
-        return ContrastResults(F=F, df_denom=self.model.df_resid,
-                    df_num=invcov.shape[0])
+            return ContrastResults(chi2=F, df_denom=J, statistic=F,
+                                   distribution='chi2', distargs=(J,))
 
     def conf_int(self, alpha=.05, cols=None, method='default'):
         """
@@ -1383,7 +1338,7 @@ def conf_int(self, alpha=.05, cols=None, method='default'):
         Parameters
         ----------
         alpha : float, optional
-            The `alpha` level for the confidence interval.
+            The significance level for the confidence interval.
             ie., The default `alpha` = .05 returns a 95% confidence interval.
         cols : array-like, optional
             `cols` specifies which confidence intervals to return
@@ -1391,7 +1346,7 @@ def conf_int(self, alpha=.05, cols=None, method='default'):
             Not Implemented Yet
             Method to estimate the confidence_interval.
             "Default" : uses self.bse which is based on inverse Hessian for MLE
-            "jhj" :
+            "hjjh" :
             "jac" :
             "boot-bse"
             "boot_quant"
@@ -1401,7 +1356,9 @@ def conf_int(self, alpha=.05, cols=None, method='default'):
         Returns
         --------
         conf_int : array
-            Each row contains [lower, upper] confidence interval
+            Each row contains [lower, upper] limits of the confidence interval
+            for the corresponding parameter. The first column contains all
+            lower, the second column contains all upper limits.
 
         Examples
         --------
@@ -1430,8 +1387,13 @@ def conf_int(self, alpha=.05, cols=None, method='default'):
         method.
         """
         bse = self.bse
-        dist = stats.norm
-        q = dist.ppf(1 - alpha / 2)
+
+        if self.use_t:
+            dist = stats.t
+            q = dist.ppf(1 - alpha / 2, self.df_resid)
+        else:
+            dist = stats.norm
+            q = dist.ppf(1 - alpha / 2)
 
         if cols is None:
             lower = self.params - q * bse
@@ -1440,7 +1402,7 @@ def conf_int(self, alpha=.05, cols=None, method='default'):
             cols = np.asarray(cols)
             lower = self.params[cols] - q * bse[cols]
             upper = self.params[cols] + q * bse[cols]
-        return np.asarray(zip(lower, upper))
+        return np.asarray(lzip(lower, upper))
 
     def save(self, fname, remove_data=False):
         '''
@@ -1498,8 +1460,8 @@ def remove_data(self):
 
         .. warning:: Since data and some intermediate results have been removed
            calculating new statistics that require them will raise exceptions.
-           The exception will occur the first time an attribute is accessed that
-           has been set to None.
+           The exception will occur the first time an attribute is accessed
+           that has been set to None.
 
         Not fully tested for time series models, tsa, and might delete too much
         for prediction or not all that would be possible.
@@ -1516,17 +1478,17 @@ def wipe(obj, att):
             try:
                 obj_ = reduce(getattr, [obj] + p)
 
-                #print repr(obj), repr(att)
-                #print hasattr(obj_, att_)
+                #print(repr(obj), repr(att))
+                #print(hasattr(obj_, att_))
                 if hasattr(obj_, att_):
-                    #print 'removing3', att_
+                    #print('removing3', att_)
                     setattr(obj_, att_, None)
             except AttributeError:
                 pass
 
-        model_attr = ['model.'+ i for i in self.model._data_attr]
+        model_attr = ['model.' + i for i in self.model._data_attr]
         for att in self._data_attr + model_attr:
-            #print 'removing', att
+            #print('removing', att)
             wipe(self, att)
 
         data_in_cache = getattr(self, 'data_in_cache', [])
@@ -1602,10 +1564,10 @@ def covjac(self):
         log-likelihood
 
         '''
-##        if not hasattr(self, '_results'):
-##            raise ValueError('need to call fit first')
-##            #self.fit()
-##        self.jacv = jacv = self.jac(self._results.params)
+        ##  if not hasattr(self, '_results'):
+        ##      raise ValueError('need to call fit first')
+        ##      #self.fit()
+        ##  self.jacv = jacv = self.jac(self._results.params)
         jacv = self.jacv
         return np.linalg.inv(np.dot(jacv.T, jacv))
 
@@ -1618,9 +1580,9 @@ def covjhj(self):
         name should be covhjh
         '''
         jacv = self.jacv
-##        hessv = self.hessv
-##        hessinv = np.linalg.inv(hessv)
-##        self.hessinv = hessinv
+        ##  hessv = self.hessv
+        ##  hessinv = np.linalg.inv(hessv)
+        ##  self.hessinv = hessinv
         hessinv = self.cov_params()
         return np.dot(hessinv, np.dot(np.dot(jacv.T, jacv), hessinv))
 
@@ -1637,7 +1599,7 @@ def bsejac(self):
         return np.sqrt(np.diag(self.covjac))
 
     def bootstrap(self, nrep=100, method='nm', disp=0, store=1):
-        '''simple bootstrap to get mean and variance of estimator
+        """simple bootstrap to get mean and variance of estimator
 
         see notes
 
@@ -1670,11 +1632,11 @@ def bootstrap(self, nrep=100, method='nm', disp=0, store=1):
 
         This will be moved to apply only to models with independently
         distributed observations.
-        '''
+        """
         results = []
-        print self.model.__class__
+        print(self.model.__class__)
         hascloneattr = True if hasattr(self, 'cloneattr') else False
-        for i in xrange(nrep):
+        for i in range(nrep):
             rvsind = np.random.randint(self.nobs - 1, size=self.nobs)
             #this needs to set startparam and get other defining attributes
             #need a clone method on model
@@ -1750,8 +1712,6 @@ class GenericLikelihoodModelResults(LikelihoodModelResults, ResultMixin):
     """
 
     def __init__(self, model, mlefit):
-#        super(DiscreteResults, self).__init__(model, params,
-#                np.linalg.inv(-hessian), scale=1.)
         self.model = model
         self.endog = model.endog
         self.exog = model.exog
@@ -1810,15 +1770,15 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
                     ('Date:', None),
                     ('Time:', None),
                     ('No. Observations:', None),
-                    ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
-                    ('Df Model:', None), #[self.df_model])
+                    ('Df Residuals:', None),  # [self.df_resid]),
+                    ('Df Model:', None),  # [self.df_model])
                     ]
 
-        top_right = [#('R-squared:', ["%#8.3f" % self.rsquared]),
-                     #('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
-                     #('F-statistic:', ["%#8.4g" % self.fvalue] ),
-                     #('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]),
-                     ('Log-Likelihood:', None), #["%#6.4g" % self.llf]),
+        top_right = [  # ('R-squared:', ["%#8.3f" % self.rsquared]),
+                       # ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
+                       # ('F-statistic:', ["%#8.4g" % self.fvalue] ),
+                       # ('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]),
+                     ('Log-Likelihood:', None),  # ["%#6.4g" % self.llf]),
                      ('AIC:', ["%#8.4g" % self.aic]),
                      ('BIC:', ["%#8.4g" % self.bic])
                      ]
@@ -1830,8 +1790,8 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         from statsmodels.iolib.summary import Summary
         smry = Summary()
         smry.add_table_2cols(self, gleft=top_left, gright=top_right,
-                          yname=yname, xname=xname, title=title)
+                             yname=yname, xname=xname, title=title)
         smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
-                             use_t=False)
+                              use_t=False)
 
         return smry
diff --git a/statsmodels/base/optimizer.py b/statsmodels/base/optimizer.py
new file mode 100644
index 00000000000..79f9f424590
--- /dev/null
+++ b/statsmodels/base/optimizer.py
@@ -0,0 +1,550 @@
+"""
+Functions that are general enough to use for any model fitting. The idea is
+to untie these from LikelihoodModel so that they may be re-used generally.
+"""
+from __future__ import print_function
+import distutils.version
+
+from scipy import __version__ as scipy_version
+import numpy as np
+from scipy import optimize
+
+def _check_method(method, methods):
+    if method not in methods:
+        message = "Unknown fit method %s" % method
+        raise ValueError(message)
+
+
+class Optimizer(object):
+
+    def _fit(self, objective, gradient, start_params, fargs, kwargs,
+             hessian=None, method='newton', maxiter=100, full_output=True,
+             disp=True, callback=None, retall=False):
+        """
+        Fit function for any model with an objective function.
+
+        Parameters
+        ----------
+        start_params : array-like, optional
+            Initial guess of the solution for the loglikelihood maximization.
+            The default is an array of zeros.
+        method : str {'newton','nm','bfgs','powell','cg','ncg','basinhopping'}
+            Method can be 'newton' for Newton-Raphson, 'nm' for Nelder-Mead,
+            'bfgs' for Broyden-Fletcher-Goldfarb-Shanno, 'powell' for modified
+            Powell's method, 'cg' for conjugate gradient, 'ncg' for Newton-
+            conjugate gradient or 'basinhopping' for global basin-hopping
+            solver, if available. `method` determines which solver from
+            scipy.optimize is used. The explicit arguments in `fit` are passed
+            to the solver, with the exception of the basin-hopping solver. Each
+            solver has several optional arguments that are not the same across
+            solvers. See the notes section below (or scipy.optimize) for the
+            available arguments and for the list of explicit arguments that the
+            basin-hopping solver supports..
+        maxiter : int
+            The maximum number of iterations to perform.
+        full_output : bool
+            Set to True to have all available output in the Results object's
+            mle_retvals attribute. The output is dependent on the solver.
+            See LikelihoodModelResults notes section for more information.
+        disp : bool
+            Set to True to print convergence messages.
+        fargs : tuple
+            Extra arguments passed to the likelihood function, i.e.,
+            loglike(x,*args)
+        callback : callable callback(xk)
+            Called after each iteration, as callback(xk), where xk is the
+            current parameter vector.
+        retall : bool
+            Set to True to return list of solutions at each iteration.
+            Available in Results object's mle_retvals attribute.
+
+        Returns
+        -------
+        xopt : array
+            The solution to the objective function
+        retvals : dict, None
+            If `full_output` is True then this is a dictionary which holds
+            information returned from the solver used. If it is False, this is
+            None.
+        optim_settings : dict
+            A dictionary that contains the parameters passed to the solver.
+
+        Notes
+        -----
+        The 'basinhopping' solver ignores `maxiter`, `retall`, `full_output`
+        explicit arguments.
+
+        Optional arguments for the solvers (available in Results.mle_settings)::
+
+            'newton'
+                tol : float
+                    Relative error in params acceptable for convergence.
+            'nm' -- Nelder Mead
+                xtol : float
+                    Relative error in params acceptable for convergence
+                ftol : float
+                    Relative error in loglike(params) acceptable for
+                    convergence
+                maxfun : int
+                    Maximum number of function evaluations to make.
+            'bfgs'
+                gtol : float
+                    Stop when norm of gradient is less than gtol.
+                norm : float
+                    Order of norm (np.Inf is max, -np.Inf is min)
+                epsilon
+                    If fprime is approximated, use this value for the step
+                    size. Only relevant if LikelihoodModel.score is None.
+            'cg'
+                gtol : float
+                    Stop when norm of gradient is less than gtol.
+                norm : float
+                    Order of norm (np.Inf is max, -np.Inf is min)
+                epsilon : float
+                    If fprime is approximated, use this value for the step
+                    size. Can be scalar or vector.  Only relevant if
+                    Likelihoodmodel.score is None.
+            'ncg'
+                fhess_p : callable f'(x,*args)
+                    Function which computes the Hessian of f times an arbitrary
+                    vector, p.  Should only be supplied if
+                    LikelihoodModel.hessian is None.
+                avextol : float
+                    Stop when the average relative error in the minimizer
+                    falls below this amount.
+                epsilon : float or ndarray
+                    If fhess is approximated, use this value for the step size.
+                    Only relevant if Likelihoodmodel.hessian is None.
+            'powell'
+                xtol : float
+                    Line-search error tolerance
+                ftol : float
+                    Relative error in loglike(params) for acceptable for
+                    convergence.
+                maxfun : int
+                    Maximum number of function evaluations to make.
+                start_direc : ndarray
+                    Initial direction set.
+            'basinhopping'
+                niter : integer
+                    The number of basin hopping iterations.
+                niter_success : integer
+                    Stop the run if the global minimum candidate remains the
+                    same for this number of iterations.
+                T : float
+                    The "temperature" parameter for the accept or reject
+                    criterion. Higher "temperatures" mean that larger jumps
+                    in function value will be accepted. For best results
+                    `T` should be comparable to the separation (in function
+                    value) between local minima.
+                stepsize : float
+                    Initial step size for use in the random displacement.
+                interval : integer
+                    The interval for how often to update the `stepsize`.
+                minimizer : dict
+                    Extra keyword arguments to be passed to the minimizer
+                    `scipy.optimize.minimize()`, for example 'method' - the
+                    minimization method (e.g. 'L-BFGS-B'), or 'tol' - the
+                    tolerance for termination. Other arguments are mapped from
+                    explicit argument of `fit`:
+                    - `args` <- `fargs`
+                    - `jac` <- `score`
+                    - `hess` <- `hess`
+        """
+        #TODO: generalize the regularization stuff
+        # Extract kwargs specific to fit_regularized calling fit
+        extra_fit_funcs = kwargs.setdefault('extra_fit_funcs', dict())
+
+        methods = ['newton', 'nm', 'bfgs', 'lbfgs', 'powell', 'cg', 'ncg',
+                'basinhopping']
+        methods += extra_fit_funcs.keys()
+        method = method.lower()
+        _check_method(method, methods)
+
+        fit_funcs = {
+            'newton': _fit_newton,
+            'nm': _fit_nm,  # Nelder-Mead
+            'bfgs': _fit_bfgs,
+            'lbfgs': _fit_lbfgs,
+            'cg': _fit_cg,
+            'ncg': _fit_ncg,
+            'powell': _fit_powell,
+            'basinhopping': _fit_basinhopping,
+        }
+
+        #NOTE: fit_regularized checks the methods for these but it should be
+        #      moved up probably
+        if extra_fit_funcs:
+            fit_funcs.update(extra_fit_funcs)
+
+        func = fit_funcs[method]
+        xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
+                            disp=disp, maxiter=maxiter, callback=callback,
+                            retall=retall, full_output=full_output,
+                            hess=hessian)
+
+        # this is stupid TODO: just change this to something sane
+        # no reason to copy scipy here
+        if not full_output: # xopt should be None and retvals is argmin
+            xopt = retvals
+            retvals = None
+
+        optim_settings = {'optimizer': method, 'start_params': start_params,
+                        'maxiter': maxiter, 'full_output': full_output,
+                        'disp': disp, 'fargs': fargs, 'callback': callback,
+                        'retall': retall}
+        optim_settings.update(kwargs)
+        # set as attributes or return?
+        return xopt, retvals, optim_settings
+
+    def _fit_constrained(self, params):
+        """
+        TODO: how to add constraints?
+
+        Something like
+        sm.add_constraint(Model, func)
+
+        or
+
+        model_instance.add_constraint(func)
+        model_instance.add_constraint("x1 + x2 = 2")
+        result = model_instance.fit()
+        """
+        pass
+
+
+    def _fit_regularized(self, params):
+        #TODO: code won't necessarily be general here. 3 options.
+        # 1) setup for scipy.optimize.fmin_sqlsqp
+        # 2) setup for cvxopt
+        # 3) setup for openopt
+        pass
+
+########################################
+# Helper functions to fit
+
+def _fit_newton(f, score, start_params, fargs, kwargs, disp=True,
+                    maxiter=100, callback=None, retall=False,
+                    full_output=True, hess=None, ridge_factor=1e-10):
+    tol = kwargs.setdefault('tol', 1e-8)
+    iterations = 0
+    oldparams = np.inf
+    newparams = np.asarray(start_params)
+    if retall:
+        history = [oldparams, newparams]
+    while (iterations < maxiter and np.any(np.abs(newparams -
+            oldparams) > tol)):
+        H = np.asarray(hess(newparams))
+        # regularize Hessian, not clear what ridge factor should be
+        # keyword option with absolute default 1e-10, see #1847
+        if not np.all(ridge_factor == 0):
+            H[np.diag_indices(H.shape[0])] += ridge_factor
+        oldparams = newparams
+        newparams = oldparams - np.dot(np.linalg.inv(H),
+                score(oldparams))
+        if retall:
+            history.append(newparams)
+        if callback is not None:
+            callback(newparams)
+        iterations += 1
+    fval = f(newparams, *fargs)  # this is the negative likelihood
+    if iterations == maxiter:
+        warnflag = 1
+        if disp:
+            print("Warning: Maximum number of iterations has been "
+                   "exceeded.")
+            print("         Current function value: %f" % fval)
+            print("         Iterations: %d" % iterations)
+    else:
+        warnflag = 0
+        if disp:
+            print("Optimization terminated successfully.")
+            print("         Current function value: %f" % fval)
+            print("         Iterations %d" % iterations)
+    if full_output:
+        (xopt, fopt, niter,
+         gopt, hopt) = (newparams, f(newparams, *fargs),
+                        iterations, score(newparams),
+                        hess(newparams))
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'iterations': niter, 'score': gopt,
+                   'Hessian': hopt, 'warnflag': warnflag,
+                   'converged': converged}
+        if retall:
+            retvals.update({'allvecs': history})
+
+    else:
+        retvals = newparams
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_bfgs(f, score, start_params, fargs, kwargs, disp=True,
+                    maxiter=100, callback=None, retall=False,
+                    full_output=True, hess=None):
+    gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05)
+    norm = kwargs.setdefault('norm', np.Inf)
+    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
+    retvals = optimize.fmin_bfgs(f, start_params, score, args=fargs,
+                                 gtol=gtol, norm=norm, epsilon=epsilon,
+                                 maxiter=maxiter, full_output=full_output,
+                                 disp=disp, retall=retall, callback=callback)
+    if full_output:
+        if not retall:
+            xopt, fopt, gopt, Hinv, fcalls, gcalls, warnflag = retvals
+        else:
+            (xopt, fopt, gopt, Hinv, fcalls,
+             gcalls, warnflag, allvecs) = retvals
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'gopt': gopt, 'Hinv': Hinv,
+                'fcalls': fcalls, 'gcalls': gcalls, 'warnflag':
+                warnflag, 'converged': converged}
+        if retall:
+            retvals.update({'allvecs': allvecs})
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_lbfgs(f, score, start_params, fargs, kwargs, disp=True,
+                   maxiter=100, callback=None, retall=False,
+                   full_output=True, hess=None):
+    """
+    Parameters
+    ----------
+    f : function
+        Returns negative log likelihood given parameters.
+    score : function
+        Returns gradient of negative log likelihood with respect to params.
+
+    Notes
+    -----
+    Within the mle part of statsmodels, the log likelihood function and
+    its gradient with respect to the parameters do not have notationally
+    consistent sign.
+    """
+
+    # Use unconstrained optimization by default.
+    bounds = kwargs.setdefault('bounds', [(None, None)] * len(start_params))
+
+    # Pass the following keyword argument names through to fmin_l_bfgs_b
+    # if they are present in kwargs, otherwise use the fmin_l_bfgs_b
+    # default values.
+    names = ('m', 'pgtol', 'factr', 'maxfun', 'epsilon', 'approx_grad')
+    extra_kwargs = dict((x, kwargs[x]) for x in names if x in kwargs)
+
+    # Extract values for the options related to the gradient.
+    approx_grad = kwargs.get('approx_grad', False)
+    loglike_and_score = kwargs.get('loglike_and_score', None)
+    epsilon = kwargs.get('epsilon', None)
+
+    # The approx_grad flag has superpowers nullifying the score function arg.
+    if approx_grad:
+        score = None
+
+    # Choose among three options for dealing with the gradient (the gradient
+    # of a log likelihood function with respect to its parameters
+    # is more specifically called the score in statistics terminology).
+    # The first option is to use the finite-differences
+    # approximation that is built into the fmin_l_bfgs_b optimizer.
+    # The second option is to use the provided score function.
+    # The third option is to use the score component of a provided
+    # function that simultaneously evaluates the log likelihood and score.
+    if epsilon and not approx_grad:
+        raise ValueError('a finite-differences epsilon was provided '
+                         'even though we are not using approx_grad')
+    if approx_grad and loglike_and_score:
+        raise ValueError('gradient approximation was requested '
+                         'even though an analytic loglike_and_score function '
+                         'was given')
+    if loglike_and_score:
+        func = lambda p, *a : tuple(-x for x in loglike_and_score(p, *a))
+    elif score:
+        func = f
+        extra_kwargs['fprime'] = score
+    elif approx_grad:
+        func = f
+
+    # Customize the fmin_l_bfgs_b call according to the scipy version.
+    # Old scipy does not support maxiter and callback.
+    scipy_version_curr = distutils.version.LooseVersion(scipy_version)
+    scipy_version_12 = distutils.version.LooseVersion('0.12.0')
+    if scipy_version_curr < scipy_version_12:
+        retvals = optimize.fmin_l_bfgs_b(func, start_params, args=fargs,
+                                         bounds=bounds, disp=disp,
+                                         **extra_kwargs)
+    else:
+        retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
+                                         callback=callback, args=fargs,
+                                         bounds=bounds, disp=disp,
+                                         **extra_kwargs)
+
+    if full_output:
+        xopt, fopt, d = retvals
+        # The warnflag is
+        # 0 if converged
+        # 1 if too many function evaluations or too many iterations
+        # 2 if stopped for another reason, given in d['task']
+        warnflag = d['warnflag']
+        converged = (warnflag == 0)
+        gopt = d['grad']
+        fcalls = d['funcalls']
+        retvals = {'fopt': fopt, 'gopt': gopt, 'fcalls': fcalls,
+                   'warnflag': warnflag, 'converged': converged}
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_nm(f, score, start_params, fargs, kwargs, disp=True,
+                maxiter=100, callback=None, retall=False,
+                full_output=True, hess=None):
+    xtol = kwargs.setdefault('xtol', 0.0001)
+    ftol = kwargs.setdefault('ftol', 0.0001)
+    maxfun = kwargs.setdefault('maxfun', None)
+    retvals = optimize.fmin(f, start_params, args=fargs, xtol=xtol,
+                            ftol=ftol, maxiter=maxiter, maxfun=maxfun,
+                            full_output=full_output, disp=disp, retall=retall,
+                            callback=callback)
+    if full_output:
+        if not retall:
+            xopt, fopt, niter, fcalls, warnflag = retvals
+        else:
+            xopt, fopt, niter, fcalls, warnflag, allvecs = retvals
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'iterations': niter,
+                   'fcalls': fcalls, 'warnflag': warnflag,
+                   'converged': converged}
+        if retall:
+            retvals.update({'allvecs': allvecs})
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_cg(f, score, start_params, fargs, kwargs, disp=True,
+                maxiter=100, callback=None, retall=False,
+                full_output=True, hess=None):
+    gtol = kwargs.setdefault('gtol', 1.0000000000000001e-05)
+    norm = kwargs.setdefault('norm', np.Inf)
+    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
+    retvals = optimize.fmin_cg(f, start_params, score, gtol=gtol, norm=norm,
+                               epsilon=epsilon, maxiter=maxiter,
+                               full_output=full_output, disp=disp,
+                               retall=retall, callback=callback)
+    if full_output:
+        if not retall:
+            xopt, fopt, fcalls, gcalls, warnflag = retvals
+        else:
+            xopt, fopt, fcalls, gcalls, warnflag, allvecs = retvals
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
+                   'warnflag': warnflag, 'converged': converged}
+        if retall:
+            retvals.update({'allvecs': allvecs})
+
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_ncg(f, score, start_params, fargs, kwargs, disp=True,
+                 maxiter=100, callback=None, retall=False,
+                 full_output=True, hess=None):
+    fhess_p = kwargs.setdefault('fhess_p', None)
+    avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05)
+    epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
+    retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p,
+                                fhess=hess, args=fargs, avextol=avextol,
+                                epsilon=epsilon, maxiter=maxiter,
+                                full_output=full_output, disp=disp,
+                                retall=retall, callback=callback)
+    if full_output:
+        if not retall:
+            xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals
+        else:
+            xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\
+                retvals
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
+                   'hcalls': hcalls, 'warnflag': warnflag,
+                   'converged': converged}
+        if retall:
+            retvals.update({'allvecs': allvecs})
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_powell(f, score, start_params, fargs, kwargs, disp=True,
+                    maxiter=100, callback=None, retall=False,
+                    full_output=True, hess=None):
+    xtol = kwargs.setdefault('xtol', 0.0001)
+    ftol = kwargs.setdefault('ftol', 0.0001)
+    maxfun = kwargs.setdefault('maxfun', None)
+    start_direc = kwargs.setdefault('start_direc', None)
+    retvals = optimize.fmin_powell(f, start_params, args=fargs, xtol=xtol,
+                                   ftol=ftol, maxiter=maxiter, maxfun=maxfun,
+                                   full_output=full_output, disp=disp,
+                                   retall=retall, callback=callback,
+                                   direc=start_direc)
+    if full_output:
+        if not retall:
+            xopt, fopt, direc, niter, fcalls, warnflag = retvals
+        else:
+            xopt, fopt, direc, niter, fcalls, warnflag, allvecs =\
+                retvals
+        converged = not warnflag
+        retvals = {'fopt': fopt, 'direc': direc, 'iterations': niter,
+                   'fcalls': fcalls, 'warnflag': warnflag,
+                   'converged': converged}
+        if retall:
+            retvals.update({'allvecs': allvecs})
+    else:
+        xopt = None
+
+    return xopt, retvals
+
+
+def _fit_basinhopping(f, score, start_params, fargs, kwargs, disp=True,
+                          maxiter=100, callback=None, retall=False,
+                          full_output=True, hess=None):
+    if not 'basinhopping' in vars(optimize):
+        msg = 'basinhopping solver is not available, use e.g. bfgs instead!'
+        raise ValueError(msg)
+
+    from copy import copy
+    kwargs = copy(kwargs)
+    niter = kwargs.setdefault('niter', 100)
+    niter_success = kwargs.setdefault('niter_success', None)
+    T = kwargs.setdefault('T', 1.0)
+    stepsize = kwargs.setdefault('stepsize', 0.5)
+    interval = kwargs.setdefault('interval', 50)
+    minimizer_kwargs = kwargs.get('minimizer', {})
+    minimizer_kwargs['args'] = fargs
+    minimizer_kwargs['jac'] = score
+    method = minimizer_kwargs.get('method', None)
+    if method and method != 'L-BFGS-B': # l_bfgs_b doesn't take a hessian
+        minimizer_kwargs['hess'] = hess
+
+    res = optimize.basinhopping(f, start_params,
+                                minimizer_kwargs=minimizer_kwargs,
+                                niter=niter, niter_success=niter_success,
+                                T=T, stepsize=stepsize, disp=disp,
+                                callback=callback, interval=interval)
+    if full_output:
+        xopt, fopt, niter, fcalls = res.x, res.fun, res.nit, res.nfev
+        converged = 'completed successfully' in res.message[0]
+        retvals = {'fopt': fopt, 'iterations': niter,
+                   'fcalls': fcalls, 'converged': converged}
+
+    else:
+        xopt = None
+
+    return xopt, retvals
diff --git a/statsmodels/base/tests/test_data.py b/statsmodels/base/tests/test_data.py
index f6ff572e460..a87fa74dd62 100644
--- a/statsmodels/base/tests/test_data.py
+++ b/statsmodels/base/tests/test_data.py
@@ -1,4 +1,5 @@
 import numpy as np
+from numpy.testing import assert_equal, assert_
 import pandas
 import pandas.util.testing as ptesting
 
@@ -386,9 +387,7 @@ def test_endogexog(self):
         np.testing.assert_equal(self.data.exog, self.exog.values[:,None])
 
 def test_alignment():
-    """
-    Fix Issue #206
-    """
+    #Fix Issue #206
     from statsmodels.regression.linear_model import OLS
     from statsmodels.datasets.macrodata import load_pandas
 
@@ -504,6 +503,11 @@ def setupClass(cls):
         X[14,2] = np.nan
         cls.y, cls.X = y, X
 
+    def test_raise_no_missing(self):
+        # smoke test for #1700
+        sm_data.handle_data(np.random.random(20), np.random.random((20, 2)),
+                            'raise')
+
     def test_raise(self):
         np.testing.assert_raises(Exception, sm_data.handle_data,
                                             (self.y, self.X, 'raise'))
@@ -565,6 +569,12 @@ def setupClass(cls):
         X[14,2] = np.nan
         cls.y, cls.X = pandas.Series(y), pandas.DataFrame(X)
 
+    def test_raise_no_missing(self):
+        # smoke test for #1700
+        sm_data.handle_data(pandas.Series(np.random.random(20)),
+                            pandas.DataFrame(np.random.random((20, 2))),
+                            'raise')
+
     def test_raise(self):
         np.testing.assert_raises(Exception, sm_data.handle_data,
                                             (self.y, self.X, 'raise'))
@@ -643,6 +653,150 @@ def test_array_noconstant(self):
         np.testing.assert_equal(data.k_constant, 0)
         np.testing.assert_equal(data.const_idx, None)
 
+
+class TestHandleMissing(object):
+
+    def test_pandas(self):
+        df = ptesting.makeDataFrame()
+        df.values[[2, 5, 10], [2, 3, 1]] = np.nan
+        y, X = df[df.columns[0]], df[df.columns[1:]]
+        data, _ = sm_data.handle_missing(y, X, missing='drop')
+
+        df = df.dropna()
+        y_exp, X_exp = df[df.columns[0]], df[df.columns[1:]]
+        ptesting.assert_frame_equal(data['exog'], X_exp)
+        ptesting.assert_series_equal(data['endog'], y_exp)
+
+    def test_arrays(self):
+        arr = np.random.randn(20, 4)
+        arr[[2, 5, 10], [2, 3, 1]] = np.nan
+        y, X = arr[:,0], arr[:,1:]
+        data, _ = sm_data.handle_missing(y, X, missing='drop')
+
+        bools_mask = np.ones(20, dtype=bool)
+        bools_mask[[2, 5, 10]] = False
+        y_exp = arr[bools_mask, 0]
+        X_exp = arr[bools_mask, 1:]
+        np.testing.assert_array_equal(data['endog'], y_exp)
+        np.testing.assert_array_equal(data['exog'], X_exp)
+
+    def test_pandas_array(self):
+        df = ptesting.makeDataFrame()
+        df.values[[2, 5, 10], [2, 3, 1]] = np.nan
+        y, X = df[df.columns[0]], df[df.columns[1:]].values
+        data, _ = sm_data.handle_missing(y, X, missing='drop')
+
+        df = df.dropna()
+        y_exp, X_exp = df[df.columns[0]], df[df.columns[1:]].values
+        np.testing.assert_array_equal(data['exog'], X_exp)
+        ptesting.assert_series_equal(data['endog'], y_exp)
+
+    def test_array_pandas(self):
+        df = ptesting.makeDataFrame()
+        df.values[[2, 5, 10], [2, 3, 1]] = np.nan
+        y, X = df[df.columns[0]].values, df[df.columns[1:]]
+        data, _ = sm_data.handle_missing(y, X, missing='drop')
+
+        df = df.dropna()
+        y_exp, X_exp = df[df.columns[0]].values, df[df.columns[1:]]
+        ptesting.assert_frame_equal(data['exog'], X_exp)
+        np.testing.assert_array_equal(data['endog'], y_exp)
+
+    def test_noop(self):
+        df = ptesting.makeDataFrame()
+        df.values[[2, 5, 10], [2, 3, 1]] = np.nan
+        y, X = df[df.columns[0]], df[df.columns[1:]]
+        data, _ = sm_data.handle_missing(y, X, missing='none')
+
+        y_exp, X_exp = df[df.columns[0]], df[df.columns[1:]]
+        ptesting.assert_frame_equal(data['exog'], X_exp)
+        ptesting.assert_series_equal(data['endog'], y_exp)
+
+
+class CheckHasConstant(object):
+
+    def test_hasconst(self):
+        for x, result in zip(self.exogs, self.results):
+            mod = self.mod(self.y, x)
+            assert_equal(mod.k_constant, result[0]) #['k_constant'])
+            assert_equal(mod.data.k_constant, result[0])
+            if result[1] is None:
+                assert_(mod.data.const_idx is None)
+            else:
+                assert_equal(mod.data.const_idx, result[1])
+
+            # extra check after fit, some models raise on singular
+            try:
+                res = mod.fit()
+                assert_equal(res.model.k_constant, result[0])
+                assert_equal(res.model.data.k_constant, result[0])
+            except:
+                pass
+
+
+    @classmethod
+    def setup_class(cls):
+        # create data
+        np.random.seed(0)
+        cls.y_c = np.random.randn(20)
+        cls.y_bin = (cls.y_c > 0).astype(int)
+        x1 = np.column_stack((np.ones(20), np.zeros(20)))
+        result1 = (1, 0)
+        x2 = np.column_stack((np.arange(20) < 10.5,
+                              np.arange(20) > 10.5)).astype(float)
+        result2 = (1, None)
+        x3 = np.column_stack((np.arange(20), np.zeros(20.)))
+        result3 = (0, None)
+        x4 = np.column_stack((np.arange(20), np.zeros((20., 2))))
+        result4 = (0, None)
+        x5 = np.column_stack((np.zeros(20), 0.5 * np.ones(20)))
+        result5 = (1, 1)
+        x5b = np.column_stack((np.arange(20), np.ones((20, 3))))
+        result5b = (1, 1)
+        x5c = np.column_stack((np.arange(20), np.ones((20, 3)) * [0.5, 1, 1]))
+        result5c = (1, 2)
+        # implicit and zero column
+        x6 = np.column_stack((np.arange(20) < 10.5,
+                              np.arange(20) > 10.5,
+                              np.zeros(20.))).astype(float)
+        result6 = (1, None)
+        x7 = np.column_stack((np.arange(20) < 10.5,
+                              np.arange(20) > 10.5,
+                              np.zeros((20., 2)))).astype(float)
+        result7 = (1, None)
+
+        cls.exogs = (x1, x2, x3, x4, x5, x5b, x5c, x6, x7)
+        cls.results = (result1, result2, result3, result4, result5, result5b,
+                       result5c, result6, result7)
+
+
+class TestHasConstantOLS(CheckHasConstant):
+
+    def __init__(self):
+        self.setup_class()  # why does nose do it properly
+        from statsmodels.regression.linear_model import OLS
+        self.mod = OLS
+        self.y = self.y_c
+
+
+class TestHasConstantGLM(CheckHasConstant):
+
+    def __init__(self):
+        self.setup_class()  # why does nose do it properly
+        from statsmodels.genmod.generalized_linear_model import GLM
+        from statsmodels.genmod import families
+        self.mod = lambda y, x : GLM(y, x, family=families.Binomial())
+        self.y = self.y_bin
+
+class TestHasConstantLogit(CheckHasConstant):
+
+    def __init__(self):
+        self.setup_class()  # why does nose do it properly
+        from statsmodels.discrete.discrete_model import Logit
+        self.mod = Logit
+        self.y = self.y_bin
+
+
 if __name__ == "__main__":
     import nose
     #nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/statsmodels/base/tests/test_generic_methods.py b/statsmodels/base/tests/test_generic_methods.py
new file mode 100644
index 00000000000..46b8d76078d
--- /dev/null
+++ b/statsmodels/base/tests/test_generic_methods.py
@@ -0,0 +1,245 @@
+# -*- coding: utf-8 -*-
+"""Tests that use cross-checks for generic methods
+
+Should be easy to check consistency across models
+Does not cover tsa
+
+Initial cases copied from test_shrink_pickle
+
+Created on Wed Oct 30 14:01:27 2013
+
+Author: Josef Perktold
+"""
+from statsmodels.compat.python import range
+import numpy as np
+import statsmodels.api as sm
+from statsmodels.compat.scipy import NumpyVersion
+
+from numpy.testing import assert_, assert_allclose
+
+from nose import SkipTest
+import platform
+
+
+iswin = platform.system() == 'Windows'
+npversionless15 = NumpyVersion(np.__version__) < '1.5.0'
+winoldnp = iswin & npversionless15
+
+
+
+class CheckGenericMixin(object):
+
+    def __init__(self):
+        self.predict_kwds = {}
+
+    @classmethod
+    def setup_class(self):
+
+        nobs = 500
+        np.random.seed(987689)
+        x = np.random.randn(nobs, 3)
+        x = sm.add_constant(x)
+        self.exog = x
+        self.xf = 0.25 * np.ones((2, 4))
+
+    def test_ttest_tvalues(self):
+        # test that t_test has same results a params, bse, tvalues, ...
+        res = self.results
+        mat = np.eye(len(res.params))
+        tt = res.t_test(mat)
+
+        assert_allclose(tt.effect, res.params, rtol=1e-12)
+        # TODO: tt.sd and tt.tvalue are 2d also for single regressor, squeeze
+        assert_allclose(np.squeeze(tt.sd), res.bse, rtol=1e-10)
+        assert_allclose(np.squeeze(tt.tvalue), res.tvalues, rtol=1e-12)
+        assert_allclose(tt.pvalue, res.pvalues, rtol=5e-10)
+        assert_allclose(tt.conf_int(), res.conf_int(), rtol=1e-10)
+
+        # test params table frame returned by t_test
+        table_res = np.column_stack((res.params, res.bse, res.tvalues,
+                                    res.pvalues, res.conf_int()))
+        table1 = np.column_stack((tt.effect, tt.sd, tt.tvalue, tt.pvalue,
+                                 tt.conf_int()))
+        table2 = tt.summary_frame().values
+        assert_allclose(table2, table_res, rtol=1e-12)
+
+        # move this to test_attributes ?
+        assert_(hasattr(res, 'use_t'))
+
+        tt = res.t_test(mat[0])
+        tt.summary()   # smoke test for #1323
+        assert_allclose(tt.pvalue, res.pvalues[0], rtol=5e-10)
+
+
+    def test_ftest_pvalues(self):
+        res = self.results
+        use_t = res.use_t
+        k_vars = len(res.params)
+        # check default use_t
+        pvals = [res.wald_test(np.eye(k_vars)[k], use_f=use_t).pvalue
+                                                   for k in range(k_vars)]
+        assert_allclose(pvals, res.pvalues, rtol=5e-10, atol=1e-25)
+
+        # sutomatic use_f based on results class use_t
+        pvals = [res.wald_test(np.eye(k_vars)[k]).pvalue
+                                                   for k in range(k_vars)]
+        assert_allclose(pvals, res.pvalues, rtol=5e-10, atol=1e-25)
+
+        # label for pvalues in summary
+        string_use_t = 'P>|z|' if use_t is False else 'P>|t|'
+        summ = str(res.summary())
+        assert_(string_use_t in summ)
+        summ = str(res.summary2())
+        assert_(string_use_t in summ)
+
+
+    # TODO The following is not (yet) guaranteed across models
+    #@knownfailureif(True)
+    def test_fitted(self):
+        # ignore wrapper for isinstance check
+        from statsmodels.genmod.generalized_linear_model import GLMResults
+        from statsmodels.discrete.discrete_model import DiscreteResults
+        results = self.results._results
+        if (isinstance(results, GLMResults) or
+            isinstance(results, DiscreteResults)):
+            raise SkipTest
+
+        res = self.results
+        fitted = res.fittedvalues
+        assert_allclose(res.model.endog - fitted, res.resid, rtol=1e-12)
+        assert_allclose(fitted, res.predict(), rtol=1e-12)
+
+    def test_predict_types(self):
+        res = self.results
+        # squeeze to make 1d for single regressor test case
+        p_exog = np.squeeze(np.asarray(res.model.exog[:2]))
+
+        # ignore wrapper for isinstance check
+        from statsmodels.genmod.generalized_linear_model import GLMResults
+        from statsmodels.discrete.discrete_model import DiscreteResults
+        results = self.results._results
+        if (isinstance(results, GLMResults) or
+            isinstance(results, DiscreteResults)):
+            # SMOKE test only  TODO
+            res.predict(p_exog)
+            res.predict(p_exog.tolist())
+            res.predict(p_exog[0].tolist())
+        else:
+            fitted = res.fittedvalues[:2]
+            assert_allclose(fitted, res.predict(p_exog), rtol=1e-12)
+            # this needs reshape to column-vector:
+            assert_allclose(fitted, res.predict(np.squeeze(p_exog).tolist()),
+                            rtol=1e-12)
+            # only one prediction:
+            assert_allclose(fitted[:1], res.predict(p_exog[0].tolist()),
+                            rtol=1e-12)
+            assert_allclose(fitted[:1], res.predict(p_exog[0]),
+                            rtol=1e-12)
+
+            # predict doesn't preserve DataFrame, e.g. dot converts to ndarray
+#             import pandas
+#             predicted = res.predict(pandas.DataFrame(p_exog))
+#             assert_(isinstance(predicted, pandas.DataFrame))
+#             assert_allclose(predicted, fitted, rtol=1e-12)
+
+
+#########  subclasses for individual models, unchanged from test_shrink_pickle
+# TODO: check if setup_class is faster than setup
+
+class TestGenericOLS(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        np.random.seed(987689)
+        y = x.sum(1) + np.random.randn(x.shape[0])
+        self.results = sm.OLS(y, self.exog).fit()
+
+
+class TestGenericOLSOneExog(CheckGenericMixin):
+    # check with single regressor (no constant)
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog[:, 1]
+        np.random.seed(987689)
+        y = x + np.random.randn(x.shape[0])
+        self.results = sm.OLS(y, x).fit()
+
+
+class TestGenericWLS(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        np.random.seed(987689)
+        y = x.sum(1) + np.random.randn(x.shape[0])
+        self.results = sm.WLS(y, self.exog, weights=np.ones(len(y))).fit()
+
+
+class TestGenericPoisson(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        np.random.seed(987689)
+        y_count = np.random.poisson(np.exp(x.sum(1) - x.mean()))
+        model = sm.Poisson(y_count, x)  #, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default
+        # use start_params to converge faster
+        start_params = np.array([0.75334818, 0.99425553, 1.00494724, 1.00247112])
+        self.results = model.fit(start_params=start_params, method='bfgs',
+                                 disp=0)
+
+        #TODO: temporary, fixed in master
+        self.predict_kwds = dict(exposure=1, offset=0)
+
+class TestGenericNegativeBinomial(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        np.random.seed(987689)
+        data = sm.datasets.randhie.load()
+        exog = sm.add_constant(data.exog, prepend=False)
+        mod = sm.NegativeBinomial(data.endog, data.exog)
+        start_params = np.array([-0.0565406 , -0.21213599,  0.08783076,
+                                 -0.02991835,  0.22901974,  0.0621026,
+                                  0.06799283,  0.08406688,  0.18530969,
+                                  1.36645452])
+        self.results = mod.fit(start_params=start_params, disp=0)
+
+
+class TestGenericLogit(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        nobs = x.shape[0]
+        np.random.seed(987689)
+        y_bin = (np.random.rand(nobs) < 1.0 / (1 + np.exp(x.sum(1) - x.mean()))).astype(int)
+        model = sm.Logit(y_bin, x)  #, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default
+        # use start_params to converge faster
+        start_params = np.array([-0.73403806, -1.00901514, -0.97754543, -0.95648212])
+        self.results = model.fit(start_params=start_params, method='bfgs', disp=0)
+
+
+class TestGenericRLM(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        np.random.seed(987689)
+        y = x.sum(1) + np.random.randn(x.shape[0])
+        self.results = sm.RLM(y, self.exog).fit()
+
+
+class TestGenericGLM(CheckGenericMixin):
+
+    def setup(self):
+        #fit for each test, because results will be changed by test
+        x = self.exog
+        np.random.seed(987689)
+        y = x.sum(1) + np.random.randn(x.shape[0])
+        self.results = sm.GLM(y, self.exog).fit()
+
+if __name__ == '__main__':
+    pass
diff --git a/statsmodels/base/tests/test_optimize.py b/statsmodels/base/tests/test_optimize.py
index 0973da846c9..711a8c1d33c 100644
--- a/statsmodels/base/tests/test_optimize.py
+++ b/statsmodels/base/tests/test_optimize.py
@@ -1,15 +1,15 @@
 from numpy.testing import assert_
-from statsmodels.base.model import (_fit_mle_newton, _fit_mle_nm,
-                                    _fit_mle_bfgs, _fit_mle_cg,
-                                    _fit_mle_ncg, _fit_mle_powell)
+from statsmodels.base.optimizer import (_fit_newton, _fit_nm,
+                                        _fit_bfgs, _fit_cg,
+                                        _fit_ncg, _fit_powell)
 
 fit_funcs = {
-    'newton': _fit_mle_newton,
-    'nm': _fit_mle_nm,  # Nelder-Mead
-    'bfgs': _fit_mle_bfgs,
-    'cg': _fit_mle_cg,
-    'ncg': _fit_mle_ncg,
-    'powell': _fit_mle_powell
+    'newton': _fit_newton,
+    'nm': _fit_nm,  # Nelder-Mead
+    'bfgs': _fit_bfgs,
+    'cg': _fit_cg,
+    'ncg': _fit_ncg,
+    'powell': _fit_powell
             }
 
 
diff --git a/statsmodels/base/tests/test_shrink_pickle.py b/statsmodels/base/tests/test_shrink_pickle.py
index 07034861536..05b42492a4c 100644
--- a/statsmodels/base/tests/test_shrink_pickle.py
+++ b/statsmodels/base/tests/test_shrink_pickle.py
@@ -5,8 +5,8 @@
 
 Author: Josef Perktold
 """
-
-import pickle
+from __future__ import print_function
+from statsmodels.compat.python import iterkeys, cPickle, BytesIO
 import numpy as np
 import statsmodels.api as sm
 
@@ -22,12 +22,11 @@
 
 
 def check_pickle(obj):
-    from statsmodels.compatnp.py3k import BytesIO
     fh = BytesIO()
-    pickle.dump(obj, fh)
+    cPickle.dump(obj, fh, protocol=cPickle.HIGHEST_PROTOCOL)
     plen = fh.tell()
     fh.seek(0, 0)
-    res = pickle.load(fh)
+    res = cPickle.load(fh)
     fh.close()
     return res, plen
 
@@ -77,8 +76,9 @@ def test_remove_data_pickle(self):
         #for testing attach res
         self.res = res
 
-        #Note: 10000 is just a guess for the limit on the length of the pickle
-        assert_(l < 10000, msg='pickle length not %d < %d' % (l, 10000))
+        #Note: l_max is just a guess for the limit on the length of the pickle
+        l_max = 20000
+        assert_(l < l_max, msg='pickle length not %d < %d' % (l, l_max))
 
         pred3 = results.predict(xf, **pred_kwds)
         np.testing.assert_equal(pred3, pred1)
@@ -88,8 +88,6 @@ def test_remove_data_docstring(self):
 
     def test_pickle_wrapper(self):
 
-        from statsmodels.compatnp.py3k import BytesIO
-
         fh = BytesIO()  # use cPickle with binary content
 
         # test unwrapped results load save pickle
@@ -107,20 +105,20 @@ def test_pickle_wrapper(self):
         # print type(res_unpickled)
         assert_(type(res_unpickled) is type(self.results))
 
-        before = sorted(self.results.__dict__.keys())
-        after = sorted(res_unpickled.__dict__.keys())
+        before = sorted(iterkeys(self.results.__dict__))
+        after = sorted(iterkeys(res_unpickled.__dict__))
         assert_(before == after, msg='not equal %r and %r' % (before, after))
 
-        before = sorted(self.results._results.__dict__.keys())
-        after = sorted(res_unpickled._results.__dict__.keys())
+        before = sorted(iterkeys(self.results._results.__dict__))
+        after = sorted(iterkeys(res_unpickled._results.__dict__))
         assert_(before == after, msg='not equal %r and %r' % (before, after))
 
-        before = sorted(self.results.model.__dict__.keys())
-        after = sorted(res_unpickled.model.__dict__.keys())
+        before = sorted(iterkeys(self.results.model.__dict__))
+        after = sorted(iterkeys(res_unpickled.model.__dict__))
         assert_(before == after, msg='not equal %r and %r' % (before, after))
 
-        before = sorted(self.results._cache.keys())
-        after = sorted(res_unpickled._cache.keys())
+        before = sorted(iterkeys(self.results._cache))
+        after = sorted(iterkeys(res_unpickled._cache))
         assert_(before == after, msg='not equal %r and %r' % (before, after))
 
 
@@ -209,7 +207,7 @@ def setup(self):
                 TestRemoveDataPickleNegativeBinomial,
                 TestRemoveDataPickleLogit, TestRemoveDataPickleRLM,
                 TestRemoveDataPickleGLM]:
-        print cls
+        print(cls)
         cls.setup_class()
         tt = cls()
         tt.setup()
diff --git a/statsmodels/base/wrapper.py b/statsmodels/base/wrapper.py
index 46236d87849..90572d82a33 100644
--- a/statsmodels/base/wrapper.py
+++ b/statsmodels/base/wrapper.py
@@ -2,7 +2,7 @@
 import functools
 
 import numpy as np
-
+from statsmodels.compat.python import get_function_name, iteritems
 
 class ResultsWrapper(object):
     """
@@ -93,11 +93,7 @@ def wrapper(self, *args, **kwargs):
     formatted = inspect.formatargspec(argspec[0], varargs=argspec[1],
                                       defaults=argspec[3])
 
-    try:
-        func_name = func.im_func.func_name
-    except AttributeError:
-        #Python 3
-        func_name = func.__name__
+    func_name = get_function_name(func)
 
     wrapper.__doc__ = "%s%s\n%s" % (func_name, formatted, wrapper.__doc__)
 
@@ -105,7 +101,7 @@ def wrapper(self, *args, **kwargs):
 
 
 def populate_wrapper(klass, wrapping):
-    for meth, how in klass._wrap_methods.iteritems():
+    for meth, how in iteritems(klass._wrap_methods):
         if not hasattr(wrapping, meth):
             continue
 
diff --git a/statsmodels/compat/__init__.py b/statsmodels/compat/__init__.py
new file mode 100644
index 00000000000..5de8af21252
--- /dev/null
+++ b/statsmodels/compat/__init__.py
@@ -0,0 +1 @@
+from .python import *
diff --git a/statsmodels/compatnp/collections.py b/statsmodels/compat/collections.py
similarity index 84%
rename from statsmodels/compatnp/collections.py
rename to statsmodels/compat/collections.py
index 41c7e976f02..c6366b5810d 100644
--- a/statsmodels/compatnp/collections.py
+++ b/statsmodels/compat/collections.py
@@ -8,7 +8,7 @@
 except ImportError:
     #http://code.activestate.com/recipes/576693/
     #author: Raymond Hettinger
-    from ordereddict import OrderedDict
+    from .ordereddict import OrderedDict
 
 try:
     #python >= 2.7
@@ -16,4 +16,4 @@
 except ImportError:
     #http://code.activestate.com/recipes/576611/
     #author: Raymond Hettinger
-    from counter import Counter
+    from .counter import Counter
diff --git a/statsmodels/compatnp/counter.py b/statsmodels/compat/counter.py
similarity index 93%
rename from statsmodels/compatnp/counter.py
rename to statsmodels/compat/counter.py
index a34b16ab596..f6da1e55c1a 100644
--- a/statsmodels/compatnp/counter.py
+++ b/statsmodels/compat/counter.py
@@ -6,10 +6,11 @@
 
 '''
 
-
+from __future__ import print_function
+from .python import filter, iteritems
 from operator import itemgetter
 from heapq import nlargest
-from itertools import repeat, ifilter
+from itertools import repeat
 
 class Counter(dict):
     '''Dict subclass for counting hashable objects.  Sometimes called a bag
@@ -46,8 +47,8 @@ def most_common(self, n=None):
 
         '''
         if n is None:
-            return sorted(self.iteritems(), key=itemgetter(1), reverse=True)
-        return nlargest(n, self.iteritems(), key=itemgetter(1))
+            return sorted(iteritems(self), key=itemgetter(1), reverse=True)
+        return nlargest(n, iteritems(self), key=itemgetter(1))
 
     def elements(self):
         '''Iterator over elements repeating each as many times as its count.
@@ -60,7 +61,7 @@ def elements(self):
         elements() will ignore it.
 
         '''
-        for elem, count in self.iteritems():
+        for elem, count in iteritems(self):
             for _ in repeat(None, count):
                 yield elem
 
@@ -88,7 +89,7 @@ def update(self, iterable=None, **kwds):
             if hasattr(iterable, 'iteritems'):
                 if self:
                     self_get = self.get
-                    for elem, count in iterable.iteritems():
+                    for elem, count in iteritems(iterable):
                         self[elem] = self_get(elem, 0) + count
                 else:
                     dict.update(self, iterable) # fast path when counter is empty
@@ -186,7 +187,7 @@ def __and__(self, other):
         result = Counter()
         if len(self) < len(other):
             self, other = other, self
-        for elem in ifilter(self.__contains__, other):
+        for elem in filter(self.__contains__, other):
             newcount = _min(self[elem], other[elem])
             if newcount > 0:
                 result[elem] = newcount
@@ -195,4 +196,4 @@ def __and__(self, other):
 
 if __name__ == '__main__':
     import doctest
-    print doctest.testmod()
+    print(doctest.testmod())
diff --git a/statsmodels/compatnp/np_compat.py b/statsmodels/compat/numpy.py
similarity index 55%
rename from statsmodels/compatnp/np_compat.py
rename to statsmodels/compat/numpy.py
index 393a3aa7f17..c73d34d1882 100644
--- a/statsmodels/compatnp/np_compat.py
+++ b/statsmodels/compat/numpy.py
@@ -1,4 +1,4 @@
-'''Compatibility functions for numpy versions in lib
+"""Compatibility functions for numpy versions in lib
 
 np.unique
 ---------
@@ -44,12 +44,12 @@
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 
-'''
-
-
+"""
+from __future__ import absolute_import
+from .scipy import NumpyVersion
 import numpy as np
 
-if np.__version__ < '1.6.2':
+if NumpyVersion(np.__version__) < '1.6.2':
     npc_unique = np.unique
 else:
 
@@ -157,3 +157,92 @@ def npc_unique(ar, return_index=False, return_inverse=False):
             ar.sort()
             flag = np.concatenate(([True], ar[1:] != ar[:-1]))
             return ar[flag]
+
+if NumpyVersion(np.__version__) >= '1.7.1':
+    np_matrix_rank = np.linalg.matrix_rank
+else:
+    def np_matrix_rank(M, tol=None):
+        """
+        Return matrix rank of array using SVD method
+
+        Rank of the array is the number of SVD singular values of the array that are
+        greater than `tol`.
+
+        Parameters
+        ----------
+        M : {(M,), (M, N)} array_like
+            array of <=2 dimensions
+        tol : {None, float}, optional
+        threshold below which SVD values are considered zero. If `tol` is
+        None, and ``S`` is an array with singular values for `M`, and
+        ``eps`` is the epsilon value for datatype of ``S``, then `tol` is
+        set to ``S.max() * max(M.shape) * eps``.
+
+        Notes
+        -----
+        The default threshold to detect rank deficiency is a test on the magnitude
+        of the singular values of `M`.  By default, we identify singular values less
+        than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
+        the symbols defined above). This is the algorithm MATLAB uses [1].  It also
+        appears in *Numerical recipes* in the discussion of SVD solutions for linear
+        least squares [2].
+
+        This default threshold is designed to detect rank deficiency accounting for
+        the numerical errors of the SVD computation.  Imagine that there is a column
+        in `M` that is an exact (in floating point) linear combination of other
+        columns in `M`. Computing the SVD on `M` will not produce a singular value
+        exactly equal to 0 in general: any difference of the smallest SVD value from
+        0 will be caused by numerical imprecision in the calculation of the SVD.
+        Our threshold for small SVD values takes this numerical imprecision into
+        account, and the default threshold will detect such numerical rank
+        deficiency.  The threshold may declare a matrix `M` rank deficient even if
+        the linear combination of some columns of `M` is not exactly equal to
+        another column of `M` but only numerically very close to another column of
+        `M`.
+
+        We chose our default threshold because it is in wide use.  Other thresholds
+        are possible.  For example, elsewhere in the 2007 edition of *Numerical
+        recipes* there is an alternative threshold of ``S.max() *
+        np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
+        this threshold as being based on "expected roundoff error" (p 71).
+
+        The thresholds above deal with floating point roundoff error in the
+        calculation of the SVD.  However, you may have more information about the
+        sources of error in `M` that would make you consider other tolerance values
+        to detect *effective* rank deficiency.  The most useful measure of the
+        tolerance depends on the operations you intend to use on your matrix.  For
+        example, if your data come from uncertain measurements with uncertainties
+        greater than floating point epsilon, choosing a tolerance near that
+        uncertainty may be preferable.  The tolerance may be absolute if the
+        uncertainties are absolute rather than relative.
+
+        References
+        ----------
+        .. [1] MATLAB reference documention, "Rank"
+            http://www.mathworks.com/help/techdoc/ref/rank.html
+        .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
+            "Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
+            page 795.
+
+        Examples
+        --------
+        >>> from numpy.linalg import matrix_rank
+        >>> matrix_rank(np.eye(4)) # Full rank matrix
+        4
+        >>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
+        >>> matrix_rank(I)
+        3
+        >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
+        1
+        >>> matrix_rank(np.zeros((4,)))
+        0
+        """
+        M = np.asarray(M)
+        if M.ndim > 2:
+            raise TypeError('array should have 2 or fewer dimensions')
+        if M.ndim < 2:
+            return int(not all(M == 0))
+        S = np.linalg.svd(M, compute_uv=False)
+        if tol is None:
+            tol = S.max() * max(M.shape) * np.finfo(S.dtype).eps
+        return np.sum(S > tol)
diff --git a/statsmodels/compatnp/ordereddict.py b/statsmodels/compat/ordereddict.py
similarity index 98%
rename from statsmodels/compatnp/ordereddict.py
rename to statsmodels/compat/ordereddict.py
index 9a08a835a99..9a4da3b09da 100644
--- a/statsmodels/compatnp/ordereddict.py
+++ b/statsmodels/compat/ordereddict.py
@@ -4,7 +4,7 @@
 #Author: Raymond Hettinger
 #License: MIT License
 #http://code.activestate.com/recipes/576693/ revision 9, downloaded 2012-03-28
-
+from .python import iterkeys, iteritems
 try:
     from thread import get_ident as _get_ident
 except ImportError:
@@ -165,12 +165,12 @@ def update(*args, **kwds):
             for key in other:
                 self[key] = other[key]
         elif hasattr(other, 'keys'):
-            for key in other.keys():
+            for key in iterkeys(other):
                 self[key] = other[key]
         else:
             for key, value in other:
                 self[key] = value
-        for key, value in kwds.items():
+        for key, value in iteritems(kwds):
             self[key] = value
 
     __update = update  # let subclasses override update without breaking __init__
diff --git a/statsmodels/compat/python.py b/statsmodels/compat/python.py
new file mode 100644
index 00000000000..2b4b43ca60e
--- /dev/null
+++ b/statsmodels/compat/python.py
@@ -0,0 +1,233 @@
+"""
+Compatibility tools for differences between Python 2 and 3
+"""
+
+import functools
+import itertools
+import sys
+
+PY3 = (sys.version_info[0] >= 3)
+PY3_2 = sys.version_info[:2] == (3, 2)
+
+try:
+    import __builtin__ as builtins
+    # not writeable when instantiated with string, doesn't handle unicode well
+    from cStringIO import StringIO as cStringIO
+    # always writeable
+    from StringIO import StringIO
+
+    BytesIO = StringIO
+    import cPickle
+    pickle = cPickle
+    import urllib2
+    import urlparse
+except ImportError:
+    import builtins
+    from io import StringIO, BytesIO
+
+    cStringIO = StringIO
+    import pickle as cPickle
+    pickle = cPickle
+    import urllib.request
+    import urllib.parse
+    from urllib.request import HTTPError, urlretrieve
+
+if PY3:
+    import io
+    bytes = bytes
+    str = str
+    asunicode = str
+
+    def asbytes(s):
+        if isinstance(s, bytes):
+            return s
+        return s.encode('latin1')
+
+    def asstr(s):
+        if isinstance(s, str):
+            return s
+        return s.decode('latin1')
+
+    def asstr2(s):  #added JP, not in numpy version
+        if isinstance(s, str):
+            return s
+        elif isinstance(s, bytes):
+            return s.decode('latin1')
+        else:
+            return str(s)
+
+    def isfileobj(f):
+        return isinstance(f, io.FileIO)
+
+    def open_latin1(filename, mode='r'):
+        return open(filename, mode=mode, encoding='iso-8859-1')
+
+    strchar = 'U'
+
+    # have to explicitly put builtins into the namespace
+    range = range
+    map = map
+    zip = zip
+    filter = filter
+    reduce = functools.reduce
+    long = int
+    unichr = chr
+    zip_longest = itertools.zip_longest
+
+    # list-producing versions of the major Python iterating functions
+    def lrange(*args, **kwargs):
+        return list(range(*args, **kwargs))
+
+    def lzip(*args, **kwargs):
+        return list(zip(*args, **kwargs))
+
+    def lmap(*args, **kwargs):
+        return list(map(*args, **kwargs))
+
+    def lfilter(*args, **kwargs):
+        return list(filter(*args, **kwargs))
+
+    urlopen = urllib.request.urlopen
+    urljoin = urllib.parse.urljoin
+    urlretrieve = urllib.request.urlretrieve
+    string_types = str
+    input = input
+
+else:
+    bytes = str
+    str = str
+    asbytes = str
+    asstr = str
+    asstr2 = str
+    strchar = 'S'
+
+    def isfileobj(f):
+        return isinstance(f, file)
+
+    def asunicode(s):
+        if isinstance(s, str):
+            return s
+        return s.decode('ascii')
+
+    def open_latin1(filename, mode='r'):
+        return open(filename, mode=mode)
+
+    # import iterator versions of these functions
+    range = xrange
+    zip = itertools.izip
+    filter = itertools.ifilter
+    map = itertools.imap
+    reduce = reduce
+    long = long
+    unichr = unichr
+    zip_longest = itertools.izip_longest
+
+    # Python 2-builtin ranges produce lists
+    lrange = builtins.range
+    lzip = builtins.zip
+    lmap = builtins.map
+    lfilter = builtins.filter
+
+    urlopen = urllib2.urlopen
+    urljoin = urlparse.urljoin
+    HTTPError = urllib2.HTTPError
+    string_types = basestring
+
+    input = raw_input
+
+
+def getexception():
+    return sys.exc_info()[1]
+
+
+def asbytes_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, str)):
+        return [asbytes_nested(y) for y in x]
+    else:
+        return asbytes(x)
+
+
+def asunicode_nested(x):
+    if hasattr(x, '__iter__') and not isinstance(x, (bytes, str)):
+        return [asunicode_nested(y) for y in x]
+    else:
+        return asunicode(x)
+
+
+try:
+    advance_iterator = next
+except NameError:
+    def advance_iterator(it):
+        return it.next()
+next = advance_iterator
+
+
+try:
+    callable = callable
+except NameError:
+    def callable(obj):
+        return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
+
+def iteritems(obj, **kwargs):
+    """replacement for six's iteritems for Python2/3 compat
+       uses 'iteritems' if available and otherwise uses 'items'.
+
+       Passes kwargs to method.
+    """
+    func = getattr(obj, "iteritems", None)
+    if not func:
+        func = obj.items
+    return func(**kwargs)
+
+
+def iterkeys(obj, **kwargs):
+    func = getattr(obj, "iterkeys", None)
+    if not func:
+        func = obj.keys
+    return func(**kwargs)
+
+
+def itervalues(obj, **kwargs):
+    func = getattr(obj, "itervalues", None)
+    if not func:
+        func = obj.values
+    return func(**kwargs)
+
+
+def get_function_name(func):
+    try:
+        return func.im_func.func_name
+    except AttributeError:
+        #Python 3
+        return func.__name__
+
+def get_class(func):
+    try:
+        return func.im_class
+    except AttributeError:
+        #Python 3
+        return func.__self__.__class__
+
+try:
+    combinations = itertools.combinations
+except:
+    # Python 2.6 only
+    def combinations(iterable, r):
+        # combinations('ABCD', 2) --> AB AC AD BC BD CD
+        # combinations(lrange(4), 3) --> 012 013 023 123
+        pool = tuple(iterable)
+        n = len(pool)
+        if r > n:
+            return
+        indices = lrange(r)
+        yield tuple(pool[i] for i in indices)
+        while True:
+            for i in reversed(lrange(r)):
+                if indices[i] != i + n - r:
+                    break
+            else:
+                return
+            indices[i] += 1
+            for j in range(i+1, r):
+                indices[j] = indices[j-1] + 1
+            yield tuple(pool[i] for i in indices)
diff --git a/statsmodels/compat/scipy.py b/statsmodels/compat/scipy.py
new file mode 100644
index 00000000000..72b1bf215a7
--- /dev/null
+++ b/statsmodels/compat/scipy.py
@@ -0,0 +1,208 @@
+from __future__ import absolute_import
+import numpy as np
+
+
+def _bit_length_26(x):
+    if x == 0:
+        return 0
+    elif x == 1:
+        return 1
+    else:
+        return len(bin(x)) - 2
+
+
+try:
+    from scipy.lib._version import NumpyVersion
+except ImportError:
+    import re
+    from .python import string_types
+
+    class NumpyVersion():
+        """Parse and compare numpy version strings.
+
+        Numpy has the following versioning scheme (numbers given are examples; they
+        can be >9) in principle):
+
+        - Released version: '1.8.0', '1.8.1', etc.
+        - Alpha: '1.8.0a1', '1.8.0a2', etc.
+        - Beta: '1.8.0b1', '1.8.0b2', etc.
+        - Release candidates: '1.8.0rc1', '1.8.0rc2', etc.
+        - Development versions: '1.8.0.dev-f1234afa' (git commit hash appended)
+        - Development versions after a1: '1.8.0a1.dev-f1234afa',
+                                        '1.8.0b2.dev-f1234afa',
+                                        '1.8.1rc1.dev-f1234afa', etc.
+        - Development versions (no git hash available): '1.8.0.dev-Unknown'
+
+        Comparing needs to be done against a valid version string or other
+        `NumpyVersion` instance.
+
+        Parameters
+        ----------
+        vstring : str
+            Numpy version string (``np.__version__``).
+
+        Notes
+        -----
+        All dev versions of the same (pre-)release compare equal.
+
+        Examples
+        --------
+        >>> from scipy.lib._version import NumpyVersion
+        >>> if NumpyVersion(np.__version__) < '1.7.0':
+        ...     print('skip')
+        skip
+
+        >>> NumpyVersion('1.7')  # raises ValueError, add ".0"
+
+        """
+
+        def __init__(self, vstring):
+            self.vstring = vstring
+            ver_main = re.match(r'\d[.]\d+[.]\d+', vstring)
+            if not ver_main:
+                raise ValueError("Not a valid numpy version string")
+
+            self.version = ver_main.group()
+            self.major, self.minor, self.bugfix = [int(x) for x in
+                                                   self.version.split('.')]
+            if len(vstring) == ver_main.end():
+                self.pre_release = 'final'
+            else:
+                alpha = re.match(r'a\d', vstring[ver_main.end():])
+                beta = re.match(r'b\d', vstring[ver_main.end():])
+                rc = re.match(r'rc\d', vstring[ver_main.end():])
+                pre_rel = [m for m in [alpha, beta, rc] if m is not None]
+                if pre_rel:
+                    self.pre_release = pre_rel[0].group()
+                else:
+                    self.pre_release = ''
+
+            self.is_devversion = bool(re.search(r'.dev-', vstring))
+
+        def _compare_version(self, other):
+            """Compare major.minor.bugfix"""
+            if self.major == other.major:
+                if self.minor == other.minor:
+                    if self.bugfix == other.bugfix:
+                        vercmp = 0
+                    elif self.bugfix > other.bugfix:
+                        vercmp = 1
+                    else:
+                        vercmp = -1
+                elif self.minor > other.minor:
+                    vercmp = 1
+                else:
+                    vercmp = -1
+            elif self.major > other.major:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare_pre_release(self, other):
+            """Compare alpha/beta/rc/final."""
+            if self.pre_release == other.pre_release:
+                vercmp = 0
+            elif self.pre_release == 'final':
+                vercmp = 1
+            elif other.pre_release == 'final':
+                vercmp = -1
+            elif self.pre_release > other.pre_release:
+                vercmp = 1
+            else:
+                vercmp = -1
+
+            return vercmp
+
+        def _compare(self, other):
+            if not isinstance(other, (string_types, NumpyVersion)):
+                raise ValueError("Invalid object to compare with NumpyVersion.")
+
+            if isinstance(other, string_types):
+                other = NumpyVersion(other)
+
+            vercmp = self._compare_version(other)
+            if vercmp == 0:
+                # Same x.y.z version, check for alpha/beta/rc
+                vercmp = self._compare_pre_release(other)
+                if vercmp == 0:
+                    # Same version and same pre-release, check if dev version
+                    if self.is_devversion is other.is_devversion:
+                        vercmp = 0
+                    elif self.is_devversion:
+                        vercmp = -1
+                    else:
+                        vercmp = 1
+
+            return vercmp
+
+        def __lt__(self, other):
+            return self._compare(other) < 0
+
+        def __le__(self, other):
+            return self._compare(other) <= 0
+
+        def __eq__(self, other):
+            return self._compare(other) == 0
+
+        def __ne__(self, other):
+            return self._compare(other) != 0
+
+        def __gt__(self, other):
+            return self._compare(other) > 0
+
+        def __ge__(self, other):
+            return self._compare(other) >= 0
+
+        def __repr(self):
+            return "NumpyVersion(%s)" % self.vstring
+
+
+def _next_regular(target):
+    """
+    Find the next regular number greater than or equal to target.
+    Regular numbers are composites of the prime factors 2, 3, and 5.
+    Also known as 5-smooth numbers or Hamming numbers, these are the optimal
+    size for inputs to FFTPACK.
+
+    Target must be a positive integer.
+    """
+    if target <= 6:
+        return target
+
+    # Quickly check if it's already a power of 2
+    if not (target & (target - 1)):
+        return target
+
+    match = float('inf')  # Anything found will be smaller
+    p5 = 1
+    while p5 < target:
+        p35 = p5
+        while p35 < target:
+            # Ceiling integer division, avoiding conversion to float
+            # (quotient = ceil(target / p35))
+            quotient = -(-target // p35)
+            # Quickly find next power of 2 >= quotient
+            try:
+                p2 = 2 ** ((quotient - 1).bit_length())
+            except AttributeError:
+                # Fallback for Python <2.7
+                p2 = 2 ** _bit_length_26(quotient - 1)
+
+            N = p2 * p35
+            if N == target:
+                return N
+            elif N < match:
+                match = N
+            p35 *= 3
+            if p35 == target:
+                return p35
+        if p35 < match:
+            match = p35
+        p5 *= 5
+        if p5 == target:
+            return p5
+    if p5 < match:
+        match = p5
+    return match
diff --git a/statsmodels/compatnp/__init__.py b/statsmodels/compat/tests/__init__.py
similarity index 100%
rename from statsmodels/compatnp/__init__.py
rename to statsmodels/compat/tests/__init__.py
diff --git a/statsmodels/compat/tests/test_collections.py b/statsmodels/compat/tests/test_collections.py
new file mode 100644
index 00000000000..0948ecf6d33
--- /dev/null
+++ b/statsmodels/compat/tests/test_collections.py
@@ -0,0 +1,11 @@
+from statsmodels.compat import iteritems
+from statsmodels.compat.collections import Counter
+from numpy.testing import assert_
+
+
+def test_counter():
+    #just check a basic example
+    c = Counter('gallahad')
+    res = [('a', 3), ('d', 1), ('g', 1), ('h', 1), ('l', 2)]
+    msg = 'gallahad fails\n'+repr(sorted(iteritems(c)))
+    assert_(sorted(iteritems(c)) == res, msg=msg)
diff --git a/statsmodels/compatnp/tests/test_itercompat.py b/statsmodels/compat/tests/test_itercompat.py
similarity index 90%
rename from statsmodels/compatnp/tests/test_itercompat.py
rename to statsmodels/compat/tests/test_itercompat.py
index 88a27a6c506..9479b2ccbc5 100644
--- a/statsmodels/compatnp/tests/test_itercompat.py
+++ b/statsmodels/compat/tests/test_itercompat.py
@@ -5,10 +5,9 @@
 
 Author: Josef Perktold
 """
-
+from statsmodels.compat import lrange, zip_longest, combinations
 from numpy.testing import assert_
 
-from statsmodels.compatnp.iter_compat import zip_longest, combinations
 
 def test_zip_longest():
     lili = [['a0', 'b0', 'c0', 'd0'],
@@ -31,7 +30,7 @@ def test_combinations():
                ('C', 'D')]
     assert_(actual == desired, '%r not equal %r' % (actual, desired))
 
-    actual = list(combinations(range(4), 3))
+    actual = list(combinations(lrange(4), 3))
     desired = [(0, 1, 2), (0, 1, 3), (0, 2, 3), (1, 2, 3)]
     assert_(actual == desired, '%r not equal %r' % (actual, desired))
 
diff --git a/statsmodels/compat/tests/test_scipy_compat.py b/statsmodels/compat/tests/test_scipy_compat.py
new file mode 100644
index 00000000000..028c2f2fb82
--- /dev/null
+++ b/statsmodels/compat/tests/test_scipy_compat.py
@@ -0,0 +1,85 @@
+from numpy.testing import assert_equal
+from statsmodels.compat.scipy import _next_regular
+
+
+def test_next_regular():
+    hams = {
+        1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 8, 8: 8, 14: 15, 15: 15,
+        16: 16, 17: 18, 1021: 1024, 1536: 1536, 51200000: 51200000,
+        510183360: 510183360, 510183360+1: 512000000, 511000000: 512000000,
+        854296875: 854296875, 854296875+1: 859963392,
+        196608000000: 196608000000, 196608000000+1: 196830000000,
+        8789062500000: 8789062500000, 8789062500000+1: 8796093022208,
+        206391214080000: 206391214080000, 206391214080000+1: 206624260800000,
+        470184984576000: 470184984576000, 470184984576000+1: 470715894135000,
+        7222041363087360: 7222041363087360,
+        7222041363087360+1: 7230196133913600,
+
+        # power of 5    5**23
+        11920928955078125: 11920928955078125,
+        11920928955078125-1: 11920928955078125,
+
+        # power of 3    3**34
+        16677181699666569: 16677181699666569,
+        16677181699666569-1: 16677181699666569,
+
+        # power of 2   2**54
+        18014398509481984: 18014398509481984,
+        18014398509481984-1: 18014398509481984,
+
+        # above this, int(ceil(n)) == int(ceil(n+1))
+        19200000000000000: 19200000000000000,
+        19200000000000000+1: 19221679687500000,
+
+        288230376151711744:   288230376151711744,
+        288230376151711744+1: 288325195312500000,
+        288325195312500000-1: 288325195312500000,
+        288325195312500000:   288325195312500000,
+        288325195312500000+1: 288555831593533440,
+
+        # power of 3    3**83
+        3990838394187339929534246675572349035227-1:
+        3990838394187339929534246675572349035227,
+        3990838394187339929534246675572349035227:
+        3990838394187339929534246675572349035227,
+
+        # power of 2     2**135
+        43556142965880123323311949751266331066368-1:
+        43556142965880123323311949751266331066368,
+        43556142965880123323311949751266331066368:
+        43556142965880123323311949751266331066368,
+
+        # power of 5      5**57
+        6938893903907228377647697925567626953125-1:
+        6938893903907228377647697925567626953125,
+        6938893903907228377647697925567626953125:
+        6938893903907228377647697925567626953125,
+
+        # http://www.drdobbs.com/228700538
+        # 2**96 * 3**1 * 5**13
+        290142196707511001929482240000000000000-1:
+        290142196707511001929482240000000000000,
+        290142196707511001929482240000000000000:
+        290142196707511001929482240000000000000,
+        290142196707511001929482240000000000000+1:
+        290237644800000000000000000000000000000,
+
+        # 2**36 * 3**69 * 5**7
+        4479571262811807241115438439905203543080960000000-1:
+        4479571262811807241115438439905203543080960000000,
+        4479571262811807241115438439905203543080960000000:
+        4479571262811807241115438439905203543080960000000,
+        4479571262811807241115438439905203543080960000000+1:
+        4480327901140333639941336854183943340032000000000,
+
+        # 2**37 * 3**44 * 5**42
+        30774090693237851027531250000000000000000000000000000000000000-1:
+        30774090693237851027531250000000000000000000000000000000000000,
+        30774090693237851027531250000000000000000000000000000000000000:
+        30774090693237851027531250000000000000000000000000000000000000,
+        30774090693237851027531250000000000000000000000000000000000000+1:
+        30778180617309082445871527002041377406962596539492679680000000,
+    }
+
+    for x, y in hams.items():
+        assert_equal(_next_regular(x), y)
diff --git a/statsmodels/compatnp/iter_compat.py b/statsmodels/compatnp/iter_compat.py
deleted file mode 100644
index 2daebbb1702..00000000000
--- a/statsmodels/compatnp/iter_compat.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-
-Created on Wed Feb 29 10:12:38 2012
-
-Author: Josef Perktold
-License: BSD-3
-
-"""
-
-import itertools
-
-try:
-    #python 2.6, 2.7
-    zip_longest = itertools.izip_longest
-    pass
-except AttributeError:
-    #python 3.2
-    zip_longest = itertools.zip_longest
-
-try:
-    from itertools import combinations
-except ImportError:
-    #from python 2.6 documentation
-    def combinations(iterable, r):
-        # combinations('ABCD', 2) --> AB AC AD BC BD CD
-        # combinations(range(4), 3) --> 012 013 023 123
-        pool = tuple(iterable)
-        n = len(pool)
-        if r > n:
-            return
-        indices = range(r)
-        yield tuple(pool[i] for i in indices)
-        while True:
-            for i in reversed(range(r)):
-                if indices[i] != i + n - r:
-                    break
-            else:
-                return
-            indices[i] += 1
-            for j in range(i+1, r):
-                indices[j] = indices[j-1] + 1
-            yield tuple(pool[i] for i in indices)
diff --git a/statsmodels/compatnp/py3k.py b/statsmodels/compatnp/py3k.py
deleted file mode 100644
index aab8807205e..00000000000
--- a/statsmodels/compatnp/py3k.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""
-Python 3 compatibility tools.
-
-"""
-
-__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
-           'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
-           'asstr', 'open_latin1']
-
-import sys
-
-if sys.version_info[0] >= 3:
-    import io
-    bytes = bytes
-    unicode = str
-    asunicode = str
-    def asbytes(s):
-        if isinstance(s, bytes):
-            return s
-        return s.encode('latin1')
-    def asstr(s):
-        if isinstance(s, str):
-            return s
-        return s.decode('latin1')
-    def asstr2(s):  #added JP, not in numpy version
-        if isinstance(s, str):
-            return s
-        elif isinstance(s, bytes):
-            return s.decode('latin1')
-        else:
-            return str(s)
-    def isfileobj(f):
-        return isinstance(f, io.FileIO)
-    def open_latin1(filename, mode='r'):
-        return open(filename, mode=mode, encoding='iso-8859-1')
-    strchar = 'U'
-    from io import BytesIO, StringIO  #statsmodels
-else:
-    bytes = str
-    unicode = unicode
-    asbytes = str
-    asstr = str
-    asstr2 = str
-    strchar = 'S'
-    def isfileobj(f):
-        return isinstance(f, file)
-    def asunicode(s):
-        if isinstance(s, unicode):
-            return s
-        return s.decode('ascii')
-    def open_latin1(filename, mode='r'):
-        return open(filename, mode=mode)
-    from StringIO import StringIO
-    BytesIO = StringIO
-
-def getexception():
-    return sys.exc_info()[1]
-
-def asbytes_nested(x):
-    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
-        return [asbytes_nested(y) for y in x]
-    else:
-        return asbytes(x)
-
-def asunicode_nested(x):
-    if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
-        return [asunicode_nested(y) for y in x]
-    else:
-        return asunicode(x)
diff --git a/statsmodels/compatnp/tests/test_collections.py b/statsmodels/compatnp/tests/test_collections.py
deleted file mode 100644
index 34cbc43d030..00000000000
--- a/statsmodels/compatnp/tests/test_collections.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-from numpy.testing import assert_
-from statsmodels.compatnp.collections import Counter
-
-
-def test_counter():
-    #just check a basic example
-    c = Counter('gallahad')
-    res = [('a', 3), ('d', 1), ('g', 1), ('h', 1), ('l', 2)]
-    msg = 'gallahad fails\n'+repr(sorted(c.items()))
-    assert_(sorted(c.items()) == res, msg=msg)
diff --git a/statsmodels/datasets/__init__.py b/statsmodels/datasets/__init__.py
index 7f618bbc795..b8ec13c4787 100644
--- a/statsmodels/datasets/__init__.py
+++ b/statsmodels/datasets/__init__.py
@@ -3,7 +3,7 @@
 """
 #__all__ = filter(lambda s:not s.startswith('_'),dir())
 from . import (anes96, cancer, committee, ccard, copper, cpunish, elnino,
-               engel, grunfeld, longley, macrodata, nile, randhie, scotland,
-               spector, stackloss, star98, strikes, sunspots, fair, heart,
-               statecrime)
-from utils import get_rdataset, get_data_home, clear_data_home
+               engel, grunfeld, longley, macrodata, modechoice, nile, randhie,
+               scotland, spector, stackloss, star98, strikes, sunspots, fair,
+               heart, statecrime, co2)
+from .utils import get_rdataset, get_data_home, clear_data_home
diff --git a/statsmodels/datasets/anes96/__init__.py b/statsmodels/datasets/anes96/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/anes96/__init__.py
+++ b/statsmodels/datasets/anes96/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/anes96/data.py b/statsmodels/datasets/anes96/data.py
index fb57c860c4e..8056456c34f 100644
--- a/statsmodels/datasets/anes96/data.py
+++ b/statsmodels/datasets/anes96/data.py
@@ -14,74 +14,75 @@
 
 DESCRLONG   = DESCRSHORT
 
-NOTE        = """
-Number of observations - 944
-Numner of variables - 10
+NOTE        = """::
 
-Variables name definitions::
+    Number of observations - 944
+    Number of variables - 10
 
-        popul - Census place population in 1000s
-        TVnews - Number of times per week that respondent watches TV news.
-        PID - Party identification of respondent.
-            0 - Strong Democrat
-            1 - Weak Democrat
-            2 - Independent-Democrat
-            3 - Independent-Indpendent
-            4 - Independent-Republican
-            5 - Weak Republican
-            6 - Strong Republican
-        age : Age of respondent.
-        educ - Education level of respondent
-            1 - 1-8 grades
-            2 - Some high school
-            3 - High school graduate
-            4 - Some college
-            5 - College degree
-            6 - Master's degree
-            7 - PhD
-        income - Income of household
-            1  - None or less than $2,999
-            2  - $3,000-$4,999
-            3  - $5,000-$6,999
-            4  - $7,000-$8,999
-            5  - $9,000-$9,999
-            6  - $10,000-$10,999
-            7  - $11,000-$11,999
-            8  - $12,000-$12,999
-            9  - $13,000-$13,999
-            10 - $14,000-$14.999
-            11 - $15,000-$16,999
-            12 - $17,000-$19,999
-            13 - $20,000-$21,999
-            14 - $22,000-$24,999
-            15 - $25,000-$29,999
-            16 - $30,000-$34,999
-            17 - $35,000-$39,999
-            18 - $40,000-$44,999
-            19 - $45,000-$49,999
-            20 - $50,000-$59,999
-            21 - $60,000-$74,999
-            22 - $75,000-89,999
-            23 - $90,000-$104,999
-            24 - $105,000 and over
-        vote - Expected vote
-            0 - Clinton
-            1 - Dole
-        The following 3 variables all take the values:
-            1 - Extremely liberal
-            2 - Liberal
-            3 - Slightly liberal
-            4 - Moderate
-            5 - Slightly conservative
-            6 - Conservative
-            7 - Extremely Conservative
-        selfLR - Respondent's self-reported political leanings from "Left"
-            to "Right".
-        ClinLR - Respondents impression of Bill Clinton's political
-            leanings from "Left" to "Right".
-        DoleLR  - Respondents impression of Bob Dole's political leanings
-            from "Left" to "Right".
-        logpopul - log(popul + .1)
+    Variables name definitions::
+
+            popul - Census place population in 1000s
+            TVnews - Number of times per week that respondent watches TV news.
+            PID - Party identification of respondent.
+                0 - Strong Democrat
+                1 - Weak Democrat
+                2 - Independent-Democrat
+                3 - Independent-Indpendent
+                4 - Independent-Republican
+                5 - Weak Republican
+                6 - Strong Republican
+            age : Age of respondent.
+            educ - Education level of respondent
+                1 - 1-8 grades
+                2 - Some high school
+                3 - High school graduate
+                4 - Some college
+                5 - College degree
+                6 - Master's degree
+                7 - PhD
+            income - Income of household
+                1  - None or less than $2,999
+                2  - $3,000-$4,999
+                3  - $5,000-$6,999
+                4  - $7,000-$8,999
+                5  - $9,000-$9,999
+                6  - $10,000-$10,999
+                7  - $11,000-$11,999
+                8  - $12,000-$12,999
+                9  - $13,000-$13,999
+                10 - $14,000-$14.999
+                11 - $15,000-$16,999
+                12 - $17,000-$19,999
+                13 - $20,000-$21,999
+                14 - $22,000-$24,999
+                15 - $25,000-$29,999
+                16 - $30,000-$34,999
+                17 - $35,000-$39,999
+                18 - $40,000-$44,999
+                19 - $45,000-$49,999
+                20 - $50,000-$59,999
+                21 - $60,000-$74,999
+                22 - $75,000-89,999
+                23 - $90,000-$104,999
+                24 - $105,000 and over
+            vote - Expected vote
+                0 - Clinton
+                1 - Dole
+            The following 3 variables all take the values:
+                1 - Extremely liberal
+                2 - Liberal
+                3 - Slightly liberal
+                4 - Moderate
+                5 - Slightly conservative
+                6 - Conservative
+                7 - Extremely Conservative
+            selfLR - Respondent's self-reported political leanings from "Left"
+                to "Right".
+            ClinLR - Respondents impression of Bill Clinton's political
+                leanings from "Left" to "Right".
+            DoleLR  - Respondents impression of Bob Dole's political leanings
+                from "Left" to "Right".
+            logpopul - log(popul + .1)
 """
 
 from numpy import recfromtxt, column_stack, array, log
diff --git a/statsmodels/datasets/cancer/__init__.py b/statsmodels/datasets/cancer/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/cancer/__init__.py
+++ b/statsmodels/datasets/cancer/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/cancer/data.py b/statsmodels/datasets/cancer/data.py
index b515f3a345d..55e48e5ee13 100755
--- a/statsmodels/datasets/cancer/data.py
+++ b/statsmodels/datasets/cancer/data.py
@@ -7,7 +7,7 @@
 SOURCE      = """
 This is the breast cancer data used in Owen's empirical likelihood.  It is taken from
 Rice, J.A. Mathematical Statistics and Data Analysis.
-http://www.thomsonedu.com/statistics/discipline_content/dataLibrary.html
+http://www.cengage.com/statistics/discipline_content/dataLibrary.html
 """
 
 DESCRSHORT  = """Breast Cancer and county population"""
@@ -15,13 +15,14 @@
 DESCRLONG   = """The number of breast cancer observances in various counties"""
 
 #suggested notes
-NOTE        = """
-Number of observations: 301
-Number of variables: 2
-Variable name definitions:
+NOTE        = """::
 
-    cancer - The number of breast cancer observances
-    population - The population of the county
+    Number of observations: 301
+    Number of variables: 2
+    Variable name definitions:
+
+        cancer - The number of breast cancer observances
+        population - The population of the county
 
 """
 
diff --git a/statsmodels/datasets/ccard/__init__.py b/statsmodels/datasets/ccard/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/ccard/__init__.py
+++ b/statsmodels/datasets/ccard/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/ccard/data.py b/statsmodels/datasets/ccard/data.py
index 6ddb5698dde..6343fee450f 100644
--- a/statsmodels/datasets/ccard/data.py
+++ b/statsmodels/datasets/ccard/data.py
@@ -18,10 +18,12 @@
 homepage for Greene's `Econometric Analysis`. See source.
 """
 
-NOTE        = """
-Number of observations - 72
-Number of variables - 5
-Variable name definitions - See Source for more information on the variables.
+NOTE        = """::
+
+    Number of observations - 72
+    Number of variables - 5
+    Variable name definitions - See Source for more information on the
+                                variables.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/co2/__init__.py b/statsmodels/datasets/co2/__init__.py
new file mode 100644
index 00000000000..df22d8bbb44
--- /dev/null
+++ b/statsmodels/datasets/co2/__init__.py
@@ -0,0 +1 @@
+from .data import *
diff --git a/statsmodels/datasets/co2/co2.csv b/statsmodels/datasets/co2/co2.csv
new file mode 100644
index 00000000000..506036d959f
--- /dev/null
+++ b/statsmodels/datasets/co2/co2.csv
@@ -0,0 +1,2285 @@
+date,co2
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diff --git a/statsmodels/datasets/co2/data.py b/statsmodels/datasets/co2/data.py
new file mode 100644
index 00000000000..3512c413547
--- /dev/null
+++ b/statsmodels/datasets/co2/data.py
@@ -0,0 +1,80 @@
+#! /usr/bin/env python
+
+"""Mauna Loa Weekly Atmospheric CO2 Data"""
+
+__docformat__ = 'restructuredtext'
+
+COPYRIGHT   = """This is public domain."""
+TITLE       = """Mauna Loa Weekly Atmospheric CO2 Data"""
+SOURCE      = """
+Data obtained from http://cdiac.ornl.gov/trends/co2/sio-keel-flask/sio-keel-flaskmlo_c.html
+
+Obtained on 3/15/2014.
+
+Citation:
+
+Keeling, C.D. and T.P. Whorf. 2004. Atmospheric CO2 concentrations derived from flask air samples at sites in the SIO network. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A.
+"""
+
+DESCRSHORT  = """Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory, Hawaii, U.S.A."""
+
+DESCRLONG   = """
+Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory, Hawaii, U.S.A.
+
+Period of Record: March 1958 - December 2001
+
+Methods: An Applied Physics Corporation (APC) nondispersive infrared gas analyzer was used to obtain atmospheric CO2 concentrations, based on continuous data (four measurements per hour) from atop intake lines on several towers. Steady data periods of not less than six hours per day are required; if no such six-hour periods are available on any given day, then no data are used that day. Weekly averages were calculated for most weeks throughout the approximately 44 years of record. The continuous data for year 2000 is compared with flask data from the same site in the graphics section."""
+
+#suggested notes
+NOTE        = """::
+
+    Number of observations: 2225
+    Number of variables: 2
+    Variable name definitions:
+
+        date - sample date in YYMMDD format
+        co2 - CO2 Concentration ppmv
+
+    The data returned by load_pandas contains the dates as the index.
+"""
+
+import numpy as np
+from statsmodels.datasets import utils as du
+from os.path import dirname, abspath
+
+import pandas as pd
+
+def load():
+    """
+    Load the data and return a Dataset class instance.
+
+    Returns
+    -------
+    Dataset instance:
+        See DATASET_PROPOSAL.txt for more information.
+    """
+    data = _get_data()
+    names = data.dtype.names
+    return du.Dataset(data=data, names=names)
+
+
+def load_pandas():
+    data = load()
+    # pandas <= 0.12.0 fails in the to_datetime regex on Python 3
+    index = pd.DatetimeIndex(start=data.data['date'][0].decode('utf-8'),
+                             periods=len(data.data), format='%Y%m%d',
+                             freq='W-SAT')
+    dataset = pd.DataFrame(data.data['co2'], index=index, columns=['co2'])
+    #NOTE: this is how I got the missing values in co2.csv
+    #new_index = pd.DatetimeIndex(start='1958-3-29', end=index[-1],
+    #                             freq='W-SAT')
+    #data.data = dataset.reindex(new_index)
+    data.data = dataset
+    return data
+
+
+def _get_data():
+    filepath = dirname(abspath(__file__))
+    data = np.recfromtxt(open(filepath + '/co2.csv', 'rb'),
+                         delimiter=",", names=True, dtype=['a8', float])
+    return data
diff --git a/statsmodels/datasets/co2/src/maunaloa_c.dat b/statsmodels/datasets/co2/src/maunaloa_c.dat
new file mode 100644
index 00000000000..8f35826b15d
--- /dev/null
+++ b/statsmodels/datasets/co2/src/maunaloa_c.dat
@@ -0,0 +1,2252 @@
+*******************************************************************
+*** Weekly averages derived from continuous air samples         *** 
+*** for the Mauna Loa Observatory, Hawaii, U.S.A.               ***
+***                                                             ***
+***                                                             ***
+*** September 2004                                              *** 
+***                                                             ***
+*** Source: Carbon Dioxide Research Group                       ***
+***         Scripps Institution of Oceanography                 ***
+***         University of California, San Diego                 ***
+***         La Jolla, California, USA 92023-0444                ***
+*******************************************************************
+
+These weekly averages are ultimately based on measurements of 4 air 
+samples per hour taken atop intake lines on several towers during 
+steady periods of CO2 concentration of not less than 6 hours per day; 
+if no such periods are available on a given day, then no data are 
+used for that day. The "Weight" column gives the number of days used 
+in each weekly average. Flag codes are explained in the NDP writeup, available 
+electronically from the home page of this data set. CO2 concentrations 
+are in terms of the 1999 calibration scale (Keeling et al., 2002) 
+available electronically from the references in the NDP writeup which 
+can be accessed from the home page of this data set.
+
+Station Sample Weight Flag  CO2 concentration
+ code   date          code     (ppmv)
+
+MLO    580329   4    0         316.10
+MLO    580405   6    0         317.30
+MLO    580412   4    0         317.60
+MLO    580419   6    0         317.50
+MLO    580426   2    0         316.40
+MLO    580503   6    0         316.90
+MLO    580517   1    0         317.50
+MLO    580524   5    0         317.90
+MLO    580705   2    0         315.80
+MLO    580712   4    0         315.80
+MLO    580719   4    0         315.40
+MLO    580726   7    0         315.50
+MLO    580802   3    0         315.60
+MLO    580809   4    0         315.10
+MLO    580816   3    0         315.00
+MLO    580830   5    0         314.10
+MLO    580906   1    0         313.50
+MLO    581108   4    0         313.00
+MLO    581115   7    0         313.20
+MLO    581122   5    0         313.50
+MLO    581129   7    0         314.00
+MLO    581206   6    0         314.50
+MLO    581213   7    0         314.40
+MLO    581220   4    0         314.70
+MLO    581227   2    0         315.20
+MLO    590103   3    0         315.20
+MLO    590110   7    0         315.50
+MLO    590117   7    0         315.60
+MLO    590124   5    0         315.80
+MLO    590131   2    0         315.40
+MLO    590214   1    0         316.90
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+MLO    590228   3    0         316.60
+MLO    590307   7    0         316.80
+MLO    590321   4    0         316.70
+MLO    590328   6    0         316.70
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+MLO    590411   7    0         317.10
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+MLO    590425   5    0         318.30
+MLO    590502   3    0         318.20
+MLO    590509   1    0         318.70
+MLO    590516   7    0         318.00
+MLO    590523   5    0         318.40
+MLO    590606   7    0         318.50
+MLO    590613   5    0         318.10
+MLO    590620   5    0         317.80
+MLO    590627   2    0         317.70
+MLO    590704   7    0         316.80
+MLO    590711   7    0         316.80
+MLO    590718   5    0         316.40
+MLO    590725   4    0         316.10
+MLO    590801   7    0         315.60
+MLO    590808   3    0         314.90
+MLO    590822   1    0         315.00
+MLO    590829   4    0         314.10
+MLO    590905   5    0         314.40
+MLO    590912   3    0         313.90
+MLO    590919   5    0         313.50
+MLO    590926   4    0         313.50
+MLO    591003   6    0         313.00
+MLO    591010   3    0         313.10
+MLO    591017   7    0         313.40
+MLO    591024   7    0         313.40
+MLO    591031   7    0         314.10
+MLO    591107   2    0         314.40
+MLO    591114   5    0         314.80
+MLO    591121   6    0         315.20
+MLO    591128   5    0         315.10
+MLO    591205   4    0         315.00
+MLO    591212   5    0         315.60
+MLO    591219   6    0         315.80
+MLO    591226   6    0         315.70
+MLO    600102   4    0         315.70
+MLO    600109   5    0         316.40
+MLO    600116   6    0         316.70
+MLO    600123   5    0         316.50
+MLO    600130   6    0         316.60
+MLO    600206   2    0         316.60
+MLO    600213   6    0         316.90
+MLO    600220   4    0         317.40
+MLO    600227   5    0         317.00
+MLO    600305   7    0         316.90
+MLO    600312   7    0         317.70
+MLO    600319   4    0         318.00
+MLO    600326   6    0         317.70
+MLO    600402   5    0         318.60
+MLO    600409   3    0         319.30
+MLO    600416   7    0         319.00
+MLO    600423   7    0         319.00
+MLO    600430   1    0         319.70
+MLO    600507   6    0         319.90
+MLO    600514   5    0         319.80
+MLO    600521   6    0         320.00
+MLO    600528   7    0         320.00
+MLO    600604   3    0         319.40
+MLO    600611   5    0         320.00
+MLO    600618   3    0         319.40
+MLO    600625   2    0         319.00
+MLO    600702   5    0         318.10
+MLO    600709   4    0         318.60
+MLO    600716   5    0         318.40
+MLO    600723   6    0         317.90
+MLO    600730   5    0         317.30
+MLO    600806   3    0         316.60
+MLO    600813   6    0         316.70
+MLO    600820   5    0         315.10
+MLO    600827   5    0         314.70
+MLO    600903   7    0         314.70
+MLO    600910   7    0         314.50
+MLO    600917   6    0         314.20
+MLO    600924   5    0         313.30
+MLO    601001   2    0         313.60
+MLO    601008   4    0         313.30
+MLO    601015   5    0         313.90
+MLO    601022   6    0         314.20
+MLO    601029   3    0         314.20
+MLO    601105   7    0         314.50
+MLO    601112   6    0         315.10
+MLO    601119   7    0         315.10
+MLO    601126   7    0         315.40
+MLO    601203   5    0         315.80
+MLO    601210   7    0         316.00
+MLO    601217   7    0         316.20
+MLO    601224   5    0         316.40
+MLO    601231   7    0         316.60
+MLO    610107   6    0         316.70
+MLO    610114   7    0         317.00
+MLO    610121   7    0         317.00
+MLO    610128   7    0         317.00
+MLO    610204   6    0         317.00
+MLO    610211   6    0         317.70
+MLO    610218   7    0         317.90
+MLO    610225   7    0         318.00
+MLO    610304   5    0         318.00
+MLO    610311   4    0         318.50
+MLO    610318   6    0         318.90
+MLO    610325   7    0         318.70
+MLO    610401   7    0         319.40
+MLO    610408   6    0         319.50
+MLO    610415   7    0         318.90
+MLO    610422   6    0         319.70
+MLO    610429   6    0         319.60
+MLO    610506   7    0         320.60
+MLO    610513   3    0         320.40
+MLO    610520   6    0         320.60
+MLO    610527   7    0         320.30
+MLO    610603   6    0         320.00
+MLO    610610   5    0         320.00
+MLO    610617   7    0         319.60
+MLO    610624   6    0         319.40
+MLO    610701   5    0         319.20
+MLO    610708   6    0         319.00
+MLO    610715   5    0         318.40
+MLO    610722   1    0         317.10
+MLO    610729   3    0         317.90
+MLO    610805   7    0         317.90
+MLO    610812   2    0         317.30
+MLO    610819   5    0         316.70
+MLO    610826   6    0         315.20
+MLO    610902   5    0         315.20
+MLO    610909   5    0         314.90
+MLO    610916   5    0         314.50
+MLO    610923   4    0         315.00
+MLO    610930   4    0         315.60
+MLO    611007   5    0         314.90
+MLO    611014   7    0         315.40
+MLO    611021   7    0         315.40
+MLO    611028   6    0         315.70
+MLO    611104   6    0         315.80
+MLO    611111   5    0         315.70
+MLO    611118   7    0         316.20
+MLO    611125   6    0         316.50
+MLO    611202   5    0         316.50
+MLO    611209   5    0         317.00
+MLO    611216   6    0         316.90
+MLO    611223   3    0         317.10
+MLO    611230   7    0         317.40
+MLO    620106   5    0         317.90
+MLO    620113   7    0         318.10
+MLO    620120   5    0         318.00
+MLO    620127   7    0         317.70
+MLO    620203   7    0         318.00
+MLO    620210   4    0         318.30
+MLO    620217   4    0         318.90
+MLO    620224   1    0         319.30
+MLO    620303   6    0         319.20
+MLO    620310   6    0         319.40
+MLO    620317   7    0         319.90
+MLO    620324   4    0         319.70
+MLO    620331   5    0         320.20
+MLO    620407   3    0         320.20
+MLO    620414   6    0         321.00
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+MLO    620602   6    0         320.90
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+MLO    970927   7    0         359.80
+MLO    971004   7    0         360.50
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+MLO    971227   7    0         365.00
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+MLO    980131   7    0         365.60
+MLO    980207   7    0         366.00
+MLO    980214   7    0         365.50
+MLO    980221   3    0         366.00
+MLO    980228   5    0         367.30
+MLO    980307   7    0         367.00
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+MLO    980321   7    0         367.40
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+MLO    980404   7    0         368.60
+MLO    980411   6    0         368.60
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+MLO    980509   4    0         368.80
+MLO    980516   6    0         368.50
+MLO    980523   7    0         369.60
+MLO    980530   7    0         369.70
+MLO    980606   6    0         369.40
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+MLO    980627   6    0         368.30
+MLO    980704   6    0         368.00
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+MLO    980718   5    0         367.60
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+MLO    980801   7    0         366.80
+MLO    980808   6    0         366.40
+MLO    980815   6    0         365.60
+MLO    980822   3    0         365.60
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+MLO    980919   7    0         363.50
+MLO    980926   7    0         363.60
+MLO    981003   7    0         364.00
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+MLO    981024   7    0         364.40
+MLO    981031   6    0         364.70
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+MLO    981219   7    0         367.30
+MLO    981226   7    0         367.30
+MLO    990102   7    0         367.50
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+MLO    990605   6    0         370.90
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+MLO    000923   7    0         366.20
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+MLO    001007   7    0         366.30
+MLO    001014   7    0         366.60
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+MLO    001028   7    0         367.30
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+MLO    001202   5    0         369.00
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+MLO    010106   6    0         369.80
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+MLO    010602   6    0         373.80
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+MLO    010623   7    0         372.90
+MLO    010630   6    0         372.70
+MLO    010707   4    0         372.10
+MLO    010714   7    0         371.30
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+MLO    010728   6    0         370.60
+MLO    010804   6    0         369.90
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+MLO    010825   7    0         369.00
+MLO    010901   6    0         368.40
+MLO    010908   7    0         368.20
+MLO    010915   7    0         368.00
+MLO    010922   7    0         367.40
+MLO    010929   7    0         367.40
+MLO    011006   7    0         367.80
+MLO    011013   7    0         367.60
+MLO    011020   7    0         368.10
+MLO    011027   6    0         368.70
+MLO    011103   7    0         368.70
+MLO    011110   3    0         368.80
+MLO    011117   7    0         369.70
+MLO    011124   7    0         370.30
+MLO    011201   7    0         370.30
+MLO    011208   7    0         370.80
+MLO    011215   7    0         371.20
+MLO    011222   6    0         371.30
+MLO    011229   6    0         371.50
diff --git a/statsmodels/datasets/committee/__init__.py b/statsmodels/datasets/committee/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/committee/__init__.py
+++ b/statsmodels/datasets/committee/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/committee/data.py b/statsmodels/datasets/committee/data.py
index f8cd04d2cb9..cbe9945c94c 100644
--- a/statsmodels/datasets/committee/data.py
+++ b/statsmodels/datasets/committee/data.py
@@ -26,23 +26,23 @@
 The data returned by load are not cleaned to represent the above example.
 """
 
-NOTE = """Number of Observations - 20
-
-Number of Variables - 6
-
-Variable name definitions::
-
-    BILLS104 - Number of bill assignments in the first 100 days of the 104th
-               House of Representatives.
-    SIZE     - Number of members on the committee.
-    SUBS     - Number of subcommittees.
-    STAFF    - Number of staff members assigned to the committee.
-    PRESTIGE - PRESTIGE == 1 is a high prestige committee.
-    BILLS103 - Number of bill assignments in the first 100 days of the 103rd
-               House of Representatives.
-
-Committee names are included as a variable in the data file though not
-returned by load.
+NOTE = """::
+
+    Number of Observations - 20
+    Number of Variables - 6
+    Variable name definitions::
+
+        BILLS104 - Number of bill assignments in the first 100 days of the
+                   104th House of Representatives.
+        SIZE     - Number of members on the committee.
+        SUBS     - Number of subcommittees.
+        STAFF    - Number of staff members assigned to the committee.
+        PRESTIGE - PRESTIGE == 1 is a high prestige committee.
+        BILLS103 - Number of bill assignments in the first 100 days of the
+                   103rd House of Representatives.
+
+    Committee names are included as a variable in the data file though not
+    returned by load.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/copper/__init__.py b/statsmodels/datasets/copper/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/copper/__init__.py
+++ b/statsmodels/datasets/copper/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/cpunish/__init__.py b/statsmodels/datasets/cpunish/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/cpunish/__init__.py
+++ b/statsmodels/datasets/cpunish/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/cpunish/data.py b/statsmodels/datasets/cpunish/data.py
index 500f67a647e..73cfa1158a7 100644
--- a/statsmodels/datasets/cpunish/data.py
+++ b/statsmodels/datasets/cpunish/data.py
@@ -24,23 +24,22 @@
 of the population with a college degree of some kind.
 """
 
-NOTE        = """
-Number of Observations - 17
-
-Number of Variables - 7
-
-Variable name definitions::
-
-    EXECUTIONS - Executions in 1996
-    INCOME - Median per capita income in 1996 dollars
-    PERPOVERTY - Percent of the population classified as living in poverty
-    PERBLACK - Percent of black citizens in the population
-    VC100k96 - Rate of violent crimes per 100,00 residents for 1996
-    SOUTH - SOUTH == 1 indicates a state in the South
-    DEGREE - An esimate of the proportion of the state population with a
-        college degree of some kind
-
-State names are included in the data file, though not returned by load.
+NOTE        = """::
+
+    Number of Observations - 17
+    Number of Variables - 7
+    Variable name definitions::
+
+        EXECUTIONS - Executions in 1996
+        INCOME - Median per capita income in 1996 dollars
+        PERPOVERTY - Percent of the population classified as living in poverty
+        PERBLACK - Percent of black citizens in the population
+        VC100k96 - Rate of violent crimes per 100,00 residents for 1996
+        SOUTH - SOUTH == 1 indicates a state in the South
+        DEGREE - An esimate of the proportion of the state population with a
+            college degree of some kind
+
+    State names are included in the data file, though not returned by load.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/elnino/__init__.py b/statsmodels/datasets/elnino/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/elnino/__init__.py
+++ b/statsmodels/datasets/elnino/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/elnino/data.py b/statsmodels/datasets/elnino/data.py
index ec90ffe6eaf..b5525a3db68 100644
--- a/statsmodels/datasets/elnino/data.py
+++ b/statsmodels/datasets/elnino/data.py
@@ -21,16 +21,16 @@
 NOAA.
 """
 
-NOTE = """
-Number of Observations - 61 x 12
+NOTE = """::
 
-Number of Variables - 1
+    Number of Observations - 61 x 12
 
-Variable name definitions::
+    Number of Variables - 1
 
-    TEMPERATURE - average sea surface temperature in degrees Celcius
-                  (12 columns, one per month).
+    Variable name definitions::
 
+        TEMPERATURE - average sea surface temperature in degrees Celcius
+                      (12 columns, one per month).
 """
 
 
diff --git a/statsmodels/datasets/engel/__init__.py b/statsmodels/datasets/engel/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/engel/__init__.py
+++ b/statsmodels/datasets/engel/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/engel/data.py b/statsmodels/datasets/engel/data.py
index f9ea0de3c73..24b8c50d3c6 100644
--- a/statsmodels/datasets/engel/data.py
+++ b/statsmodels/datasets/engel/data.py
@@ -22,12 +22,13 @@
 DESCRLONG   = """Data on income and food expenditure for 235 working class households in 1857 Belgium."""
 
 #suggested notes
-NOTE        = """
-Number of observations: 235
-Number of variables: 2
-Variable name definitions:
-    income - annual household income (Belgian francs)
-    foodexp - annual household food expenditure (Belgian francs)
+NOTE        = """::
+
+    Number of observations: 235
+    Number of variables: 2
+    Variable name definitions:
+        income - annual household income (Belgian francs)
+        foodexp - annual household food expenditure (Belgian francs)
 """
 
 import numpy as np
diff --git a/statsmodels/datasets/fair/__init__.py b/statsmodels/datasets/fair/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/fair/__init__.py
+++ b/statsmodels/datasets/fair/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/fair/data.py b/statsmodels/datasets/fair/data.py
index 12219413703..759d9dde9f9 100644
--- a/statsmodels/datasets/fair/data.py
+++ b/statsmodels/datasets/fair/data.py
@@ -21,32 +21,33 @@
 with censored data."""
 
 #suggested notes
-NOTE        = """
-Number of observations: 6366
-Number of variables: 9
-Variable name definitions:
+NOTE        = """::
 
-    rate_marriage   : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,
-                      4 = good, 5 = very good
-    age             : Age
-    yrs_married     : No. years married. Interval approximations. See
-                      original paper for detailed explanation.
-    children        : No. children
-    religious       : How relgious, 1 = not, 2 = mildly, 3 = fairly,
-                      4 = strongly
-    educ            : Level of education, 9 = grade school, 12 = high school,
-                      14 = some college, 16 = college graduate, 17 = some
-                      graduate school, 20 = advanced degree
-    occupation      : 1 = student, 2 = farming, agriculture; semi-skilled,
-                      or unskilled worker; 3 = white-colloar; 4 = teacher
-                      counselor social worker, nurse; artist, writers;
-                      technician, skilled worker, 5 = managerial,
-                      administrative, business, 6 = professional with
-                      advanced degree
-    occupation_husb : Husband's occupation. Same as occupation.
-    affairs         : measure of time spent in extramarital affairs
+    Number of observations: 6366
+    Number of variables: 9
+    Variable name definitions:
 
-See the original paper for more details.
+        rate_marriage   : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,
+                        4 = good, 5 = very good
+        age             : Age
+        yrs_married     : No. years married. Interval approximations. See
+                        original paper for detailed explanation.
+        children        : No. children
+        religious       : How relgious, 1 = not, 2 = mildly, 3 = fairly,
+                        4 = strongly
+        educ            : Level of education, 9 = grade school, 12 = high
+                        school, 14 = some college, 16 = college graduate,
+                        17 = some graduate school, 20 = advanced degree
+        occupation      : 1 = student, 2 = farming, agriculture; semi-skilled,
+                        or unskilled worker; 3 = white-colloar; 4 = teacher
+                        counselor social worker, nurse; artist, writers;
+                        technician, skilled worker, 5 = managerial,
+                        administrative, business, 6 = professional with
+                        advanced degree
+        occupation_husb : Husband's occupation. Same as occupation.
+        affairs         : measure of time spent in extramarital affairs
+
+    See the original paper for more details.
 """
 
 import numpy as np
diff --git a/statsmodels/datasets/grunfeld/__init__.py b/statsmodels/datasets/grunfeld/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/grunfeld/__init__.py
+++ b/statsmodels/datasets/grunfeld/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/grunfeld/data.py b/statsmodels/datasets/grunfeld/data.py
index a392ad22ce9..c40c3071e87 100644
--- a/statsmodels/datasets/grunfeld/data.py
+++ b/statsmodels/datasets/grunfeld/data.py
@@ -19,22 +19,24 @@
 
 DESCRLONG   = DESCRSHORT
 
-NOTE        = """Number of observations - 220 (20 years for 11 firms)
+NOTE        = """::
 
-Number of variables - 5
+    Number of observations - 220 (20 years for 11 firms)
 
-Variables name definitions::
+    Number of variables - 5
 
-    invest  - Gross investment in 1947 dollars
-    value   - Market value as of Dec. 31 in 1947 dollars
-    capital - Stock of plant and equipment in 1947 dollars
-    firm    - General Motors, US Steel, General Electric, Chrysler,
-              Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear,
-              Diamond Match, American Steel
-    year    - 1935 - 1954
+    Variables name definitions::
 
-Note that raw_data has firm expanded to dummy variables, since it is a
-string categorical variable.
+        invest  - Gross investment in 1947 dollars
+        value   - Market value as of Dec. 31 in 1947 dollars
+        capital - Stock of plant and equipment in 1947 dollars
+        firm    - General Motors, US Steel, General Electric, Chrysler,
+                Atlantic Refining, IBM, Union Oil, Westinghouse, Goodyear,
+                Diamond Match, American Steel
+        year    - 1935 - 1954
+
+    Note that raw_data has firm expanded to dummy variables, since it is a
+    string categorical variable.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/heart/__init__.py b/statsmodels/datasets/heart/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/heart/__init__.py
+++ b/statsmodels/datasets/heart/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/heart/data.py b/statsmodels/datasets/heart/data.py
index aa76f924ece..65f9577c769 100644
--- a/statsmodels/datasets/heart/data.py
+++ b/statsmodels/datasets/heart/data.py
@@ -12,20 +12,19 @@
 
 DESCRSHORT  = """Survival times after receiving a heart transplant"""
 
-DESCRLONG   = """This data contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored.  
+DESCRLONG   = """This data contains the survival time after receiving a heart transplant, the age of the patient and whether or not the survival time was censored.
 """
 
-NOTE = """
-Number of Observations - 69
+NOTE = """::
 
-Number of Variables - 3
-
-Variable name definitions::
-    death - Days after surgery until death
-    age - age at the time of surgery
-    censored - indicates if an observation is censored.  1 is uncensored
+    Number of Observations - 69
 
+    Number of Variables - 3
 
+    Variable name definitions::
+        death - Days after surgery until death
+        age - age at the time of surgery
+        censored - indicates if an observation is censored.  1 is uncensored
 """
 
 import numpy as np
diff --git a/statsmodels/datasets/longley/__init__.py b/statsmodels/datasets/longley/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/longley/__init__.py
+++ b/statsmodels/datasets/longley/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/longley/data.py b/statsmodels/datasets/longley/data.py
index 3973a499a39..407f916e148 100644
--- a/statsmodels/datasets/longley/data.py
+++ b/statsmodels/datasets/longley/data.py
@@ -22,20 +22,21 @@
 variables that are known to be highly collinear.  It has been used to appraise
 the accuracy of least squares routines."""
 
-NOTE        = """
-Number of Observations - 16
+NOTE        = """::
 
-Number of Variables - 6
+    Number of Observations - 16
 
-Variable name definitions::
+    Number of Variables - 6
 
-        TOTEMP - Total Employment
-        GNPDEFL - GNP deflator
-        GNP - GNP
-        UNEMP - Number of unemployed
-        ARMED - Size of armed forces
-        POP - Population
-        YEAR - Year (1947 - 1962)
+    Variable name definitions::
+
+            TOTEMP - Total Employment
+            GNPDEFL - GNP deflator
+            GNP - GNP
+            UNEMP - Number of unemployed
+            ARMED - Size of armed forces
+            POP - Population
+            YEAR - Year (1947 - 1962)
 """
 
 from numpy import recfromtxt, array, column_stack
diff --git a/statsmodels/datasets/macrodata/__init__.py b/statsmodels/datasets/macrodata/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/macrodata/__init__.py
+++ b/statsmodels/datasets/macrodata/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/macrodata/data.py b/statsmodels/datasets/macrodata/data.py
index ea2281452da..32e60b2373f 100644
--- a/statsmodels/datasets/macrodata/data.py
+++ b/statsmodels/datasets/macrodata/data.py
@@ -21,36 +21,36 @@
 
 DESCRLONG   = DESCRSHORT
 
-NOTE        = """
-Number of Observations - 203
-
-Number of Variables - 14
-
-Variable name definitions::
-
-    year      - 1959q1 - 2009q3
-    quarter   - 1-4
-    realgdp   - Real gross domestic product (Bil. of chained 2005 US$,
-                seasonally adjusted annual rate)
-    realcons  - Real personal consumption expenditures (Bil. of chained 2005
-                US$,
-                seasonally adjusted annual rate)
-    realinv   - Real gross private domestic investment (Bil. of chained 2005
-                US$, seasonally adjusted annual rate)
-    realgovt  - Real federal consumption expenditures & gross investment
-                (Bil. of chained 2005 US$, seasonally adjusted annual rate)
-    realdpi   - Real private disposable income (Bil. of chained 2005
-                US$, seasonally adjusted annual rate)
-    cpi       - End of the quarter consumer price index for all urban
-                consumers: all items (1982-84 = 100, seasonally adjusted).
-    m1        - End of the quarter M1 nominal money stock (Seasonally adjusted)
-    tbilrate  - Quarterly monthly average of the monthly 3-month treasury bill:
-                secondary market rate
-    unemp     - Seasonally adjusted unemployment rate (%)
-    pop       - End of the quarter total population: all ages incl. armed
-                forces over seas
-    infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
-    realint   - Real interest rate (tbilrate - infl)
+NOTE        = """::
+    Number of Observations - 203
+
+    Number of Variables - 14
+
+    Variable name definitions::
+
+        year      - 1959q1 - 2009q3
+        quarter   - 1-4
+        realgdp   - Real gross domestic product (Bil. of chained 2005 US$,
+                    seasonally adjusted annual rate)
+        realcons  - Real personal consumption expenditures (Bil. of chained
+                    2005 US$, seasonally adjusted annual rate)
+        realinv   - Real gross private domestic investment (Bil. of chained
+                    2005 US$, seasonally adjusted annual rate)
+        realgovt  - Real federal consumption expenditures & gross investment
+                    (Bil. of chained 2005 US$, seasonally adjusted annual rate)
+        realdpi   - Real private disposable income (Bil. of chained 2005
+                    US$, seasonally adjusted annual rate)
+        cpi       - End of the quarter consumer price index for all urban
+                    consumers: all items (1982-84 = 100, seasonally adjusted).
+        m1        - End of the quarter M1 nominal money stock (Seasonally
+                    adjusted)
+        tbilrate  - Quarterly monthly average of the monthly 3-month
+                    treasury bill: secondary market rate
+        unemp     - Seasonally adjusted unemployment rate (%)
+        pop       - End of the quarter total population: all ages incl. armed
+                    forces over seas
+        infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)
+        realint   - Real interest rate (tbilrate - infl)
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/modechoice/__init__.py b/statsmodels/datasets/modechoice/__init__.py
new file mode 100644
index 00000000000..df22d8bbb44
--- /dev/null
+++ b/statsmodels/datasets/modechoice/__init__.py
@@ -0,0 +1 @@
+from .data import *
diff --git a/statsmodels/datasets/modechoice/data.py b/statsmodels/datasets/modechoice/data.py
new file mode 100644
index 00000000000..88a650ffba2
--- /dev/null
+++ b/statsmodels/datasets/modechoice/data.py
@@ -0,0 +1,90 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""Travel Mode Choice"""
+
+__docformat__ = 'restructuredtext'
+
+COPYRIGHT = """This is public domain."""
+TITLE = __doc__
+SOURCE = """
+Greene, W.H. and D. Hensher (1997) Multinomial logit and discrete choice models
+in Greene, W. H. (1997) LIMDEP version 7.0 user's manual revised, Plainview,
+New York econometric software, Inc.
+Download from on-line complements to Greene, W.H. (2011) Econometric Analysis,
+Prentice Hall, 7th Edition (data table F18-2)
+http://people.stern.nyu.edu/wgreene/Text/Edition7/TableF18-2.csv
+"""
+
+DESCRSHORT = """Data used to study travel mode choice between Australian cities
+"""
+
+DESCRLONG = """The data, collected as part of a 1987 intercity mode choice
+study, are a sub-sample of 210 non-business trips between Sydney, Canberra and
+Melbourne in which the traveler chooses a mode from four alternatives (plane,
+car, bus and train). The sample, 840 observations, is choice based with
+over-sampling of the less popular modes (plane, train and bus) and under-sampling
+of the more popular mode, car. The level of service data was derived from highway
+and transport networks in Sydney, Melbourne, non-metropolitan N.S.W. and Victoria,
+including the Australian Capital Territory."""
+
+NOTE = """::
+
+    Number of observations: 840 Observations On 4 Modes for 210 Individuals.
+    Number of variables: 8
+    Variable name definitions::
+
+        individual = 1 to 210
+        mode =
+            1 - air
+            2 - train
+            3 - bus
+            4 - car
+        choice =
+            0 - no
+            1 - yes
+        ttme = terminal waiting time for plane, train and bus (minutes); 0
+               for car.
+        invc = in vehicle cost for all stages (dollars).
+        invt = travel time (in-vehicle time) for all stages (minutes).
+        gc = generalized cost measure:invc+(invt*value of travel time savings)
+            (dollars).
+        hinc = household income ($1000s).
+        psize = traveling group size in mode chosen (number)."""
+
+import numpy as np
+from statsmodels.datasets import utils as du
+from os.path import dirname, abspath
+
+def load():
+    """
+    Load the data modechoice data and return a Dataset class instance.
+
+    Returns
+    -------
+    Dataset instance:
+        See DATASET_PROPOSAL.txt for more information.
+    """
+    data = _get_data()
+    return du.process_recarray(data, endog_idx=2, exog_idx=[3,4,5,6,7,8],
+                               dtype=float)
+
+def load_pandas():
+    """
+    Load the data modechoice data and return a Dataset class instance.
+
+    Returns
+    -------
+    Dataset instance:
+        See DATASET_PROPOSAL.txt for more information.
+    """
+
+    data = _get_data()
+    return du.process_recarray_pandas(data, endog_idx = 2, exog_idx=[3,4,5,6,7,8],
+                                      dtype=float)
+
+def _get_data():
+    filepath = dirname(abspath(__file__))
+    data = np.recfromtxt(open(filepath + '/modechoice.csv', 'rb'),
+            delimiter=";", names = True, dtype=float)
+    return data
diff --git a/statsmodels/datasets/modechoice/modechoice.csv b/statsmodels/datasets/modechoice/modechoice.csv
new file mode 100644
index 00000000000..f279010d8cd
--- /dev/null
+++ b/statsmodels/datasets/modechoice/modechoice.csv
@@ -0,0 +1,841 @@
+individual;mode;choice;ttme;invc;invt;gc;hinc;psize
+1;1;0;69;59;100;70;35;1
+1;2;0;34;31;372;71;35;1
+1;3;0;35;25;417;70;35;1
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+2;1;0;64;58;68;68;30;2
+2;2;0;44;31;354;84;30;2
+2;3;0;53;25;399;85;30;2
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diff --git a/statsmodels/datasets/modechoice/src/TableF18-2.csv b/statsmodels/datasets/modechoice/src/TableF18-2.csv
new file mode 100644
index 00000000000..152c1249e9c
--- /dev/null
+++ b/statsmodels/datasets/modechoice/src/TableF18-2.csv
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+0,69,60,133,74,34,1
+0,34,31,397,74,34,1
+1,15,21,240,47,34,1
+0,0,14,351,52,34,1
+0,64,81,129,101,8,2
+0,44,35,677,138,8,2
+1,15,31,565,117,8,2
+0,0,12,588,101,8,2
+0,69,59,117,72,30,1
+0,34,32,397,75,30,1
+0,35,26,442,74,30,1
+1,0,17,180,37,30,1
+0,69,111,276,141,32,1
+0,34,93,1047,207,32,1
+1,30,35,860,128,32,1
+0,0,42,1008,151,32,1
+0,69,59,88,68,26,1
+0,34,30,361,70,26,1
+1,15,21,295,53,26,1
+0,0,13,299,45,26,1
+0,69,59,114,71,42,1
+0,34,31,389,73,42,1
+1,15,21,243,47,42,1
+0,0,13,334,49,42,1
+1,90,55,115,72,50,1
+0,44,72,602,163,50,1
+0,53,33,596,123,50,1
+0,0,24,589,113,50,1
+1,15,87,110,104,70,2
+0,44,31,348,84,70,2
+0,53,25,393,85,70,2
+0,0,7,320,56,70,2
+0,69,62,76,71,30,1
+0,34,39,377,80,30,1
+0,35,33,422,79,30,1
+1,0,12,300,45,30,1
+0,69,59,121,72,26,1
+0,34,31,386,73,26,1
+0,35,25,431,72,26,1
+1,0,14,270,43,26,1
+0,64,51,90,64,26,3
+1,20,61,350,114,26,3
+0,53,22,405,83,26,3
+0,0,6,327,56,26,3
+0,64,64,215,97,18,2
+1,50,41,420,104,18,2
+0,53,15,385,73,18,2
+0,0,9,321,58,18,2
+0,64,58,74,69,35,2
+1,30,21,295,66,35,2
+0,53,24,389,83,35,2
+0,0,7,315,55,35,2
+0,64,71,119,89,50,3
+0,44,58,617,152,50,3
+1,15,35,615,128,50,3
+0,0,11,586,100,50,3
+1,15,50,75,58,4,1
+0,34,15,361,55,4,1
+0,35,12,406,56,4,1
+0,0,13,315,47,4,1
+1,10,61,80,73,30,4
+0,44,24,350,77,30,4
+0,53,19,395,79,30,4
+0,0,4,314,52,30,4
+0,69,59,100,70,45,1
+0,34,31,372,71,45,1
+0,35,25,417,70,45,1
+1,0,13,210,36,45,1
+0,69,58,73,66,60,1
+0,34,31,324,66,60,1
+0,35,25,334,61,60,1
+1,0,18,240,44,60,1
+0,69,62,184,82,4,1
+0,34,19,454,68,4,1
+0,35,16,464,66,4,1
+1,0,18,225,42,4,1
+1,30,93,85,102,30,1
+0,34,33,365,72,30,1
+0,35,27,410,71,30,1
+0,0,13,308,46,30,1
+1,20,76,95,86,60,1
+0,34,33,344,70,60,1
+0,35,27,389,69,60,1
+0,0,13,315,47,60,1
+0,64,100,190,129,4,2
+0,44,49,678,152,4,2
+1,30,48,430,113,4,2
+0,0,14,629,109,4,2
+0,64,48,63,58,18,3
+1,10,21,255,60,18,3
+0,53,13,397,73,18,3
+0,0,4,324,53,18,3
+0,64,58,93,72,15,2
+1,10,30,315,78,15,2
+0,53,13,346,65,15,2
+0,0,6,316,54,15,2
+0,69,59,94,69,26,1
+0,34,30,361,70,26,1
+0,35,24,406,68,26,1
+1,0,10,270,39,26,1
+1,30,111,105,122,40,1
+0,34,71,596,136,40,1
+0,35,32,590,96,40,1
+0,0,27,577,90,40,1
+1,5,64,80,76,70,2
+0,44,30,348,83,70,2
+0,53,24,393,84,70,2
+0,0,7,291,51,70,2
+1,5,60,85,73,45,4
+0,44,24,379,81,45,4
+0,53,19,424,83,45,4
+0,0,3,337,54,45,4
+0,64,97,188,125,26,3
+0,44,76,825,201,26,3
+1,16,38,810,161,26,3
+0,0,15,803,137,26,3
+0,69,91,117,104,35,1
+0,34,77,630,145,35,1
+0,35,38,624,106,35,1
+1,0,29,540,88,35,1
+0,69,59,114,72,4,1
+0,34,16,378,57,4,1
+1,5,14,370,54,4,1
+0,0,17,335,53,4,1
+0,69,60,66,67,20,1
+0,34,34,352,72,20,1
+1,10,37,250,64,20,1
+0,0,13,307,46,20,1
+0,69,107,144,123,45,1
+0,34,89,879,184,45,1
+1,30,35,775,119,45,1
+0,0,37,868,131,45,1
+0,69,81,124,94,40,1
+0,34,69,605,135,40,1
+0,35,30,599,95,40,1
+1,0,30,720,108,40,1
+1,45,126,135,141,40,1
+0,34,70,596,135,40,1
+0,35,31,590,95,40,1
+0,0,32,577,95,40,1
+0,69,59,86,68,2,1
+0,34,15,360,54,2,1
+1,50,29,265,57,2,1
+0,0,13,300,46,2,1
+0,69,81,124,94,20,1
+0,34,35,605,100,20,1
+0,35,31,599,96,20,1
+1,0,27,510,82,20,1
+0,64,66,140,87,70,4
+0,44,54,670,156,70,4
+0,53,33,664,134,70,4
+1,0,12,540,94,70,4
diff --git a/statsmodels/datasets/nile/__init__.py b/statsmodels/datasets/nile/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/nile/__init__.py
+++ b/statsmodels/datasets/nile/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/nile/data.py b/statsmodels/datasets/nile/data.py
index b8f6cb1932c..8e016f82663 100644
--- a/statsmodels/datasets/nile/data.py
+++ b/statsmodels/datasets/nile/data.py
@@ -1,29 +1,33 @@
-"""Name of dataset."""
+"""Nile River Flows."""
 
 __docformat__ = 'restructuredtext'
 
-COPYRIGHT   = """E.g., This is public domain."""
-TITLE       = """Title of the dataset"""
+COPYRIGHT   = """This is public domain."""
+TITLE       = """Nile River flows at Ashwan 1871-1970"""
 SOURCE      = """
-This section should provide a link to the original dataset if possible and
-attribution and correspondance information for the dataset's original author
-if so desired.
+This data is first analyzed in:
+
+    Cobb, G. W. 1978. "The Problem of the Nile: Conditional Solution to a
+        Changepoint Problem." *Biometrika*. 65.2, 243-51.
 """
 
-DESCRSHORT  = """A short description."""
+DESCRSHORT  = """This dataset contains measurements on the annual flow of
+the Nile as measured at Ashwan for 100 years from 1871-1970."""
 
-DESCRLONG   = """A longer description of the dataset."""
+DESCRLONG   = DESCRSHORT + " There is an apparent changepoint near 1898."
 
 #suggested notes
-NOTE        = """
-Number of observations:
-Number of variables:
-Variable name definitions:
+NOTE        = """::
+
+    Number of observations: 100
+    Number of variables: 2
+    Variable name definitions:
 
-Any other useful information that does not fit into the above categories.
+        year - the year of the observations
+        volumne - the discharge at Aswan in 10^8, m^3
 """
 
-from numpy import recfromtxt, column_stack, array
+from numpy import recfromtxt, array
 from pandas import Series, DataFrame
 
 from statsmodels.datasets.utils import Dataset
diff --git a/statsmodels/datasets/randhie/__init__.py b/statsmodels/datasets/randhie/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/randhie/__init__.py
+++ b/statsmodels/datasets/randhie/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/randhie/data.py b/statsmodels/datasets/randhie/data.py
index 2dc0bb1b902..42e840e8a87 100644
--- a/statsmodels/datasets/randhie/data.py
+++ b/statsmodels/datasets/randhie/data.py
@@ -8,7 +8,7 @@
 The data was collected by the RAND corporation as part of the Health
 Insurance Experiment (HIE).
 
-http://www.rand.org/health/projects/hie/
+http://www.rand.org/health/projects/hie.html
 
 This data was used in::
 
@@ -26,22 +26,23 @@
 
 DESCRLONG   = """"""
 
-NOTE        = """
-Number of observations - 20,190
-Number of variables - 10
-Variable name definitions::
-
-    mdvis   - Number of outpatient visits to an MD
-    lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100
-    idp     - 1 if individual deductible plan, 0 otherwise
-    lpi     - ln(max(1, annual participation incentive payment))
-    fmde    - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise
-    physlm  - 1 if the person has a physical limitation
-    disea   - number of chronic diseases
-    hlthg   - 1 if self-rated health is good
-    hlthf   - 1 if self-rated health is fair
-    hlthp   - 1 if self-rated health is poor
-    (Omitted category is excellent self-rated health)
+NOTE        = """::
+
+    Number of observations - 20,190
+    Number of variables - 10
+    Variable name definitions::
+
+        mdvis   - Number of outpatient visits to an MD
+        lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100
+        idp     - 1 if individual deductible plan, 0 otherwise
+        lpi     - ln(max(1, annual participation incentive payment))
+        fmde    - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise
+        physlm  - 1 if the person has a physical limitation
+        disea   - number of chronic diseases
+        hlthg   - 1 if self-rated health is good
+        hlthf   - 1 if self-rated health is fair
+        hlthp   - 1 if self-rated health is poor
+        (Omitted category is excellent self-rated health)
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/scotland/__init__.py b/statsmodels/datasets/scotland/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/scotland/__init__.py
+++ b/statsmodels/datasets/scotland/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/scotland/data.py b/statsmodels/datasets/scotland/data.py
index 2dcf6a48d52..b04548bc980 100644
--- a/statsmodels/datasets/scotland/data.py
+++ b/statsmodels/datasets/scotland/data.py
@@ -27,26 +27,28 @@
 /scotland/src/
 """
 
-NOTE        = """
-Number of Observations - 32 (1 for each Scottish district)
-
-Number of Variables - 8
-
-Variable name definitions::
-
-    YES    - Proportion voting yes to granting taxation powers to the Scottish
-             parliament.
-    COUTAX - Amount of council tax collected in pounds steling as of April '97
-    UNEMPF - Female percentage of total unemployment benefits claims as of
-             January 1998
-    MOR    - The standardized mortality rate (UK is 100)
-    ACT    - Labor force participation (Short for active)
-    GDP    - GDP per county
-    AGE    - Percentage of children aged 5 to 15 in the county
-    COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF
-
-Council district names are included in the data file, though are not returned
-by load.
+NOTE        = """::
+
+    Number of Observations - 32 (1 for each Scottish district)
+
+    Number of Variables - 8
+
+    Variable name definitions::
+
+        YES    - Proportion voting yes to granting taxation powers to the
+                 Scottish parliament.
+        COUTAX - Amount of council tax collected in pounds steling as of
+                 April '97
+        UNEMPF - Female percentage of total unemployment benefits claims as of
+                January 1998
+        MOR    - The standardized mortality rate (UK is 100)
+        ACT    - Labor force participation (Short for active)
+        GDP    - GDP per county
+        AGE    - Percentage of children aged 5 to 15 in the county
+        COUTAX_FEMALEUNEMP - Interaction between COUTAX and UNEMPF
+
+    Council district names are included in the data file, though are not
+    returned by load.
 """
 
 import numpy as np
diff --git a/statsmodels/datasets/spector/__init__.py b/statsmodels/datasets/spector/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/spector/__init__.py
+++ b/statsmodels/datasets/spector/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/spector/data.py b/statsmodels/datasets/spector/data.py
index dbab9a01dcf..9b2ee3ac215 100644
--- a/statsmodels/datasets/spector/data.py
+++ b/statsmodels/datasets/spector/data.py
@@ -17,18 +17,19 @@
 
 DESCRLONG   = DESCRSHORT
 
-NOTE        = """
-Number of Observations - 32
+NOTE        = """::
 
-Number of Variables - 4
+    Number of Observations - 32
 
-Variable name definitions::
+    Number of Variables - 4
 
-    Grade - binary variable indicating whether or not a student's grade
-            improved.  1 indicates an improvement.
-    TUCE  - Test score on economics test
-    PSI   - participation in program
-    GPA   - Student's grade point average
+    Variable name definitions::
+
+        Grade - binary variable indicating whether or not a student's grade
+                improved.  1 indicates an improvement.
+        TUCE  - Test score on economics test
+        PSI   - participation in program
+        GPA   - Student's grade point average
 """
 
 import numpy as np
diff --git a/statsmodels/datasets/stackloss/__init__.py b/statsmodels/datasets/stackloss/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/stackloss/__init__.py
+++ b/statsmodels/datasets/stackloss/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/stackloss/data.py b/statsmodels/datasets/stackloss/data.py
index 8e9d8c0c31e..9ab911d1987 100644
--- a/statsmodels/datasets/stackloss/data.py
+++ b/statsmodels/datasets/stackloss/data.py
@@ -15,18 +15,19 @@
 21 days of measurements from a plant's oxidation of ammonia to nitric acid.
 The nitric oxide pollutants are captured in an absorption tower."""
 
-NOTE        = """
-Number of Observations - 21
+NOTE        = """::
 
-Number of Variables - 4
+    Number of Observations - 21
 
-Variable name definitions::
+    Number of Variables - 4
 
-    STACKLOSS - 10 times the percentage of ammonia going into the plant that
-                escapes from the absoroption column
-    AIRFLOW   - Rate of operation of the plant
-    WATERTEMP - Cooling water temperature in the absorption tower
-    ACIDCONC  - Acid concentration of circulating acid minus 50 times 10.
+    Variable name definitions::
+
+        STACKLOSS - 10 times the percentage of ammonia going into the plant
+                    that escapes from the absoroption column
+        AIRFLOW   - Rate of operation of the plant
+        WATERTEMP - Cooling water temperature in the absorption tower
+        ACIDCONC  - Acid concentration of circulating acid minus 50 times 10.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/star98/__init__.py b/statsmodels/datasets/star98/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/star98/__init__.py
+++ b/statsmodels/datasets/star98/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/star98/data.py b/statsmodels/datasets/star98/data.py
index 81a88d4d5dc..6e89b8ac803 100644
--- a/statsmodels/datasets/star98/data.py
+++ b/statsmodels/datasets/star98/data.py
@@ -24,42 +24,44 @@
 The data used in this example is only a subset of the original source.
 """
 
-NOTE        = """
-Number of Observations - 303 (counties in California).
-
-Number of Variables - 13 and 8 interaction terms.
-
-Definition of variables names::
-
-    NABOVE   - Total number of students above the national median for the math
-               section.
-    NBELOW   - Total number of students below the national median for the math
-               section.
-    LOWINC   - Percentage of low income students
-    PERASIAN - Percentage of Asian student
-    PERBLACK - Percentage of black students
-    PERHISP  - Percentage of Hispanic students
-    PERMINTE - Percentage of minority teachers
-    AVYRSEXP - Sum of teachers' years in educational service divided by the
-               number of teachers.
-    AVSALK   - Total salary budget including benefits divided by the number of
-               full-time teachers (in thousands)
-    PERSPENK - Per-pupil spending (in thousands)
-    PTRATIO  - Pupil-teacher ratio.
-    PCTAF    - Percentage of students taking UC/CSU prep courses
-    PCTCHRT  - Percentage of charter schools
-    PCTYRRND - Percentage of year-round schools
-
-    The below variables are interaction terms of the variables defined above.
-
-    PERMINTE_AVYRSEXP
-    PEMINTE_AVSAL
-    AVYRSEXP_AVSAL
-    PERSPEN_PTRATIO
-    PERSPEN_PCTAF
-    PTRATIO_PCTAF
-    PERMINTE_AVTRSEXP_AVSAL
-    PERSPEN_PTRATIO_PCTAF
+NOTE        = """::
+
+    Number of Observations - 303 (counties in California).
+
+    Number of Variables - 13 and 8 interaction terms.
+
+    Definition of variables names::
+
+        NABOVE   - Total number of students above the national median for the
+                   math section.
+        NBELOW   - Total number of students below the national median for the
+                   math section.
+        LOWINC   - Percentage of low income students
+        PERASIAN - Percentage of Asian student
+        PERBLACK - Percentage of black students
+        PERHISP  - Percentage of Hispanic students
+        PERMINTE - Percentage of minority teachers
+        AVYRSEXP - Sum of teachers' years in educational service divided by the
+                number of teachers.
+        AVSALK   - Total salary budget including benefits divided by the number
+                   of full-time teachers (in thousands)
+        PERSPENK - Per-pupil spending (in thousands)
+        PTRATIO  - Pupil-teacher ratio.
+        PCTAF    - Percentage of students taking UC/CSU prep courses
+        PCTCHRT  - Percentage of charter schools
+        PCTYRRND - Percentage of year-round schools
+
+        The below variables are interaction terms of the variables defined
+        above.
+
+        PERMINTE_AVYRSEXP
+        PEMINTE_AVSAL
+        AVYRSEXP_AVSAL
+        PERSPEN_PTRATIO
+        PERSPEN_PCTAF
+        PTRATIO_PCTAF
+        PERMINTE_AVTRSEXP_AVSAL
+        PERSPEN_PTRATIO_PCTAF
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/statecrime/__init__.py b/statsmodels/datasets/statecrime/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/statecrime/__init__.py
+++ b/statsmodels/datasets/statecrime/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/statecrime/data.py b/statsmodels/datasets/statecrime/data.py
index 66f2cdd3755..62ea963ae7c 100644
--- a/statsmodels/datasets/statecrime/data.py
+++ b/statsmodels/datasets/statecrime/data.py
@@ -15,41 +15,43 @@
 DESCRLONG   = DESCRSHORT
 
 #suggested notes
-NOTE        = """
-Number of observations: 51
-Number of variables: 8
-Variable name definitions:
+NOTE        = """::
 
-state
-    All 50 states plus DC.
-violent
-    Rate of violent crimes / 100,000 population. Includes murder, forcible
-    rape, robbery, and aggravated assault. Numbers for Illinois and Minnesota
-    do not include forcible rapes. Footnote included with the American
-    Statistical Abstract table reads:
-    "The data collection methodology for the offense of forcible
-    rape used by the Illinois and the Minnesota state Uniform Crime Reporting
-    (UCR) Programs (with the exception of Rockford, Illinois, and Minneapolis
-    and St. Paul, Minnesota) does not comply with national UCR guidelines.
-    Consequently, their state figures for forcible rape and violent crime (of
-    which forcible rape is a part) are not published in this table."
-murder
-    Rate of murders / 100,000 population.
-hs_grad
-    Precent of population having graduated from high school or higher.
-poverty
-    % of individuals below the poverty line
-white
-    Percent of population that is one race - white only. From 2009 American
-    Community Survey
-single
-    Calculated from 2009 1-year American Community Survey obtained obtained
-    from Census. Variable is Male householder, no wife present, family
-    household combined with Female household, no husband prsent, family
-    household, divided by the total number of Family households.
-urban
-    % of population in Urbanized Areas as of 2010 Census. Urbanized Areas are
-    area of 50,000 or more people."""
+    Number of observations: 51
+    Number of variables: 8
+    Variable name definitions:
+
+    state
+        All 50 states plus DC.
+    violent
+        Rate of violent crimes / 100,000 population. Includes murder, forcible
+        rape, robbery, and aggravated assault. Numbers for Illinois and
+        Minnesota do not include forcible rapes. Footnote included with the
+        American Statistical Abstract table reads:
+        "The data collection methodology for the offense of forcible
+        rape used by the Illinois and the Minnesota state Uniform Crime
+        Reporting (UCR) Programs (with the exception of Rockford, Illinois,
+        and Minneapolis and St. Paul, Minnesota) does not comply with
+        national UCR guidelines. Consequently, their state figures for
+        forcible rape and violent crime (of which forcible rape is a part)
+        are not published in this table."
+    murder
+        Rate of murders / 100,000 population.
+    hs_grad
+        Precent of population having graduated from high school or higher.
+    poverty
+        % of individuals below the poverty line
+    white
+        Percent of population that is one race - white only. From 2009 American
+        Community Survey
+    single
+        Calculated from 2009 1-year American Community Survey obtained obtained
+        from Census. Variable is Male householder, no wife present, family
+        household combined with Female household, no husband prsent, family
+        household, divided by the total number of Family households.
+    urban
+        % of population in Urbanized Areas as of 2010 Census. Urbanized
+        Areas are area of 50,000 or more people."""
 
 import numpy as np
 from statsmodels.datasets import utils as du
diff --git a/statsmodels/datasets/strikes/__init__.py b/statsmodels/datasets/strikes/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/strikes/__init__.py
+++ b/statsmodels/datasets/strikes/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/strikes/data.py b/statsmodels/datasets/strikes/data.py
index d2e526efc13..a7fc50d0cc8 100644
--- a/statsmodels/datasets/strikes/data.py
+++ b/statsmodels/datasets/strikes/data.py
@@ -24,15 +24,16 @@
 seasonal issues."""
 
 #suggested notes
-NOTE        = """
-Number of observations - 62
+NOTE        = """::
 
-Number of variables - 2
+    Number of observations - 62
 
-Variable name definitions::
+    Number of variables - 2
 
-            duration - duration of the strike in days
-            iprod - unanticipated industrial production
+    Variable name definitions::
+
+                duration - duration of the strike in days
+                iprod - unanticipated industrial production
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/sunspots/__init__.py b/statsmodels/datasets/sunspots/__init__.py
index 2fb5ecda9eb..df22d8bbb44 100644
--- a/statsmodels/datasets/sunspots/__init__.py
+++ b/statsmodels/datasets/sunspots/__init__.py
@@ -1 +1 @@
-from data import *
+from .data import *
diff --git a/statsmodels/datasets/sunspots/data.py b/statsmodels/datasets/sunspots/data.py
index 8b7855135a4..9dfd18e3df0 100644
--- a/statsmodels/datasets/sunspots/data.py
+++ b/statsmodels/datasets/sunspots/data.py
@@ -5,7 +5,7 @@
 COPYRIGHT   = """This data is public domain."""
 TITLE       = __doc__
 SOURCE      = """
-http://www.ngdc.noaa.gov/stp/SOLAR/ftpsunspotnumber.html
+http://www.ngdc.noaa.gov/stp/solar/solarda3.html
 
 The original dataset contains monthly data on sunspot activity in the file
 ./src/sunspots_yearly.dat.  There is also sunspots_monthly.dat.
@@ -16,14 +16,15 @@
 
 DESCRLONG   = DESCRSHORT
 
-NOTE        = """
-Number of Observations - 309 (Annual 1700 - 2008)
-Number of Variables - 1
-Variable name definitions::
+NOTE        = """::
 
-    SUNACTIVITY - Number of sunspots for each year
+    Number of Observations - 309 (Annual 1700 - 2008)
+    Number of Variables - 1
+    Variable name definitions::
 
-The data file contains a 'YEAR' variable that is not returned by load.
+        SUNACTIVITY - Number of sunspots for each year
+
+    The data file contains a 'YEAR' variable that is not returned by load.
 """
 
 from numpy import recfromtxt, column_stack, array
diff --git a/statsmodels/datasets/template_data.py b/statsmodels/datasets/template_data.py
index 41fdc2c4b3a..919fac6fcc3 100644
--- a/statsmodels/datasets/template_data.py
+++ b/statsmodels/datasets/template_data.py
@@ -18,9 +18,11 @@
 
 #suggested notes
 NOTE        = """
-Number of observations:
-Number of variables:
-Variable name definitions:
+::
+
+    Number of observations:
+    Number of variables:
+    Variable name definitions:
 
 Any other useful information that does not fit into the above categories.
 """
@@ -29,6 +31,7 @@
 from statsmodels.datasets import utils as du
 from os.path import dirname, abspath
 
+
 def load():
     """
     Load the data and return a Dataset class instance.
@@ -43,6 +46,7 @@ def load():
     #NOTE: None for exog_idx is the complement of endog_idx
     return du.process_recarray(data, endog_idx=0, exog_idx=None, dtype=float)
 
+
 def load_pandas():
     data = _get_data()
     ##### SET THE INDICES #####
@@ -50,9 +54,10 @@ def load_pandas():
     return du.process_recarray_pandas(data, endog_idx=0, exog_idx=None,
                                       dtype=float)
 
+
 def _get_data():
     filepath = dirname(abspath(__file__))
     ##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####
     data = np.recfromtxt(open(filepath + '/DatasetName.csv', 'rb'),
-            delimiter=",", names = True, dtype=float)
+                         delimiter=",", names=True, dtype=float)
     return data
diff --git a/statsmodels/datasets/tests/test_data.py b/statsmodels/datasets/tests/test_data.py
new file mode 100644
index 00000000000..28d3881deb3
--- /dev/null
+++ b/statsmodels/datasets/tests/test_data.py
@@ -0,0 +1,5 @@
+from statsmodels.datasets import co2
+
+def test_co2_python3():
+    # this failed in pd.to_datetime on Python 3 with pandas <= 0.12.0
+    dta = co2.load_pandas()
diff --git a/statsmodels/datasets/utils.py b/statsmodels/datasets/utils.py
index e26ed325275..e11e7cd0162 100644
--- a/statsmodels/datasets/utils.py
+++ b/statsmodels/datasets/utils.py
@@ -1,6 +1,7 @@
+from statsmodels.compat.python import (range, StringIO, urlopen, HTTPError, lrange,
+                                cPickle)
 import sys
 import shutil
-import pickle
 from os import environ
 from os import makedirs
 from os.path import basename
@@ -8,9 +9,9 @@
 from os.path import exists
 from os.path import expanduser
 from os.path import join
-from StringIO import StringIO
+
 import time
-from urllib2 import urlopen, HTTPError
+
 
 import numpy as np
 from numpy import genfromtxt, array
@@ -53,7 +54,7 @@ def process_recarray(data, endog_idx=0, exog_idx=None, stack=True, dtype=None):
             endog = data[endog_name]
 
     if exog_idx is None:
-        exog_name = [names[i] for i in xrange(len(names))
+        exog_name = [names[i] for i in range(len(names))
                  if i not in endog_idx]
     else:
         exog_name = [names[i] for i in exog_idx]
@@ -113,7 +114,7 @@ def _maybe_reset_index(data):
     real index. Strip this for a zero-based index
     """
     from pandas import Index
-    if data.index.equals(Index(range(1,len(data)+1))):
+    if data.index.equals(Index(lrange(1,len(data)+1))):
         data = data.reset_index(drop=True)
     return data
 
@@ -132,10 +133,10 @@ def _cache_it(data, cache_path):
         # for some reason encode("zip") won't work for me in Python 3?
         import zlib
         # use protocol 2 so can open with python 2.x if cached in 3.x
-        open(cache_path, "wb").write(zlib.compress(pickle.dumps(data,
+        open(cache_path, "wb").write(zlib.compress(cPickle.dumps(data,
                                                                 protocol=2)))
     else:
-        open(cache_path, "wb").write(pickle.dumps(data).encode("zip"))
+        open(cache_path, "wb").write(cPickle.dumps(data).encode("zip"))
 
 def _open_cache(cache_path):
     if sys.version_info[0] >= 3:
@@ -144,10 +145,10 @@ def _open_cache(cache_path):
         import zlib
         data = zlib.decompress(open(cache_path, 'rb').read())
         # return as bytes object encoded in utf-8 for cross-compat of cached
-        data = pickle.loads(data).encode('utf-8')
+        data = cPickle.loads(data).encode('utf-8')
     else:
         data = open(cache_path, 'rb').read().decode('zip')
-        data = pickle.loads(data)
+        data = cPickle.loads(data)
     return data
 
 def _urlopen_cached(url, cache):
@@ -178,15 +179,13 @@ def _get_data(base_url, dataname, cache, extension="csv"):
     url = base_url + (dataname + ".%s") % extension
     try:
         data, from_cache = _urlopen_cached(url, cache)
-    except HTTPError, err:
+    except HTTPError as err:
         if '404' in str(err):
             raise ValueError("Dataset %s was not found." % dataname)
         else:
             raise err
 
-    #Python 3, always decode as unicode
-    if sys.version[0] == '3':  # pragma: no cover
-        data = data.decode('utf-8', errors='strict')
+    data = data.decode('utf-8', 'strict')
     return StringIO(data), from_cache
 
 
@@ -198,7 +197,7 @@ def _get_dataset_meta(dataname, package, cache):
     data, _ = _urlopen_cached(index_url, cache)
     #Python 3
     if sys.version[0] == '3':  # pragma: no cover
-        data = data.decode('ascii', errors='strict')
+        data = data.decode('utf-8', 'strict')
     index = read_csv(StringIO(data))
     idx = np.logical_and(index.Item == dataname, index.Package == package)
     dataset_meta = index.ix[idx]
diff --git a/statsmodels/discrete/discrete_margins.py b/statsmodels/discrete/discrete_margins.py
index 2b64a00182e..40aedbd5a44 100644
--- a/statsmodels/discrete/discrete_margins.py
+++ b/statsmodels/discrete/discrete_margins.py
@@ -1,5 +1,6 @@
 #Splitting out maringal effects to see if they can be generalized
 
+from statsmodels.compat.python import lzip, callable, range
 import numpy as np
 from scipy.stats import norm
 from statsmodels.tools.decorators import cache_readonly, resettable_cache
@@ -484,7 +485,7 @@ def conf_int(self, alpha=.05):
         q = norm.ppf(1 - alpha / 2)
         lower = self.margeff - q * me_se
         upper = self.margeff + q * me_se
-        return np.asarray(zip(lower, upper))
+        return np.asarray(lzip(lower, upper))
 
     def summary(self, alpha=.05):
         """
diff --git a/statsmodels/discrete/discrete_model.py b/statsmodels/discrete/discrete_model.py
index f430c687e9b..f48c2eb8bf2 100644
--- a/statsmodels/discrete/discrete_model.py
+++ b/statsmodels/discrete/discrete_model.py
@@ -18,6 +18,7 @@
 
 __all__ = ["Poisson", "Logit", "Probit", "MNLogit", "NegativeBinomial"]
 
+from statsmodels.compat.python import lmap, lzip, range
 import numpy as np
 from scipy.special import gammaln
 from scipy import stats, special, optimize  # opt just for nbin
@@ -33,6 +34,7 @@
 import statsmodels.base.model as base
 import statsmodels.regression.linear_model as lm
 import statsmodels.base.wrapper as wrap
+from statsmodels.compat.numpy import np_matrix_rank
 
 from statsmodels.base.l1_slsqp import fit_l1_slsqp
 try:
@@ -69,10 +71,10 @@
     *Attributes*
 
     aic : float
-        Akaike information criterion.  -2*(`llf` - p) where p is the number
+        Akaike information criterion.  `-2*(llf - p)` where `p` is the number
         of regressors including the intercept.
     bic : float
-        Bayesian information criterion. -2*`llf` + ln(`nobs`)*p where p is the
+        Bayesian information criterion. `-2*llf + ln(nobs)*p` where `p` is the
         number of regressors including the intercept.
     bse : array
         The standard errors of the coefficients.
@@ -87,13 +89,13 @@
     llnull : float
         Value of the constant-only loglikelihood
     llr : float
-        Likelihood ratio chi-squared statistic; -2*(`llnull` - `llf`)
+        Likelihood ratio chi-squared statistic; `-2*(llnull - llf)`
     llr_pvalue : float
         The chi-squared probability of getting a log-likelihood ratio
         statistic greater than llr.  llr has a chi-squared distribution
         with degrees of freedom `df_model`.
     prsquared : float
-        McFadden's pseudo-R-squared. 1 - (`llf`/`llnull`)
+        McFadden's pseudo-R-squared. `1 - (llf / llnull)`
 %(extra_attr)s"""
 
 _l1_results_attr = """    nnz_params : Integer
@@ -123,8 +125,10 @@ def initialize(self):
         statsmodels.model.LikelihoodModel.__init__
         and should contain any preprocessing that needs to be done for a model.
         """
-        self.df_model = float(tools.rank(self.exog) - 1)  # assumes constant
-        self.df_resid = float(self.exog.shape[0] - tools.rank(self.exog))
+        # assumes constant
+        self.df_model = float(np_matrix_rank(self.exog) - 1)
+        self.df_resid = (float(self.exog.shape[0] -
+                         np_matrix_rank(self.exog)))
 
     def cdf(self, X):
         """
@@ -152,7 +156,7 @@ def fit(self, start_params=None, method='newton', maxiter=35,
         Fit the model using maximum likelihood.
 
         The rest of the docstring is from
-        statsmodels.LikelihoodModel.fit
+        statsmodels.base.model.LikelihoodModel.fit
         """
         if callback is None:
             callback = self._check_perfect_pred
@@ -219,6 +223,9 @@ def fit_regularized(self, start_params=None, method='l1',
 
         Notes
         -----
+        Extra parameters are not penalized if alpha is given as a scalar.
+        An example is the shape parameter in NegativeBinomial `nb1` and `nb2`.
+
         Optional arguments for the solvers (available in Results.mle_settings)::
 
             'l1'
@@ -427,7 +434,7 @@ def _derivative_exog(self, params, exog=None, transform='dydx',
         """
         #note, this form should be appropriate for
         ## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
-        if exog == None:
+        if exog is None:
             exog = self.exog
         margeff = np.dot(self.pdf(np.dot(exog, params))[:,None],
                                                           params[None,:])
@@ -555,7 +562,7 @@ def _derivative_predict(self, params, exog=None, transform='dydx'):
 
         eXB = np.exp(np.dot(exog, params))
         sum_eXB = (1 + eXB.sum(1))[:,None]
-        J, K = map(int, [self.J, self.K])
+        J, K = lmap(int, [self.J, self.K])
         repeat_eXB = np.repeat(eXB, J, axis=1)
         X = np.tile(exog, J-1)
         # this is the derivative wrt the base level
@@ -601,7 +608,7 @@ def _derivative_exog(self, params, exog=None, transform='dydx',
         K = int(self.K) # number of variables
         #note, this form should be appropriate for
         ## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
-        if exog == None:
+        if exog is None:
             exog = self.exog
         if params.ndim == 1: # will get flatted from approx_fprime
             params = params.reshape(K, J-1, order='F')
@@ -653,7 +660,16 @@ def _check_inputs(self, offset, exposure, endog):
                 raise ValueError("exposure is not the same length as endog")
         self.exposure = exposure
 
-    #TODO: are these two methods only for Poisson? or also Negative Binomial?
+
+    def _get_init_kwds(self):
+        # this is a temporary fixup because exposure has been transformed
+        # see #1609
+        kwds = super(CountModel, self)._get_init_kwds()
+        if 'exposure' in kwds and kwds['exposure'] is not None:
+            kwds['exposure'] = np.exp(kwds['exposure'])
+        return kwds
+
+
     def predict(self, params, exog=None, exposure=None, offset=None,
                 linear=False):
         """
@@ -716,7 +732,7 @@ def _derivative_exog(self, params, exog=None, transform="dydx",
         but checks are done in the results in get_margeff.
         """
         # group 3 poisson, nbreg, zip, zinb
-        if exog == None:
+        if exog is None:
             exog = self.exog
         margeff = self.predict(params, exog)[:,None] * params[None,:]
         if 'ex' in transform:
@@ -782,7 +798,15 @@ class Poisson(CountModel):
     exog : array
         A reference to the exogenous design.
     """ % {'params' : base._model_params_doc,
-           'extra_params' : base._missing_param_doc}
+           'extra_params' :
+           """offset : array_like
+        Offset is added to the linear prediction with coefficient equal to 1.
+    exposure : array_like
+        Log(exposure) is added to the linear prediction with coefficient
+        equal to 1.
+
+    """ + base._missing_param_doc}
+
 
     def cdf(self, X):
         """
@@ -896,6 +920,96 @@ def loglikeobs(self, params):
         #np.sum(stats.poisson.logpmf(endog, np.exp(XB)))
         return -np.exp(XB) +  endog*XB - gammaln(endog+1)
 
+    def fit(self, start_params=None, method='newton', maxiter=35,
+            full_output=1, disp=1, callback=None, **kwargs):
+        cntfit = super(CountModel, self).fit(start_params=start_params,
+                method=method, maxiter=maxiter, full_output=full_output,
+                disp=disp, callback=callback, **kwargs)
+        discretefit = PoissonResults(self, cntfit)
+        return PoissonResultsWrapper(discretefit)
+    fit.__doc__ = DiscreteModel.fit.__doc__
+
+    def fit_regularized(self, start_params=None, method='l1',
+            maxiter='defined_by_method', full_output=1, disp=1, callback=None,
+            alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
+            qc_tol=0.03, **kwargs):
+        cntfit = super(CountModel, self).fit_regularized(
+                start_params=start_params, method=method, maxiter=maxiter,
+                full_output=full_output, disp=disp, callback=callback,
+                alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
+                size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
+        if method in ['l1', 'l1_cvxopt_cp']:
+            discretefit = L1PoissonResults(self, cntfit)
+        else:
+            raise Exception(
+                    "argument method == %s, which is not handled" % method)
+        return L1PoissonResultsWrapper(discretefit)
+
+    fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__
+
+
+    def fit_constrained(self, constraints, start_params=None, **fit_kwds):
+        """fit the model subject to linear equality constraints
+
+        The constraints are of the form   `R params = q`
+        where R is the constraint_matrix and q is the vector of
+        constraint_values.
+
+        The estimation creates a new model with transformed design matrix,
+        exog, and converts the results back to the original parameterization.
+
+        Parameters
+        ----------
+        constraints : formula expression or tuple
+            If it is a tuple, then the constraint needs to be given by two
+            arrays (constraint_matrix, constraint_value), i.e. (R, q).
+            Otherwise, the constraints can be given as strings or list of
+            strings.
+            see t_test for details
+        start_params : None or array_like
+            starting values for the optimization. `start_params` needs to be
+            given in the original parameter space and are internally
+            transformed.
+        **fit_kwds : keyword arguments
+            fit_kwds are used in the optimization of the transformed model.
+
+        Returns
+        -------
+        results : Results instance
+
+        """
+
+        #constraints = (R, q)
+        # TODO: temporary trailing underscore to not overwrite the monkey
+        #       patched version
+        # TODO: decide whether to move the imports
+        from patsy import DesignInfo
+        from statsmodels.base._constraints import fit_constrained
+
+        # same pattern as in base.LikelihoodModel.t_test
+        lc = DesignInfo(self.exog_names).linear_constraint(constraints)
+        R, q = lc.coefs, lc.constants
+
+        # TODO: add start_params option, need access to tranformation
+        #       fit_constrained needs to do the transformation
+        params, cov, res_constr = fit_constrained(self, R, q,
+                                                  start_params=start_params,
+                                                  fit_kwds=fit_kwds)
+        #create dummy results Instance, TODO: wire up properly
+        res = self.fit(maxiter=0, method='nm', disp=0) # we get a wrapper back
+        res.mle_retvals['fcall'] = res_constr.mle_retvals.get('fcall', np.nan)
+        res.mle_retvals['iterations'] = res_constr.mle_retvals.get(
+                                                        'iterations', np.nan)
+        res.mle_retvals['converged'] = res_constr.mle_retvals['converged']
+        res._results.params = params
+        res._results.normalized_cov_params = cov
+        k_constr = len(q)
+        res._results.df_resid += k_constr
+        res._results.df_model -= k_constr
+        res._results.constraints = lc
+        res._results.k_constr = k_constr
+        res._results.results_constrained = res_constr
+        return res
 
 
     def score(self, params):
@@ -1420,7 +1534,7 @@ class MNLogit(MultinomialModel):
         is done.
     exog : array-like
         A nobs x k array where `nobs` is the number of observations and `k`
-        is the number of regressors. An interecept is not included by default
+        is the number of regressors. An intercept is not included by default
         and should be added by the user. See `statsmodels.tools.add_constant`.
     %(extra_params)s
 
@@ -1565,6 +1679,21 @@ def score(self, params):
         #NOTE: might need to switch terms if params is reshaped
         return np.dot(firstterm.T, self.exog).flatten()
 
+    def loglike_and_score(self, params):
+        """
+        Returns log likelihood and score, efficiently reusing calculations.
+
+        Note that both of these returned quantities will need to be negated
+        before being minimized by the maximum likelihood fitting machinery.
+
+        """
+        params = params.reshape(self.K, -1, order='F')
+        cdf_dot_exog_params = self.cdf(np.dot(self.exog, params))
+        loglike_value = np.sum(self.wendog * np.log(cdf_dot_exog_params))
+        firstterm = self.wendog[:, 1:] - cdf_dot_exog_params[:, 1:]
+        score_array = np.dot(firstterm.T, self.exog).flatten()
+        return loglike_value, score_array
+
     def jac(self, params):
         """
         Jacobian matrix for multinomial logit model log-likelihood
@@ -1732,9 +1861,16 @@ class NegativeBinomial(CountModel):
         Log-likelihood type. 'nb2','nb1', or 'geometric'.
         Fitted value :math:`\\mu`
         Heterogeneity parameter :math:`\\alpha`
-        nb2: Variance equal to :math:`\\mu + \\alpha\\mu^2` (most common)
-        nb1: Variance equal to :math:`\\mu + \\alpha\\mu`
-        geometric: Variance equal to :math:`\\mu + \\mu^2`
+
+        - nb2: Variance equal to :math:`\\mu + \\alpha\\mu^2` (most common)
+        - nb1: Variance equal to :math:`\\mu + \\alpha\\mu`
+        - geometric: Variance equal to :math:`\\mu + \\mu^2`
+    offset : array_like
+        Offset is added to the linear prediction with coefficient equal to 1.
+    exposure : array_like
+        Log(exposure) is added to the linear prediction with coefficient
+        equal to 1.
+
     """ + base._missing_param_doc}
     def __init__(self, endog, exog, loglike_method='nb2', offset=None,
                        exposure=None, missing='none'):
@@ -1745,6 +1881,12 @@ def __init__(self, endog, exog, loglike_method='nb2', offset=None,
         self._initialize()
         if loglike_method in ['nb2', 'nb1']:
             self.exog_names.append('alpha')
+            self.k_extra = 1
+        else:
+            self.k_extra = 0
+        # store keys for extras if we need to recreate model instance
+        # we need to append keys that don't go to super
+        self._init_keys.append('loglike_method')
 
     def _initialize(self):
         if self.loglike_method == 'nb2':
@@ -1779,7 +1921,7 @@ def __setstate__(self, indict):
 
     def _ll_nbin(self, params, alpha, Q=0):
         endog = self.endog
-        mu = np.exp(np.dot(self.exog, params))
+        mu = self.predict(params)
         size = 1/alpha * mu**Q
         prob = size/(size+mu)
         coeff = (gammaln(size+endog) - gammaln(endog+1) -
@@ -1845,7 +1987,7 @@ def loglike(self, params):
     def _score_geom(self, params):
         exog = self.exog
         y = self.endog[:,None]
-        mu = np.exp(np.dot(exog, params))[:,None]
+        mu = self.predict(params)[:,None]
         dparams = exog * (y-mu)/(mu+1)
         return dparams.sum(0)
 
@@ -1860,7 +2002,7 @@ def _score_nbin(self, params, Q=0):
         params = params[:-1]
         exog = self.exog
         y = self.endog[:,None]
-        mu = np.exp(np.dot(exog, params))[:,None]
+        mu = self.predict(params)[:,None]
         a1 = 1/alpha * mu**Q
         if Q: # nb1
             dparams = exog*mu/alpha*(np.log(1/(alpha + 1)) +
@@ -1881,7 +2023,10 @@ def _score_nbin(self, params, Q=0):
                         - np.log(a1+mu) - (a1+y)/(a1+mu) + 1).sum()*da1
 
         #multiply above by constant outside sum to reduce rounding error
-        return np.r_[dparams.sum(0), dalpha]
+        if self._transparams:
+            return np.r_[dparams.sum(0), dalpha*alpha]
+        else:
+            return np.r_[dparams.sum(0), dalpha]
 
     def _score_nb1(self, params):
         return self._score_nbin(params, Q=1)
@@ -1889,7 +2034,7 @@ def _score_nb1(self, params):
     def _hessian_geom(self, params):
         exog = self.exog
         y = self.endog[:,None]
-        mu = np.exp(np.dot(exog, params))[:,None]
+        mu = self.predict(params)[:,None]
 
         # for dl/dparams dparams
         dim = exog.shape[1]
@@ -1918,7 +2063,7 @@ def _hessian_nb1(self, params):
         params = params[:-1]
         exog = self.exog
         y = self.endog[:,None]
-        mu = np.exp(np.dot(exog, params))[:,None]
+        mu = self.predict(params)[:,None]
 
         a1 = mu/alpha
 
@@ -1985,7 +2130,7 @@ def _hessian_nb2(self, params):
 
         exog = self.exog
         y = self.endog[:,None]
-        mu = np.exp(np.dot(exog, params))[:,None]
+        mu = self.predict(params)[:,None]
 
         # for dl/dparams dparams
         dim = exog.shape[1]
@@ -2019,7 +2164,7 @@ def _hessian_nb2(self, params):
         return hess_arr
 
     #TODO: replace this with analytic where is it used?
-    def scoreobs(self, params):
+    def jac(self, params):
         sc = approx_fprime_cs(params, self.loglikeobs)
         return sc
 
@@ -2031,7 +2176,7 @@ def fit(self, start_params=None, method='bfgs', maxiter=35,
         elif self.loglike_method.startswith('nb'): # method is newton/ncg
             self._transparams = False # because we need to step in alpha space
 
-        if start_params == None:
+        if start_params is None:
             # Use poisson fit as first guess.
             start_params = Poisson(self.endog, self.exog).fit(disp=0).params
             if self.loglike_method.startswith('nb'):
@@ -2048,11 +2193,51 @@ def fit(self, start_params=None, method='bfgs', maxiter=35,
             if method not in ["newton", "ncg"]:
                 mlefit._results.params[-1] = np.exp(mlefit._results.params[-1])
 
-            nbinfit = NegativeBinomialAncillaryResults(self, mlefit._results)
-            return NegativeBinomialAncillaryResultsWrapper(nbinfit)
+            nbinfit = NegativeBinomialResults(self, mlefit._results)
+            return NegativeBinomialResultsWrapper(nbinfit)
         else:
             return mlefit
 
+
+    def fit_regularized(self, start_params=None, method='l1',
+            maxiter='defined_by_method', full_output=1, disp=1, callback=None,
+            alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
+            qc_tol=0.03, **kwargs):
+
+        if self.loglike_method.startswith('nb') and (np.size(alpha) == 1 and
+                                                     alpha != 0):
+            # don't penalize alpha if alpha is scalar
+            alpha = alpha * np.ones(len(start_params))
+            alpha[-1] = 0
+
+        # alpha for regularized poisson to get starting values
+        alpha_p = alpha[:-1] if (self.k_extra and np.size(alpha) > 1) else alpha
+
+        self._transparams = False
+        if start_params is None:
+            # Use poisson fit as first guess.
+            start_params = Poisson(self.endog, self.exog).fit_regularized(
+                start_params=start_params, method=method, maxiter=maxiter,
+                full_output=full_output, disp=0, callback=callback,
+                alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
+                size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params
+            if self.loglike_method.startswith('nb'):
+                start_params = np.append(start_params, 0.1)
+
+        cntfit = super(CountModel, self).fit_regularized(
+                start_params=start_params, method=method, maxiter=maxiter,
+                full_output=full_output, disp=disp, callback=callback,
+                alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
+                size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)
+        if method in ['l1', 'l1_cvxopt_cp']:
+            discretefit = L1NegativeBinomialResults(self, cntfit)
+        else:
+            raise Exception(
+                    "argument method == %s, which is not handled" % method)
+
+        return L1NegativeBinomialResultsWrapper(discretefit)
+
+
 ### Results Class ###
 
 class DiscreteResults(base.LikelihoodModelResults):
@@ -2092,10 +2277,13 @@ def llr_pvalue(self):
 
     @cache_readonly
     def llnull(self):
+
         model = self.model
+        kwds = model._get_init_kwds()
         #TODO: what parameters to pass to fit?
-        null = model.__class__(model.endog, np.ones(self.nobs)).fit(disp=0)
-        return null.llf
+        mod_null = model.__class__(model.endog, np.ones(self.nobs), **kwds)
+        res_null = mod_null.fit(disp=0)
+        return res_null.llf
 
     @cache_readonly
     def fittedvalues(self):
@@ -2185,6 +2373,8 @@ def get_margeff(self, at='overall', method='dydx', atexog=None,
 
     def margeff(self, at='overall', method='dydx', atexog=None, dummy=False,
             count=False):
+        """DEPRECATED: marginal effects, use get_margeff instead
+        """
         import warnings
         warnings.warn("This method is deprecated and will be removed in 0.6.0."
                 " Use get_margeff instead", FutureWarning)
@@ -2250,7 +2440,11 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05,
                           yname=yname, xname=xname, title=title)
         # for parameters, etc
         smry.add_table_params(self, yname=yname_list, xname=xname, alpha=alpha,
-                             use_t=False)
+                             use_t=self.use_t)
+
+        if hasattr(self, 'constraints'):
+            smry.add_extra_txt(['Model has been estimated subject to linear '
+                          'equality constraints.'])
 
         #diagnostic table not used yet
         #smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
@@ -2294,6 +2488,10 @@ def summary2(self, yname=None, xname=None, title=None, alpha=.05,
         smry.add_base(results=self, alpha=alpha, float_format=float_format,
                 xname=xname, yname=yname, title=title)
 
+        if hasattr(self, 'constraints'):
+            smry.add_text('Model has been estimated subject to linear '
+                          'equality constraints.')
+
         return smry
 
 
@@ -2318,12 +2516,10 @@ def resid(self):
         """
         return self.model.endog - self.predict()
 
-class NegativeBinomialAncillaryResults(CountResults):
+class NegativeBinomialResults(CountResults):
     __doc__ = _discrete_results_docs % {
         "one_line_description" : "A results class for NegativeBinomial 1 and 2",
                     "extra_attr" : ""}
-    def __init__(self, model, mlefit):
-        super(NegativeBinomialAncillaryResults, self).__init__(model, mlefit)
 
     @cache_readonly
     def lnalpha(self):
@@ -2336,13 +2532,15 @@ def lnalpha_std_err(self):
     @cache_readonly
     def aic(self):
         # + 1 because we estimate alpha
-        return -2*(self.llf - (self.df_model + self.k_constant + 1))
+        k_extra = getattr(self.model, 'k_extra', 0)
+        return -2*(self.llf - (self.df_model + self.k_constant + k_extra))
 
     @cache_readonly
     def bic(self):
         # + 1 because we estimate alpha
+        k_extra = getattr(self.model, 'k_extra', 0)
         return -2*self.llf + np.log(self.nobs)*(self.df_model +
-                                                self.k_constant + 1)
+                                                self.k_constant + k_extra)
 
 class L1CountResults(DiscreteResults):
     __doc__ = _discrete_results_docs % {"one_line_description" :
@@ -2356,12 +2554,52 @@ def __init__(self, model, cntfit):
         # entry in params has been set zero'd out.
         self.trimmed = cntfit.mle_retvals['trimmed']
         self.nnz_params = (self.trimmed == False).sum()
-        #update degrees of freedom
+        # update degrees of freedom
         self.model.df_model = self.nnz_params - 1
         self.model.df_resid = float(self.model.endog.shape[0] - self.nnz_params)
+        # adjust for extra parameter in NegativeBinomial nb1 and nb2
+        # extra parameter is not included in df_model
+        k_extra = getattr(self.model, 'k_extra', 0)
+        self.model.df_model -= k_extra
+        self.model.df_resid += k_extra
         self.df_model = self.model.df_model
         self.df_resid = self.model.df_resid
 
+class PoissonResults(CountResults):
+    def predict_prob(self, n=None, exog=None, exposure=None, offset=None,
+                     transform=True):
+        """
+        Return predicted probability of each count level for each observation
+
+        Parameters
+        ----------
+        n : array-like or int
+            The counts for which you want the probabilities. If n is None
+            then the probabilities for each count from 0 to max(y) are
+            given.
+
+        Returns
+        -------
+        ndarray
+            A nobs x n array where len(`n`) columns are indexed by the count
+            n. If n is None, then column 0 is the probability that each
+            observation is 0, column 1 is the probability that each
+            observation is 1, etc.
+        """
+        if n is not None:
+            counts = np.atleast_2d(n)
+        else:
+            counts = np.atleast_2d(np.arange(0, np.max(self.model.endog)+1))
+        mu = self.predict(exog=exog, exposure=exposure, offset=offset,
+                          transform=transform, linear=False)[:,None]
+        # uses broadcasting
+        return stats.poisson.pmf(counts, mu)
+
+class L1PoissonResults(L1CountResults, PoissonResults):
+    pass
+
+class L1NegativeBinomialResults(L1CountResults, NegativeBinomialResults):
+    pass
 
 class OrderedResults(DiscreteResults):
     __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for ordered discrete data." , "extra_attr" : ""}
@@ -2605,7 +2843,7 @@ def pred_table(self):
         """
         J = self.model.J
         # these are the actual, predicted indices
-        idx = zip(self.model.endog, self.predict().argmax(1))
+        idx = lzip(self.model.endog, self.predict().argmax(1))
         return np.histogram2d(self.model.endog, self.predict().argmax(1),
                               bins=J)[0]
 
@@ -2722,14 +2960,38 @@ class CountResultsWrapper(lm.RegressionResultsWrapper):
     pass
 wrap.populate_wrapper(CountResultsWrapper, CountResults)
 
-class NegativeBinomialAncillaryResultsWrapper(lm.RegressionResultsWrapper):
+class NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper):
+    pass
+wrap.populate_wrapper(NegativeBinomialResultsWrapper,
+                      NegativeBinomialResults)
+
+class PoissonResultsWrapper(lm.RegressionResultsWrapper):
     pass
-wrap.populate_wrapper(NegativeBinomialAncillaryResultsWrapper,
-                      NegativeBinomialAncillaryResults)
+    #_methods = {
+    #        "predict_prob" : "rows",
+    #        }
+    #_wrap_methods = lm.wrap.union_dicts(
+    #                            lm.RegressionResultsWrapper._wrap_methods,
+    #                            _methods)
+wrap.populate_wrapper(PoissonResultsWrapper, PoissonResults)
 
 class L1CountResultsWrapper(lm.RegressionResultsWrapper):
     pass
-wrap.populate_wrapper(L1CountResultsWrapper, L1CountResults)
+
+class L1PoissonResultsWrapper(lm.RegressionResultsWrapper):
+    pass
+    #_methods = {
+    #        "predict_prob" : "rows",
+    #        }
+    #_wrap_methods = lm.wrap.union_dicts(
+    #                            lm.RegressionResultsWrapper._wrap_methods,
+    #                            _methods)
+wrap.populate_wrapper(L1PoissonResultsWrapper, L1PoissonResults)
+
+class L1NegativeBinomialResultsWrapper(lm.RegressionResultsWrapper):
+    pass
+wrap.populate_wrapper(L1NegativeBinomialResultsWrapper,
+                      L1NegativeBinomialResults)
 
 class BinaryResultsWrapper(lm.RegressionResultsWrapper):
     _attrs = {"resid_dev" : "rows",
diff --git a/statsmodels/discrete/tests/results/predict_prob_poisson.csv b/statsmodels/discrete/tests/results/predict_prob_poisson.csv
new file mode 100644
index 00000000000..414e893a9d0
--- /dev/null
+++ b/statsmodels/discrete/tests/results/predict_prob_poisson.csv
@@ -0,0 +1,100 @@
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+3.667351615308579726e-02,1.212316575714769973e-01,2.003777703803729937e-01,2.207962929096410065e-01,1.824715992825579891e-01,1.206392882995020038e-01,6.646622423541169444e-02,3.138820357814629813e-02,1.296999969980200049e-02,4.763881487983540053e-03,1.574796501245009960e-03,4.732550407189550171e-04,1.303699487138589898e-04,3.315107642951420157e-05,7.827680508214350635e-06,1.725064447033169917e-06,3.564091411675569944e-07,6.930481446548139533e-08,1.272783077494139934e-08,2.214441867179409974e-09,3.660140699806459851e-10,5.761585062275659759e-11,8.657306423710390639e-12,1.244280932119739982e-12,1.713841664101940064e-13,2.266178840194519972e-14,2.881272435687100149e-15,3.527638328425570177e-16,4.164755530964130263e-17,4.747391064695470094e-18,5.231150871113779640e-19,5.578263743948540178e-20,5.762521491895889849e-21,5.772475663014290469e-22,5.612375057513309809e-23,5.300808730708199855e-24,4.867468228227479547e-25,4.348754525047629747e-26,3.783073589142530035e-27,3.206591777971869826e-28,2.650007942897260126e-29,2.136617648031550055e-30,1.681670915730689959e-31,1.292814010288440116e-32,9.712854114760189782e-34,7.135063528721190440e-35,5.127474449752560247e-36,3.606360514278259923e-37,2.483655718573480080e-38,1.675555457127449910e-39,1.107776874039400082e-40,7.180349945846680275e-42,4.564631883991050156e-43,2.847038776192090106e-44,1.742862456360189996e-45,1.047523844636680038e-46,6.183571442986500031e-48,3.586146436150379708e-49,2.043918234947270067e-50,1.145183280975199929e-51,6.309388078863389840e-53,3.419171605164850077e-54,1.823025321707859995e-55,9.565675496443530379e-57,4.940821478058910104e-58,2.512750094014659800e-59,1.258545307408900032e-60,6.209513052476710105e-62,3.018645569899840204e-63,1.446193900513559966e-64,6.829548253262870211e-66,3.179780351542369957e-67,1.459916837237680094e-68,6.611024239602200641e-70,2.953252104496869993e-71,1.301675561041950024e-72,5.661775582235429918e-74,2.430666739293579759e-75
diff --git a/statsmodels/discrete/tests/results/results_count_robust_cluster.py b/statsmodels/discrete/tests/results/results_count_robust_cluster.py
new file mode 100644
index 00000000000..22670e3468f
--- /dev/null
+++ b/statsmodels/discrete/tests/results/results_count_robust_cluster.py
@@ -0,0 +1,695 @@
+import numpy as np
+
+est = dict(
+           rank = 3,
+           N = 34,
+           ic = 1,
+           k = 3,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           N_clust = 5,
+           ll = -354.2436413025559,
+           k_eq_model = 1,
+           ll_0 = -356.2029100704882,
+           df_m = 2,
+           chi2 = 5.204189583786304,
+           p = .0741181533729996,
+           r2_p = .0055004288638308,
+           cmdline = "poisson accident yr_con op_75_79, vce(cluster ship)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           clustvar = "ship",
+           vce = "cluster",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+    -.02172061893549,  .19933709357097, -.10896426022065,  .91323083771076,
+    -.41241414311748,  .36897290524649, np.nan,  1.9599639845401,
+                   0,  .22148585072024,  .11093628220713,  1.9965140918162,
+     .04587799343723,  .00405473301549,  .43891696842499, np.nan,
+     1.9599639845401,                0,  2.2697077143215,  1.1048569901548,
+      2.054299999499,  .03994666479943,  .10422780555076,  4.4351876230922,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+     .03973527687332,  .00976206273414, -.21171095768584,  .00976206273414,
+     .01230685870994, -.06297293767114, -.21171095768584, -.06297293767114,
+     1.2207089686939]).reshape(3,3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_poisson_clu = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 3,
+           N = 34,
+           ic = 1,
+           k = 3,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           ll = -354.2436413025559,
+           k_eq_model = 1,
+           ll_0 = -356.2029100704882,
+           df_m = 2,
+           chi2 = .1635672212515404,
+           p = .9214713337295277,
+           r2_p = .0055004288638308,
+           cmdline = "poisson accident yr_con op_75_79, vce(robust)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           vce = "robust",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+    -.02172061893549,  .19233713248134, -.11292993014545,  .91008610728406,
+    -.39869447148862,  .35525323361764, np.nan,  1.9599639845401,
+                   0,  .22148585072024,  .55301404772037,    .400506735106,
+     .68878332380143,  -.8624017657564,  1.3053734671969, np.nan,
+     1.9599639845401,                0,  2.2697077143215,  .66532523368388,
+     3.4114258702533,  .00064624070669,  .96569421829539,  3.5737212103476,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+     .03699357253114, -.01521223175214, -.09585501859714, -.01521223175214,
+     .30582453697607,  -.1649339692102, -.09585501859714,  -.1649339692102,
+     .44265766657651]).reshape(3,3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_hc1 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 3,
+           N = 34,
+           ic = 4,
+           k = 3,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           ll = -91.28727940081573,
+           k_eq_model = 1,
+           ll_0 = -122.0974139280415,
+           df_m = 2,
+           chi2 = 61.62026905445154,
+           p = 4.16225408420e-14,
+           r2_p = .2523405986746273,
+           cmdline = "poisson accident yr_con op_75_79, exposure(service)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(service)",
+           gof = "poiss_g",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .30633819450439,  .05790831365493,   5.290055523458,  1.222792336e-07,
+     .19283998533528,   .4198364036735, np.nan,  1.9599639845401,
+                   0,  .35592229608495,  .12151759298719,  2.9289775030556,
+     .00340079035234,  .11775219034206,  .59409240182785, np.nan,
+     1.9599639845401,                0,  -6.974712802772,  .13252425018256,
+    -52.629709605328,                0,  -7.234455560208,  -6.714970045336,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+     .00335337279036, -.00315267340017, -.00589654294427, -.00315267340017,
+      .0147665254054, -.00165060980569, -.00589654294427, -.00165060980569,
+     .01756267688645]).reshape(3,3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_nonrobust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 3,
+           N = 34,
+           ic = 4,
+           k = 3,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           ll = -91.28727940081573,
+           k_eq_model = 1,
+           ll_0 = -122.0974139280415,
+           df_m = 2,
+           chi2 = 15.1822804640621,
+           p = .0005049050167458,
+           r2_p = .2523405986746273,
+           cmdline = "poisson accident yr_con op_75_79, exposure(service) vce(robust)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(service)",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           vce = "robust",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .30633819450439,  .09144457613957,  3.3499875819514,  .00080815183366,
+     .12711011868929,  .48556627031949, np.nan,  1.9599639845401,
+                   0,  .35592229608495,  .16103531267836,  2.2102127177276,
+     .02709040275274,  .04029888299621,  .67154570917369, np.nan,
+     1.9599639845401,                0,  -6.974712802772,   .2558675415017,
+    -27.259076168227,  1.29723387e-163, -7.4762039689282, -6.4732216366159,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+     .00836211050535,  .00098797681063, -.01860743122756,  .00098797681063,
+     .02593237192942, -.02395236210603, -.01860743122756, -.02395236210603,
+     .06546819879413]).reshape(3,3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_hc1 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 3,
+           N = 34,
+           ic = 4,
+           k = 3,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           N_clust = 5,
+           ll = -91.28727940081573,
+           k_eq_model = 1,
+           ll_0 = -122.0974139280415,
+           df_m = 2,
+           chi2 = 340.7343047354823,
+           p = 1.02443835269e-74,
+           r2_p = .2523405986746273,
+           cmdline = "poisson accident yr_con op_75_79, exposure(service) vce(cluster ship)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(service)",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           clustvar = "ship",
+           vce = "cluster",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .30633819450439,  .03817694295902,  8.0241677504982,  1.022165435e-15,
+     .23151276126487,  .38116362774391, np.nan,  1.9599639845401,
+                   0,  .35592229608495,  .09213163536669,  3.8631930787765,
+     .00011191448109,  .17534760892947,  .53649698324044, np.nan,
+     1.9599639845401,                0,  -6.974712802772,   .0968656626603,
+    -72.003975518463,                0, -7.1645660129248, -6.7848595926192,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons'.split()
+
+cov = np.array([
+      .0014574789737, -.00277745275086,  .00108765624666, -.00277745275086,
+     .00848823823534, -.00469929607507,  .00108765624666, -.00469929607507,
+     .00938295660262]).reshape(3,3)
+
+cov_colnames = 'yr_con op_75_79 _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons'.split()
+
+
+results_poisson_exposure_clu = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 4,
+           N = 34,
+           ic = 2,
+           k = 4,
+           k_eq = 2,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           N_clust = 5,
+           ll = -109.0877965183258,
+           k_eq_model = 1,
+           ll_0 = -109.1684720604314,
+           rank0 = 2,
+           df_m = 2,
+           chi2 = 5.472439553195301,
+           p = .0648148991694882,
+           k_aux = 1,
+           alpha = 2.330298308905143,
+           cmdline = "nbreg accident yr_con op_75_79, vce(cluster ship)",
+           cmd = "nbreg",
+           predict = "nbreg_p",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           clustvar = "ship",
+           vce = "cluster",
+           title = "Negative binomial regression",
+           diparm1 = "lnalpha, exp label(",
+           user = "nbreg_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+    -.03536709401845,  .27216090050938, -.12994921001605,  .89660661037787,
+    -.56879265701682,  .49805846897992, np.nan,  1.9599639845401,
+                   0,  .23211570238882,  .09972456245386,  2.3275680201277,
+     .01993505322091,  .03665915160525,  .42757225317239, np.nan,
+     1.9599639845401,                0,  2.2952623989519,  1.2335785495143,
+     1.8606536242509,  .06279310688494, -.12250713019722,  4.7130319281011,
+    np.nan,  1.9599639845401,                0,  .84599628895555,
+     .22483100011931, np.nan, np.nan,  .40533562611357,
+     1.2866569517975, np.nan,  1.9599639845401,                0,
+     2.3302983089051,  .52392329936749, np.nan, np.nan,
+     1.4998057895818,  3.6206622525444, np.nan,  1.9599639845401,
+                   0]).reshape(5,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+     .07407155576607, -.00421355148283, -.32663130963457,  .02015715724983,
+    -.00421355148283,  .00994498835661,  .00992613461881, -.00714955450361,
+    -.32663130963457,  .00992613461881,  1.5217160378218, -.09288283512096,
+     .02015715724983, -.00714955450361, -.09288283512096,  .05054897861465
+    ]).reshape(4,4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_clu = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 4,
+           N = 34,
+           ic = 2,
+           k = 4,
+           k_eq = 2,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           ll = -109.0877965183258,
+           k_eq_model = 1,
+           ll_0 = -109.1684720604314,
+           rank0 = 2,
+           df_m = 2,
+           chi2 = .1711221347493475,
+           p = .9179970816706797,
+           r2_p = .0007390003778831,
+           k_aux = 1,
+           alpha = 2.330298308905143,
+           cmdline = "nbreg accident yr_con op_75_79, vce(robust)",
+           cmd = "nbreg",
+           predict = "nbreg_p",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           vce = "robust",
+           title = "Negative binomial regression",
+           diparm1 = "lnalpha, exp label(",
+           user = "nbreg_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+    -.03536709401845,  .26106873337039, -.13547043172065,  .89223994079058,
+     -.5470524089139,    .476318220877, np.nan,  1.9599639845401,
+                   0,  .23211570238882,  .56245325203342,  .41268443475019,
+     .67983783029986, -.87027241458412,  1.3345038193618, np.nan,
+     1.9599639845401,                0,  2.2952623989519,  .76040210713867,
+     3.0184850586341,  .00254041928465,  .80490165519179,  3.7856231427121,
+    np.nan,  1.9599639845401,                0,  .84599628895555,
+     .24005700345444, np.nan, np.nan,  .37549320794823,
+     1.3164993699629, np.nan,  1.9599639845401,                0,
+     2.3302983089051,  .55940442919073, np.nan, np.nan,
+     1.4557092049439,  3.7303399539165, np.nan,  1.9599639845401,
+                   0]).reshape(5,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+     .06815688354362, -.03840590969835, -.16217402790798,  .02098165591138,
+    -.03840590969835,  .31635366072297, -.11049674936104, -.02643483668568,
+    -.16217402790798, -.11049674936104,  .57821136454093, -.03915049342584,
+     .02098165591138, -.02643483668568, -.03915049342584,  .05762736490753
+    ]).reshape(4,4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_hc1 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 4,
+           N = 34,
+           ic = 4,
+           k = 4,
+           k_eq = 2,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           ll = -82.49115612464289,
+           k_eq_model = 1,
+           ll_0 = -84.68893065247886,
+           rank0 = 2,
+           df_m = 2,
+           chi2 = 4.39554905567195,
+           p = .1110500222994781,
+           ll_c = -91.28727940081573,
+           chi2_c = 17.5922465523457,
+           r2_p = .0259511427397111,
+           k_aux = 1,
+           alpha = .2457422083490335,
+           cmdline = "nbreg accident yr_con op_75_79, exposure(service)",
+           cmd = "nbreg",
+           predict = "nbreg_p",
+           offset = "ln(service)",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2_ct = "LR",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Negative binomial regression",
+           diparm1 = "lnalpha, exp label(",
+           user = "nbreg_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .28503762550355,  .14983643534827,  1.9023251910727,  .05712865433138,
+    -.00863639135093,  .57871164235802, np.nan,  1.9599639845401,
+                   0,  .17127003537767,  .27580549562862,  .62098122804736,
+     .53461197443513, -.36929880279264,  .71183887354798, np.nan,
+     1.9599639845401,                0, -6.5908639033905,  .40391814231008,
+     -16.31732574748,  7.432080344e-60, -7.3825289150206, -5.7991988917604,
+    np.nan,  1.9599639845401,                0, -1.4034722260565,
+     .51305874839271, np.nan, np.nan, -2.4090488948595,
+    -.39789555725363, np.nan,  1.9599639845401,                0,
+     .24574220834903,  .12608018984282, np.nan, np.nan,
+       .089900758997,  .67173218155228, np.nan,  1.9599639845401,
+                   0]).reshape(5,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+     .02245095735788, -.01097939549632, -.05127649084781,  .00045725833006,
+    -.01097939549632,  .07606867141895,  -.0197375670989, -.00926008351523,
+    -.05127649084781,  -.0197375670989,  .16314986568722,  .02198323898312,
+     .00045725833006, -.00926008351523,  .02198323898312,  .26322927930229
+    ]).reshape(4,4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_exposure_nonrobust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 4,
+           N = 34,
+           ic = 4,
+           k = 4,
+           k_eq = 2,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 0,
+           N_clust = 5,
+           ll = -82.49115612464289,
+           k_eq_model = 1,
+           ll_0 = -84.68893065247886,
+           rank0 = 2,
+           df_m = 2,
+           chi2 = 5.473741859983782,
+           p = .0647727084656973,
+           k_aux = 1,
+           alpha = .2457422083490335,
+           cmdline = "nbreg accident yr_con op_75_79, exposure(service) vce(cluster ship)",
+           cmd = "nbreg",
+           predict = "nbreg_p",
+           offset = "ln(service)",
+           dispers = "mean",
+           diparm_opt2 = "noprob",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           clustvar = "ship",
+           vce = "cluster",
+           title = "Negative binomial regression",
+           diparm1 = "lnalpha, exp label(",
+           user = "nbreg_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "accident",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .28503762550355,  .14270989695062,  1.9973220610073,  .04579020833966,
+     .00533136724292,  .56474388376418, np.nan,  1.9599639845401,
+                   0,  .17127003537767,  .17997186802799,  .95164892854829,
+     .34127505843023, -.18146834418759,  .52400841494293, np.nan,
+     1.9599639845401,                0, -6.5908639033905,  .62542746996715,
+    -10.538174640357,  5.760612980e-26, -7.8166792194681, -5.3650485873129,
+    np.nan,  1.9599639845401,                0, -1.4034722260565,
+     .86579403765571, np.nan, np.nan, -3.1003973578913,
+     .29345290577817, np.nan,  1.9599639845401,                0,
+     .24574220834903,  .21276213878894, np.nan, np.nan,
+      .0450313052935,  1.3410500222158, np.nan,  1.9599639845401,
+                   0]).reshape(5,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'yr_con op_75_79 _cons _cons alpha'.split()
+
+cov = np.array([
+     .02036611468766, -.00330004038514, -.08114367170947, -.07133030733881,
+    -.00330004038514,  .03238987328148, -.03020509748676, -.09492663454187,
+    -.08114367170947, -.03020509748676,  .39115952018952,  .43276143586693,
+    -.07133030733881, -.09492663454187,  .43276143586693,  .74959931564018
+    ]).reshape(4,4)
+
+cov_colnames = 'yr_con op_75_79 _cons _cons'.split()
+
+cov_rownames = 'yr_con op_75_79 _cons _cons'.split()
+
+
+results_negbin_exposure_clu = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/discrete/tests/results/results_discrete.py b/statsmodels/discrete/tests/results/results_discrete.py
index 44c41d0e8c7..a221a051306 100644
--- a/statsmodels/discrete/tests/results/results_discrete.py
+++ b/statsmodels/discrete/tests/results/results_discrete.py
@@ -10,7 +10,7 @@
 
 cur_dir = os.path.abspath(os.path.dirname(__file__))
 
-class Anes():
+class Anes(object):
     def __init__(self):
         """
         Results are from Stata 11 (checked vs R nnet package).
@@ -368,7 +368,7 @@ def mnlogit(self):
         self.bic = 3174.6431733460686
 
 
-class Spector():
+class Spector(object):
     """
     Results are from Stata 11
     """
@@ -716,7 +716,7 @@ def probit(self):
         self.pred_table = np.array([[18, 3], [3, 8]])
 
 
-class RandHIE():
+class RandHIE(object):
     """
     Results obtained from Stata 11
     """
@@ -937,14 +937,14 @@ def negativebinomial_geometric_bfgs(self):
         self.z = [-10.41567024, -12.92281571, 10.8755779 , -12.27364916,
                 9.95160202,  28.76323587,  -2.36514487,   0.59040434,
                 2.70792943,  29.54148082]
-        self.aic = 87101.160011780419
-        self.bic = 87180.289438893495
+        self.aic = 87101.159433012392 # old value 87101.160011780419
+        self.bic = 87180.288860125467 # old value 87180.289438893495
         self.df_model = 9.0
         self.df_resid = 20180.0
         self.llf = -43540.58000589021
-        self.llnull = -44199.27443567125
-        self.llr = 1317.3888595620811
-        self.llr_pvalue = 5.4288002863296022e-278
+        self.llnull = -44586.650971362695 # old value -44199.27443567125
+        self.llr = 2092.1425097129977 # old value 1317.3888595620811
+        self.llr_pvalue = 0 # old value 5.4288002863296022e-278
         self.fittedvalues = [ 0.89348994,  0.89348994,  0.89348994,
                 0.89348994,  0.89348994, 0.9365745 ,  0.9365745 ,  0.9365745 ,
                 0.9365745 ,  0.9365745 ]
diff --git a/statsmodels/discrete/tests/results/results_glm_logit_constrained.py b/statsmodels/discrete/tests/results/results_glm_logit_constrained.py
new file mode 100644
index 00000000000..e052a397cbc
--- /dev/null
+++ b/statsmodels/discrete/tests/results/results_glm_logit_constrained.py
@@ -0,0 +1,704 @@
+import numpy as np
+
+est = dict(
+           k_eq_model = 0,
+           phi = 1,
+           vf = 1,
+           df = 28,
+           df_m = 3,
+           power = 0,
+           canonical = 1,
+           rank = 4,
+           aic = 1.055602138883215,
+           rc = 0,
+           p = .0388431588742135,
+           chi2 = 8.376256383189103,
+           ll = -12.88963422213144,
+           k_autoCns = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 3,
+           N = 32,
+           nbml = 0,
+           bic = -71.26133683412948,
+           dispers_ps = .9734684585933382,
+           deviance_ps = 27.25711684061347,
+           dispers_p = .9734684585933382,
+           deviance_p = 27.25711684061347,
+           dispers_s = .920688158723674,
+           deviance_s = 25.77926844426287,
+           dispers = .920688158723674,
+           deviance = 25.77926844426287,
+           cmdline = "glm grade  gpa tuce psi, family(binomial)",
+           cmd = "glm",
+           predict = "glim_p",
+           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
+           marginsok = "default",
+           hac_lag = "30",
+           vcetype = "OIM",
+           vce = "oim",
+           linkt = "Logit",
+           linkf = "ln(u/(1-u))",
+           varfunct = "Bernoulli",
+           varfuncf = "u*(1-u)",
+           opt1 = "ML",
+           oim = "oim",
+           a = "1",
+           m = "1",
+           varfunc = "glim_v2",
+           link = "glim_l02",
+           chi2type = "Wald",
+           opt = "moptimize",
+           title = "Generalized linear models",
+           user = "glim_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     2.8261124216999,  1.2629410221647,  2.2377231969675,  .02523911156938,
+     .35079350365878,   5.301431339741, np.nan,  1.9599639845401,
+                   0,  .09515765001172,    .141554201358,  .67223472774972,
+     .50143427587633, -.18228348651028,  .37259878653373, np.nan,
+     1.9599639845401,                0,  2.3786875040587,  1.0645642078703,
+     2.2344237073472,  .02545520725424,  .29217999740245,  4.4651950107149,
+    np.nan,  1.9599639845401,                0, -13.021345912635,
+      4.931323890811, -2.6405375515688,  .00827746189686, -22.686563134726,
+    -3.3561286905433, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+     1.5950200254665, -.03692058012179,  .42761557297075, -4.5734780841711,
+    -.03692058012179,   .0200375919221,  .01491263753083, -.34625566662867,
+     .42761557297075,  .01491263753083,  1.1332969526786, -2.3591604492672,
+    -4.5734780841711, -.34625566662867, -2.3591604492672,  24.317955316083
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.889634222131,                4,
+     33.779268444263,  39.642212055462])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_noconstraint = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model = 0,
+           phi = 1,
+           vf = 1,
+           df = 28,
+           df_m = 3,
+           power = 0,
+           canonical = 1,
+           rank = 4,
+           aic = 1.055602138883215,
+           rc = 0,
+           p = .0248623136764981,
+           chi2 = 9.360530997638559,
+           ll = -12.88963422213144,
+           k_autoCns = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 3,
+           N = 32,
+           nbml = 0,
+           bic = -71.26133683412948,
+           dispers_ps = .9734684585933382,
+           deviance_ps = 27.25711684061347,
+           dispers_p = .9734684585933382,
+           deviance_p = 27.25711684061347,
+           dispers_s = .920688158723674,
+           deviance_s = 25.77926844426287,
+           dispers = .920688158723674,
+           deviance = 25.77926844426287,
+           cmdline = "glm grade  gpa tuce psi, family(binomial) vce(robust)",
+           cmd = "glm",
+           predict = "glim_p",
+           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
+           marginsok = "default",
+           hac_lag = "30",
+           vcetype = "Robust",
+           vce = "robust",
+           linkt = "Logit",
+           linkf = "ln(u/(1-u))",
+           varfunct = "Bernoulli",
+           varfuncf = "u*(1-u)",
+           opt1 = "ML",
+           oim = "oim",
+           a = "1",
+           m = "1",
+           varfunc = "glim_v2",
+           link = "glim_l02",
+           chi2type = "Wald",
+           opt = "moptimize",
+           title = "Generalized linear models",
+           user = "glim_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     2.8261124216999,   1.287827879216,  2.1944799202672,  .02820092594159,
+     .30201616014984,  5.3502086832499, np.nan,  1.9599639845401,
+                   0,  .09515765001172,   .1198091371814,  .79424367999287,
+     .42705358424294, -.13966394388263,  .32997924390608, np.nan,
+     1.9599639845401,                0,  2.3786875040587,  .97985082470462,
+     2.4276016757712,  .01519902587997,  .45821517741577,  4.2991598307016,
+    np.nan,  1.9599639845401,                0, -13.021345912635,
+     5.2807513766642, -2.4658130981467,  .01367026437574, -23.371428422207,
+    -2.6712634030626, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+     1.6585006464861,  .00630184631279,  .20368998146717, -5.7738061195745,
+     .00630184631279,  .01435422935215,  .01997066738212, -.34768562593344,
+     .20368998146717,  .01997066738212,  .96010763867432, -1.5315997267117,
+    -5.7738061195745, -.34768562593344, -1.5315997267117,  27.886335102141
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.889634222131,                4,
+     33.779268444263,  39.642212055462])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_noconstraint_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model = 0,
+           phi = 1,
+           vf = 1,
+           df = 29,
+           df_m = 2,
+           power = 0,
+           canonical = 1,
+           rank = 3,
+           aic = .993115540206396,
+           rc = 0,
+           p = .0600760311411508,
+           chi2 = 5.624288666552698,
+           ll = -12.88984864330234,
+           k_autoCns = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 3,
+           N = 32,
+           nbml = 0,
+           bic = -74.7266438945874,
+           dispers_ps = .9340711710496038,
+           deviance_ps = 27.08806396043851,
+           dispers_p = .9340711710496038,
+           deviance_p = 27.08806396043851,
+           dispers_s = .8889550788484368,
+           deviance_s = 25.77969728660467,
+           dispers = .8889550788484368,
+           deviance = 25.77969728660467,
+           cmdline = "glm grade  gpa tuce psi, family(binomial) constraints(1)",
+           cmd = "glm",
+           predict = "glim_p",
+           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
+           marginsok = "default",
+           hac_lag = "30",
+           vcetype = "OIM",
+           vce = "oim",
+           linkt = "Logit",
+           linkf = "ln(u/(1-u))",
+           varfunct = "Bernoulli",
+           varfuncf = "u*(1-u)",
+           opt1 = "ML",
+           oim = "oim",
+           a = "1",
+           m = "1",
+           varfunc = "glim_v2",
+           link = "glim_l02",
+           chi2type = "Wald",
+           opt = "moptimize",
+           title = "Generalized linear models",
+           user = "glim_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+                 2.8, np.nan, np.nan, np.nan,
+    np.nan, np.nan, np.nan,  1.9599639845401,
+                   0,  .09576464077943,  .13824841412912,  .69269974185736,
+     .48849800113543, -.17519727183342,  .36672655339228, np.nan,
+     1.9599639845401,                0,  2.3717067235827,  1.0071435928909,
+     2.3548843882081,  .01852846934254,  .39774155425619,  4.3456718929091,
+    np.nan,  1.9599639845401,                0, -12.946549758905,
+     3.3404275889275, -3.8757163309928,  .00010631147941, -19.493667526167,
+    -6.3994319916434, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+                   0,                0,                0,                0,
+                   0,  .01911262400922,  .02461998233256, -.45036648979107,
+                   0,  .02461998233256,  1.0143382167012,  -1.126241119498,
+                   0, -.45036648979107,  -1.126241119498,  11.158456476868
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.889848643302,                3,
+     31.779697286605,  36.176904995004])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_constraint1 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model = 0,
+           phi = 1,
+           vf = 1,
+           df = 29,
+           df_m = 2,
+           power = 0,
+           canonical = 1,
+           rank = 3,
+           aic = .9965088127779717,
+           rc = 0,
+           p = .0151376593316312,
+           chi2 = 8.381139289068923,
+           ll = -12.94414100444755,
+           k_autoCns = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 3,
+           N = 32,
+           nbml = 0,
+           bic = -74.61805917229698,
+           dispers_ps = .9101961406899989,
+           deviance_ps = 26.39568808000997,
+           dispers_p = .9101961406899989,
+           deviance_p = 26.39568808000997,
+           dispers_s = .892699379617072,
+           deviance_s = 25.88828200889509,
+           dispers = .892699379617072,
+           deviance = 25.88828200889509,
+           cmdline = "glm grade  gpa tuce psi, family(binomial) constraints(2)",
+           cmd = "glm",
+           predict = "glim_p",
+           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
+           marginsok = "default",
+           hac_lag = "30",
+           vcetype = "OIM",
+           vce = "oim",
+           linkt = "Logit",
+           linkf = "ln(u/(1-u))",
+           varfunct = "Bernoulli",
+           varfuncf = "u*(1-u)",
+           opt1 = "ML",
+           oim = "oim",
+           a = "1",
+           m = "1",
+           varfunc = "glim_v2",
+           link = "glim_l02",
+           chi2type = "Wald",
+           opt = "moptimize",
+           title = "Generalized linear models",
+           user = "glim_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     2.5537914524884,  .92662050289421,  2.7560273537138,  .00585081038138,
+     .73764863947939,  4.3699342654975, np.nan,  1.9599639845401,
+                   0,  .10791139824293,  .13554656123081,  .79612051580696,
+     .42596199070477, -.15775497999771,  .37357777648357, np.nan,
+     1.9599639845401,                0,  2.5537914524884,  .92662050289421,
+     2.7560273537138,  .00585081038138,  .73764863947939,  4.3699342654975,
+    np.nan,  1.9599639845401,                0, -12.527922070831,
+     4.6393777844052, -2.7003453163357,  .00692675392223,  -21.62093543894,
+    -3.4349087027211, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+     .85862555638391, -.00408642741742,  .85862555638391, -3.1725052764862,
+    -.00408642741742,   .0183728702615, -.00408642741742, -.40376368789892,
+     .85862555638391, -.00408642741742,  .85862555638391, -3.1725052764862,
+    -3.1725052764862, -.40376368789892, -3.1725052764862,  21.523826226433
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.944141004448,                3,
+     31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+predict_mu = np.array([
+     .02720933393726,  .05877785527304,  .17341537851768,  .02240274574181,
+     .48834788561471,  .03262255746648,  .02545734725406,  .05057489993471,
+     .10986224061161,  .64848146294279,  .02525325609066,  .17259131542841,
+     .28314297612096,  .18171413480391,  .33018645131295,  .02988039105483,
+     .05693576903037,  .03731338966779,  .61672273095571,  .68861137241716,
+     .08792248035539,  .90822178043053,  .25295501355621,  .85758484919326,
+     .83972248507748,  .54158048311843,  .62661357692624,  .36224489285202,
+     .83387563062407,  .93837010344092,  .55200183830167,  .13940358008872
+    ])
+
+predict_mu_colnames = 'predict_mu'.split()
+
+predict_mu_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32'.split()
+
+predict_linpred_std = np.array([
+     1.2186852972383,  .98250143329647,  .71300625338041,  1.7281112031272,
+     .58278126610648,  1.2588643933597,   1.323097466817,  1.0187451680624,
+     .92583226681839,  .97445803529749,  1.2426520057509,  .66674211884633,
+     .53877733839827,  .77006015103931,  .70670367147137,  1.2036701125873,
+     1.1407798755705,  1.1376397495763,  .57331962577752,  .65764380198652,
+     .85122884445037,  1.1282943138296,  1.2981327331615,  .91561885084703,
+      .8524827403359,  .75030433039358,  1.0902299962647,  .53350768600347,
+     .96511132361274,  1.2127047415358,  .61923877005984,  .80300912367498
+    ])
+
+predict_linpred_std_colnames = 'predict_linpred_std'.split()
+
+predict_linpred_std_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32'.split()
+
+predict_hat = np.array([
+     .03931157544567,  .05340381182541,  .07287215399916,  .06540404284993,
+      .0848623883214,   .0500117280211,  .04343078449564,  .04983412818394,
+     .08382437063813,  .21645722203914,  .03801090644315,  .06348261316195,
+     .05891921860299,  .08817451110282,  .11045563375857,  .04199779738721,
+     .06987634275981,  .04648995770552,   .0776956378885,  .09273814423054,
+     .05810645039404,  .10611489289649,  .31844046474321,  .10239122636412,
+     .09780916971071,  .13976583081559,  .27809589396914,  .06575633167064,
+     .12902962938834,  .08505028097419,  .09482722348113,  .07735963673184
+    ])
+
+predict_hat_colnames = 'predict_hat'.split()
+
+predict_hat_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32'.split()
+
+
+results_constraint2 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                predict_mu=predict_mu,
+                predict_mu_colnames=predict_mu_colnames,
+                predict_mu_rownames=predict_mu_rownames,
+                predict_linpred_std=predict_linpred_std,
+                predict_linpred_std_colnames=predict_linpred_std_colnames,
+                predict_linpred_std_rownames=predict_linpred_std_rownames,
+                predict_hat=predict_hat,
+                predict_hat_colnames=predict_hat_colnames,
+                predict_hat_rownames=predict_hat_rownames,
+                **est
+                )
+
+est = dict(
+           k_eq_model = 0,
+           phi = 1,
+           vf = 1,
+           df = 29,
+           df_m = 2,
+           power = 0,
+           canonical = 1,
+           rank = 3,
+           aic = .9965088127779717,
+           rc = 0,
+           p = .0085760854232441,
+           chi2 = 9.517555427941099,
+           ll = -12.94414100444755,
+           k_autoCns = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 3,
+           N = 32,
+           nbml = 0,
+           bic = -74.61805917229698,
+           dispers_ps = .9101961406899989,
+           deviance_ps = 26.39568808000997,
+           dispers_p = .9101961406899989,
+           deviance_p = 26.39568808000997,
+           dispers_s = .892699379617072,
+           deviance_s = 25.88828200889509,
+           dispers = .892699379617072,
+           deviance = 25.88828200889509,
+           cmdline = "glm grade  gpa tuce psi, family(binomial) constraints(2) vce(robust)",
+           cmd = "glm",
+           predict = "glim_p",
+           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
+           marginsok = "default",
+           hac_lag = "30",
+           vcetype = "Robust",
+           vce = "robust",
+           linkt = "Logit",
+           linkf = "ln(u/(1-u))",
+           varfunct = "Bernoulli",
+           varfuncf = "u*(1-u)",
+           opt1 = "ML",
+           oim = "oim",
+           a = "1",
+           m = "1",
+           varfunc = "glim_v2",
+           link = "glim_l02",
+           chi2type = "Wald",
+           opt = "moptimize",
+           title = "Generalized linear models",
+           user = "glim_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     2.5537914524884,  .83609404798719,  3.0544308485827,  .00225487991353,
+     .91507723074524,  4.1925056742316, np.nan,  1.9599639845401,
+                   0,  .10791139824293,  .12275592600281,   .8790728216287,
+     .37936179287834, -.13268579561143,   .3485085920973, np.nan,
+     1.9599639845401,                0,  2.5537914524884,  .83609404798719,
+     3.0544308485827,  .00225487991353,  .91507723074524,  4.1925056742316,
+    np.nan,  1.9599639845401,                0, -12.527922070831,
+      4.510414281113, -2.7775546302454,  .00547696322683, -21.368171617167,
+    -3.6876725244938, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+      .6990532570796,  .01512804251258,   .6990532570796, -2.9662622048441,
+     .01512804251258,  .01506901736881,  .01512804251258,  -.3968065659911,
+      .6990532570796,  .01512804251258,   .6990532570796, -2.9662622048441,
+    -2.9662622048441,  -.3968065659911, -2.9662622048441,  20.343836987269
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.944141004448,                3,
+     31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_constraint2_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
+est = dict(
+           N_cds = 0,
+           N_cdf = 0,
+           p = .0151376589433054,
+           chi2 = 8.381139340374848,
+           df_m = 2,
+           k_eq_model = 1,
+           ll = -12.94414100444751,
+           k_autoCns = 0,
+           rc = 0,
+           converged = 1,
+           k_dv = 1,
+           k_eq = 1,
+           k = 4,
+           ic = 5,
+           N = 32,
+           rank = 3,
+           cmdline = "logit grade  gpa tuce psi, constraints(2)",
+           cmd = "logit",
+           estat_cmd = "logit_estat",
+           predict = "logit_p",
+           marginsnotok = "stdp DBeta DEviance DX2 DDeviance Hat Number Residuals RStandard SCore",
+           title = "Logistic regression",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vce = "oim",
+           user = "mopt__logit_d2()",
+           crittype = "log likelihood",
+           ml_method = "d2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "grade",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     2.5537916456996,  .92662056628814,  2.7560273736742,  .00585081002433,
+     .73764870844071,  4.3699345829585, np.nan,  1.9599639845401,
+                   0,  .10791141442743,  .13554656655573,  .79612060393329,
+     .42596193948753, -.15775497424986,  .37357780310472, np.nan,
+     1.9599639845401,                0,  2.5537916456996,  .92662056628814,
+     2.7560273736742,  .00585081002433,  .73764870844071,  4.3699345829585,
+    np.nan,  1.9599639845401,                0, -12.527923225554,
+     4.6393781670436, -2.7003453425175,  .00692675337706,  -21.62093734362,
+    -3.4349091074867, np.nan,  1.9599639845401,                0
+    ]).reshape(4,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'gpa tuce psi _cons'.split()
+
+cov = np.array([
+     .85862567386816, -.00408642236043,  .85862567386816,  -3.172505858545,
+    -.00408642236043,  .01837287170505, -.00408642236043, -.40376374127778,
+     .85862567386816, -.00408642236043,  .85862567386816,  -3.172505858545,
+     -3.172505858545, -.40376374127778,  -3.172505858545,  21.523829776841
+    ]).reshape(4,4)
+
+cov_colnames = 'gpa tuce psi _cons'.split()
+
+cov_rownames = 'gpa tuce psi _cons'.split()
+
+infocrit = np.array([
+                  32, np.nan, -12.944141004448,                3,
+     31.888282008895,  36.285489717294])
+
+infocrit_colnames = 'N ll0 ll df AIC BIC'.split()
+
+infocrit_rownames = '.'.split()
+
+
+results_logit_constraint2 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                infocrit=infocrit,
+                infocrit_colnames=infocrit_colnames,
+                infocrit_rownames=infocrit_rownames,
+                **est
+                )
+
diff --git a/statsmodels/discrete/tests/results/results_poisson_constrained.py b/statsmodels/discrete/tests/results/results_poisson_constrained.py
new file mode 100644
index 00000000000..3f780a90992
--- /dev/null
+++ b/statsmodels/discrete/tests/results/results_poisson_constrained.py
@@ -0,0 +1,649 @@
+import numpy as np
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+est = dict(
+           rank = 7,
+           N = 10,
+           ic = 3,
+           k = 8,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -33.45804471711131,
+           k_eq_model = 1,
+           ll_0 = -349.6684656479622,
+           df_m = 6,
+           chi2 = 632.4208418617018,
+           p = 2.3617193197e-133,
+           r2_p = .9043149497192691,
+           cmdline = "poisson deaths lnpyears smokes i.agecat",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           gof = "poiss_g",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .66308184237808,  .63593388706566,  1.0426899019923,  .29709193621918,
+    -.58332567281917,  1.9094893575753, np.nan,  1.9599639845401,
+                   0,  .84966723812924,  .94279599903649,  .90122066597395,
+     .36747100512904, -.99817896475073,  2.6975134410092, np.nan,
+     1.9599639845401,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,  1.9599639845401,                0,  1.3944392032504,
+     .25613243411925,  5.4442117338454,  5.203529593e-08,   .8924288571041,
+     1.8964495493967, np.nan,  1.9599639845401,                0,
+      2.389284381366,  .48305517266329,  4.9461935542328,  7.567871319e-07,
+     1.4425136404002,  3.3360551223318, np.nan,  1.9599639845401,
+                   0,  2.8385093615484,  .98099727008295,  2.8934936397003,
+     .00380982006764,  .91579004325369,  4.7612286798431, np.nan,
+     1.9599639845401,                0,  2.9103531988515,   1.500316321385,
+     1.9398263935201,  .05240079188831, -.03021275648066,  5.8509191541838,
+    np.nan,  1.9599639845401,                0,  -4.724924181641,
+     6.0276019460727, -.78388125558284,  .43310978942119, -16.538806909087,
+      7.088958545805, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+     .40441190871844, -.59566294916097,                0,   .1055698685775,
+     .28413388045122,  .61269322798077,  .94624135329227, -3.8311942353131,
+    -.59566294916097,  .88886429579921,                0, -.15587944298625,
+     -.4190789999425, -.90299843943229, -1.3940094688194,  5.6335527795822,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+      .1055698685775, -.15587944298625,                0,  .06560382380785,
+     .10360281461667,  .18937107288073,  .27643306166968,  -1.029211453947,
+     .28413388045122,  -.4190789999425,                0,  .10360281461667,
+     .23334229983676,  .45990880867889,  .69424104947043, -2.7206801001387,
+     .61269322798077, -.90299843943229,                0,  .18937107288073,
+     .45990880867889,  .96235564391021,  1.4630024143274, -5.8333014154113,
+     .94624135329227, -1.3940094688194,                0,  .27643306166968,
+     .69424104947043,  1.4630024143274,  2.2509490642142,  -8.993394678922,
+    -3.8311942353131,  5.6335527795822,                0,  -1.029211453947,
+    -2.7206801001387, -5.8333014154113,  -8.993394678922,  36.331985220299
+    ]).reshape(8,8)
+
+cov_colnames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+
+results_noexposure_noconstraint = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 6,
+           N = 10,
+           ic = 3,
+           k = 7,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -33.6001534405213,
+           k_eq_model = 1,
+           ll_0 = -495.0676356770329,
+           df_m = 5,
+           chi2 = 922.9349644730232,
+           p = 2.8920463572e-197,
+           r2_p = .9321301757191799,
+           cmdline = "poisson deaths smokes i.agecat, exposure(pyears)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(pyears)",
+           gof = "poiss_g",
+           chi2type = "LR",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .35453563725291,  .10737411818853,  3.3018723993653,  .00096041750265,
+     .14408623273163,  .56498504177418, np.nan,  1.9599639845401,
+                   0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+     1.9599639845401,                0,  1.4840070063099,  .19510337263434,
+      7.606260139291,  2.821411159e-14,  1.1016114226842,  1.8664025899355,
+    np.nan,  1.9599639845401,                0,  2.6275051184579,
+     .18372726944827,  14.301116684248,  2.153264398e-46,  2.2674062873614,
+     2.9876039495544, np.nan,  1.9599639845401,                0,
+      3.350492785161,  .18479918093323,  18.130452571495,  1.832448146e-73,
+     2.9882930461593,  3.7126925241626, np.nan,  1.9599639845401,
+                   0,  3.7000964518246,  .19221951212105,   19.24932807807,
+     1.430055953e-82,  3.3233531309415,  4.0768397727077, np.nan,
+     1.9599639845401,                0,  -7.919325711822,  .19176181876223,
+    -41.297719029467,                0, -8.2951719702059, -7.5434794534381,
+    np.nan,  1.9599639845401,                0]).reshape(7,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+     .01152920125677,                0, -.00061561668833, -.00090117889461,
+    -.00087280941113, -.00045274641397, -.00921219275997,                0,
+                   0,                0,                0,                0,
+                   0,                0, -.00061561668833,                0,
+      .0380653260133,  .02945988432334,  .02945836949789,   .0294359396881,
+     -.0289198676971, -.00090117889461,                0,  .02945988432334,
+     .03375570953892,   .0294799877675,  .02944715358419, -.02869169455392,
+    -.00087280941113,                0,  .02945836949789,   .0294799877675,
+     .03415073727359,  .02944603952766, -.02871436265941, -.00045274641397,
+                   0,   .0294359396881,  .02944715358419,  .02944603952766,
+     .03694834084006, -.02905000614546, -.00921219275997,                0,
+     -.0289198676971, -.02869169455392, -.02871436265941, -.02905000614546,
+       .036772595135]).reshape(7,7)
+
+cov_colnames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_exposure_noconstraint = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 6,
+           N = 10,
+           ic = 4,
+           k = 8,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -33.46699798755848,
+           k_eq_model = 1,
+           df_m = 5,
+           chi2 = 452.5895246742914,
+           p = 1.35732711092e-95,
+           r2_p = np.nan,
+           cmdline = "poisson deaths lnpyears smokes i.agecat, constraints(1)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .57966535352347,  .13107152221057,  4.4225117992619,  9.756001957e-06,
+     .32276989059191,  .83656081645503, np.nan,  1.9599639845401,
+                   0,  .97254074124891,  .22289894431919,  4.3631464663029,
+     .00001282050472,   .5356668381913,  1.4094146443065, np.nan,
+     1.9599639845401,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,  1.9599639845401,                0,  1.3727621378494,
+     .19798042377276,  6.9338276567436,  4.096036246e-12,  .98472763761078,
+      1.760796638088, np.nan,  1.9599639845401,                0,
+     2.3307703209845,  .20530981936838,     11.352454199,  7.210981748e-30,
+       1.92837046935,  2.7331701726189, np.nan,  1.9599639845401,
+                   0,    2.71338890728,  .29962471107816,  9.0559583604312,
+     1.353737255e-19,  2.1261352646886,  3.3006425498714, np.nan,
+     1.9599639845401,                0,    2.71338890728,  .29962471107816,
+     9.0559583604312,  1.353737255e-19,  2.1261352646886,  3.3006425498714,
+    np.nan,  1.9599639845401,                0, -3.9347864312059,
+     1.2543868840549, -3.1368204508696,  .00170790683415, -6.3933395466329,
+     -1.476233315779, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+      .0171797439346, -.02561346650005,                0,  .00445310785396,
+     .01204526460873,  .03142116278001,  .03142116278001, -.16245493266167,
+    -.02561346650005,  .04968393937861,                0,  -.0069699735991,
+    -.01845598801461, -.04723465558226, -.04723465558226,   .2326939064726,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+     .00445310785396,  -.0069699735991,                0,  .03919624819724,
+     .03254829669461,  .03756752462584,  .03756752462584, -.07124751761252,
+     .01204526460873, -.01845598801461,                0,  .03254829669461,
+     .04215212192908,  .05145895528528,  .05145895528528, -.14290240509701,
+     .03142116278001, -.04723465558226,                0,  .03756752462584,
+     .05145895528528,  .08977496748867,  .08977496748867, -.32621483141938,
+     .03142116278001, -.04723465558226,                0,  .03756752462584,
+     .05145895528528,  .08977496748867,  .08977496748867, -.32621483141938,
+    -.16245493266167,   .2326939064726,                0, -.07124751761252,
+    -.14290240509701, -.32621483141938, -.32621483141938,  1.5734864548889
+    ]).reshape(8,8)
+
+cov_colnames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_noexposure_constraint = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 5,
+           N = 10,
+           ic = 3,
+           k = 7,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -38.45090497564205,
+           k_eq_model = 1,
+           df_m = 4,
+           chi2 = 641.6446542589836,
+           p = 1.5005477751e-137,
+           r2_p = np.nan,
+           cmdline = "poisson deaths smokes i.agecat, exposure(pyears) constraints(1)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(pyears)",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .34304077058284,   .1073083520206,   3.196776058186,  .00138972774083,
+     .13272026538212,  .55336127578356, np.nan,  1.9599639845401,
+                   0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+     1.9599639845401,                0,  1.4846230896448,  .19510453584194,
+     7.6093724999174,  2.754298692e-14,  1.1022252261742,  1.8670209531154,
+    np.nan,  1.9599639845401,                0,  2.6284071093765,
+     .18373002757074,  14.305811326156,  2.012766793e-46,  2.2683028724593,
+     2.9885113462937, np.nan,  1.9599639845401,                0,
+     3.4712405808805,  .17983994458502,  19.301833020969,  5.183735658e-83,
+     3.1187607665121,  3.8237203952488, np.nan,  1.9599639845401,
+                   0,  3.4712405808805,  .17983994458502,  19.301833020969,
+     5.183735658e-83,  3.1187607665121,  3.8237203952488, np.nan,
+     1.9599639845401,                0, -7.9101515866812,  .19164951521841,
+    -41.274049546467,                0, -8.2857777341639, -7.5345254391986,
+    np.nan,  1.9599639845401,                0]).reshape(7,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+     .01151508241338,                0, -.00061947268694, -.00090708285562,
+    -.00074959767622, -.00074959767622, -.00917958318314,                0,
+                   0,                0,                0,                0,
+                   0,                0, -.00061947268694,                0,
+      .0380657799061,  .02946056271023,   .0294520905375,   .0294520905375,
+    -.02891793401778, -.00090708285562,                0,  .02946056271023,
+     .03375672303114,  .02947081310555,  .02947081310555, -.02868865719866,
+    -.00074959767622,                0,   .0294520905375,  .02947081310555,
+     .03234240566834,  .03234240566834, -.02881420109427, -.00074959767622,
+                   0,   .0294520905375,  .02947081310555,  .03234240566834,
+     .03234240566834, -.02881420109427, -.00917958318314,                0,
+    -.02891793401778, -.02868865719866, -.02881420109427, -.02881420109427,
+     .03672953668345]).reshape(7,7)
+
+cov_colnames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_exposure_constraint = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 6,
+           N = 10,
+           ic = 3,
+           k = 8,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -33.78306559091298,
+           k_eq_model = 1,
+           df_m = 5,
+           chi2 = 526.719430888018,
+           p = 1.3614066522e-111,
+           r2_p = np.nan,
+           cmdline = "poisson deaths lnpyears smokes i.agecat, constraints(2)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     1.1598786864273,  .13082965708054,  8.8655639119598,  7.611783820e-19,
+     .90345727043975,  1.4163001024149, np.nan,  1.9599639845401,
+                   0,  .12111539473831,  .22317899375276,  .54268277090847,
+     .58734823873758, -.31630739512299,  .55853818459962, np.nan,
+     1.9599639845401,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,  1.9599639845401,                0,  1.5276244194375,
+     .19848759770871,  7.6963217705896,  1.400389019e-14,  1.1385958765506,
+     1.9166529623245, np.nan,  1.9599639845401,                0,
+     2.7415571106656,  .20647039325801,  13.278209371354,  3.097119459e-40,
+     2.3368825760061,  3.1462316453252, np.nan,  1.9599639845401,
+                   0,   3.587300073596,  .30160673316211,  11.893965482753,
+     1.272196529e-32,  2.9961617391034,  4.1784384080885, np.nan,
+     1.9599639845401,                0,   4.087300073596,  .30160673316211,
+     13.551753406643,  7.735990122e-42,  3.4961617391034,  4.6784384080885,
+    np.nan,  1.9599639845401,                0, -9.4376201542802,
+     1.2537557101599, -7.5274792990385,  5.172920628e-14, -11.894936191605,
+    -6.9803041169553, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+     .01711639917181, -.02559852137367,                0,  .00475026273828,
+       .012305588195,  .03167368550108,  .03167368550108, -.16210959536359,
+    -.02559852137367,   .0498088632525,                0, -.00783669874902,
+    -.01946551099054,  -.0482099128044,  -.0482099128044,  .23336630265161,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+     .00475026273828, -.00783669874902,                0,  .03939732644417,
+      .0328943776068,   .0382554606876,   .0382554606876, -.07382466315002,
+       .012305588195, -.01946551099054,                0,   .0328943776068,
+     .04263002329212,  .05226051095238,  .05226051095238, -.14512177326509,
+     .03167368550108,  -.0482099128044,                0,   .0382554606876,
+     .05226051095238,  .09096662148872,  .09096662148872, -.32873181469848,
+     .03167368550108,  -.0482099128044,                0,   .0382554606876,
+     .05226051095238,  .09096662148872,  .09096662148872, -.32873181469848,
+    -.16210959536359,  .23336630265161,                0, -.07382466315002,
+    -.14512177326509, -.32873181469848, -.32873181469848,  1.5719033807586
+    ]).reshape(8,8)
+
+cov_colnames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'lnpyears smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_noexposure_constraint2 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           rank = 5,
+           N = 10,
+           ic = 3,
+           k = 7,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -34.5367006700131,
+           k_eq_model = 1,
+           df_m = 4,
+           chi2 = 554.4168921897579,
+           p = 1.1331093797e-118,
+           r2_p = np.nan,
+           cmdline = "poisson deaths smokes i.agecat, exposure(pyears) constraints(2)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(pyears)",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vce = "oim",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log likelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .35978347114582,  .10730668667519,  3.3528522992687,  .00079983377167,
+     .14946622996212,  .57010071232952, np.nan,  1.9599639845401,
+                   0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+     1.9599639845401,                0,  1.4837272702102,  .19510269288329,
+     7.6048528509946,  2.852282383e-14,  1.1013330188722,  1.8661215215483,
+    np.nan,  1.9599639845401,                0,  2.6270956495127,
+     .18372567328363,  14.299012231442,  2.219372691e-46,  2.2669999468414,
+      2.987191352184, np.nan,  1.9599639845401,                0,
+     3.2898291023835,  .17982035319735,  18.295087535352,  9.055555257e-75,
+     2.9373876864294,  3.6422705183376, np.nan,  1.9599639845401,
+                   0,  3.7898291023835,  .17982035319735,  21.075640409983,
+     1.330935038e-98,  3.4373876864294,  4.1422705183376, np.nan,
+     1.9599639845401,                0, -7.9235211042587,  .19177810950798,
+    -41.316087245761,                0, -8.2993992919175, -7.5476429165999,
+    np.nan,  1.9599639845401,                0]).reshape(7,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+     .01151472500521,                0, -.00061274288972, -.00089685568608,
+    -.00069335681347, -.00069335681347, -.00921031399899,                0,
+                   0,                0,                0,                0,
+                   0,                0, -.00061274288972,                0,
+     .03806506077031,  .02945948985187,  .02944866089267,  .02944866089267,
+    -.02892164840477, -.00089685568608,                0,  .02945948985187,
+     .03375512302352,  .02946576868665,  .02946576868665,  -.0286943943397,
+    -.00069335681347,                0,  .02944866089267,  .02946576868665,
+     .03233535942402,  .03233535942402, -.02885716752919, -.00069335681347,
+                   0,  .02944866089267,  .02946576868665,  .03233535942402,
+     .03233535942402, -.02885716752919, -.00921031399899,                0,
+    -.02892164840477,  -.0286943943397, -.02885716752919, -.02885716752919,
+     .03677884328645]).reshape(7,7)
+
+cov_colnames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_exposure_constraint2 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+
+est = dict(
+           rank = 5,
+           N = 10,
+           ic = 3,
+           k = 7,
+           k_eq = 1,
+           k_dv = 1,
+           converged = 1,
+           rc = 0,
+           k_autoCns = 1,
+           ll = -34.5367006700131,
+           k_eq_model = 1,
+           df_m = 4,
+           chi2 = 582.5215805315736,
+           p = 9.3932644024e-125,
+           r2_p = np.nan,
+           cmdline = "poisson deaths smokes i.agecat, exposure(pyears) constraints(2) vce(robust)",
+           cmd = "poisson",
+           predict = "poisso_p",
+           estat_cmd = "poisson_estat",
+           offset = "ln(pyears)",
+           gof = "poiss_g",
+           chi2type = "Wald",
+           opt = "moptimize",
+           vcetype = "Robust",
+           vce = "robust",
+           title = "Poisson regression",
+           user = "poiss_lf",
+           crittype = "log pseudolikelihood",
+           ml_method = "e2",
+           singularHmethod = "m-marquardt",
+           technique = "nr",
+           which = "max",
+           depvar = "deaths",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .35978347114582,   .1172393358046,  3.0687948603312,  .00214924117257,
+      .1299985953974,  .58956834689424, np.nan,  1.9599639845401,
+                   0,                0, np.nan, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+     1.9599639845401,                0,  1.4837272702102,  .21969092615175,
+     6.7537030145039,  1.441186055e-11,  1.0531409672225,  1.9143135731979,
+    np.nan,  1.9599639845401,                0,  2.6270956495127,
+     .20894895542061,  12.572906355164,  2.975796525e-36,   2.217563222281,
+     3.0366280767443, np.nan,  1.9599639845401,                0,
+     3.2898291023835,   .2211846822073,  14.873675109654,  4.885611722e-50,
+     2.8563150913252,  3.7233431134417, np.nan,  1.9599639845401,
+                   0,  3.7898291023835,   .2211846822073,  17.134229479922,
+     8.243780087e-66,  3.3563150913252,  4.2233431134417, np.nan,
+     1.9599639845401,                0, -7.9235211042587,   .2479876721169,
+    -31.951270144281,  5.18748229e-224, -8.4095680102177, -7.4374741982996,
+    np.nan,  1.9599639845401,                0]).reshape(7,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov = np.array([
+      .0137450618599,                0,  .00249770233028,  .00412347653263,
+     .00486142402447,  .00486142402447, -.01620342093134,                0,
+                   0,                0,                0,                0,
+                   0,                0,  .00249770233028,                0,
+     .04826410303341,  .04389964215014,  .04391744129373,  .04391744129373,
+    -.04609122424924,  .00412347653263,                0,  .04389964215014,
+     .04365966597136,  .04367917402468,  .04367917402468, -.04726310745444,
+     .00486142402447,                0,  .04391744129373,  .04367917402468,
+     .04892266364314,  .04892266364314, -.04794543190806,  .00486142402447,
+                   0,  .04391744129373,  .04367917402468,  .04892266364314,
+     .04892266364314, -.04794543190806, -.01620342093134,                0,
+    -.04609122424924, -.04726310745444, -.04794543190806, -.04794543190806,
+     .06149788552196]).reshape(7,7)
+
+cov_colnames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+cov_rownames = 'smokes 1b.agecat 2.agecat 3.agecat 4.agecat 5.agecat _cons'.split()
+
+
+results_exposure_constraint2_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/discrete/tests/results/ships.csv b/statsmodels/discrete/tests/results/ships.csv
new file mode 100644
index 00000000000..4857c893442
--- /dev/null
+++ b/statsmodels/discrete/tests/results/ships.csv
@@ -0,0 +1,41 @@
+ship,yr_con,yr_op,service,accident,op_75_79,co_65_69,co_70_74,co_75_79
+1,1,1,127,0,0,0,0,0
+1,1,2,63,0,1,0,0,0
+1,2,1,1095,3,0,1,0,0
+1,2,2,1095,4,1,1,0,0
+1,3,1,1512,6,0,0,1,0
+1,3,2,3353,18,1,0,1,0
+1,4,1,,,0,0,0,1
+1,4,2,2244,11,1,0,0,1
+2,1,1,44882,39,0,0,0,0
+2,1,2,17176,29,1,0,0,0
+2,2,1,28609,58,0,1,0,0
+2,2,2,20370,53,1,1,0,0
+2,3,1,7064,12,0,0,1,0
+2,3,2,13099,44,1,0,1,0
+2,4,1,,,0,0,0,1
+2,4,2,7117,18,1,0,0,1
+3,1,1,1179,1,0,0,0,0
+3,1,2,552,1,1,0,0,0
+3,2,1,781,0,0,1,0,0
+3,2,2,676,1,1,1,0,0
+3,3,1,783,6,0,0,1,0
+3,3,2,1948,2,1,0,1,0
+3,4,1,,,0,0,0,1
+3,4,2,274,1,1,0,0,1
+4,1,1,251,0,0,0,0,0
+4,1,2,105,0,1,0,0,0
+4,2,1,288,0,0,1,0,0
+4,2,2,192,0,1,1,0,0
+4,3,1,349,2,0,0,1,0
+4,3,2,1208,11,1,0,1,0
+4,4,1,,,0,0,0,1
+4,4,2,2051,4,1,0,0,1
+5,1,1,45,0,0,0,0,0
+5,1,2,,,1,0,0,0
+5,2,1,789,7,0,1,0,0
+5,2,2,437,7,1,1,0,0
+5,3,1,1157,5,0,0,1,0
+5,3,2,2161,12,1,0,1,0
+5,4,1,,,0,0,0,1
+5,4,2,542,1,1,0,0,1
diff --git a/statsmodels/discrete/tests/test_constrained.py b/statsmodels/discrete/tests/test_constrained.py
new file mode 100644
index 00000000000..88eb6357fc4
--- /dev/null
+++ b/statsmodels/discrete/tests/test_constrained.py
@@ -0,0 +1,510 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri May 30 16:22:29 2014
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+from statsmodels.compat.python import StringIO
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal, assert_
+from nose import SkipTest
+
+import pandas as pd
+import patsy
+
+from statsmodels.discrete.discrete_model import Poisson
+from statsmodels.discrete.discrete_model import Logit
+from statsmodels.genmod.generalized_linear_model import GLM
+from statsmodels.genmod import families
+from statsmodels.base._constraints import fit_constrained
+
+from statsmodels.tools.tools import add_constant
+from statsmodels import datasets
+
+
+spector_data = datasets.spector.load()
+spector_data.exog = add_constant(spector_data.exog, prepend=False)
+
+from .results import results_poisson_constrained as results
+from .results import results_glm_logit_constrained as reslogit
+
+DEBUG = False
+
+ss='''\
+agecat	smokes	deaths	pyears
+1	1	32	52407
+2	1	104	43248
+3	1	206	28612
+4	1	186	12663
+5	1	102	5317
+1	0	2	18790
+2	0	12	10673
+3	0	28	5710
+4	0	28	2585
+5	0	31	1462'''
+
+data = pd.read_csv(StringIO(ss), delimiter='\t')
+data['logpyears'] = np.log(data['pyears'])
+
+
+class CheckPoissonConstrainedMixin(object):
+
+    def test_basic(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_allclose(res1[0], res2.params[self.idx], rtol=1e-6)
+        # see below Stata has nan, we have zero
+        bse1 = np.sqrt(np.diag(res1[1]))
+        mask = (bse1 == 0) & np.isnan(res2.bse[self.idx])
+        assert_allclose(bse1[~mask], res2.bse[self.idx][~mask], rtol=1e-6)
+
+
+    def test_basic_method(self):
+        if hasattr(self, 'res1m'):
+            res1 = (self.res1m if not hasattr(self.res1m, '_results')
+                               else self.res1m._results)
+            res2 = self.res2
+            assert_allclose(res1.params, res2.params[self.idx], rtol=1e-6)
+
+            # when a parameter is fixed, the Stata has bse=nan, we have bse=0
+            mask = (res1.bse == 0) & np.isnan(res2.bse[self.idx])
+            assert_allclose(res1.bse[~mask], res2.bse[self.idx][~mask], rtol=1e-6)
+
+            tvalues = res2.params_table[self.idx, 2]
+            # when a parameter is fixed, the Stata has tvalue=nan, we have tvalue=inf
+            mask = np.isinf(res1.tvalues) & np.isnan(tvalues)
+            assert_allclose(res1.tvalues[~mask], tvalues[~mask], rtol=1e-6)
+            pvalues = res2.params_table[self.idx, 3]
+            # note most pvalues are very small
+            # examples so far agree at 8 or more decimal, but rtol is stricter
+            # see above
+            mask = (res1.pvalues == 0) & np.isnan(pvalues)
+            assert_allclose(res1.pvalues[~mask], pvalues[~mask], rtol=5e-5)
+
+            ci_low = res2.params_table[self.idx, 4]
+            ci_upp = res2.params_table[self.idx, 5]
+            ci = np.column_stack((ci_low, ci_upp))
+            # note most pvalues are very small
+            # examples so far agree at 8 or more decimal, but rtol is stricter
+            # see above: nan versus value
+            assert_allclose(res1.conf_int()[~np.isnan(ci)], ci[~np.isnan(ci)], rtol=5e-5)
+
+            #other
+            assert_allclose(res1.llf, res2.ll, rtol=1e-6)
+            assert_equal(res1.df_model, res2.df_m)
+            # Stata doesn't have df_resid
+            df_r = res2.N - res2.df_m - 1
+            assert_equal(res1.df_resid, df_r)
+        else:
+            raise SkipTest("not available yet")
+
+    def test_other(self):
+        # some results may not be valid or available for all models
+        if hasattr(self, 'res1m'):
+            res1 = self.res1m
+            res2 = self.res2
+
+            if hasattr(res2, 'll_0'):
+                assert_allclose(res1.llnull, res2.ll_0, rtol=1e-6)
+            else:
+                if DEBUG:
+                    import warnings
+                    message = ('test: ll_0 not available, llnull=%6.4F'
+                                                                % res1.llnull)
+                    warnings.warn(message)
+
+        else:
+            raise SkipTest("not available yet")
+
+
+class TestPoissonConstrained1a(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_noexposure_constraint
+        cls.idx = [7, 3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ logpyears + smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data)
+        #res1a = mod1a.fit()
+        # get start_params, example fails to converge on one py TravisCI
+        k_vars = len(mod.exog_names)
+        start_params = np.zeros(k_vars)
+        start_params[0] = np.log(mod.endog.mean())
+        # if we need it, this is desired params
+        p = np.array([-3.93478643,  1.37276214,  2.33077032,  2.71338891,
+                      2.71338891, 0.57966535,  0.97254074])
+
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                        start_params=start_params,
+                                        fit_kwds={'method':'bfgs'})
+        # TODO: Newton fails
+
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr, start_params=start_params,
+                                        method='bfgs')
+
+    def test_smoke(self):
+        # trailing text in summary, assumes it's the first extra string
+        summ = self.res1m.summary()
+        assert_('linear equality constraints' in summ.extra_txt)
+        summ = self.res1m.summary2()
+        assert_('linear equality constraints' in summ.extra_txt[0])
+
+
+class TestPoissonConstrained1b(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_exposure_constraint
+        #cls.idx = [3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+        cls.idx = [6, 2, 3, 4, 5, 0]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   exposure=data['pyears'].values)
+                                   #offset=np.log(data['pyears'].values))
+        #res1a = mod1a.fit()
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                   fit_kwds={'method':'newton'})
+        cls.constraints = lc
+        # TODO: bfgs fails
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr, method='newton')
+
+
+class TestPoissonConstrained1c(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_exposure_constraint
+        #cls.idx = [3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+        cls.idx = [6, 2, 3, 4, 5, 0]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   offset=np.log(data['pyears'].values))
+        #res1a = mod1a.fit()
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                   fit_kwds={'method':'newton'})
+        cls.constraints = lc
+        # TODO: bfgs fails
+
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr, method='newton')
+
+
+class TestPoissonNoConstrained(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_exposure_noconstraint
+        cls.idx = [6, 2, 3, 4, 5, 0] # 1 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   #exposure=data['pyears'].values)
+                                   offset=np.log(data['pyears'].values))
+        res1 = mod.fit()._results
+        # res1 is duplicate check, so we can follow the same pattern
+        cls.res1 = (res1.params, res1.cov_params())
+        cls.res1m = res1
+
+
+class TestPoissonConstrained2a(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_noexposure_constraint2
+        cls.idx = [7, 3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ logpyears + smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data)
+
+        # get start_params, example fails to converge on one py TravisCI
+        k_vars = len(mod.exog_names)
+        start_params = np.zeros(k_vars)
+        start_params[0] = np.log(mod.endog.mean())
+        # if we need it, this is desired params
+        p = np.array([-9.43762015,  1.52762442,  2.74155711,  3.58730007,
+                      4.08730007,  1.15987869,  0.12111539])
+
+        constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                   start_params=start_params,
+                                   fit_kwds={'method':'bfgs'})
+        # TODO: Newton fails
+
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr, start_params=start_params,
+                                        method='bfgs')
+
+
+class TestPoissonConstrained2b(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_exposure_constraint2
+        #cls.idx = [3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+        cls.idx = [6, 2, 3, 4, 5, 0]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   exposure=data['pyears'].values)
+                                   #offset=np.log(data['pyears'].values))
+        #res1a = mod1a.fit()
+        constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                   fit_kwds={'method':'newton'})
+        cls.constraints = lc
+        # TODO: bfgs fails
+
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr,
+                                        fit_kwds={'method':'bfgs'})
+
+
+class TestPoissonConstrained2c(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+
+        cls.res2 = results.results_exposure_constraint2
+        #cls.idx = [3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+        cls.idx = [6, 2, 3, 4, 5, 0]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   offset=np.log(data['pyears'].values))
+
+        constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
+                                       fit_kwds={'method':'newton'})
+        cls.constraints = lc
+        # TODO: bfgs fails
+
+        # test method of Poisson, not monkey patched
+        cls.res1m = mod.fit_constrained(constr,
+                                        fit_kwds={'method':'bfgs'})
+
+
+class TestGLMPoissonConstrained1a(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+        from statsmodels.genmod.generalized_linear_model import GLM
+        from statsmodels.genmod import families
+        from statsmodels.base._constraints import fit_constrained
+
+        cls.res2 = results.results_noexposure_constraint
+        cls.idx = [7, 3, 4, 5, 6, 0, 1]  # 2 is dropped baseline for categorical
+
+        # example without offset
+        formula = 'deaths ~ logpyears + smokes + C(agecat)'
+        mod = GLM.from_formula(formula, data=data,
+                                    family=families.Poisson())
+
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants)
+        cls.constraints = lc
+        cls.res1m = mod.fit_constrained(constr)
+
+
+class TestGLMPoissonConstrained1b(CheckPoissonConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+        from statsmodels.genmod.generalized_linear_model import GLM
+        from statsmodels.genmod import families
+        from statsmodels.base._constraints import fit_constrained
+
+        cls.res2 = results.results_exposure_constraint
+        cls.idx = [6, 2, 3, 4, 5, 0]  # 2 is dropped baseline for categorical
+
+        # example with offset
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = GLM.from_formula(formula, data=data,
+                                    family=families.Poisson(),
+                                    offset=np.log(data['pyears'].values))
+
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
+        cls.res1 = fit_constrained(mod, lc.coefs, lc.constants)
+        cls.constraints = lc
+        cls.res1m = mod.fit_constrained(constr)._results
+
+    def test_compare_glm_poisson(self):
+        res1 = self.res1m
+        res2 = self.res2
+
+        formula = 'deaths ~ smokes + C(agecat)'
+        mod = Poisson.from_formula(formula, data=data,
+                                   exposure=data['pyears'].values)
+                                   #offset=np.log(data['pyears'].values))
+
+        constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
+        res2 = mod.fit_constrained(constr, start_params=self.res1m.params,
+                                        method='newton')
+
+        # we get high precision because we use the params as start_params
+
+        # basic, just as check that we have the same model
+        assert_allclose(res1.params, res2.params, rtol=1e-12)
+        assert_allclose(res1.bse, res2.bse, rtol=1e-12)
+
+        # check predict, fitted, ...
+
+        predicted = res1.predict()
+        assert_allclose(predicted, res2.predict(), rtol=1e-10)
+        assert_allclose(res1.mu, predicted, rtol=1e-10)
+        assert_allclose(res1.fittedvalues, predicted, rtol=1e-10)
+        assert_allclose(res2.predict(linear=True), res2.predict(linear=True),
+                        rtol=1e-10)
+
+
+class CheckGLMConstrainedMixin(CheckPoissonConstrainedMixin):
+    # add tests for some GLM specific attributes
+
+    def test_glm(self):
+        res2 = self.res2  # reference results
+        res1 = self.res1m
+
+        #assert_allclose(res1.aic, res2.aic, rtol=1e-10)  # far away
+        # Stata aic in ereturn and in estat ic are very different
+        # we have the same as estat ic
+        # see issue #1733
+        assert_allclose(res1.aic, res2.infocrit[4], rtol=1e-10)
+
+        assert_allclose(res1.bic, res2.bic, rtol=1e-10)
+        # bic is deviance based
+        #assert_allclose(res1.bic, res2.infocrit[5], rtol=1e-10)
+        assert_allclose(res1.deviance, res2.deviance, rtol=1e-10)
+        # TODO: which chi2 are these
+        #assert_allclose(res1.pearson_chi2, res2.chi2, rtol=1e-10)
+
+
+class TestGLMLogitConstrained1(CheckGLMConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.idx = slice(None)
+        # params sequence same as Stata, but Stata reports param = nan
+        # and we have param = value = 0
+
+        #res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
+        cls.res2 = reslogit.results_constraint1
+
+        mod1 = GLM(spector_data.endog, spector_data.exog,
+                   family=families.Binomial())
+
+        constr = 'x1 = 2.8'
+        cls.res1m = mod1.fit_constrained(constr)
+
+        R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
+        cls.res1 = fit_constrained(mod1, R, q)
+
+
+class TestGLMLogitConstrained2(CheckGLMConstrainedMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.idx = slice(None)  # params sequence same as Stata
+        #res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
+        cls.res2 = reslogit.results_constraint2
+
+        mod1 = GLM(spector_data.endog, spector_data.exog,
+                   family=families.Binomial())
+
+        constr = 'x1 - x3 = 0'
+        cls.res1m = mod1.fit_constrained(constr)
+
+        R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
+        cls.res1 = fit_constrained(mod1, R, q)
+        cls.constraints_rq = (R, q)
+
+
+    def test_predict(self):
+        # results only available for this case
+        res2 = self.res2  # reference results
+        res1 = self.res1m
+
+        predicted = res1.predict()
+        assert_allclose(predicted, res2.predict_mu, atol=1e-7)
+        assert_allclose(res1.mu, predicted, rtol=1e-10)
+        assert_allclose(res1.fittedvalues, predicted, rtol=1e-10)
+
+
+    def test_smoke(self):
+        # trailing text in summary, assumes it's the first extra string
+        summ = self.res1m.summary()
+        assert_('linear equality constraints' in summ.extra_txt)
+        summ = self.res1m.summary2()
+        assert_('linear equality constraints' in summ.extra_txt[0])
+
+
+    def test_fit_constrained_wrap(self):
+        # minimal test
+        res2 = self.res2  # reference results
+
+        from statsmodels.base._constraints import fit_constrained_wrap
+        res_wrap = fit_constrained_wrap(self.res1m.model, self.constraints_rq)
+        assert_allclose(res_wrap.params, res2.params, rtol=1e-6)
+        assert_allclose(res_wrap.params, res2.params, rtol=1e-6)
+
+
+def junk():
+    # Singular Matrix in mod1a.fit()
+
+    formula1 = 'deaths ~ smokes + C(agecat)'
+
+    formula2 = 'deaths ~ C(agecat) + C(smokes) : C(agecat)'  # same as Stata default
+
+    mod = Poisson.from_formula(formula2, data=data, exposure=data['pyears'].values)
+
+    res0 = mod.fit()
+
+    constraints = 'C(smokes)[T.1]:C(agecat)[3] = C(smokes)[T.1]:C(agecat)[4]'
+
+    import patsy
+    lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constraints)
+    R, q = lc.coefs, lc.constants
+
+    resc = mod.fit_constrained(R,q, fit_kwds={'method':'bfgs'})
+
+    # example without offset
+    formula1a = 'deaths ~ logpyears + smokes + C(agecat)'
+    mod1a = Poisson.from_formula(formula1a, data=data)
+    print(mod1a.exog.shape)
+
+    res1a = mod1a.fit()
+    lc_1a = patsy.DesignInfo(mod1a.exog_names).linear_constraint('C(agecat)[T.4] = C(agecat)[T.5]')
+    resc1a = mod1a.fit_constrained(lc_1a.coefs, lc_1a.constants, fit_kwds={'method':'newton'})
+    print(resc1a[0])
+    print(resc1a[1])
diff --git a/statsmodels/discrete/tests/test_discrete.py b/statsmodels/discrete/tests/test_discrete.py
index 53340325115..ad219cc873b 100644
--- a/statsmodels/discrete/tests/test_discrete.py
+++ b/statsmodels/discrete/tests/test_discrete.py
@@ -8,19 +8,21 @@
 tests.
 """
 # pylint: disable-msg=E1101
-
+from statsmodels.compat.python import range
 import os
 import numpy as np
 from numpy.testing import (assert_, assert_raises, assert_almost_equal,
-                           assert_equal, assert_array_equal)
+                           assert_equal, assert_array_equal, assert_allclose,
+                           assert_array_less)
 
 from statsmodels.discrete.discrete_model import (Logit, Probit, MNLogit,
                                                  Poisson, NegativeBinomial)
 from statsmodels.discrete.discrete_margins import _iscount, _isdummy
 import statsmodels.api as sm
 from nose import SkipTest
-from results.results_discrete import Spector, DiscreteL1
+from .results.results_discrete import Spector, DiscreteL1, RandHIE, Anes
 from statsmodels.tools.sm_exceptions import PerfectSeparationError
+
 try:
     import cvxopt
     has_cvxopt = True
@@ -52,7 +54,7 @@ def test_params(self):
         assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4)
 
     def test_conf_int(self):
-        assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_4)
+        assert_allclose(self.res1.conf_int(), self.res2.conf_int, rtol=8e-5)
 
     def test_zstat(self):
         assert_almost_equal(self.res1.tvalues, self.res2.z, DECIMAL_4)
@@ -361,8 +363,23 @@ def setupClass(cls):
         res2 = Spector()
         res2.probit()
         cls.res2 = res2
-        cls.res1 = Probit(data.endog, data.exog).fit(method="cg",
-            disp=0, maxiter=500, gtol=1e-08)
+
+        # fmin_cg fails to converge on some machines - reparameterize
+        from statsmodels.tools.transform_model import StandardizeTransform
+        transf = StandardizeTransform(data.exog)
+        exog_st = transf(data.exog)
+        res1_st = Probit(data.endog, exog_st).fit(method="cg",
+                                             disp=0, maxiter=500, gtol=1e-08)
+        start_params = transf.transform_params(res1_st.params)
+        assert_allclose(start_params, res2.params, rtol=1e-5, atol=1e-6)
+
+        cls.res1 = Probit(data.endog, data.exog).fit(start_params=start_params,
+                                                     method="cg",
+                                                     maxiter=500, gtol=1e-08,
+                                                     disp=0)
+
+        assert_array_less(cls.res1.mle_retvals['fcalls'], 100)
+
 
 class TestProbitNCG(CheckBinaryResults):
     @classmethod
@@ -389,7 +406,7 @@ def setupClass(cls):
         cls.res2 = res2
         fit = Probit(data.endog, data.exog).fit
         cls.res1 = fit(method="basinhopping", disp=0, niter=5,
-                       minimizer={'method' : 'L-BFGS-B', 'tol' : 1e-8})
+                        minimizer={'method' : 'L-BFGS-B', 'tol' : 1e-8})
 
 class CheckLikelihoodModelL1(object):
     """
@@ -523,15 +540,18 @@ class CheckL1Compatability(object):
     def test_params(self):
         m = self.m
         assert_almost_equal(
-            self.res_unreg.params, self.res_reg.params[:m], DECIMAL_4)
+            self.res_unreg.params[:m], self.res_reg.params[:m], DECIMAL_4)
         # The last entry should be close to zero
-        assert_almost_equal(0, self.res_reg.params[m:], DECIMAL_4)
+        # handle extra parameter of NegativeBinomial
+        kvars = self.res_reg.model.exog.shape[1]
+        assert_almost_equal(0, self.res_reg.params[m:kvars], DECIMAL_4)
 
     def test_cov_params(self):
         m = self.m
         # The restricted cov_params should be equal
         assert_almost_equal(
-            self.res_unreg.cov_params(), self.res_reg.cov_params()[:m, :m],
+            self.res_unreg.cov_params()[:m, :m],
+            self.res_reg.cov_params()[:m, :m],
             DECIMAL_1)
 
     def test_df(self):
@@ -541,20 +561,24 @@ def test_df(self):
     def test_t_test(self):
         m = self.m
         kvars = self.kvars
-        t_unreg = self.res_unreg.t_test(np.eye(m))
-        t_reg = self.res_reg.t_test(np.eye(kvars))
-        assert_almost_equal(t_unreg.effect, t_reg.effect[:m], DECIMAL_3)
-        assert_almost_equal(t_unreg.sd, t_reg.sd[:m], DECIMAL_3)
+        # handle extra parameter of NegativeBinomial
+        extra = getattr(self, 'k_extra', 0)
+        t_unreg = self.res_unreg.t_test(np.eye(len(self.res_unreg.params)))
+        t_reg = self.res_reg.t_test(np.eye(kvars + extra))
+        assert_almost_equal(t_unreg.effect[:m], t_reg.effect[:m], DECIMAL_3)
+        assert_almost_equal(t_unreg.sd[:m], t_reg.sd[:m], DECIMAL_3)
         assert_almost_equal(np.nan, t_reg.sd[m])
-        assert_almost_equal(t_unreg.tvalue, t_reg.tvalue[:m], DECIMAL_3)
+        assert_allclose(t_unreg.tvalue[:m], t_reg.tvalue[:m], atol=3e-3)
         assert_almost_equal(np.nan, t_reg.tvalue[m])
 
     def test_f_test(self):
         m = self.m
         kvars = self.kvars
-        f_unreg = self.res_unreg.f_test(np.eye(m))
-        f_reg = self.res_reg.f_test(np.eye(kvars)[:m])
-        assert_almost_equal(f_unreg.fvalue, f_reg.fvalue, DECIMAL_2)
+        # handle extra parameter of NegativeBinomial
+        extra = getattr(self, 'k_extra', 0)
+        f_unreg = self.res_unreg.f_test(np.eye(len(self.res_unreg.params))[:m])
+        f_reg = self.res_reg.f_test(np.eye(kvars + extra)[:m])
+        assert_allclose(f_unreg.fvalue, f_reg.fvalue, rtol=3e-5, atol=1e-3)
         assert_almost_equal(f_unreg.pvalue, f_reg.pvalue, DECIMAL_3)
 
     def test_bad_r_matrix(self):
@@ -572,8 +596,8 @@ def setupClass(cls):
         rand_exog = sm.add_constant(rand_exog, prepend=True)
         # Drop some columns and do an unregularized fit
         exog_no_PSI = rand_exog[:, :cls.m]
-        cls.res_unreg = sm.Poisson(
-            rand_data.endog, exog_no_PSI).fit(method="newton", disp=False)
+        mod_unreg = sm.Poisson(rand_data.endog, exog_no_PSI)
+        cls.res_unreg = mod_unreg.fit(method="newton", disp=False)
         # Do a regularized fit with alpha, effectively dropping the last column
         alpha = 10 * len(rand_data.endog) * np.ones(cls.kvars)
         alpha[:cls.m] = 0
@@ -582,6 +606,56 @@ def setupClass(cls):
             trim_mode='auto')
 
 
+class TestNegativeBinomialL1Compatability(CheckL1Compatability):
+    @classmethod
+    def setupClass(cls):
+        cls.kvars = 10 # Number of variables
+        cls.m = 7 # Number of unregularized parameters
+        rand_data = sm.datasets.randhie.load()
+        rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)
+        rand_exog_st = (rand_exog - rand_exog.mean(0)) / rand_exog.std(0)
+        rand_exog = sm.add_constant(rand_exog_st, prepend=True)
+        # Drop some columns and do an unregularized fit
+        exog_no_PSI = rand_exog[:, :cls.m]
+        mod_unreg = sm.NegativeBinomial(rand_data.endog, exog_no_PSI)
+        cls.res_unreg = mod_unreg.fit(method="newton", disp=False)
+        # Do a regularized fit with alpha, effectively dropping the last column
+        alpha = 10 * len(rand_data.endog) * np.ones(cls.kvars + 1)
+        alpha[:cls.m] = 0
+        alpha[-1] = 0  # don't penalize alpha
+
+        mod_reg = sm.NegativeBinomial(rand_data.endog, rand_exog)
+        cls.res_reg = mod_reg.fit_regularized(
+            method='l1', alpha=alpha, disp=False, acc=1e-10, maxiter=2000,
+            trim_mode='auto')
+        cls.k_extra = 1  # 1 extra parameter in nb2
+
+
+class TestNegativeBinomialGeoL1Compatability(CheckL1Compatability):
+    @classmethod
+    def setupClass(cls):
+        cls.kvars = 10 # Number of variables
+        cls.m = 7 # Number of unregularized parameters
+        rand_data = sm.datasets.randhie.load()
+        rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)
+        rand_exog = sm.add_constant(rand_exog, prepend=True)
+        # Drop some columns and do an unregularized fit
+        exog_no_PSI = rand_exog[:, :cls.m]
+        mod_unreg = sm.NegativeBinomial(rand_data.endog, exog_no_PSI,
+                                         loglike_method='geometric')
+        cls.res_unreg = mod_unreg.fit(method="newton", disp=False)
+        # Do a regularized fit with alpha, effectively dropping the last columns
+        alpha = 10 * len(rand_data.endog) * np.ones(cls.kvars)
+        alpha[:cls.m] = 0
+        mod_reg = sm.NegativeBinomial(rand_data.endog, rand_exog,
+                                      loglike_method='geometric')
+        cls.res_reg = mod_reg.fit_regularized(
+            method='l1', alpha=alpha, disp=False, acc=1e-10, maxiter=2000,
+            trim_mode='auto')
+
+        assert_equal(mod_reg.loglike_method, 'geometric')
+
+
 class TestLogitL1Compatability(CheckL1Compatability):
     @classmethod
     def setupClass(cls):
@@ -759,27 +833,16 @@ def test_dummy_exog2(self):
 class TestLogitBFGS(CheckBinaryResults, CheckMargEff):
     @classmethod
     def setupClass(cls):
-        #import scipy
-        #major, minor, micro = scipy.__version__.split('.')[:3]
-        #if int(minor) < 9:
-        #    raise SkipTest
-        #Skip this unconditionally for release 0.3.0
-        #since there are still problems with scipy 0.9.0 on some machines
-        #Ralf on mailing list 2011-03-26
-        raise SkipTest
-
         data = sm.datasets.spector.load()
         data.exog = sm.add_constant(data.exog, prepend=False)
         res2 = Spector()
         res2.logit()
         cls.res2 = res2
-        cls.res1 = Logit(data.endog, data.exog).fit(method="bfgs",
-            disp=0)
+        cls.res1 = Logit(data.endog, data.exog).fit(method="bfgs", disp=0)
 
 class TestPoissonNewton(CheckModelResults):
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = Poisson(data.endog, exog).fit(method='newton', disp=0)
@@ -804,10 +867,18 @@ def test_margeff_dummy_overall(self):
     def test_resid(self):
         assert_almost_equal(self.res1.resid, self.res2.resid, 2)
 
+    def test_predict_prob(self):
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        probs_res = np.loadtxt(os.path.join(cur_dir, "results",
+                            "predict_prob_poisson.csv"), delimiter=",")
+
+        # just check the first 100 obs. vs R to save memory
+        probs = self.res1.predict_prob()[:100]
+        assert_almost_equal(probs, probs_res, 8)
+
 class TestNegativeBinomialNB2Newton(CheckModelResults):
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = NegativeBinomial(data.endog, exog, 'nb2').fit(method='newton', disp=0)
@@ -860,7 +931,6 @@ def no_info(self):
 class TestNegativeBinomialNB1Newton(CheckModelResults):
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = NegativeBinomial(data.endog, exog, 'nb1').fit(
@@ -901,7 +971,6 @@ def test_predict_xb(self):
 class TestNegativeBinomialNB2BFGS(CheckModelResults):
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = NegativeBinomial(data.endog, exog, 'nb2').fit(
@@ -956,7 +1025,6 @@ def no_info(self):
 class TestNegativeBinomialNB1BFGS(CheckModelResults):
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = NegativeBinomial(data.endog, exog, 'nb1').fit(method="bfgs",
@@ -1003,7 +1071,6 @@ class TestNegativeBinomialGeometricBFGS(CheckModelResults):
 
     @classmethod
     def setupClass(cls):
-        from results.results_discrete import RandHIE
         data = sm.datasets.randhie.load()
         exog = sm.add_constant(data.exog, prepend=False)
         cls.res1 = NegativeBinomial(data.endog, exog, 'geometric').fit(method='bfgs', disp=0)
@@ -1011,11 +1078,13 @@ def setupClass(cls):
         res2.negativebinomial_geometric_bfgs()
         cls.res2 = res2
 
+    # the following are regression tests, could be inherited instead
+
     def test_aic(self):
-        assert_almost_equal(self.res1.aic, self.res2.aic, DECIMAL_1)
+        assert_almost_equal(self.res1.aic, self.res2.aic, DECIMAL_3)
 
     def test_bic(self):
-        assert_almost_equal(self.res1.bic, self.res2.bic, DECIMAL_1)
+        assert_almost_equal(self.res1.bic, self.res2.bic, DECIMAL_3)
 
     def test_conf_int(self):
         assert_almost_equal(self.res1.conf_int(), self.res2.conf_int, DECIMAL_3)
@@ -1053,18 +1122,7 @@ def test_bse(self):
     test_jac = no_info
 
 
-class TestMNLogitNewtonBaseZero(CheckModelResults):
-    @classmethod
-    def setupClass(cls):
-        from results.results_discrete import Anes
-        data = sm.datasets.anes96.load()
-        cls.data = data
-        exog = data.exog
-        exog = sm.add_constant(exog, prepend=False)
-        cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
-        res2 = Anes()
-        res2.mnlogit_basezero()
-        cls.res2 = res2
+class CheckMNLogitBaseZero(CheckModelResults):
 
     def test_margeff_overall(self):
         me = self.res1.get_margeff()
@@ -1169,6 +1227,39 @@ def test_pred_table(self):
     def test_resid(self):
         assert_array_equal(self.res1.resid_misclassified, self.res2.resid)
 
+
+class TestMNLogitNewtonBaseZero(CheckMNLogitBaseZero):
+    @classmethod
+    def setupClass(cls):
+
+        data = sm.datasets.anes96.load()
+        cls.data = data
+        exog = data.exog
+        exog = sm.add_constant(exog, prepend=False)
+        cls.res1 = MNLogit(data.endog, exog).fit(method="newton", disp=0)
+        res2 = Anes()
+        res2.mnlogit_basezero()
+        cls.res2 = res2
+
+class TestMNLogitLBFGSBaseZero(CheckMNLogitBaseZero):
+    @classmethod
+    def setupClass(cls):
+        data = sm.datasets.anes96.load()
+        cls.data = data
+        exog = data.exog
+        exog = sm.add_constant(exog, prepend=False)
+        mymodel = MNLogit(data.endog, exog)
+        cls.res1 = mymodel.fit(method="lbfgs", disp=0, maxiter=50000,
+                #m=12, pgtol=1e-7, factr=1e3, # 5 failures
+                #m=20, pgtol=1e-8, factr=1e2, # 3 failures
+                #m=30, pgtol=1e-9, factr=1e1, # 1 failure
+                m=40, pgtol=1e-10, factr=5e0,
+                loglike_and_score=mymodel.loglike_and_score)
+        res2 = Anes()
+        res2.mnlogit_basezero()
+        cls.res2 = res2
+
+
 def test_perfect_prediction():
     cur_dir = os.path.dirname(os.path.abspath(__file__))
     iris_dir = os.path.join(cur_dir, '..', '..', 'genmod', 'tests', 'results')
diff --git a/statsmodels/discrete/tests/test_sandwich_cov.py b/statsmodels/discrete/tests/test_sandwich_cov.py
new file mode 100644
index 00000000000..c622d042555
--- /dev/null
+++ b/statsmodels/discrete/tests/test_sandwich_cov.py
@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Mon Dec 09 21:29:20 2013
+
+Author: Josef Perktold
+"""
+
+import os
+import numpy as np
+import pandas as pd
+import statsmodels.discrete.discrete_model as smd
+import statsmodels.stats.sandwich_covariance as sc
+from statsmodels.tools.tools import add_constant
+
+from numpy.testing import assert_allclose
+
+
+# get data and results as module global for now, TODO: move to class
+from .results import results_count_robust_cluster as results_st
+
+cur_dir = os.path.dirname(os.path.abspath(__file__))
+
+filepath = os.path.join(cur_dir, "results", "ships.csv")
+data_raw = pd.read_csv(filepath, index_col=False)
+data = data_raw.dropna()
+
+#mod = smd.Poisson.from_formula('accident ~ yr_con + op_75_79', data=dat)
+# Don't use formula for tests against Stata because intercept needs to be last
+endog = data['accident']
+exog_data = data['yr_con op_75_79'.split()]
+exog = add_constant(exog_data, prepend=False)
+group = np.asarray(data['ship'], int)
+exposure = np.asarray(data['service'])
+
+
+# TODO get the test methods from regression/tests
+class CheckCountRobustMixin(object):
+
+
+    def test_basic(self):
+        res1 = self.res1
+        res2 = self.res2
+
+        if len(res1.params) == (len(res2.params) - 1):
+            # Stata includes lnalpha in table for NegativeBinomial
+            mask = np.ones(len(res2.params), np.bool_)
+            mask[-2] = False
+            res2_params = res2.params[mask]
+            res2_bse = res2.bse[mask]
+        else:
+            res2_params = res2.params
+            res2_bse = res2.bse
+
+        assert_allclose(res1._results.params, res2_params, 1e-4)
+
+        assert_allclose(self.bse_rob / self.corr_fact, res2_bse, 1e-5)
+
+    @classmethod
+    def get_robust_clu(cls):
+        res1 = cls.res1
+        cov_clu = sc.cov_cluster(res1, group)
+        cls.bse_rob = sc.se_cov(cov_clu)
+
+        nobs, k_vars = res1.model.exog.shape
+        k_params = len(res1.params)
+        #n_groups = len(np.unique(group))
+        corr_fact = (nobs-1.) / float(nobs - k_params)
+        # for bse we need sqrt of correction factor
+        cls.corr_fact = np.sqrt(corr_fact)
+
+
+    def test_oth(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_allclose(res1._results.llf, res2.ll, 1e-4)
+        assert_allclose(res1._results.llnull, res2.ll_0, 1e-4)
+
+
+class TestPoissonClu(CheckCountRobustMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.res2 = results_st.results_poisson_clu
+        mod = smd.Poisson(endog, exog)
+        cls.res1 = mod.fit(disp=False)
+        cls.get_robust_clu()
+
+
+class TestPoissonCluExposure(CheckCountRobustMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.res2 = results_st.results_poisson_exposure_clu #nonrobust
+        mod = smd.Poisson(endog, exog, exposure=exposure)
+        cls.res1 = mod.fit(disp=False)
+        cls.get_robust_clu()
+
+
+class TestNegbinClu(CheckCountRobustMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.res2 = results_st.results_negbin_clu
+        mod = smd.NegativeBinomial(endog, exog)
+        cls.res1 = mod.fit(disp=False)
+        cls.get_robust_clu()
+
+
+class TestNegbinCluExposure(CheckCountRobustMixin):
+
+    @classmethod
+    def setup_class(cls):
+        cls.res2 = results_st.results_negbin_exposure_clu #nonrobust
+        mod = smd.NegativeBinomial(endog, exog, exposure=exposure)
+        cls.res1 = mod.fit(disp=False)
+        cls.get_robust_clu()
+
+
+#        mod_nbe = smd.NegativeBinomial(endog, exog, exposure=data['service'])
+#        res_nbe = mod_nbe.fit()
+#        mod_nb = smd.NegativeBinomial(endog, exog)
+#        res_nb = mod_nb.fit()
+#
+#        cov_clu_nb = sc.cov_cluster(res_nb, group)
+#        k_params = k_vars + 1
+#        print sc.se_cov(cov_clu_nb / ((nobs-1.) / float(nobs - k_params)))
+#
+#        wt = res_nb.wald_test(np.eye(len(res_nb.params))[1:3], cov_p=cov_clu_nb/((nobs-1.) / float(nobs - k_params)))
+#        print wt
+#
+#        print dir(results_st)
+
+if __name__ == '__main__':
+    tt = TestPoissonClu()
+    tt.setup_class()
+    tt.test_basic()
+    tt = TestNegbinClu()
+    tt.setup_class()
+    tt.test_basic()
diff --git a/statsmodels/distributions/__init__.py b/statsmodels/distributions/__init__.py
index 207c586ba16..1ba2a57a765 100644
--- a/statsmodels/distributions/__init__.py
+++ b/statsmodels/distributions/__init__.py
@@ -1,4 +1,4 @@
-from empirical_distribution import ECDF, monotone_fn_inverter, StepFunction
+from .empirical_distribution import ECDF, monotone_fn_inverter, StepFunction
 
 from statsmodels import NoseWrapper as Tester
 test = Tester().test
diff --git a/statsmodels/distributions/empirical_distribution.py b/statsmodels/distributions/empirical_distribution.py
index 2a5f795c815..f96923e6327 100644
--- a/statsmodels/distributions/empirical_distribution.py
+++ b/statsmodels/distributions/empirical_distribution.py
@@ -61,16 +61,16 @@ class StepFunction(object):
     >>> y = np.arange(20)
     >>> f = StepFunction(x, y)
     >>>
-    >>> print f(3.2)
+    >>> print(f(3.2))
     3.0
-    >>> print f([[3.2,4.5],[24,-3.1]])
+    >>> print(f([[3.2,4.5],[24,-3.1]]))
     [[  3.   4.]
      [ 19.   0.]]
     >>> f2 = StepFunction(x, y, side='right')
     >>>
-    >>> print f(3.0)
+    >>> print(f(3.0))
     2.0
-    >>> print f2(3.0)
+    >>> print(f2(3.0))
     3.0
     """
 
@@ -164,9 +164,9 @@ def monotone_fn_inverter(fn, x, vectorized=True, **keywords):
 if __name__ == "__main__":
     #TODO: Make sure everything is correctly aligned and make a plotting
     # function
+    from statsmodels.compat.python import urlopen
     import matplotlib.pyplot as plt
-    import urllib
-    nerve_data = urllib.urlopen('http://www.statsci.org/data/general/nerve.txt')
+    nerve_data = urlopen('http://www.statsci.org/data/general/nerve.txt')
     nerve_data = np.loadtxt(nerve_data)
     x = nerve_data / 50. # was in 1/50 seconds
     cdf = ECDF(x)
diff --git a/statsmodels/distributions/mixture_rvs.py b/statsmodels/distributions/mixture_rvs.py
index e7732e96ff9..84681daeca4 100644
--- a/statsmodels/distributions/mixture_rvs.py
+++ b/statsmodels/distributions/mixture_rvs.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import range
 import numpy as np
 
 def _make_index(prob,size):
diff --git a/statsmodels/compatnp/tests/__init__.py b/statsmodels/duration/__init__.py
similarity index 100%
rename from statsmodels/compatnp/tests/__init__.py
rename to statsmodels/duration/__init__.py
diff --git a/statsmodels/duration/hazard_regression.py b/statsmodels/duration/hazard_regression.py
new file mode 100644
index 00000000000..bd42822d938
--- /dev/null
+++ b/statsmodels/duration/hazard_regression.py
@@ -0,0 +1,1800 @@
+import numpy as np
+from statsmodels.base import model
+import statsmodels.base.model as base
+from statsmodels.tools.decorators import cache_readonly
+from scipy.optimize import brent
+
+"""
+Implementation of proportional hazards regression models for duration
+data that may be censored ("Cox models").
+
+References
+----------
+T Therneau (1996).  Extending the Cox model.  Technical report.
+http://www.mayo.edu/research/documents/biostat-58pdf/DOC-10027288
+
+G Rodriguez (2005).  Non-parametric estimation in survival models.
+http://data.princeton.edu/pop509/NonParametricSurvival.pdf
+
+B Gillespie (2006).  Checking the assumptions in the Cox proportional
+hazards model.
+http://www.mwsug.org/proceedings/2006/stats/MWSUG-2006-SD08.pdf
+"""
+
+
+class PHSurvivalTime(object):
+
+    def __init__(self, time, status, exog, strata=None, entry=None,
+                 offset=None):
+        """
+        Represent a collection of survival times with possible
+        stratification and left truncation.
+
+        Parameters
+        ----------
+        time : array_like
+            The times at which either the event (failure) occurs or
+            the observation is censored.
+        status : array_like
+            Indicates whether the event (failure) occurs at `time`
+            (`status` is 1), or if `time` is a censoring time (`status`
+            is 0).
+        exog : array_like
+            The exogeneous (covariate) data matrix, cases are rows and
+            variables are columns.
+        strata : array_like
+            Grouping variable defining the strata.  If None, all
+            observations are in a single stratum.
+        entry : array_like
+            Entry (left truncation) times.  The observation is not
+            part of the risk set for times before the entry time.  If
+            None, the entry time is treated as being zero, which
+            gives no left truncation.  The entry time must be less
+            than or equal to `time`.
+        offset : array-like
+            An optional array of offsets
+        """
+
+        # Default strata
+        if strata is None:
+            strata = np.zeros(len(time), dtype=np.int32)
+
+        # Default entry times
+        if entry is None:
+            entry = np.zeros(len(time))
+
+        # Parameter validity checks.
+        n1, n2, n3, n4 = len(time), len(status), len(strata),\
+            len(entry)
+        nv = [n1, n2, n3, n4]
+        if max(nv) != min(nv):
+            raise ValueError("endog, status, strata, and " +
+                             "entry must all have the same length")
+        if min(time) < 0:
+            raise ValueError("endog must be non-negative")
+        if min(entry) < 0:
+            raise ValueError("entry time must be non-negative")
+
+        # In Stata, this is entry >= time, in R it is >.
+        if np.any(entry > time):
+            raise ValueError("entry times may not occur " +
+                             "after event or censoring times")
+
+        # Get the row indices for the cases in each stratum
+        if strata is not None:
+            stu = np.unique(strata)
+            #sth = {x: [] for x in stu} # needs >=2.7
+            sth = dict([(x, []) for x in stu])
+            for i,k in enumerate(strata):
+                sth[k].append(i)
+            stratum_rows = [np.asarray(sth[k], dtype=np.int32) for k in stu]
+            stratum_names = stu
+        else:
+            stratum_rows = [np.arange(len(time)),]
+            stratum_names = [0,]
+
+        # Remove strata with no events
+        ix = [i for i,ix in enumerate(stratum_rows) if status[ix].sum() > 0]
+        stratum_rows = [stratum_rows[i] for i in ix]
+        stratum_names = [stratum_names[i] for i in ix]
+
+        # The number of strata
+        nstrat = len(stratum_rows)
+        self.nstrat = nstrat
+
+        # Remove subjects whose entry time occurs after the last event
+        # in their stratum.
+        for stx,ix in enumerate(stratum_rows):
+            last_failure = max(time[ix][status[ix] == 1])
+
+            # Stata uses < here, R uses <=
+            ii = [i for i,t in enumerate(entry[ix]) if
+                  t <= last_failure]
+            stratum_rows[stx] = stratum_rows[stx][ii]
+
+        # Remove subjects who are censored before the first event in
+        # their stratum.
+        for stx,ix in enumerate(stratum_rows):
+            first_failure = min(time[ix][status[ix] == 1])
+
+            ii = [i for i,t in enumerate(time[ix]) if
+                  t >= first_failure]
+            stratum_rows[stx] = stratum_rows[stx][ii]
+
+        # Order by time within each stratum
+        for stx,ix in enumerate(stratum_rows):
+            ii = np.argsort(time[ix])
+            stratum_rows[stx] = stratum_rows[stx][ii]
+
+        if offset is not None:
+            self.offset_s = []
+            for stx in range(nstrat):
+                self.offset_s.append(offset[stratum_rows[stx]])
+        else:
+            self.offset_s = None
+
+        # Number of informative subjects
+        self.n_obs = sum([len(ix) for ix in stratum_rows])
+
+        # Split everything by stratum
+        self.time_s = []
+        self.exog_s = []
+        self.status_s = []
+        self.entry_s = []
+        for ix in stratum_rows:
+            self.time_s.append(time[ix])
+            self.exog_s.append(exog[ix,:])
+            self.status_s.append(status[ix])
+            self.entry_s.append(entry[ix])
+
+        self.stratum_rows = stratum_rows
+        self.stratum_names = stratum_names
+
+        # Precalculate some indices needed to fit Cox models.
+        # Distinct failure times within a stratum are always taken to
+        # be sorted in ascending order.
+        #
+        # ufailt_ix[stx][k] is a list of indices for subjects who fail
+        # at the k^th sorted unique failure time in stratum stx
+        #
+        # risk_enter[stx][k] is a list of indices for subjects who
+        # enter the risk set at the k^th sorted unique failure time in
+        # stratum stx
+        #
+        # risk_exit[stx][k] is a list of indices for subjects who exit
+        # the risk set at the k^th sorted unique failure time in
+        # stratum stx
+        self.ufailt_ix, self.risk_enter, self.risk_exit, self.ufailt =\
+            [], [], [], []
+
+        for stx in range(self.nstrat):
+
+            # All failure times
+            ift = np.flatnonzero(self.status_s[stx] == 1)
+            ft = self.time_s[stx][ift]
+
+            # Unique failure times
+            uft = np.unique(ft)
+            nuft = len(uft)
+
+            # Indices of cases that fail at each unique failure time
+            #uft_map = {x:i for i,x in enumerate(uft)} # requires >=2.7
+            uft_map = dict([(x, i) for i,x in enumerate(uft)]) # 2.6
+            uft_ix = [[] for k in range(nuft)]
+            for ix,ti in zip(ift,ft):
+                uft_ix[uft_map[ti]].append(ix)
+
+            # Indices of cases (failed or censored) that enter the
+            # risk set at each unique failure time.
+            risk_enter1 = [[] for k in range(nuft)]
+            for i,t in enumerate(self.time_s[stx]):
+                ix = np.searchsorted(uft, t, "right") - 1
+                if ix >= 0:
+                    risk_enter1[ix].append(i)
+
+            # Indices of cases (failed or censored) that exit the
+            # risk set at each unique failure time.
+            risk_exit1 = [[] for k in range(nuft)]
+            for i,t in enumerate(self.entry_s[stx]):
+                ix = np.searchsorted(uft, t)
+                risk_exit1[ix].append(i)
+
+            self.ufailt.append(uft)
+            self.ufailt_ix.append([np.asarray(x, dtype=np.int32) for x in uft_ix])
+            self.risk_enter.append([np.asarray(x, dtype=np.int32) for x in risk_enter1])
+            self.risk_exit.append([np.asarray(x, dtype=np.int32) for x in risk_exit1])
+
+
+
+class PHReg(model.LikelihoodModel):
+    """
+    Fit the Cox proportional hazards regression model for right
+    censored data.
+
+    Arguments
+    ---------
+    endog : array-like
+        The observed times (event or censoring)
+    exog : 2D array-like
+        The covariates or exogeneous variables
+    status : array-like
+        The censoring status values; status=1 indicates that an
+        event occured (e.g. failure or death), status=0 indicates
+        that the observation was right censored. If None, defaults
+        to status=1 for all cases.
+    entry : array-like
+        The entry times, if left truncation occurs
+    strata : array-like
+        Stratum labels.  If None, all observations are taken to be
+        in a single stratum.
+    ties : string
+        The method used to handle tied times, must be either 'breslow'
+        or 'efron'.
+    offset : array-like
+        Array of offset values
+    missing : string
+        The method used to handle missing data
+
+    Notes
+    -----
+    Proportional hazards regression models should not include an
+    explicit or implicit intercept.  The effect of an intercept is
+    not identified using the partial likelihood approach.
+
+    `endog`, `event`, `strata`, `entry`, and the first dimension
+    of `exog` all must have the same length
+    """
+
+    def __init__(self, endog, exog, status=None, entry=None,
+                 strata=None, offset=None, ties='breslow',
+                 missing='drop'):
+
+        # Default is no censoring
+        if status is None:
+            status = np.ones(len(endog))
+
+        super(PHReg, self).__init__(endog, exog, status=status,
+                                    entry=entry, strata=strata,
+                                    offset=offset, missing=missing)
+
+        # endog and exog are automatically converted, but these are
+        # not
+        if self.status is not None:
+            self.status = np.asarray(self.status)
+        if self.entry is not None:
+            self.entry = np.asarray(self.entry)
+        if self.strata is not None:
+            self.strata = np.asarray(self.strata)
+        if self.offset is not None:
+            self.offset = np.asarray(self.offset)
+
+        self.surv = PHSurvivalTime(self.endog, self.status,
+                                    self.exog, self.strata,
+                                    self.entry, self.offset)
+
+        # TODO: not used?
+        self.missing = missing
+
+        ties = ties.lower()
+        if ties not in ("efron", "breslow"):
+            raise ValueError("`ties` must be either `efron` or " +
+                             "`breslow`")
+
+        self.ties = ties
+
+    def fit(self, groups=None, **args):
+        """
+        Fit a proportional hazards regression model.
+
+        Parameters
+        ----------
+        groups : array-like
+            Labels indicating groups of observations that may be
+            dependent.  If present, the standard errors account for
+            this dependence. Does not affect fitted values.
+
+        Returns a PHregResults instance.
+        """
+
+        # TODO process for missing values
+        if groups is not None:
+            self.groups = np.asarray(groups)
+        else:
+            self.groups = None
+
+        if 'disp' not in args:
+            args['disp'] = False
+        fit_rslts = super(PHReg, self).fit(**args)
+
+        if self.groups is None:
+            cov_params = fit_rslts.cov_params()
+        else:
+            cov_params = self.robust_covariance(fit_rslts.params)
+
+        results = PHRegResults(self, fit_rslts.params, cov_params)
+
+        return results
+
+
+    def fit_regularized(self, method="coord_descent", maxiter=100,
+                        alpha=0., L1_wt=1., start_params=None,
+                        cnvrg_tol=1e-7, zero_tol=1e-8, **kwargs):
+        """
+        Return a regularized fit to a linear regression model.
+
+        Parameters
+        ----------
+        method :
+            Only the coordinate descent algorithm is implemented.
+        maxiter : integer
+            The maximum number of iteration cycles (an iteration cycle
+            involves running coordinate descent on all variables).
+        alpha : scalar or array-like
+            The penalty weight.  If a scalar, the same penalty weight
+            applies to all variables in the model.  If a vector, it
+            must have the same length as `params`, and contains a
+            penalty weight for each coefficient.
+        L1_wt : scalar
+            The fraction of the penalty given to the L1 penalty term.
+            Must be between 0 and 1 (inclusive).  If 0, the fit is
+            a ridge fit, if 1 it is a lasso fit.
+        start_params : array-like
+            Starting values for `params`.
+        cnvrg_tol : scalar
+            If `params` changes by less than this amount (in sup-norm)
+            in once iteration cycle, the algorithm terminates with
+            convergence.
+        zero_tol : scalar
+            Any estimated coefficient smaller than this value is
+            replaced with zero.
+
+        Returns
+        -------
+        A PHregResults object, of the same type returned by `fit`.
+
+        Notes
+        -----
+        The penalty is the"elastic net" penalty, which
+        is a convex combination of L1 and L2 penalties.
+
+        The function that is minimized is:
+
+        -loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)
+
+        where |*|_1 and |*|_2 are the L1 and L2 norms.
+
+        Post-estimation results are based on the same data used to
+        select variables, hence may be subject to overfitting biases.
+        """
+
+        k_exog = self.exog.shape[1]
+        n_exog = self.exog.shape[0]
+
+        if np.isscalar(alpha):
+            alpha = alpha * np.ones(k_exog, dtype=np.float64)
+
+        # regularization cannot be used with groups
+        self.groups = None
+
+        # Define starting params
+        if start_params is None:
+            params = np.zeros(k_exog, dtype=np.float64)
+        else:
+            params = start_params.copy()
+
+        # Maybe could be a shallow copy, but just in case...
+        import copy
+        surv = copy.deepcopy(self.surv)
+
+        # This is the base offset, onto which the effects of
+        # constrained variables are added.
+        if self.offset is None:
+            offset_s_base = [np.zeros(len(x)) for x in surv.stratum_rows]
+            surv.offset_s = [x.copy() for x in offset_s_base]
+        else:
+            offset_s_base = [x.copy() for x in surv.offset_s]
+
+        # Create a model instance for optimizing a single variable
+        model_1var = copy.deepcopy(self)
+        model_1var.surv = surv
+        model_1var.ties = self.ties
+
+        # All the negative penalized loglikeihood functions.
+        def gen_npfuncs(k):
+            def nploglike(params):
+                pen = alpha[k]*((1 - L1_wt)*params**2/2 + L1_wt*np.abs(params))
+                return -model_1var.loglike(np.r_[params]) / n_exog + pen
+            def npscore(params):
+                pen_grad = alpha[k]*(1 - L1_wt)*params
+                return -model_1var.score(np.r_[params])[0] / n_exog + pen_grad
+            def nphess(params):
+                pen_hess = alpha[k]*(1 - L1_wt)
+                return -model_1var.hessian(np.r_[params])[0,0] / n_exog + pen_hess
+            return nploglike, npscore, nphess
+        nploglike_funcs = [gen_npfuncs(k) for k in range(len(params))]
+
+        # 1-dimensional exog's
+        exog_s = []
+        for k in range(k_exog):
+            ex = [x[:, k][:, None] for x in surv.exog_s]
+            exog_s.append(ex)
+
+        converged = False
+        btol = 1e-8
+        params_zero = np.zeros(len(params), dtype=bool)
+
+        for itr in range(maxiter):
+
+            # Sweep through the parameters
+            params_save = params.copy()
+            for k in range(k_exog):
+
+                # Under the active set method, if a parameter becomes
+                # zero we don't try to change it again.
+                if params_zero[k]:
+                    continue
+
+                # Set exog to include only the variable whose effect
+                # is being estimated.
+                surv.exog_s = exog_s[k]
+
+                # Set the offset to account for the variables that are
+                # being held fixed.
+                params0 = params.copy()
+                params0[k] = 0
+                for stx in range(self.surv.nstrat):
+                    v = np.dot(self.surv.exog_s[stx], params0)
+                    surv.offset_s[stx] = offset_s_base[stx] + v
+
+                params[k] = _opt_1d(nploglike_funcs[k], params[k],
+                                    alpha[k]*L1_wt, tol=btol)
+
+                # Update the active set
+                if itr > 0 and np.abs(params[k]) < zero_tol:
+                    params_zero[k] = True
+                    params[k] = 0.
+
+            # Check for convergence
+            pchange = np.max(np.abs(params - params_save))
+            if pchange < cnvrg_tol:
+                converged = True
+                break
+
+        # Set approximate zero coefficients to be exactly zero
+        params *= np.abs(params) >= zero_tol
+
+        # Fit the reduced model to get standard errors and other
+        # post-estimation results.
+        ii = np.flatnonzero(params)
+        cov = np.zeros((k_exog, k_exog), dtype=np.float64)
+        if len(ii) > 0:
+            model = self.__class__(self.endog, self.exog[:, ii],
+                                   status=self.status, entry=self.entry,
+                                   strata=self.strata, offset=self.offset,
+                                   ties=self.ties, missing=self.missing)
+            rslt = model.fit()
+            cov[np.ix_(ii, ii)] = rslt.normalized_cov_params
+
+        rfit = PHRegResults(self, params, cov_params=cov)
+        rfit.converged = converged
+        rfit.regularized = True
+
+        return rfit
+
+    def loglike(self, params):
+        """
+        Returns the log partial likelihood function evaluated at
+        `params`.
+        """
+
+        if self.ties == "breslow":
+            return self.breslow_loglike(params)
+        elif self.ties == "efron":
+            return self.efron_loglike(params)
+
+    def score(self, params):
+        """
+        Returns the score function evaluated at `params`.
+        """
+
+        if self.ties == "breslow":
+            return self.breslow_gradient(params)
+        elif self.ties == "efron":
+            return self.efron_gradient(params)
+
+    def hessian(self, params):
+        """
+        Returns the Hessian matrix of the log partial likelihood
+        function evaluated at `params`.
+        """
+
+        if self.ties == "breslow":
+            return self.breslow_hessian(params)
+        else:
+            return self.efron_hessian(params)
+
+    def breslow_loglike(self, params):
+        """
+        Returns the value of the log partial likelihood function
+        evaluated at `params`, using the Breslow method to handle tied
+        times.
+        """
+
+        surv = self.surv
+
+        like = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft_ix = surv.ufailt_ix[stx]
+            exog_s = surv.exog_s[stx]
+            nuft = len(uft_ix)
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0 = 0.
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                xp0 += e_linpred[ix].sum()
+
+                # Account for all cases that fail at this point.
+                ix = uft_ix[i]
+                like += (linpred[ix] - np.log(xp0)).sum()
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                xp0 -= e_linpred[ix].sum()
+
+        return like
+
+    def efron_loglike(self, params):
+        """
+        Returns the value of the log partial likelihood function
+        evaluated at `params`, using the Efron method to handle tied
+        times.
+        """
+
+        surv = self.surv
+
+        like = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            # exog and linear predictor for this stratum
+            exog_s = surv.exog_s[stx]
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0 = 0.
+
+            # Iterate backward through the unique failure times.
+            uft_ix = surv.ufailt_ix[stx]
+            nuft = len(uft_ix)
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                xp0 += e_linpred[ix].sum()
+                xp0f = e_linpred[uft_ix[i]].sum()
+
+                # Account for all cases that fail at this point.
+                ix = uft_ix[i]
+                like += linpred[ix].sum()
+
+                m = len(ix)
+                J = np.arange(m, dtype=np.float64) / m
+                like -= np.log(xp0 - J*xp0f).sum()
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                xp0 -= e_linpred[ix].sum()
+
+        return like
+
+    def breslow_gradient(self, params):
+        """
+        Returns the gradient of the log partial likelihood, using the
+        Breslow method to handle tied times.
+        """
+
+        surv = self.surv
+
+        grad = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            # Indices of subjects in the stratum
+            strat_ix = surv.stratum_rows[stx]
+
+            # Unique failure times in the stratum
+            uft_ix = surv.ufailt_ix[stx]
+            nuft = len(uft_ix)
+
+            # exog and linear predictor for the stratum
+            exog_s = surv.exog_s[stx]
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0, xp1 = 0., 0.
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                if len(ix) > 0:
+                    v = exog_s[ix,:]
+                    xp0 += e_linpred[ix].sum()
+                    xp1 += (e_linpred[ix][:,None] * v).sum(0)
+
+                # Account for all cases that fail at this point.
+                ix = uft_ix[i]
+                grad += (exog_s[ix,:] - xp1 / xp0).sum(0)
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                if len(ix) > 0:
+                    v = exog_s[ix,:]
+                    xp0 -= e_linpred[ix].sum()
+                    xp1 -= (e_linpred[ix][:,None] * v).sum(0)
+
+        return grad
+
+    def efron_gradient(self, params):
+        """
+        Returns the gradient of the log partial likelihood evaluated
+        at `params`, using the Efron method to handle tied times.
+        """
+
+        surv = self.surv
+
+        grad = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            # Indices of cases in the stratum
+            strat_ix = surv.stratum_rows[stx]
+
+            # exog and linear predictor of the stratum
+            exog_s = surv.exog_s[stx]
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0, xp1 = 0., 0.
+
+            # Iterate backward through the unique failure times.
+            uft_ix = surv.ufailt_ix[stx]
+            nuft = len(uft_ix)
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                if len(ix) > 0:
+                    v = exog_s[ix,:]
+                    xp0 += e_linpred[ix].sum()
+                    xp1 += (e_linpred[ix][:,None] * v).sum(0)
+                ixf = uft_ix[i]
+                if len(ixf) > 0:
+                    v = exog_s[ixf,:]
+                    xp0f = e_linpred[ixf].sum()
+                    xp1f = (e_linpred[ixf][:,None] * v).sum(0)
+
+                    # Consider all cases that fail at this point.
+                    grad += v.sum(0)
+
+                    m = len(ixf)
+                    J = np.arange(m, dtype=np.float64) / m
+                    numer = xp1 - np.outer(J, xp1f)
+                    denom = xp0 - np.outer(J, xp0f)
+                    ratio = numer / denom
+                    rsum = ratio.sum(0)
+                    grad -= rsum
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                if len(ix) > 0:
+                    v = exog_s[ix,:]
+                    xp0 -= e_linpred[ix].sum()
+                    xp1 -= (e_linpred[ix][:,None] * v).sum(0)
+
+        return grad
+
+    def breslow_hessian(self, params):
+        """
+        Returns the Hessian of the log partial likelihood evaluated at
+        `params`, using the Breslow method to handle tied times.
+        """
+
+        surv = self.surv
+
+        hess = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft_ix = surv.ufailt_ix[stx]
+            nuft = len(uft_ix)
+
+            exog_s = surv.exog_s[stx]
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0, xp1, xp2 = 0., 0., 0.
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                if len(ix) > 0:
+                    xp0 += e_linpred[ix].sum()
+                    v = exog_s[ix,:]
+                    xp1 += (e_linpred[ix][:,None] * v).sum(0)
+                    mat = v[None,:,:]
+                    elx = e_linpred[ix]
+                    xp2 += (mat.T * mat * elx[None,:,None]).sum(1)
+
+                # Account for all cases that fail at this point.
+                m = len(uft_ix[i])
+                hess += m*(xp2 / xp0  - np.outer(xp1, xp1) / xp0**2)
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_exit[stx][i]
+                if len(ix) > 0:
+                    xp0 -= e_linpred[ix].sum()
+                    v = exog_s[ix,:]
+                    xp1 -= (e_linpred[ix][:,None] * v).sum(0)
+                    mat = v[None,:,:]
+                    elx = e_linpred[ix]
+                    xp2 -= (mat.T * mat * elx[None,:,None]).sum(1)
+
+        return -hess
+
+    def efron_hessian(self, params):
+        """
+        Returns the Hessian matrix of the partial log-likelihood
+        evaluated at `params`, using the Efron method to handle tied
+        times.
+        """
+
+        surv = self.surv
+
+        hess = 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            exog_s = surv.exog_s[stx]
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            xp0, xp1, xp2 = 0., 0., 0.
+
+            # Iterate backward through the unique failure times.
+            uft_ix = surv.ufailt_ix[stx]
+            nuft = len(uft_ix)
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                if len(ix) > 0:
+                    xp0 += e_linpred[ix].sum()
+                    v = exog_s[ix,:]
+                    xp1 += (e_linpred[ix][:,None] * v).sum(0)
+                    mat = v[None,:,:]
+                    elx = e_linpred[ix]
+                    xp2 += (mat.T * mat * elx[None,:,None]).sum(1)
+
+                ixf = uft_ix[i]
+                if len(ixf) > 0:
+                    v = exog_s[ixf,:]
+                    xp0f = e_linpred[ixf].sum()
+                    xp1f = (e_linpred[ixf][:,None] * v).sum(0)
+                    mat = v[None,:,:]
+                    elx = e_linpred[ixf]
+                    xp2f = (mat.T * mat * elx[None,:,None]).sum(1)
+
+                # Account for all cases that fail at this point.
+                m = len(uft_ix[i])
+                J = np.arange(m, dtype=np.float64) / m
+                c0 = xp0 - J*xp0f
+                mat = (xp2[None,:,:] - J[:,None,None]*xp2f) / c0[:,None,None]
+                hess += mat.sum(0)
+                mat = (xp1[None, :] - np.outer(J, xp1f)) / c0[:, None]
+                mat = mat[:, :, None] * mat[:, None, :]
+                hess -= mat.sum(0)
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_exit[stx][i]
+                if len(ix) > 0:
+                    xp0 -= e_linpred[ix].sum()
+                    v = exog_s[ix,:]
+                    xp1 -= (e_linpred[ix][:,None] * v).sum(0)
+                    mat = v[None,:,:]
+                    elx = e_linpred[ix]
+                    xp2 -= (mat.T * mat * elx[None,:,None]).sum(1)
+
+        return -hess
+
+    def robust_covariance(self, params):
+        """
+        Returns a covariance matrix for the proportional hazards model
+        regresion coefficient estimates that is robust to certain
+        forms of model misspecification.
+
+        Parameters
+        ----------
+        params : ndarray
+            The parameter vector at which the covariance matrix is
+            calculated.
+
+        Returns
+        -------
+        The robust covariance matrix as a square ndarray.
+
+        Notes
+        -----
+        This function uses the `groups` argument to determine groups
+        within which observations may be dependent.  The covariance
+        matrix is calculated using the Huber-White "sandwich" approach.
+        """
+
+        if self.groups is None:
+            raise ValueError("`groups` must be specified to calculate the robust covariance matrix")
+
+        hess = self.hessian(params)
+
+        score_obs = self.score_residuals(params)
+
+        # Collapse
+        grads = {}
+        for i,g in enumerate(self.groups):
+            if g not in grads:
+                grads[g] = 0.
+            grads[g] += score_obs[i, :]
+        grads = np.asarray(list(grads.values()))
+
+        mat = grads[None, :, :]
+        mat = mat.T * mat
+        mat = mat.sum(1)
+
+        hess_inv = np.linalg.inv(hess)
+        cmat = np.dot(hess_inv, np.dot(mat, hess_inv))
+
+        return cmat
+
+    def score_residuals(self, params):
+        """
+        Returns the score residuals calculated at a given vector of
+        parameters.
+
+        Parameters
+        ----------
+        params : ndarray
+            The parameter vector at which the score residuals are
+            calculated.
+
+        Returns
+        -------
+        The score residuals, returned as a ndarray having the same
+        shape as `exog`.
+
+        Notes
+        -----
+        Observations in a stratum with no observed events have undefined
+        score residuals, and contain NaN in the returned matrix.
+        """
+
+        surv = self.surv
+
+        score_resid = np.zeros(self.exog.shape, dtype=np.float64)
+
+        # Use to set undefined values to NaN.
+        mask = np.zeros(self.exog.shape[0], dtype=np.int32)
+
+        w_avg = self.weighted_covariate_averages(params)
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft_ix = surv.ufailt_ix[stx]
+            exog_s = surv.exog_s[stx]
+            nuft = len(uft_ix)
+            strat_ix = surv.stratum_rows[stx]
+
+            xp0 = 0.
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            at_risk_ix = set([])
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                at_risk_ix |= set(ix)
+                xp0 += e_linpred[ix].sum()
+
+                atr_ix = list(at_risk_ix)
+                leverage = exog_s[atr_ix, :] - w_avg[stx][i, :]
+
+                # Event indicators
+                d = np.zeros(exog_s.shape[0])
+                d[uft_ix[i]] = 1
+
+                # The increment in the cumulative hazard
+                dchaz = len(uft_ix[i]) / xp0
+
+                # Piece of the martingale residual
+                mrp = d[atr_ix] - e_linpred[atr_ix] * dchaz
+
+                # Update the score residuals
+                ii = strat_ix[atr_ix]
+                score_resid[ii,:] += leverage * mrp[:, None]
+                mask[ii] = 1
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                at_risk_ix -= set(ix)
+                xp0 -= e_linpred[ix].sum()
+
+        jj = np.flatnonzero(mask == 0)
+        if len(jj) > 0:
+            score_resid[jj, :] = np.nan
+
+        return score_resid
+
+    def weighted_covariate_averages(self, params):
+        """
+        Returns the hazard-weighted average of covariate values for
+        subjects who are at-risk at a particular time.
+
+        Arguments
+        ---------
+        params : ndarray
+            Parameter vector
+
+        Returns
+        -------
+        averages : list of ndarrays
+            averages[stx][i,:] is a row vector containing the weighted
+            average values (for all the covariates) of at-risk
+            subjects a the i^th largest observed failure time in
+            stratum `stx`, using the hazard multipliers as weights.
+
+        Notes
+        -----
+        Used to calculate leverages and score residuals.
+        """
+
+        surv = self.surv
+
+        averages = []
+        xp0, xp1 = 0., 0.
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft_ix = surv.ufailt_ix[stx]
+            exog_s = surv.exog_s[stx]
+            nuft = len(uft_ix)
+
+            average_s = np.zeros((len(uft_ix), exog_s.shape[1]),
+                                  dtype=np.float64)
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            linpred -= linpred.max()
+            e_linpred = np.exp(linpred)
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                xp0 += e_linpred[ix].sum()
+                xp1 += np.dot(e_linpred[ix], exog_s[ix, :])
+
+                average_s[i, :] = xp1 / xp0
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                xp0 -= e_linpred[ix].sum()
+                xp1 -= np.dot(e_linpred[ix], exog_s[ix, :])
+
+            averages.append(average_s)
+
+        return averages
+
+    def baseline_cumulative_hazard(self, params):
+        """
+        Estimate the baseline cumulative hazard and survival
+        functions.
+
+        Parameters
+        ----------
+        params : ndarray
+            The model parameters.
+
+        Returns
+        -------
+        A list of triples (time, hazard, survival) containing the time
+        values and corresponding cumulative hazard and survival
+        function values for each stratum.
+
+        Notes
+        -----
+        Uses the Nelson-Aalen estimator.
+        """
+
+        # TODO: some disagreements with R, not the same algorithm but
+        # hard to deduce what R is doing.  Our results are reasonable.
+
+        surv = self.surv
+        rslt = []
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft = surv.ufailt[stx]
+            uft_ix = surv.ufailt_ix[stx]
+            exog_s = surv.exog_s[stx]
+            nuft = len(uft_ix)
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            e_linpred = np.exp(linpred)
+
+            xp0 = 0.
+            h0 = np.zeros(nuft, dtype=np.float64)
+
+            # Iterate backward through the unique failure times.
+            for i in range(nuft)[::-1]:
+
+                # Update for new cases entering the risk set.
+                ix = surv.risk_enter[stx][i]
+                xp0 += e_linpred[ix].sum()
+
+                # Account for all cases that fail at this point.
+                ix = uft_ix[i]
+                h0[i] = len(ix) / xp0
+
+                # Update for cases leaving the risk set.
+                ix = surv.risk_exit[stx][i]
+                xp0 -= e_linpred[ix].sum()
+
+            cumhaz = np.cumsum(h0) - h0
+            surv = np.exp(-cumhaz)
+            rslt.append([uft, cumhaz, surv])
+
+        return rslt
+
+    def baseline_cumulative_hazard_function(self, params):
+        """
+        Returns a function that calculates the baseline cumulative
+        hazard function for each stratum.
+
+        Parameters
+        ----------
+        params : ndarray
+            The model parameters.
+
+        Returns
+        -------
+        A dict mapping stratum names to the estimated baseline
+        cumulative hazard function.
+        """
+
+        from scipy.interpolate import interp1d
+        surv = self.surv
+        base = self.baseline_cumulative_hazard(params)
+
+        cumhaz_f = {}
+        for stx in range(surv.nstrat):
+            time_h = base[stx][0]
+            cumhaz = base[stx][1]
+            time_h = np.r_[-np.inf, time_h, np.inf]
+            cumhaz = np.r_[cumhaz[0], cumhaz, cumhaz[-1]]
+            func = interp1d(time_h, cumhaz, kind='zero')
+            cumhaz_f[self.surv.stratum_names[stx]] = func
+
+        return cumhaz_f
+
+    def predict(self, params, cov_params=None, endog=None, exog=None,
+                strata=None, offset=None, pred_type="lhr"):
+        """
+        Returns predicted values from the proportional hazards
+        regression model.
+
+        Parameters:
+        -----------
+        params : array-like
+            The proportional hazards model parameters.
+        cov_params : array-like
+            The covariance matrix of the estimated `params` vector,
+            used to obtain prediction errors if pred_type='lhr',
+            otherwise optional.
+        endog : array-like
+            Duration (time) values at which the predictions are made.
+            Only used if pred_type is either 'cumhaz' or 'surv'.  If
+            using model `exog`, defaults to model `endog` (time), but
+            may be provided explicitly to make predictions at
+            alternative times.
+        exog : array-like
+            Data to use as `exog` in forming predictions.  If not
+            provided, the `exog` values from the model used to fit the
+            data are used.
+        strata : array-like
+            A vector of stratum values used to form the predictions.
+            Not used (may be 'None') if pred_type is 'lhr' or 'hr'.
+            If `exog` is None, the model stratum values are used.  If
+            `exog` is not None and pred_type is 'surv' or 'cumhaz',
+            stratum values must be provided (unless there is only one
+            stratum).
+        offset : array-like
+            Offset values used to create the predicted values.
+        pred_type : string
+            If 'lhr', returns log hazard ratios, if 'hr' returns
+            hazard ratios, if 'surv' returns the survival function, if
+            'cumhaz' returns the cumulative hazard function.
+
+        Returns
+        -------
+        A bunch containing two fields: `predicted_values` and
+        `standard_errors`.
+
+        Notes
+        -----
+        Standard errors are only returned when predicting the log
+        hazard ratio (pred_type is 'lhr').
+
+        Types `surv` and `cumhaz` require estimation of the cumulative
+        hazard function.
+        """
+
+        pred_type = pred_type.lower()
+        if pred_type not in ["lhr", "hr", "surv", "cumhaz"]:
+            msg = "Type %s not allowed for prediction" % pred_type
+            raise ValueError(msg)
+
+        class bunch:
+            predicted_values = None
+            standard_errors = None
+        ret_val = bunch()
+
+        # Don't do anything with offset here because we want to allow
+        # different offsets to be specified even if exog is the model
+        # exog.
+        exog_provided = True
+        if exog is None:
+            exog = self.exog
+            exog_provided = False
+
+        lhr = np.dot(exog, params)
+        if offset is not None:
+            lhr += offset
+        # Never use self.offset unless we are also using self.exog
+        elif self.offset is not None and not exog_provided:
+            lhr += self.offset
+
+        # Handle lhr and hr prediction first, since they don't make
+        # use of the hazard function.
+
+        if pred_type == "lhr":
+            ret_val.predicted_values = lhr
+            mat = np.dot(exog, cov_params)
+            va = (mat * exog).sum(1)
+            ret_val.standard_errors = np.sqrt(va)
+            return ret_val
+
+        hr = np.exp(lhr)
+
+        if pred_type == "hr":
+            ret_val.predicted_values = hr
+            return ret_val
+
+        # Makes sure endog is defined
+        if endog is None and exog_provided:
+            msg = "If `exog` is provided `endog` must be provided."
+            raise ValueError(msg)
+        # Use model endog if using model exog
+        elif endog is None and not exog_provided:
+            endog = self.endog
+
+        # Make sure strata is defined
+        if strata is None:
+            if exog_provided and self.surv.nstrat > 1:
+                raise ValueError("`strata` must be provided")
+            if self.strata is None:
+                strata = [self.surv.stratum_names[0],] * len(endog)
+            else:
+                strata = self.strata
+
+        cumhaz = np.nan * np.ones(len(endog), dtype=np.float64)
+        stv = np.unique(strata)
+        bhaz = self.baseline_cumulative_hazard_function(params)
+        for stx in stv:
+            ix = np.flatnonzero(strata == stx)
+            func = bhaz[stx]
+            cumhaz[ix] = func(endog[ix]) * hr[ix]
+
+        if pred_type == "cumhaz":
+            ret_val.predicted_values = cumhaz
+
+        elif pred_type == "surv":
+            ret_val.predicted_values = np.exp(-cumhaz)
+
+        return ret_val
+
+    def get_distribution(self, params):
+        """
+        Returns a scipy distribution object corresponding to the
+        distribution of uncensored endog (duration) values for each
+        case.
+
+        Parameters:
+        -----------
+        params : arrayh-like
+            The model proportional hazards model parameters.
+
+        Returns:
+        --------
+        A list of objects of type scipy.stats.distributions.rv_discrete
+
+        Notes:
+        ------
+        The distributions are obtained from a simple discrete estimate
+        of the survivor function that puts all mass on the observed
+        failure times wihtin a stratum.
+        """
+
+        # TODO: this returns a Python list of rv_discrete objects, so
+        # nothing can be vectorized.  It appears that rv_discrete does
+        # not allow vectorization.
+
+        from scipy.stats.distributions import rv_discrete
+
+        surv = self.surv
+        bhaz = self.baseline_cumulative_hazard(params)
+
+        # The arguments to rv_discrete_float, first obtained by
+        # stratum
+        pk, xk = [], []
+
+        for stx in range(self.surv.nstrat):
+
+            exog_s = surv.exog_s[stx]
+
+            linpred = np.dot(exog_s, params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            e_linpred = np.exp(linpred)
+
+            # The unique failure times for this stratum (the support
+            # of the distribution).
+            pts = bhaz[stx][0]
+
+            # The individual cumulative hazards for everyone in this
+            # stratum.
+            ichaz = np.outer(e_linpred, bhaz[stx][1])
+
+            # The individual survival functions.
+            usurv = np.exp(-ichaz)
+            usurv = np.concatenate((usurv, np.zeros((usurv.shape[0], 1))),
+                                   axis=1)
+
+            # The individual survival probability masses.
+            probs = -np.diff(usurv, 1)
+
+            pk.append(probs)
+            xk.append(np.outer(np.ones(probs.shape[0]), pts))
+
+        # Pad to make all strata have the same shape
+        mxc = max([x.shape[1] for x in xk])
+        for k in range(self.surv.nstrat):
+            if xk[k].shape[1] < mxc:
+                xk1 = np.zeros((xk.shape[0], mxc))
+                pk1 = np.zeros((pk.shape[0], mxc))
+                xk1[:, -mxc:] = xk
+                pk1[:, -mxc:] = pk
+                xk[k], pk[k] = xk1, pk1
+
+        xka = np.nan * np.zeros((len(self.endog), mxc), dtype=np.float64)
+        pka = np.ones((len(self.endog), mxc), dtype=np.float64) / mxc
+
+        for stx in range(self.surv.nstrat):
+            ix = self.surv.stratum_rows[stx]
+            xka[ix, :] = xk[stx]
+            pka[ix, :] = pk[stx]
+
+        dist = rv_discrete_float(xka, pka)
+
+        return dist
+
+
+class PHRegResults(base.LikelihoodModelResults):
+    '''
+    Class to contain results of fitting a Cox proportional hazards
+    survival model.
+
+    PHregResults inherits from statsmodels.LikelihoodModelResults
+
+    Parameters
+    ----------
+    See statsmodels.LikelihoodModelResults
+
+    Returns
+    -------
+    **Attributes**
+
+    model : class instance
+        PHreg model instance that called fit.
+    normalized_cov_params : array
+        The sampling covariance matrix of the estimates
+    params : array
+        The coefficients of the fitted model.  Each coefficient is the
+        log hazard ratio corresponding to a 1 unit difference in a
+        single covariate while holding the other covariates fixed.
+    bse : array
+        The standard errors of the fitted parameters.
+
+    See Also
+    --------
+    statsmodels.LikelihoodModelResults
+    '''
+
+    def __init__(self, model, params, cov_params, covariance_type="naive"):
+
+        self.covariance_type = covariance_type
+
+        super(PHRegResults, self).__init__(model, params,
+           normalized_cov_params=cov_params)
+
+    @cache_readonly
+    def standard_errors(self):
+        """
+        Returns the standard errors of the parameter estimates.
+        """
+        return np.sqrt(np.diag(self.cov_params()))
+
+    @cache_readonly
+    def bse(self):
+        """
+        Returns the standard errors of the parameter estimates.
+        """
+        return self.standard_errors
+
+    def get_distribution(self):
+        """
+        Returns a scipy distribution object corresponding to the
+        distribution of uncensored endog (duration) values for each
+        case.
+
+        Returns:
+        --------
+        A list of objects of type scipy.stats.distributions.rv_discrete
+
+        Notes:
+        ------
+        The distributions are obtained from a simple discrete estimate
+        of the survivor function that puts all mass on the observed
+        failure times wihtin a stratum.
+        """
+
+        return self.model.get_distribution(self.params)
+
+
+    def predict(self, endog=None, exog=None, strata=None,
+                offset=None, pred_type="lhr"):
+        """
+        Returns predicted values from the fitted proportional hazards
+        regression model.
+
+        Parameters:
+        -----------
+        params : array-;like
+            The proportional hazards model parameters.
+        endog : array-like
+            Duration (time) values at which the predictions are made.
+            Only used if pred_type is either 'cumhaz' or 'surv'.  If
+            using model `exog`, defaults to model `endog` (time), but
+            may be provided explicitly to make predictions at
+            alternative times.
+        exog : array-like
+            Data to use as `exog` in forming predictions.  If not
+            provided, the `exog` values from the model used to fit the
+            data are used.
+        strata : array-like
+            A vector of stratum values used to form the predictions.
+            Not used (may be 'None') if pred_type is 'lhr' or 'hr'.
+            If `exog` is None, the model stratum values are used.  If
+            `exog` is not None and pred_type is 'surv' or 'cumhaz',
+            stratum values must be provided (unless there is only one
+            stratum).
+        offset : array-like
+            Offset values used to create the predicted values.
+        pred_type : string
+            If 'lhr', returns log hazard ratios, if 'hr' returns
+            hazard ratios, if 'surv' returns the survival function, if
+            'cumhaz' returns the cumulative hazard function.
+
+        Returns
+        -------
+        A bunch containing two fields: `predicted_values` and
+        `standard_errors`.
+
+        Notes
+        -----
+        Standard errors are only returned when predicting the log
+        hazard ratio (pred_type is 'lhr').
+
+        Types `surv` and `cumhaz` require estimation of the cumulative
+        hazard function.
+        """
+
+        return self.model.predict(self.params, self.cov_params(),
+                                  endog, exog, strata, offset,
+                                  pred_type)
+
+    def _group_stats(self, groups):
+        """
+        Descriptive statistics of the groups.
+        """
+        gsize = {}
+        for x in groups:
+            if x not in gsize:
+                gsize[x] = 0
+            gsize[x] += 1
+        gsize = np.asarray(gsize.values())
+        return gsize.min(), gsize.max(), gsize.mean()
+
+    @cache_readonly
+    def weighted_covariate_averages(self):
+        """
+        The average covariate values within the at-risk set at each
+        event time point, weighted by hazard.
+        """
+        return self.model.weighted_covariate_averages(self.params)
+
+    @cache_readonly
+    def score_residuals(self):
+        """
+        A matrix containing the score residuals.
+        """
+        return self.model.score_residuals(self.params)
+
+    @cache_readonly
+    def baseline_cumulative_hazard(self):
+        """
+        A list (corresponding to the strata) containing the baseline
+        cumulative hazard function evaluated at the event points.
+        """
+        return self.model.baseline_cumulative_hazard(self.params)
+
+    @cache_readonly
+    def baseline_cumulative_hazard_function(self):
+        """
+        A list (corresponding to the strata) containing function
+        objects that calculate the cumulative hazard function.
+        """
+        return self.model.baseline_cumulative_hazard_function(self.params)
+
+    @cache_readonly
+    def schoenfeld_residuals(self):
+        """
+        A matrix containing the Schoenfeld residuals.
+
+        Notes
+        -----
+        Schoenfeld residuals for censored observations are set to zero.
+        """
+
+        surv = self.model.surv
+        w_avg = self.weighted_covariate_averages
+
+        # Initialize at NaN since rows that belong to strata with no
+        # events have undefined residuals.
+        sch_resid = np.nan*np.ones(self.model.exog.shape, dtype=np.float64)
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            uft = surv.ufailt[stx]
+            exog_s = surv.exog_s[stx]
+            time_s = surv.time_s[stx]
+            strat_ix = surv.stratum_rows[stx]
+
+            ii = np.searchsorted(uft, time_s)
+
+            # These subjects are censored after the last event in
+            # their stratum, so have empty risk sets and undefined
+            # residuals.
+            jj = np.flatnonzero(ii < len(uft))
+
+            sch_resid[strat_ix[jj], :] = exog_s[jj, :] - w_avg[stx][ii[jj], :]
+
+        jj = np.flatnonzero(self.model.status == 0)
+        sch_resid[jj, :] = np.nan
+
+        return sch_resid
+
+    @cache_readonly
+    def martingale_residuals(self):
+        """
+        The martingale residuals.
+        """
+
+        surv = self.model.surv
+
+        # Initialize at NaN since rows that belong to strata with no
+        # events have undefined residuals.
+        mart_resid = np.nan*np.ones(len(self.model.endog), dtype=np.float64)
+
+        cumhaz_f_list = self.baseline_cumulative_hazard_function
+
+        # Loop over strata
+        for stx in range(surv.nstrat):
+
+            cumhaz_f = cumhaz_f_list[stx]
+
+            exog_s = surv.exog_s[stx]
+            time_s = surv.time_s[stx]
+
+            linpred = np.dot(exog_s, self.params)
+            if surv.offset_s is not None:
+                linpred += surv.offset_s[stx]
+            e_linpred = np.exp(linpred)
+
+            ii = surv.stratum_rows[stx]
+            chaz = cumhaz_f(time_s)
+            mart_resid[ii] = self.model.status[ii] - e_linpred * chaz
+
+        return mart_resid
+
+    def summary(self, yname=None, xname=None, title=None, alpha=.05):
+        """
+        Summarize the proportional hazards regression results.
+
+        Parameters
+        -----------
+        yname : string, optional
+            Default is `y`
+        xname : list of strings, optional
+            Default is `x#` for ## in p the number of regressors
+        title : string, optional
+            Title for the top table. If not None, then this replaces
+            the default title
+        alpha : float
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be
+            printed or converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary
+            results
+        """
+
+        from statsmodels.iolib import summary2
+        from statsmodels.compat.collections import OrderedDict
+        smry = summary2.Summary()
+        float_format = "%8.3f"
+
+        info = OrderedDict()
+        info["Model:"] = "PH Reg"
+        if yname is None:
+            yname = self.model.endog_names
+        info["Dependent variable:"] = yname
+        info["Ties:"] = self.model.ties.capitalize()
+        info["Sample size:"] = str(self.model.surv.n_obs)
+        info["Num. events:"] = str(int(sum(self.model.status)))
+
+        if self.model.groups is not None:
+            mn, mx, avg = self._group_stats(self.model.groups)
+            info["Max. group size:"] = str(mx)
+            info["Min. group size:"] = str(mn)
+            info["Avg. group size:"] = str(avg)
+
+        smry.add_dict(info, align='l', float_format=float_format)
+
+        param = summary2.summary_params(self, alpha=alpha)
+        param = param.rename(columns={"Coef.": "log HR",
+                                      "Std.Err.": "log HR SE"})
+        param.insert(2, "HR", np.exp(param["log HR"]))
+        a = "[%.3f" % (alpha / 2)
+        param.loc[:, a] = np.exp(param.loc[:, a])
+        a = "%.3f]" % (1 - alpha / 2)
+        param.loc[:, a] = np.exp(param.loc[:, a])
+        if xname != None:
+            param.index = xname
+        smry.add_df(param, float_format=float_format)
+        smry.add_title(title=title, results=self)
+        smry.add_text("Confidence intervals are for the hazard ratios")
+
+        if self.model.groups is not None:
+            smry.add_text("Standard errors account for dependence within groups")
+
+        if hasattr(self, "regularized"):
+            smry.add_text("Standard errors do not account for the regularization")
+
+        return smry
+
+class rv_discrete_float(object):
+    """
+    A class representing a collection of discrete distributions.
+
+    Parameters:
+    ----------
+    xk : 2d array-like
+        The support points, should be non-decreasing within each
+        row.
+    pk : 2d array-like
+        The probabilities, should sum to one within each row.
+
+    Notes
+    -----
+    Each row of `xk`, and the corresponding row of `pk` describe a
+    discrete distribution.
+
+    `xk` and `pk` should both be two-dimensional ndarrays.  Each row
+    of `pk` should sum to 1.
+
+    This class is used as a substitute for scipy.distributions.
+    rv_discrete, since that class does not allow non-integer support
+    points, or vectorized operations.
+
+    Only a limited number of methods are implemented here compared to
+    the other scipy distribution classes.
+    """
+
+    def __init__(self, xk, pk):
+
+        self.xk = xk
+        self.pk = pk
+        self.cpk = np.cumsum(self.pk, axis=1)
+
+    def rvs(self):
+        """
+        Returns a random sample from the discrete distribution.
+
+        A vector is returned containing a single draw from each row of
+        `xk`, using the probabilities of the corresponding row of `pk`
+        """
+
+        n = self.xk.shape[0]
+        u = np.random.uniform(size=n)
+
+        ix = (self.cpk < u[:, None]).sum(1)
+        ii = np.arange(n, dtype=np.int32)
+        return self.xk[(ii,ix)]
+
+    def mean(self):
+        """
+        Returns a vector containing the mean values of the discrete
+        distributions.
+
+        A vector is returned containing the mean value of each row of
+        `xk`, using the probabilities in the corresponding row of
+        `pk`.
+        """
+
+        return (self.xk * self.pk).sum(1)
+
+    def var(self):
+        """
+        Returns a vector containing the variances of the discrete
+        distributions.
+
+        A vector is returned containing the variance for each row of
+        `xk`, using the probabilities in the corresponding row of
+        `pk`.
+        """
+
+        mn = self.mean()
+        xkc = self.xk - mn[:, None]
+
+        return (self.pk * (self.xk - xkc)**2).sum(1)
+
+    def std(self):
+        """
+        Returns a vector containing the standard deviations of the
+        discrete distributions.
+
+        A vector is returned containing the standard deviation for
+        each row of `xk`, using the probabilities in the corresponding
+        row of `pk`.
+        """
+
+        return np.sqrt(self.var())
+
+
+def _opt_1d(funcs, start, L1_wt, tol):
+    """
+    Optimize a L1-penalized smooth one-dimensional function of a
+    single variable.
+
+    Parameters:
+    -----------
+    funcs : tuple of functions
+        funcs[0] is the objective function to be minimized.  funcs[1]
+        and funcs[2] are, respectively, the first and second
+        derivatives of the smooth part of funcs[0] (i.e. excluding the
+        L1 penalty).
+    start : real
+        A starting value for the function argument
+    L1_wt : non-negative real
+        The weight for the L1 penalty function.
+    tol : non-negative real
+        A convergence threshold.
+
+    Returns:
+    --------
+    The argmin of the objective function.
+    """
+
+    # TODO: can we detect failures without calling funcs[0] twice?
+
+    x = start
+    f = funcs[0](x)
+
+    b = funcs[1](x)
+    c = funcs[2](x)
+    d = b - c*x
+
+    if L1_wt > np.abs(d):
+        return 0.
+    elif d >= 0:
+        x += (L1_wt - b) / c
+    elif d < 0:
+        x -= (L1_wt + b) / c
+
+    f1 = funcs[0](x)
+
+    # This is an expensive fall-back if the quadratic
+    # approximation is poor and sends us far off-course.
+    if f1 > f + 1e-10:
+        return brent(funcs[0], brack=(x-0.2, x+0.2), tol=tol)
+
+    return x
+
+
diff --git a/setup.cfg b/statsmodels/duration/tests/__init__.py
similarity index 100%
rename from setup.cfg
rename to statsmodels/duration/tests/__init__.py
diff --git a/statsmodels/duration/tests/phreg_gentests.py b/statsmodels/duration/tests/phreg_gentests.py
new file mode 100644
index 00000000000..6563b547453
--- /dev/null
+++ b/statsmodels/duration/tests/phreg_gentests.py
@@ -0,0 +1,60 @@
+import numpy as np
+
+"""
+Generate data sets for testing Cox proportional hazards regression
+models.
+
+After updating the test data sets, use R to run the survival.R script
+to update the R results.
+"""
+
+# The current data may not reflect this seed
+np.random.seed(5234)
+
+# Loop over pairs containing (sample size, number of variables).
+for (n,p) in (20,1), (50,1), (50,2), (100,5), (1000,10):
+
+    exog = np.random.normal(size=(5*n,p))
+    coef = np.linspace(-0.5, 0.5, p)
+    lpred = np.dot(exog, coef)
+    expected_survival_time = np.exp(-lpred)
+
+    # Survival times are exponential
+    survival_time = -np.log(np.random.uniform(size=5*n))
+    survival_time *= expected_survival_time
+
+    # Set this to get a reasonable amount of censoring
+    expected_censoring_time = np.mean(expected_survival_time)
+
+    # Theses are the observation times.
+    censoring_time = -np.log(np.random.uniform(size=5*n))
+    censoring_time *= expected_censoring_time
+
+    # Entry times
+    entry_time = -np.log(np.random.uniform(size=5*n))
+    entry_time *= 0.5*expected_censoring_time
+
+    # 1=failure (death), 0=no failure (no death)
+    status = 1*(survival_time <= censoring_time)
+
+    # The censoring time of the failure time, whichever comes first
+    time = np.where(status==1, survival_time, censoring_time)
+
+    # Round time so that we have ties
+    time = np.around(time, decimals=1)
+
+    # Only take cases where the entry time is before the failure or
+    # censoring time.  Take exactly n such cases.
+    ii = np.flatnonzero(entry_time < time)
+    ii = ii[np.random.permutation(len(ii))[0:n]]
+    status = status[ii]
+    time = time[ii]
+    exog = exog[ii,:]
+    entry_time = entry_time[ii]
+
+    data = np.concatenate((time[:,None], status[:,None],
+                           entry_time[:,None], exog),
+                          axis=1)
+
+    fname = "results/survival_data_%d_%d.csv" % (n, p)
+    np.savetxt(fname, data, fmt="%.5f")
diff --git a/statsmodels/duration/tests/results/__init__.py b/statsmodels/duration/tests/results/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/statsmodels/duration/tests/results/survival_data_1000_10.csv b/statsmodels/duration/tests/results/survival_data_1000_10.csv
new file mode 100644
index 00000000000..4d7be61b9fc
--- /dev/null
+++ b/statsmodels/duration/tests/results/survival_data_1000_10.csv
@@ -0,0 +1,1000 @@
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diff --git a/statsmodels/duration/tests/results/survival_data_100_5.csv b/statsmodels/duration/tests/results/survival_data_100_5.csv
new file mode 100644
index 00000000000..f81d97f3017
--- /dev/null
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+1.80000 1.00000 0.15619 0.29362 -1.22544 -0.09859 0.02863 -1.39324
diff --git a/statsmodels/duration/tests/results/survival_data_20_1.csv b/statsmodels/duration/tests/results/survival_data_20_1.csv
new file mode 100644
index 00000000000..8b6910ece2e
--- /dev/null
+++ b/statsmodels/duration/tests/results/survival_data_20_1.csv
@@ -0,0 +1,20 @@
+0.70000 1.00000 0.17884 -0.80807
+0.60000 0.00000 0.23284 -0.10099
+1.40000 1.00000 0.07038 -0.34916
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+1.20000 0.00000 0.27814 1.15109
+0.30000 0.00000 0.26536 -0.07403
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+0.40000 1.00000 0.19745 -0.64876
+0.30000 1.00000 0.07634 0.76335
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+0.20000 0.00000 0.07805 0.39894
+1.30000 1.00000 0.28314 0.19641
+0.70000 0.00000 0.29323 0.70037
diff --git a/statsmodels/duration/tests/results/survival_data_50_1.csv b/statsmodels/duration/tests/results/survival_data_50_1.csv
new file mode 100644
index 00000000000..0914f4f31a9
--- /dev/null
+++ b/statsmodels/duration/tests/results/survival_data_50_1.csv
@@ -0,0 +1,50 @@
+1.10000 0.00000 0.34994 -0.01447
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+0.30000 0.00000 0.10249 0.63181
+0.40000 0.00000 0.15787 -0.86227
+1.10000 1.00000 0.40295 0.73249
diff --git a/statsmodels/duration/tests/results/survival_data_50_2.csv b/statsmodels/duration/tests/results/survival_data_50_2.csv
new file mode 100644
index 00000000000..631c92789a8
--- /dev/null
+++ b/statsmodels/duration/tests/results/survival_data_50_2.csv
@@ -0,0 +1,50 @@
+1.00000 1.00000 0.03528 -0.38315 -1.11646
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+1.20000 0.00000 0.59698 0.21795 -2.65967
+1.10000 0.00000 0.90747 1.10303 2.21828
+0.20000 0.00000 0.15655 1.50321 0.88795
diff --git a/statsmodels/duration/tests/survival.R b/statsmodels/duration/tests/survival.R
new file mode 100644
index 00000000000..82de69e90f7
--- /dev/null
+++ b/statsmodels/duration/tests/survival.R
@@ -0,0 +1,68 @@
+library(survival)
+library(R2nparray)
+
+ixd = list(c(20,1), c(50,1), c(50,2), c(100,5), c(1000,10))
+
+res = list()
+
+for (ix in ixd) {
+    fname = sprintf("results/survival_data_%d_%d.csv", ix[1], ix[2])
+    data = read.table(fname)
+
+    time = data[,1]
+    status = data[,2]
+    entry = data[,3]
+    exog = data[,4:dim(data)[2]]
+    exog = as.matrix(exog)
+    n = dim(exog)[1]
+    p = dim(exog)[2]
+
+    # Needs to match the kronecker statement in test_phreg.py
+    strata = kronecker(seq(5), array(1, n/5))
+
+    for (ties in c("breslow", "efron")) {
+
+        ti = substr(ties, 1, 3)
+
+        # Base model
+        surv = Surv(time, status)
+        md = coxph(surv ~ exog, ties=ties)
+        tag = sprintf("%d_%d_%s", n, p, ti)
+        res[[sprintf("coef_%s", tag)]] = md$coef
+        res[[sprintf("se_%s", tag)]] = sqrt(diag(md$var))
+        #bhaz = basehaz(md)
+        bhaz = survfit(md, type="aalen")
+        #bhaz = survfit(md, type="efron")
+        res[[sprintf("time_%s", tag)]] = bhaz$time
+        res[[sprintf("hazard_%s", tag)]] = -log(bhaz$surv)
+
+        # With entry time
+        surv = Surv(entry, time, status)
+        md = coxph(surv ~ exog, ties=ties)
+        tag = sprintf("%d_%d_et_%s", n, p, ti)
+        res[[sprintf("coef_%s", tag)]] = md$coef
+        res[[sprintf("se_%s", tag)]] = sqrt(diag(md$var))
+        res[[sprintf("time_%s", tag)]] = c(0)
+        res[[sprintf("hazard_%s", tag)]] = c(0)
+
+        # With strata
+        surv = Surv(time, status)
+        md = coxph(surv ~ exog + strata(strata), ties=ties)
+        tag = sprintf("%d_%d_st_%s", n, p, ti)
+        res[[sprintf("coef_%s", tag)]] = md$coef
+        res[[sprintf("se_%s", tag)]] = sqrt(diag(md$var))
+        res[[sprintf("time_%s", tag)]] = c(0)
+        res[[sprintf("hazard_%s", tag)]] = c(0)
+
+        # With entry time and strata
+        surv = Surv(entry, time, status)
+        md = coxph(surv ~ exog + strata(strata), ties=ties)
+        tag = sprintf("%d_%d_et_st_%s", n, p, ti)
+        res[[sprintf("coef_%s", tag)]] = md$coef
+        res[[sprintf("se_%s", tag)]] = sqrt(diag(md$var))
+        res[[sprintf("time_%s", tag)]] = c(0)
+        res[[sprintf("hazard_%s", tag)]] = c(0)
+    }
+}
+
+R2nparray(res, fname="survival_r_results.py")
diff --git a/statsmodels/duration/tests/survival_enet_r_results.py b/statsmodels/duration/tests/survival_enet_r_results.py
new file mode 100644
index 00000000000..f1bfba0fb1b
--- /dev/null
+++ b/statsmodels/duration/tests/survival_enet_r_results.py
@@ -0,0 +1,10 @@
+import numpy as np
+
+coef_50_2_0 = np.array([-0.6748149,0.5219471])
+
+coef_50_2_1 = np.array([-0.3464841,0.211115])
+
+coef_100_5_0 = np.array([-0.4839566,-0.3130558,-0.1239565,0.3466049,0.5827503])
+
+coef_100_5_1 = np.array([-0.1314948,0,0,0.0324285,0.2364489])
+
diff --git a/statsmodels/duration/tests/survival_r_results.py b/statsmodels/duration/tests/survival_r_results.py
new file mode 100644
index 00000000000..ce4aa4e46a0
--- /dev/null
+++ b/statsmodels/duration/tests/survival_r_results.py
@@ -0,0 +1,321 @@
+import numpy as np
+
+coef_20_1_bre = np.array([-0.9185611])
+
+se_20_1_bre = np.array([0.4706831])
+
+time_20_1_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.1,1.2,1.3,1.4,1.5])
+
+hazard_20_1_bre = np.array([0,0,0.04139181,0.1755379,0.3121216,0.3121216,0.4263121,0.6196358,0.6196358,0.6196358,0.909556,1.31083,1.31083])
+
+coef_20_1_et_bre = np.array([-0.8907007])
+
+se_20_1_et_bre = np.array([0.4683384])
+
+time_20_1_et_bre = np.array([0])
+
+hazard_20_1_et_bre = np.array([0])
+
+coef_20_1_st_bre = np.array([-0.5766809])
+
+se_20_1_st_bre = np.array([0.4418918])
+
+time_20_1_st_bre = np.array([0])
+
+hazard_20_1_st_bre = np.array([0])
+
+coef_20_1_et_st_bre = np.array([-0.5785683])
+
+se_20_1_et_st_bre = np.array([0.4388437])
+
+time_20_1_et_st_bre = np.array([0])
+
+hazard_20_1_et_st_bre = np.array([0])
+
+coef_20_1_efr = np.array([-0.9975319])
+
+se_20_1_efr = np.array([0.4792421])
+
+time_20_1_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.1,1.2,1.3,1.4,1.5])
+
+hazard_20_1_efr = np.array([0,0,0.03934634,0.1663316,0.2986427,0.2986427,0.4119189,0.6077373,0.6077373,0.6077373,0.8933041,1.285732,1.285732])
+
+coef_20_1_et_efr = np.array([-0.9679541])
+
+se_20_1_et_efr = np.array([0.4766406])
+
+time_20_1_et_efr = np.array([0])
+
+hazard_20_1_et_efr = np.array([0])
+
+coef_20_1_st_efr = np.array([-0.6345294])
+
+se_20_1_st_efr = np.array([0.4455952])
+
+time_20_1_st_efr = np.array([0])
+
+hazard_20_1_st_efr = np.array([0])
+
+coef_20_1_et_st_efr = np.array([-0.6355622])
+
+se_20_1_et_st_efr = np.array([0.4423104])
+
+time_20_1_et_st_efr = np.array([0])
+
+hazard_20_1_et_st_efr = np.array([0])
+
+coef_50_1_bre = np.array([-0.6761247])
+
+se_50_1_bre = np.array([0.25133])
+
+time_50_1_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.5,1.6,1.7,1.8,1.9,2.4,2.8])
+
+hazard_50_1_bre = np.array([0,0.04895521,0.08457461,0.2073863,0.2382473,0.2793018,0.3271622,0.3842953,0.3842953,0.5310807,0.6360276,0.7648251,0.7648251,0.9294298,0.9294298,0.9294298,1.206438,1.555569,1.555569])
+
+coef_50_1_et_bre = np.array([-0.6492871])
+
+se_50_1_et_bre = np.array([0.2542493])
+
+time_50_1_et_bre = np.array([0])
+
+hazard_50_1_et_bre = np.array([0])
+
+coef_50_1_st_bre = np.array([-0.7051135])
+
+se_50_1_st_bre = np.array([0.2852093])
+
+time_50_1_st_bre = np.array([0])
+
+hazard_50_1_st_bre = np.array([0])
+
+coef_50_1_et_st_bre = np.array([-0.8672546])
+
+se_50_1_et_st_bre = np.array([0.3443235])
+
+time_50_1_et_st_bre = np.array([0])
+
+hazard_50_1_et_st_bre = np.array([0])
+
+coef_50_1_efr = np.array([-0.7119322])
+
+se_50_1_efr = np.array([0.2533563])
+
+time_50_1_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.5,1.6,1.7,1.8,1.9,2.4,2.8])
+
+hazard_50_1_efr = np.array([0,0.04773902,0.08238731,0.2022993,0.2327053,0.2736316,0.3215519,0.3787123,0.3787123,0.526184,0.6323073,0.7627338,0.7627338,0.9288858,0.9288858,0.9288858,1.206835,1.556054,1.556054])
+
+coef_50_1_et_efr = np.array([-0.7103063])
+
+se_50_1_et_efr = np.array([0.2598129])
+
+time_50_1_et_efr = np.array([0])
+
+hazard_50_1_et_efr = np.array([0])
+
+coef_50_1_st_efr = np.array([-0.7417904])
+
+se_50_1_st_efr = np.array([0.2846437])
+
+time_50_1_st_efr = np.array([0])
+
+hazard_50_1_st_efr = np.array([0])
+
+coef_50_1_et_st_efr = np.array([-0.9276112])
+
+se_50_1_et_st_efr = np.array([0.3462638])
+
+time_50_1_et_st_efr = np.array([0])
+
+hazard_50_1_et_st_efr = np.array([0])
+
+coef_50_2_bre = np.array([-0.5935189,0.5035724])
+
+se_50_2_bre = np.array([0.2172841,0.2399933])
+
+time_50_2_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.9,2.7,2.9])
+
+hazard_50_2_bre = np.array([0.02695812,0.09162381,0.1309537,0.1768423,0.2033353,0.2033353,0.3083449,0.3547287,0.4076453,0.4761318,0.5579718,0.7610905,0.918962,0.918962,1.136173,1.605757,2.457676,2.457676])
+
+coef_50_2_et_bre = np.array([-0.4001465,0.4415933])
+
+se_50_2_et_bre = np.array([0.1992302,0.2525949])
+
+time_50_2_et_bre = np.array([0])
+
+hazard_50_2_et_bre = np.array([0])
+
+coef_50_2_st_bre = np.array([-0.6574891,0.4416079])
+
+se_50_2_st_bre = np.array([0.2753398,0.269458])
+
+time_50_2_st_bre = np.array([0])
+
+hazard_50_2_st_bre = np.array([0])
+
+coef_50_2_et_st_bre = np.array([-0.3607069,0.2731982])
+
+se_50_2_et_st_bre = np.array([0.255415,0.306942])
+
+time_50_2_et_st_bre = np.array([0])
+
+hazard_50_2_et_st_bre = np.array([0])
+
+coef_50_2_efr = np.array([-0.6107485,0.5309737])
+
+se_50_2_efr = np.array([0.2177713,0.2440535])
+
+time_50_2_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.9,2.7,2.9])
+
+hazard_50_2_efr = np.array([0.02610571,0.08933637,0.1279094,0.1731699,0.19933,0.19933,0.303598,0.3497025,0.4023939,0.4706978,0.5519237,0.7545023,0.9129989,0.9129989,1.13186,1.60574,2.472615,2.472615])
+
+coef_50_2_et_efr = np.array([-0.4092002,0.4871344])
+
+se_50_2_et_efr = np.array([0.1968905,0.2608527])
+
+time_50_2_et_efr = np.array([0])
+
+hazard_50_2_et_efr = np.array([0])
+
+coef_50_2_st_efr = np.array([-0.6631286,0.4663285])
+
+se_50_2_st_efr = np.array([0.2748224,0.273603])
+
+time_50_2_st_efr = np.array([0])
+
+hazard_50_2_st_efr = np.array([0])
+
+coef_50_2_et_st_efr = np.array([-0.3656059,0.2943912])
+
+se_50_2_et_st_efr = np.array([0.2540752,0.3124632])
+
+time_50_2_et_st_efr = np.array([0])
+
+hazard_50_2_et_st_efr = np.array([0])
+
+coef_100_5_bre = np.array([-0.529776,-0.2916374,-0.1205425,0.3493476,0.6034305])
+
+se_100_5_bre = np.array([0.1789305,0.1482505,0.1347422,0.1528205,0.1647927])
+
+time_100_5_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.5,2.8,3.2,3.3])
+
+hazard_100_5_bre = np.array([0.02558588,0.05608812,0.1087773,0.1451098,0.1896703,0.2235791,0.3127521,0.3355107,0.439452,0.504983,0.5431706,0.5841462,0.5841462,0.5841462,0.6916466,0.7540191,0.8298704,1.027876,1.170335,1.379306,1.648758,1.943177,1.943177,1.943177,4.727101])
+
+coef_100_5_et_bre = np.array([-0.4000784,-0.1790941,-0.1378969,0.3288529,0.533246])
+
+se_100_5_et_bre = np.array([0.1745655,0.1513545,0.1393968,0.1487803,0.1686992])
+
+time_100_5_et_bre = np.array([0])
+
+hazard_100_5_et_bre = np.array([0])
+
+coef_100_5_st_bre = np.array([-0.53019,-0.3225739,-0.1241568,0.3246598,0.6196859])
+
+se_100_5_st_bre = np.array([0.1954581,0.1602811,0.1470644,0.17121,0.1784115])
+
+time_100_5_st_bre = np.array([0])
+
+hazard_100_5_st_bre = np.array([0])
+
+coef_100_5_et_st_bre = np.array([-0.3977171,-0.2166136,-0.1387623,0.3251726,0.5664705])
+
+se_100_5_et_st_bre = np.array([0.1951054,0.1707925,0.1501968,0.1699932,0.1843428])
+
+time_100_5_et_st_bre = np.array([0])
+
+hazard_100_5_et_st_bre = np.array([0])
+
+coef_100_5_efr = np.array([-0.5641909,-0.3233021,-0.1234858,0.3712328,0.6421963])
+
+se_100_5_efr = np.array([0.1804027,0.1496253,0.1338531,0.1529832,0.1670848])
+
+time_100_5_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.5,2.8,3.2,3.3])
+
+hazard_100_5_efr = np.array([0.02393412,0.05276399,0.1028432,0.1383859,0.1823461,0.2158107,0.3037825,0.3264864,0.4306648,0.4964367,0.5348595,0.5760305,0.5760305,0.5760305,0.6842238,0.7468135,0.8228841,1.023195,1.166635,1.379361,1.652898,1.950119,1.950119,1.950119,4.910635])
+
+coef_100_5_et_efr = np.array([-0.4338666,-0.2140139,-0.1397387,0.3535993,0.5768645])
+
+se_100_5_et_efr = np.array([0.1756485,0.1527244,0.138298,0.1488427,0.1716654])
+
+time_100_5_et_efr = np.array([0])
+
+hazard_100_5_et_efr = np.array([0])
+
+coef_100_5_st_efr = np.array([-0.5530876,-0.3331652,-0.128381,0.3503472,0.6397813])
+
+se_100_5_st_efr = np.array([0.1969338,0.1614976,0.1464088,0.171299,0.1800787])
+
+time_100_5_st_efr = np.array([0])
+
+hazard_100_5_st_efr = np.array([0])
+
+coef_100_5_et_st_efr = np.array([-0.421153,-0.2350069,-0.1433638,0.3538863,0.5934568])
+
+se_100_5_et_st_efr = np.array([0.1961729,0.1724719,0.1492979,0.170464,0.1861849])
+
+time_100_5_et_st_efr = np.array([0])
+
+hazard_100_5_et_st_efr = np.array([0])
+
+coef_1000_10_bre = np.array([-0.4699279,-0.464557,-0.308411,-0.2158298,-0.09048563,0.09359662,0.112588,0.3343705,0.3480601,0.5634985])
+
+se_1000_10_bre = np.array([0.04722914,0.04785291,0.04503528,0.04586872,0.04429793,0.0446141,0.04139944,0.04464292,0.04559903,0.04864393])
+
+time_1000_10_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4,4.1,4.2,4.3,4.4,4.6,4.8,4.9,5,5.1,5.2,5.7,5.8,5.9,6.9])
+
+hazard_1000_10_bre = np.array([0.01610374,0.04853538,0.08984849,0.1311329,0.168397,0.2230488,0.2755388,0.3312606,0.3668702,0.4146558,0.477935,0.5290705,0.5831775,0.6503129,0.7113068,0.7830385,0.8361717,0.8910061,0.9615944,1.024011,1.113399,1.165349,1.239827,1.352902,1.409548,1.53197,1.601843,1.682158,1.714907,1.751564,1.790898,1.790898,1.83393,1.83393,1.936055,1.992303,2.050778,2.118776,2.263056,2.504999,2.739343,2.895514,3.090349,3.090349,3.391772,3.728142,4.152769,4.152769,4.152769,4.725957,4.725957,5.69653,5.69653,5.69653])
+
+coef_1000_10_et_bre = np.array([-0.410889,-0.3929442,-0.2975845,-0.1851533,-0.0918359,0.1011997,0.106735,0.2899179,0.3220672,0.5069589])
+
+se_1000_10_et_bre = np.array([0.04696754,0.04732169,0.04537707,0.04605371,0.04365232,0.04450021,0.04252475,0.04482007,0.04562374,0.04859727])
+
+time_1000_10_et_bre = np.array([0])
+
+hazard_1000_10_et_bre = np.array([0])
+
+coef_1000_10_st_bre = np.array([-0.471015,-0.4766859,-0.3070839,-0.2091938,-0.09190845,0.0964942,0.1138269,0.3307131,0.3543551,0.562492])
+
+se_1000_10_st_bre = np.array([0.04814778,0.04841938,0.04572291,0.04641227,0.04502525,0.04517603,0.04203737,0.04524356,0.04635037,0.04920866])
+
+time_1000_10_st_bre = np.array([0])
+
+hazard_1000_10_st_bre = np.array([0])
+
+coef_1000_10_et_st_bre = np.array([-0.4165849,-0.4073504,-0.2980959,-0.1765194,-0.09152798,0.1013213,0.1009838,0.2859668,0.3247608,0.5044448])
+
+se_1000_10_et_st_bre = np.array([0.04809818,0.04809499,0.0460829,0.04679922,0.0445294,0.04514045,0.04339298,0.04580591,0.04652447,0.04920744])
+
+time_1000_10_et_st_bre = np.array([0])
+
+hazard_1000_10_et_st_bre = np.array([0])
+
+coef_1000_10_efr = np.array([-0.4894399,-0.4839746,-0.3227769,-0.2261293,-0.09318482,0.09767154,0.1173205,0.3493732,0.3640146,0.5879749])
+
+se_1000_10_efr = np.array([0.0474181,0.04811855,0.04507655,0.04603044,0.04440409,0.04478202,0.04136728,0.04473343,0.045768,0.04891375])
+
+time_1000_10_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4,4.1,4.2,4.3,4.4,4.6,4.8,4.9,5,5.1,5.2,5.7,5.8,5.9,6.9])
+
+hazard_1000_10_efr = np.array([0.01549698,0.04680035,0.08682564,0.1269429,0.1632388,0.2167291,0.2682311,0.3231316,0.3582936,0.4054892,0.4681098,0.5188697,0.5727059,0.639571,0.7003012,0.7718979,0.825053,0.880063,0.950935,1.013828,1.103903,1.156314,1.231707,1.346235,1.40359,1.527475,1.598231,1.6795,1.712779,1.750227,1.790455,1.790455,1.834455,1.834455,1.938997,1.996804,2.056859,2.126816,2.275217,2.524027,2.76669,2.929268,3.13247,3.13247,3.448515,3.80143,4.249649,4.249649,4.249649,4.851365,4.851365,5.877307,5.877307,5.877307])
+
+coef_1000_10_et_efr = np.array([-0.4373066,-0.4131901,-0.3177637,-0.1978493,-0.09679451,0.1092037,0.1136069,0.3088907,0.3442007,0.5394121])
+
+se_1000_10_et_efr = np.array([0.04716041,0.04755342,0.04546713,0.04627802,0.04376583,0.04474868,0.04259991,0.04491564,0.04589027,0.04890847])
+
+time_1000_10_et_efr = np.array([0])
+
+hazard_1000_10_et_efr = np.array([0])
+
+coef_1000_10_st_efr = np.array([-0.4911117,-0.4960756,-0.3226152,-0.220949,-0.09478141,0.1015735,0.1195524,0.3446977,0.3695904,0.5878576])
+
+se_1000_10_st_efr = np.array([0.04833676,0.04868554,0.04578407,0.04661755,0.04518267,0.04537135,0.04202183,0.04531266,0.0464931,0.04949831])
+
+time_1000_10_st_efr = np.array([0])
+
+hazard_1000_10_st_efr = np.array([0])
+
+coef_1000_10_et_st_efr = np.array([-0.444355,-0.4283278,-0.3198815,-0.1901781,-0.09727039,0.1106191,0.1092104,0.3034778,0.3451699,0.5382381])
+
+se_1000_10_et_st_efr = np.array([0.04830664,0.04833619,0.04617371,0.04706401,0.04472699,0.0454208,0.04350539,0.04588588,0.04675675,0.04950987])
+
+time_1000_10_et_st_efr = np.array([0])
+
+hazard_1000_10_et_st_efr = np.array([0])
diff --git a/statsmodels/duration/tests/survival_regularized.R b/statsmodels/duration/tests/survival_regularized.R
new file mode 100644
index 00000000000..da3fb5666af
--- /dev/null
+++ b/statsmodels/duration/tests/survival_regularized.R
@@ -0,0 +1,35 @@
+library(glmnet)
+library(survival)
+library(R2nparray)
+
+# List containing (sample size, # covariates) for each data set
+ixd = list(c(50,2), c(100,5))
+
+res = list()
+
+for (ix in ixd) {
+
+    fname = sprintf("results/survival_data_%d_%d.csv", ix[1], ix[2])
+    data = read.table(fname)
+
+    time = data[,1]
+    status = data[,2]
+    entry = data[,3]
+    exog = data[,4:dim(data)[2]]
+    exog = as.matrix(exog)
+    n = dim(exog)[1]
+    p = dim(exog)[2]
+
+    for (k in 1:p) {
+        exog[,k] = (exog[,k] - mean(exog[,k])) / sd(exog[,k])
+    }
+
+    surv = Surv(time, status)
+    md = glmnet(exog, surv, family="cox", lambda=c(0, 0.1))
+    #md1 = coxph(surv ~ exog, ties="breslow")
+    tag = sprintf("%d_%d", n, p)
+    res[[sprintf("coef_%s_0", tag)]] = as.vector(coef(md, s=0))
+    res[[sprintf("coef_%s_1", tag)]] = as.vector(coef(md, s=0.1))
+}
+
+R2nparray(res, fname="survival_enet_r_results.py")
diff --git a/statsmodels/duration/tests/test_phreg.py b/statsmodels/duration/tests/test_phreg.py
new file mode 100644
index 00000000000..dc5854d13d1
--- /dev/null
+++ b/statsmodels/duration/tests/test_phreg.py
@@ -0,0 +1,283 @@
+import os
+import numpy as np
+from statsmodels.duration.hazard_regression import PHReg
+from numpy.testing import assert_almost_equal
+
+# TODO: Include some corner cases: data sets with empty strata, strata
+#      with no events, entry times after censoring times, etc.
+
+# All the R results
+from . import survival_r_results
+from . import survival_enet_r_results
+
+"""
+Tests of PHReg against R coxph.
+
+Tests include entry times and stratification.
+
+phreg_gentests.py generates the test data sets and puts them into the
+results folder.
+
+survival.R runs R on all the test data sets and constructs the
+survival_r_results module.
+"""
+
+# Arguments passed to the PHReg fit method.
+args = {"method": "bfgs", "disp": 0}
+
+def get_results(n, p, ext, ties):
+    if ext is None:
+        coef_name = "coef_%d_%d_%s" % (n, p, ties)
+        se_name = "se_%d_%d_%s" % (n, p, ties)
+        time_name = "time_%d_%d_%s" % (n, p, ties)
+        hazard_name = "hazard_%d_%d_%s" % (n, p, ties)
+    else:
+        coef_name = "coef_%d_%d_%s_%s" % (n, p, ext, ties)
+        se_name = "se_%d_%d_%s_%s" % (n, p, ext, ties)
+        time_name = "time_%d_%d_%s_%s" % (n, p, ext, ties)
+        hazard_name = "hazard_%d_%d_%s_%s" % (n, p, ext, ties)
+    coef = getattr(survival_r_results, coef_name)
+    se = getattr(survival_r_results, se_name)
+    time = getattr(survival_r_results, time_name)
+    hazard = getattr(survival_r_results, hazard_name)
+    return coef, se, time, hazard
+
+class TestPHReg(object):
+
+    # Load a data file from the results directory
+    def load_file(self, fname):
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        data = np.genfromtxt(os.path.join(cur_dir, 'results', fname),
+                             delimiter=" ")
+        time = data[:,0]
+        status = data[:,1]
+        entry = data[:,2]
+        exog = data[:,3:]
+
+        return time, status, entry, exog
+
+
+    # Run a single test against R output
+    def do1(self, fname, ties, entry_f, strata_f):
+
+        # Read the test data.
+        time, status, entry, exog = self.load_file(fname)
+        n = len(time)
+
+        vs = fname.split("_")
+        n = int(vs[2])
+        p = int(vs[3].split(".")[0])
+        ties1 = ties[0:3]
+
+        # Needs to match the kronecker statement in survival.R
+        strata = np.kron(range(5), np.ones(n/5))
+
+        # No stratification or entry times
+        mod = PHReg(time, exog, status, ties=ties)
+        phrb = mod.fit(**args)
+        coef_r, se_r, time_r, hazard_r = get_results(n, p, None, ties1)
+        assert_almost_equal(phrb.params, coef_r, decimal=4)
+        assert_almost_equal(phrb.bse, se_r, decimal=4)
+        #time_h, cumhaz, surv = phrb.baseline_hazard[0]
+
+        # Entry times but no stratification
+        phrb = PHReg(time, exog, status, entry=entry,
+                     ties=ties).fit(**args)
+        coef, se, time_r, hazard_r = get_results(n, p, "et", ties1)
+        assert_almost_equal(phrb.params, coef, decimal=4)
+        assert_almost_equal(phrb.bse, se, decimal=4)
+
+        # Stratification but no entry times
+        phrb = PHReg(time, exog, status, strata=strata,
+                      ties=ties).fit(**args)
+        coef, se, time_r, hazard_r = get_results(n, p, "st", ties1)
+        assert_almost_equal(phrb.params, coef, decimal=4)
+        assert_almost_equal(phrb.bse, se, decimal=4)
+
+        # Stratification and entry times
+        phrb = PHReg(time, exog, status, entry=entry,
+                     strata=strata, ties=ties).fit(**args)
+        coef, se, time_r, hazard_r = get_results(n, p, "et_st", ties1)
+        assert_almost_equal(phrb.params, coef, decimal=4)
+        assert_almost_equal(phrb.bse, se, decimal=4)
+
+
+    # Run all the tests
+    def test_r(self):
+
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        rdir = os.path.join(cur_dir, 'results')
+        fnames = os.listdir(rdir)
+        fnames = [x for x in fnames if x.startswith("survival")
+                  and x.endswith(".csv")]
+
+        for fname in fnames:
+            for ties in "breslow","efron":
+                for entry_f in False,True:
+                    for strata_f in False,True:
+                        yield (self.do1, fname, ties, entry_f,
+                               strata_f)
+
+    def test_missing(self):
+
+        np.random.seed(34234)
+        time = 50 * np.random.uniform(size=200)
+        status = np.random.randint(0, 2, 200).astype(np.float64)
+        exog = np.random.normal(size=(200,4))
+
+        time[0:5] = np.nan
+        status[5:10] = np.nan
+        exog[10:15,:] = np.nan
+
+        md = PHReg(time, exog, status, missing='drop')
+        assert(len(md.endog) == 185)
+        assert(len(md.status) == 185)
+        assert(all(md.exog.shape == np.r_[185,4]))
+
+    def test_formula(self):
+        #Smoke test for import only
+        from statsmodels.api import PHReg
+        from statsmodels.formula.api import phreg
+
+    def test_offset(self):
+
+        np.random.seed(34234)
+        time = 50 * np.random.uniform(size=200)
+        status = np.random.randint(0, 2, 200).astype(np.float64)
+        exog = np.random.normal(size=(200,4))
+
+        mod1 = PHReg(time, exog, status)
+        rslt1 = mod1.fit()
+        offset = exog[:,0] * rslt1.params[0]
+        exog = exog[:, 1:]
+
+        mod2 = PHReg(time, exog, status, offset=offset)
+        rslt2 = mod2.fit()
+
+        assert_almost_equal(rslt2.params, rslt1.params[1:])
+
+    def test_post_estimation(self):
+        # All regression tests
+        np.random.seed(34234)
+        time = 50 * np.random.uniform(size=200)
+        status = np.random.randint(0, 2, 200).astype(np.float64)
+        exog = np.random.normal(size=(200,4))
+
+        mod = PHReg(time, exog, status)
+        rslt = mod.fit()
+        mart_resid = rslt.martingale_residuals
+        assert_almost_equal(np.abs(mart_resid).sum(), 120.72475743348433)
+
+        w_avg = rslt.weighted_covariate_averages
+        assert_almost_equal(np.abs(w_avg[0]).sum(0),
+               np.r_[7.31008415, 9.77608674,10.89515885, 13.1106801])
+
+        bc_haz = rslt.baseline_cumulative_hazard
+        v = [np.mean(np.abs(x)) for x in bc_haz[0]]
+        w = np.r_[23.482841556421608, 0.44149255358417017,
+                  0.68660114081275281]
+        assert_almost_equal(v, w)
+
+        score_resid = rslt.score_residuals
+        v = np.r_[ 0.50924792, 0.4533952, 0.4876718, 0.5441128]
+        w = np.abs(score_resid).mean(0)
+        assert_almost_equal(v, w)
+
+        groups = np.random.randint(0, 3, 200)
+        mod = PHReg(time, exog, status)
+        rslt = mod.fit(groups=groups)
+        robust_cov = rslt.cov_params()
+        v = [0.00513432, 0.01278423, 0.00810427, 0.00293147]
+        w = np.abs(robust_cov).mean(0)
+        assert_almost_equal(v, w)
+
+        s_resid = rslt.schoenfeld_residuals
+        ii = np.flatnonzero(np.isfinite(s_resid).all(1))
+        s_resid = s_resid[ii, :]
+        v = np.r_[0.85154336, 0.72993748, 0.73758071, 0.78599333]
+        assert_almost_equal(np.abs(s_resid).mean(0), v)
+
+    def test_summary(self):
+        # smoke test
+        np.random.seed(34234)
+        time = 50 * np.random.uniform(size=200)
+        status = np.random.randint(0, 2, 200).astype(np.float64)
+        exog = np.random.normal(size=(200,4))
+
+        mod = PHReg(time, exog, status)
+        rslt = mod.fit()
+        rslt.summary()
+
+    def test_predict(self):
+        # All smoke tests. We should be able to convert the lhr and hr
+        # tests into real tests against R.  There are many options to
+        # this function that may interact in complicated ways.  Only a
+        # few key combinations are tested here.
+        np.random.seed(34234)
+        endog = 50 * np.random.uniform(size=200)
+        status = np.random.randint(0, 2, 200).astype(np.float64)
+        exog = np.random.normal(size=(200,4))
+
+        mod = PHReg(endog, exog, status)
+        rslt = mod.fit()
+        rslt.predict()
+        for pred_type in 'lhr', 'hr', 'cumhaz', 'surv':
+            rslt.predict(pred_type=pred_type)
+            rslt.predict(endog=endog[0:10], pred_type=pred_type)
+            rslt.predict(endog=endog[0:10], exog=exog[0:10,:],
+                         pred_type=pred_type)
+
+    def test_get_distribution(self):
+        # Smoke test
+        np.random.seed(34234)
+        exog = np.random.normal(size=(200, 2))
+        lin_pred = exog.sum(1)
+        elin_pred = np.exp(-lin_pred)
+        time = -elin_pred * np.log(np.random.uniform(size=200))
+
+        mod = PHReg(time, exog)
+        rslt = mod.fit()
+
+        dist = rslt.get_distribution()
+
+        fitted_means = dist.mean()
+        true_means = elin_pred
+        fitted_var = dist.var()
+        fitted_sd = dist.std()
+        sample = dist.rvs()
+
+    def test_fit_regularized(self):
+
+        # Data set sizes
+        for n,p in (50,2),(100,5):
+
+            # Penalty weights
+            for js,s in enumerate([0,0.1]):
+
+                coef_name = "coef_%d_%d_%d" % (n, p, js)
+                coef = getattr(survival_enet_r_results, coef_name)
+
+                fname = "survival_data_%d_%d.csv" % (n, p)
+                time, status, entry, exog = self.load_file(fname)
+
+                exog -= exog.mean(0)
+                exog /= exog.std(0, ddof=1)
+
+                mod = PHReg(time, exog, status=status, ties='breslow')
+                rslt = mod.fit_regularized(alpha=s)
+
+                # The agreement isn't very high, the issue may be on
+                # their side.  They seem to use some approximations
+                # that we are not using.
+                assert_almost_equal(rslt.params, coef, decimal=1)
+
+                # Smoke test for summary
+                smry = rslt.summary()
+
+
+if  __name__=="__main__":
+
+    import nose
+
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
+                   exit=False)
diff --git a/statsmodels/emplike/aft_el.py b/statsmodels/emplike/aft_el.py
index cee8b22fdd9..26823e9e51c 100644
--- a/statsmodels/emplike/aft_el.py
+++ b/statsmodels/emplike/aft_el.py
@@ -29,14 +29,14 @@
 """
 
 import numpy as np
-from statsmodels.api import OLS, WLS, add_constant
+from statsmodels.regression.linear_model import OLS, WLS
+from statsmodels.tools import add_constant
 #from elregress import ElReg
 from scipy import optimize
 from scipy.stats import chi2
-from descriptive import _OptFuncts
-# ^ this will change when descriptive gets merged
+from .descriptive import _OptFuncts
 import warnings
-
+from statsmodels.tools.sm_exceptions import IterationLimitWarning
 
 class OptAFT(_OptFuncts):
     """
@@ -55,7 +55,7 @@ class OptAFT(_OptFuncts):
         likelihood of a parameter vector.
 
     """
-    def __init__(_OptFuncts):
+    def __init__(self):
         pass
 
     def _opt_wtd_nuis_regress(self, test_vals):
@@ -84,7 +84,6 @@ def _opt_wtd_nuis_regress(self, test_vals):
                                                          test_params))
         eta_star = self._modif_newton(np.zeros(self.model.nvar), est_vect,
                                          self.model._fit_weights)
-        self.eta_star = eta_star
         denom = np.sum(self.model._fit_weights) + np.dot(eta_star, est_vect.T)
         self.new_weights = self.model._fit_weights / denom
         return -1 * np.sum(np.log(self.new_weights))
@@ -151,7 +150,8 @@ def _EM_test(self, nuisance_params, params=None, param_nums=None,
         llikemax = np.sum(np.log(wtd_km[uncensored])) + \
           np.sum(np.log(survivalmax[censored]))
         if iters == maxiter:
-            warnings.warn('The EM reached the maximum number of iterations')
+            warnings.warn('The EM reached the maximum number of iterations',
+                          IterationLimitWarning)
         return -2 * (llike - llikemax)
 
     def _ci_limits_beta(self, b0, param_num=None):
@@ -478,7 +478,8 @@ def test_beta(self, b0_vals, param_nums, ftol=10 ** - 5, maxiter=30,
         uncens_endog = endog[uncensored]
         uncens_exog = exog[uncensored, :]
         reg_model = OLS(uncens_endog, uncens_exog).fit()
-        llr, pval, new_weights = reg_model.el_test(b0_vals, param_nums, return_weights=True)  # Needs to be changed
+        llr, pval, new_weights = reg_model.el_test(b0_vals, param_nums,
+                                      return_weights=True)  # Needs to be changed
         km = self.model._make_km(endog, censors).flatten()  # when merged
         uncens_nobs = self.model.uncens_nobs
         F = np.asarray(new_weights).reshape(uncens_nobs)
@@ -503,7 +504,7 @@ def test_beta(self, b0_vals, param_nums, ftol=10 ** - 5, maxiter=30,
                                    (params, param_nums, b0_vals, F, survidx,
                                     uncens_nobs, numcensbelow, km, uncensored,
                                     censored, maxiter, ftol), full_output=1,
-                                    disp = 0)
+                                    disp=0)
 
                 llr = res[1]
                 return llr, chi2.sf(llr, len(param_nums))
diff --git a/statsmodels/emplike/descriptive.py b/statsmodels/emplike/descriptive.py
index 3bfa5cd2d4f..7c3929cd97d 100644
--- a/statsmodels/emplike/descriptive.py
+++ b/statsmodels/emplike/descriptive.py
@@ -18,7 +18,7 @@
 import numpy as np
 from scipy import optimize
 from scipy.stats import chi2, skew, kurtosis
-from statsmodels.base.model import _fit_mle_newton
+from statsmodels.base.optimizer import _fit_newton
 import itertools
 from statsmodels.graphics import utils
 
@@ -66,7 +66,7 @@ class _OptFuncts(object):
     """
 
     def __init__(self, endog):
-        super(_OptFuncts, self).__init__(endog)
+        pass
 
     def _log_star(self, eta, est_vect, weights, nobs):
         """
@@ -91,7 +91,7 @@ def _log_star(self, eta, est_vect, weights, nobs):
 
         Notes
         -----
-        This function is only a placeholder for the _fit_mle_Newton.
+        This function is only a placeholder for the _fit_Newton.
         The function value is not used in optimization and the optimal value
         is disregarded when computing the log likelihood ratio.
         """
@@ -166,7 +166,7 @@ def _grad(self, eta, est_vect, weights, nobs):
     def _modif_newton(self,  eta, est_vect, weights):
         """
         Modified Newton's method for maximizing the log 'star' equation.  This
-        function calls _fit_mle_newton to find the optimal values of eta.
+        function calls _fit_newton to find the optimal values of eta.
 
         Parameters
         ----------
@@ -190,7 +190,7 @@ def _modif_newton(self,  eta, est_vect, weights):
         hess = lambda x0: - self._hess(x0, est_vect, weights, nobs)
         kwds = {'tol': 1e-8}
         eta = eta.squeeze()
-        res = _fit_mle_newton(f, grad, eta, (), kwds, hess=hess, maxiter=50, \
+        res = _fit_newton(f, grad, eta, (), kwds, hess=hess, maxiter=50, \
                               disp=0)
         return res[0]
 
@@ -686,7 +686,7 @@ def ci_var(self, lower_bound=None, upper_bound=None, sig=.05):
         self.r0 = chi2.ppf(1 - sig, 1)
         llim = optimize.brentq(self._ci_limits_var, lower_bound, endog.var())
         ulim = optimize.brentq(self._ci_limits_var, endog.var(), upper_bound)
-        return   llim, ulim
+        return llim, ulim
 
     def plot_contour(self, mu_low, mu_high, var_low, var_high, mu_step,
                         var_step,
diff --git a/statsmodels/emplike/elanova.py b/statsmodels/emplike/elanova.py
index 94ee27f06af..e94b23ca041 100644
--- a/statsmodels/emplike/elanova.py
+++ b/statsmodels/emplike/elanova.py
@@ -10,8 +10,9 @@
 Owen, A. B. (2001). Empirical Likelihood. Chapman and Hall.
 """
 
+from statsmodels.compat.python import range
 import numpy as np
-from descriptive import _OptFuncts
+from .descriptive import _OptFuncts
 from scipy import optimize
 from scipy.stats import chi2
 
diff --git a/statsmodels/emplike/elregress.py b/statsmodels/emplike/elregress.py
index 9751a00ef10..20a0bd1433c 100644
--- a/statsmodels/emplike/elregress.py
+++ b/statsmodels/emplike/elregress.py
@@ -77,6 +77,11 @@ def _opt_nuis_regress(self, nuisance_params, param_nums=None,
             eta_star = self._modif_newton(x0, est_vect, wts)
             denom = 1. + np.dot(eta_star, est_vect.T)
             self.new_weights = 1. / nobs * 1. / denom
+            # the following commented out code is to verify weights
+            # see open issue #1845
+            #self.new_weights /= self.new_weights.sum()
+            #if not np.allclose(self.new_weights.sum(), 1., rtol=0, atol=1e-10):
+            #    raise RuntimeError('weights do not sum to 1')
             llr = np.sum(np.log(nobs * self.new_weights))
             return -2 * llr
         except np.linalg.linalg.LinAlgError:
diff --git a/statsmodels/emplike/koul_and_mc.py b/statsmodels/emplike/koul_and_mc.py
index 57c85319ec8..a1ea4f78147 100644
--- a/statsmodels/emplike/koul_and_mc.py
+++ b/statsmodels/emplike/koul_and_mc.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import range
 import statsmodels.api as sm
 import numpy as np
 
diff --git a/statsmodels/emplike/tests/results/el_results.py b/statsmodels/emplike/tests/results/el_results.py
index ef41ab22483..d69e98b5e7d 100644
--- a/statsmodels/emplike/tests/results/el_results.py
+++ b/statsmodels/emplike/tests/results/el_results.py
@@ -325,7 +325,7 @@ def __init__(self):
 
         self.test_ci_beta3 = (-0.3804313225443794, 0.006934528877337928)
 
-class ANOVAResults():
+class ANOVAResults(object):
     """
     Results for ANOVA
     """
diff --git a/statsmodels/emplike/tests/test_aft.py b/statsmodels/emplike/tests/test_aft.py
index 1de64eef852..46fa8e669c9 100644
--- a/statsmodels/emplike/tests/test_aft.py
+++ b/statsmodels/emplike/tests/test_aft.py
@@ -1,15 +1,17 @@
 import numpy as np
 from numpy.testing import assert_almost_equal
-import statsmodels.api as sm
-from results.el_results import AFTRes
+from statsmodels.datasets import heart
+from statsmodels.tools import add_constant
+from statsmodels.emplike.aft_el import emplikeAFT
+from .results.el_results import AFTRes
 
 
 class GenRes(object):
     def __init__(self):
-        data = sm.datasets.heart.load()
+        data = heart.load()
         endog = np.log10(data.endog)
-        exog = sm.add_constant(data.exog)
-        self.mod1 = sm.emplike.emplikeAFT(endog, exog, data.censors)
+        exog = add_constant(data.exog)
+        self.mod1 = emplikeAFT(endog, exog, data.censors)
         self.res1 = self.mod1.fit()
         self.res2 = AFTRes()
 
diff --git a/statsmodels/emplike/tests/test_anova.py b/statsmodels/emplike/tests/test_anova.py
index e81c0951350..2cc9a466f6c 100644
--- a/statsmodels/emplike/tests/test_anova.py
+++ b/statsmodels/emplike/tests/test_anova.py
@@ -1,15 +1,16 @@
 from numpy.testing import assert_almost_equal
-import statsmodels.api as sm
-from results.el_results import ANOVAResults
+from statsmodels.datasets import star98
+from statsmodels.emplike.elanova import ANOVA
+from .results.el_results import ANOVAResults
 
-class TestANOVA():
+class TestANOVA(object):
     """
     Tests ANOVA difference in means
     """
 
     def __init__(self):
-        self.data = sm.datasets.star98.load().exog[:30, 1:3]
-        self.res1 = sm.emplike.ANOVA([self.data[:, 0], self.data[:, 1]])
+        self.data = star98.load().exog[:30, 1:3]
+        self.res1 = ANOVA([self.data[:, 0], self.data[:, 1]])
         self.res2 = ANOVAResults()
 
     def test_anova(self):
diff --git a/statsmodels/emplike/tests/test_descriptive.py b/statsmodels/emplike/tests/test_descriptive.py
index b678a01730d..d76521ba25e 100644
--- a/statsmodels/emplike/tests/test_descriptive.py
+++ b/statsmodels/emplike/tests/test_descriptive.py
@@ -1,7 +1,8 @@
 import numpy as np
 from numpy.testing import assert_almost_equal
-import statsmodels.api as sm
-from results.el_results import DescStatRes
+from statsmodels.datasets import star98
+from statsmodels.emplike.descriptive import DescStat
+from .results.el_results import DescStatRes
 
 
 class GenRes(object):
@@ -10,12 +11,12 @@ class GenRes(object):
 
     """
     def __init__(self):
-        data = sm.datasets.star98.load()
+        data = star98.load()
         desc_stat_data = data.exog[:50, 5]
         mv_desc_stat_data = data.exog[:50, 5:7]  # mv = multivariate
-        self.res1 = sm.emplike.DescStat(desc_stat_data)
+        self.res1 = DescStat(desc_stat_data)
         self.res2 = DescStatRes()
-        self.mvres1 = sm.emplike.DescStat(mv_desc_stat_data)
+        self.mvres1 = DescStat(mv_desc_stat_data)
 
 
 class TestDescriptiveStatistics(GenRes):
diff --git a/statsmodels/emplike/tests/test_origin.py b/statsmodels/emplike/tests/test_origin.py
index 82c27e2ca01..8fbf6d962f6 100644
--- a/statsmodels/emplike/tests/test_origin.py
+++ b/statsmodels/emplike/tests/test_origin.py
@@ -1,6 +1,7 @@
 from numpy.testing import assert_almost_equal
-import statsmodels.api as sm
-from results.el_results import OriginResults
+from statsmodels.emplike.originregress import ELOriginRegress
+from statsmodels.datasets import cancer
+from .results.el_results import OriginResults
 import numpy as np
 
 class GenRes(object):
@@ -9,8 +10,8 @@ class GenRes(object):
 
     """
     def __init__(self):
-        data = sm.datasets.cancer.load()
-        self.res1 = sm.emplike.ELOriginRegress(data.endog, data.exog).fit()
+        data = cancer.load()
+        self.res1 = ELOriginRegress(data.endog, data.exog).fit()
         self.res2 = OriginResults()
 
 
diff --git a/statsmodels/emplike/tests/test_regression.py b/statsmodels/emplike/tests/test_regression.py
index 0b4344acb6f..016d29d3ac0 100644
--- a/statsmodels/emplike/tests/test_regression.py
+++ b/statsmodels/emplike/tests/test_regression.py
@@ -1,7 +1,9 @@
 from numpy.testing import assert_almost_equal
 from numpy.testing.decorators import slow
-import statsmodels.api as sm
-from results.el_results import RegressionResults
+from statsmodels.regression.linear_model import OLS
+from statsmodels.tools import add_constant
+from .results.el_results import RegressionResults
+from statsmodels.datasets import stackloss
 
 
 class GenRes(object):
@@ -10,9 +12,9 @@ class GenRes(object):
 
     """
     def __init__(self):
-        data = sm.datasets.stackloss.load()
-        data.exog = sm.add_constant(data.exog)
-        self.res1 = sm.OLS(data.endog, data.exog).fit()
+        data = stackloss.load()
+        data.exog = add_constant(data.exog)
+        self.res1 = OLS(data.endog, data.exog).fit()
         self.res2 = RegressionResults()
 
 
@@ -68,13 +70,11 @@ def test_ci_beta1(self):
                                         method='powell')
         assert_almost_equal(beta1ci, self.res2.test_ci_beta1, 4)
 
-    @slow
     def test_ci_beta2(self):
         beta2ci = self.res1.conf_int_el(2, lower_bound=.59,
                                     upper_bound=2.2, method='powell')
         assert_almost_equal(beta2ci, self.res2.test_ci_beta2, 5)
 
-    @slow
     def test_ci_beta3(self):
         beta3ci = self.res1.conf_int_el(3, lower_bound=-.39, upper_bound=.01,
                                         method='powell')
@@ -144,10 +144,12 @@ def test_ci_beta1(self):
         beta1ci = self.res1.conf_int_el(1, method='nm')
         assert_almost_equal(beta1ci, self.res2.test_ci_beta1, 6)
 
+    @slow
     def test_ci_beta2(self):
         beta2ci = self.res1.conf_int_el(2, lower_bound=.59, upper_bound=2.2,  method='nm')
         assert_almost_equal(beta2ci, self.res2.test_ci_beta2, 6)
 
+    @slow
     def test_ci_beta3(self):
         beta3ci = self.res1.conf_int_el(3, method='nm')
         assert_almost_equal(beta3ci, self.res2.test_ci_beta3, 6)
diff --git a/statsmodels/examples/es_misc_poisson2.py b/statsmodels/examples/es_misc_poisson2.py
index 64e35616fd7..b2fff0fc284 100644
--- a/statsmodels/examples/es_misc_poisson2.py
+++ b/statsmodels/examples/es_misc_poisson2.py
@@ -1,5 +1,5 @@
 
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 import statsmodels.api as sm
@@ -39,19 +39,19 @@ class Dummy(object):
 start_params = res.params[1:]
 res1 = mod1.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=1000)
 
-print 'mod2'
+print('mod2')
 mod2 = PoissonZiGMLE(data_endog, data_exog[:,1:], offset=offset)
 start_params = np.r_[np.ones(6)/2.,10]
 start_params = np.r_[res.params[1:], 20.] #-100]
 res2 = mod2.fit(start_params=start_params, method='bfgs', maxiter=1000, maxfun=2000)
 
-print 'mod3'
+print('mod3')
 mod3 = PoissonZiGMLE(data_endog, data_exog, offset=None)
 start_params = np.r_[np.ones(7)/2.,10]
 start_params = np.r_[res.params, 20.]
 res3 = mod3.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=2000)
 
-print 'mod4'
+print('mod4')
 data_endog2 = np.r_[data_endog, np.zeros(nobs)]
 data_exog2 = np.r_[data_exog, data_exog]
 
@@ -59,4 +59,4 @@ class Dummy(object):
 start_params = np.r_[np.ones(7)/2.,10]
 start_params = np.r_[res.params, 0.]
 res4 = mod4.fit(start_params=start_params, method='nm', maxiter=1000, maxfun=1000)
-print res4.summary()
+print(res4.summary())
diff --git a/statsmodels/examples/ex_arch_canada.py b/statsmodels/examples/ex_arch_canada.py
index b75e8072f76..311bfa5981e 100644
--- a/statsmodels/examples/ex_arch_canada.py
+++ b/statsmodels/examples/ex_arch_canada.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.sandbox.stats.diagnostic as dia
 
@@ -98,7 +99,7 @@
 canada = np.array(canada_raw.split(), float).reshape(-1,4)
 k=2;
 resarch2 = dia.acorr_lm((canada[:,k]-canada[:,k].mean())**2, maxlag=2, autolag=None, store=1)
-print resarch2
+print(resarch2)
 resarch5 = dia.acorr_lm(canada[:,k]**2, maxlag=12, autolag=None, store=1)
 
 ss = '''\
@@ -107,8 +108,8 @@
 Chi-squared = %(chi)-8.4f df = %(df)-4d p-value = %(pval)8.4g
 '''
 resarch = resarch5
-print
-print ss % dict(chi=resarch[2], df=resarch[-1].resols.df_model, pval=resarch[3])
+print()
+print(ss % dict(chi=resarch[2], df=resarch[-1].resols.df_model, pval=resarch[3]))
 
 
 #R:FinTS: ArchTest(as.vector(Canada[,3]), lag=5)
diff --git a/statsmodels/examples/ex_emplike_1.py b/statsmodels/examples/ex_emplike_1.py
index d05d6140e02..8611ef46ae2 100644
--- a/statsmodels/examples/ex_emplike_1.py
+++ b/statsmodels/examples/ex_emplike_1.py
@@ -4,9 +4,11 @@
 it also generates plots.
 
 """
+
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
-print 'Welcome to El'
+print('Welcome to El')
 np.random.seed(634)  # No significance of the seed.
 # Let's first generate some univariate data.
 univariate = np.random.standard_normal(30)
@@ -21,8 +23,8 @@
 eldescriptive_mean = eldescriptive.endog.mean()  #.42
 
 #Let's conduct a hypothesis test to see if the mean is 0
-print 'Hypothesis test results for the mean:'
-print  eldescriptive.test_mean(0)
+print('Hypothesis test results for the mean:')
+print(eldescriptive.test_mean(0))
 
 
 # The first value is is  -2 *log-likelihood ratio, which is distributed
@@ -32,31 +34,31 @@
 eldescriptive_var = eldescriptive.endog.var()  # 1.01
 
 #Let's test if the variance is 1:
-print 'Hypothesis test results for the variance:'
-print eldescriptive.test_var(1)
+print('Hypothesis test results for the variance:')
+print(eldescriptive.test_var(1))
 
 # Let's test if Skewness and Kurtosis are 0
-print 'Hypothesis test results for Skewness:'
-print eldescriptive.test_skew(0)
-print 'Hypothesis test results for the Kurtosis:'
-print eldescriptive.test_kurt(0)
+print('Hypothesis test results for Skewness:')
+print(eldescriptive.test_skew(0))
+print('Hypothesis test results for the Kurtosis:')
+print(eldescriptive.test_kurt(0))
 # Note that the skewness and Kurtosis take longer.  This is because
 # we have to optimize over the nuisance parameters (mean, variance).
 
 # We can also test for the joint skewness and kurtoses
-print ' Joint Skewness-Kurtosis test'
+print(' Joint Skewness-Kurtosis test')
 eldescriptive.test_joint_skew_kurt(0, 0)
 
 
 # Let's try and get some confidence intervals
-print 'Confidence interval for the mean'
-print eldescriptive.ci_mean()
-print 'Confidence interval for the variance'
-print eldescriptive.ci_var()
-print 'Confidence interval for skewness'
-print eldescriptive.ci_skew()
-print 'Confidence interval for kurtosis'
-print eldescriptive.ci_kurt()
+print('Confidence interval for the mean')
+print(eldescriptive.ci_mean())
+print('Confidence interval for the variance')
+print(eldescriptive.ci_var())
+print('Confidence interval for skewness')
+print(eldescriptive.ci_skew())
+print('Confidence interval for kurtosis')
+print(eldescriptive.ci_kurt())
 
 
 # if matplotlib is installed, we can get a contour plot for the mean
@@ -75,17 +77,17 @@
 # mv mean contour and ci_corr and test_corr.
 
 # Let's test the hypthesis that x1 has a mean of 2 and x2 has a mean of 7
-print 'Multivaraite mean hypothesis test'
-print mv_elmodel.mv_test_mean(np.array([2, 7]))
+print('Multivaraite mean hypothesis test')
+print(mv_elmodel.mv_test_mean(np.array([2, 7])))
 
 # Now let's get the confidence interval for correlation
-print 'Correlation Coefficient CI'
-print mv_elmodel.ci_corr()
+print('Correlation Coefficient CI')
+print(mv_elmodel.ci_corr())
 # Note how this took much longer than previous functions.  That is
 # because the function is optimizing over 4 nuisance parameters.
 # We can also do a hypothesis test for correlation
-print 'Hypothesis test for correlation'
-print mv_elmodel.test_corr(.7)
+print('Hypothesis test for correlation')
+print(mv_elmodel.test_corr(.7))
 
 # Finally, let's create a contour plot for the means of the data
 means_contour = mv_elmodel.mv_mean_contour(1, 3, 6,9, .15,.15, plot_dta=1)
diff --git a/statsmodels/examples/ex_emplike_2.py b/statsmodels/examples/ex_emplike_2.py
index 3652f4b39f4..2ca8ea66f32 100644
--- a/statsmodels/examples/ex_emplike_2.py
+++ b/statsmodels/examples/ex_emplike_2.py
@@ -3,6 +3,7 @@
 likelihood estimation and inference in linear regression models.
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 
@@ -15,7 +16,7 @@
 # There are no distributional assumptions on the error.  I just chose
 # normal errors to demonstrate.
 
-print 'Lets play with EL Regression'
+print('Lets play with EL Regression')
 
 
 # In a model with an intercept, access EL inference through OLS results.
@@ -26,31 +27,31 @@
 
 
 # Let's test if the intercept is 0
-print 'Intercept test'
+print('Intercept test')
 test0_1 = fitted.el_test(np.array([0]), np.array([0]))
-print test0_1
+print(test0_1)
 #  Let's test if beta3 is 4
-print 'beta3 test'
+print('beta3 test')
 test1 = fitted.el_test(np.array([4]), np.array([3]))
-print test1
+print(test1)
 #  Lets test the hypothesis that beta3=4 and beta2=3
-print 'joint beta test'
+print('joint beta test')
 test2 = fitted.el_test(np.array([3, 4]), np.array([2, 3]))
-print test2
+print(test2)
 
 #  Let's get the confidence intervals for the parameters
-print 'Confidence Interval for Beta1'
+print('Confidence Interval for Beta1')
 ci_beta1 = fitted.conf_int_el(1)
-print ci_beta1
+print(ci_beta1)
 
 # Of course, we can still see the rest of the RegressionResults
-print 'R-squared'
-print fitted.rsquared
-print 'Params'
-print fitted.params
+print('R-squared')
+print(fitted.rsquared)
+print('Params')
+print(fitted.params)
 
 #  Now lets check out regression through the origin
-print 'Origin Regression'
+print('Origin Regression')
 originx = np.random.standard_normal((30, 3))
 originbeta = np.array([[1], [2], [3]])
 originy = np.dot(originx, originbeta) + np.random.standard_normal((30, 1))
@@ -62,12 +63,12 @@
 originfit = originmodel.fit()
 
 
-print 'The fitted parameters'
-print originfit.params
-print 'The MSE'
-print originfit.mse_model
-print 'The R-squared'
-print originfit.rsquared
+print('The fitted parameters')
+print(originfit.params)
+print('The MSE')
+print(originfit.mse_model)
+print('The R-squared')
+print(originfit.rsquared)
 
 # Note that the first element of param is 0 and there are 4 params.  That is
 # because the first param is the intercept term.  This is noted in the
@@ -75,17 +76,17 @@
 
 #  Now that the model is fitted, we can do some inference.
 
-print 'Test beta1 =1'
+print('Test beta1 =1')
 test_beta1 = originfit.el_test([1], [1])
-print test_beta1
+print(test_beta1)
 
 #  A confidence interval for Beta1.
-print 'confidence interval for beta1'
+print('confidence interval for beta1')
 ci_beta2 = originfit.conf_int_el(1)
-print ci_beta2
+print(ci_beta2)
 
 # Finally, since we initiated an EL model, normal inference is not available
 try:
     originfit.conf_int()
 except:
-    print 'No normal inference available'
+    print('No normal inference available')
diff --git a/statsmodels/examples/ex_emplike_3.py b/statsmodels/examples/ex_emplike_3.py
index e762d08501c..1f3b70fccf5 100644
--- a/statsmodels/examples/ex_emplike_3.py
+++ b/statsmodels/examples/ex_emplike_3.py
@@ -6,6 +6,7 @@
 
 """
 
+from __future__ import print_function
 import statsmodels.api as sm
 import numpy as np
 
@@ -18,10 +19,10 @@
 
 # We need to fit the model to get the parameters
 fitted = model.fit()
-print fitted.params()
+print(fitted.params())
 test1 = fitted.test_beta([4],[0])  # Test that the intercept is 4
-print test1
+print(test1)
 test2 = fitted.test_beta([-.05], [1]) # Test that the slope is -.05
-print test2
+print(test2)
 ci_beta1 = fitted.ci_beta(1, .1, -.1)
-print ci_beta1
+print(ci_beta1)
diff --git a/statsmodels/examples/ex_feasible_gls_het.py b/statsmodels/examples/ex_feasible_gls_het.py
index f121b6867d4..07ce40f9f3c 100644
--- a/statsmodels/examples/ex_feasible_gls_het.py
+++ b/statsmodels/examples/ex_feasible_gls_het.py
@@ -21,6 +21,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -47,35 +48,35 @@
     X2 = X
 
     res_ols = OLS(y2, X2).fit()
-    print 'OLS beta estimates'
-    print res_ols.params
-    print 'OLS stddev of beta'
-    print res_ols.bse
-    print '\nWLS'
+    print('OLS beta estimates')
+    print(res_ols.params)
+    print('OLS stddev of beta')
+    print(res_ols.bse)
+    print('\nWLS')
     mod0 = GLSHet2(y2, X2, exog_var=w)
     res0 = mod0.fit()
-    print 'new version'
+    print('new version')
     mod1 = GLSHet(y2, X2, exog_var=w)
     res1 = mod1.iterative_fit(2)
-    print 'WLS beta estimates'
-    print res1.params
-    print res0.params
-    print 'WLS stddev of beta'
-    print res1.bse
+    print('WLS beta estimates')
+    print(res1.params)
+    print(res0.params)
+    print('WLS stddev of beta')
+    print(res1.bse)
     #compare with previous version GLSHet2, refactoring check
     #assert_almost_equal(res1.params, np.array([ 0.37642521,  1.51447662]))
     #this fails ???  more iterations? different starting weights?
 
 
-    print res1.model.weights/res1.model.weights.max()
+    print(res1.model.weights/res1.model.weights.max())
     #why is the error so small in the estimated weights ?
     assert_almost_equal(res1.model.weights/res1.model.weights.max(), 1./w, 14)
-    print 'residual regression params'
-    print res1.results_residual_regression.params
-    print 'scale of model ?'
-    print res1.scale
-    print 'unweighted residual variance, note unweighted mean is not zero'
-    print res1.resid.var()
+    print('residual regression params')
+    print(res1.results_residual_regression.params)
+    print('scale of model ?')
+    print(res1.scale)
+    print('unweighted residual variance, note unweighted mean is not zero')
+    print(res1.resid.var())
     #Note weighted mean is zero:
     #(res1.model.weights * res1.resid).mean()
 
@@ -93,21 +94,21 @@
     z = (w[:,None] == np.unique(w)).astype(float) #dummy variable
     mod2 = GLSHet(y2, X2, exog_var=z)
     res2 = mod2.iterative_fit(2)
-    print res2.params
+    print(res2.params)
 
     import statsmodels.api as sm
     z = sm.add_constant(w)
     mod3 = GLSHet(y2, X2, exog_var=z)
     res3 = mod3.iterative_fit(8)
-    print res3.params
-    print "np.array(res3.model.history['ols_params'])"
+    print(res3.params)
+    print("np.array(res3.model.history['ols_params'])")
 
-    print np.array(res3.model.history['ols_params'])
-    print "np.array(res3.model.history['self_params'])"
-    print np.array(res3.model.history['self_params'])
+    print(np.array(res3.model.history['ols_params']))
+    print("np.array(res3.model.history['self_params'])")
+    print(np.array(res3.model.history['self_params']))
 
-    print np.unique(res2.model.weights) #for discrete z only, only a few uniques
-    print np.unique(res3.model.weights)
+    print(np.unique(res2.model.weights)) #for discrete z only, only a few uniques
+    print(np.unique(res3.model.weights))
 
     if doplots:
         plt.figure()
diff --git a/statsmodels/examples/ex_feasible_gls_het_0.py b/statsmodels/examples/ex_feasible_gls_het_0.py
index 02cf43adc3a..c7f93db12c9 100644
--- a/statsmodels/examples/ex_feasible_gls_het_0.py
+++ b/statsmodels/examples/ex_feasible_gls_het_0.py
@@ -16,6 +16,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -63,35 +64,35 @@
     X2 = X  #correctly specigied
 
     res_ols = OLS(y2, X2).fit()
-    print 'OLS beta estimates'
-    print res_ols.params
-    print 'OLS stddev of beta'
-    print res_ols.bse
-    print '\nWLS'
+    print('OLS beta estimates')
+    print(res_ols.params)
+    print('OLS stddev of beta')
+    print(res_ols.bse)
+    print('\nWLS')
     mod0 = GLSHet2(y2, X2, exog_var=winv)
     res0 = mod0.fit()
-    print 'new version'
+    print('new version')
     mod1 = GLSHet(y2, X2, exog_var=winv)
     res1 = mod1.iterative_fit(2)
-    print 'WLS beta estimates'
-    print res1.params
-    print res0.params
-    print 'WLS stddev of beta'
-    print res1.bse
+    print('WLS beta estimates')
+    print(res1.params)
+    print(res0.params)
+    print('WLS stddev of beta')
+    print(res1.bse)
     #compare with previous version GLSHet2, refactoring check
     #assert_almost_equal(res1.params, np.array([ 0.37642521,  1.51447662]))
     #this fails ???  more iterations? different starting weights?
 
 
-    print res1.model.weights/res1.model.weights.max()
+    print(res1.model.weights/res1.model.weights.max())
     #why is the error so small in the estimated weights ?
     assert_almost_equal(res1.model.weights/res1.model.weights.max(), weights_dgp, 14)
-    print 'residual regression params'
-    print res1.results_residual_regression.params
-    print 'scale of model ?'
-    print res1.scale
-    print 'unweighted residual variance, note unweighted mean is not zero'
-    print res1.resid.var()
+    print('residual regression params')
+    print(res1.results_residual_regression.params)
+    print('scale of model ?')
+    print(res1.scale)
+    print('unweighted residual variance, note unweighted mean is not zero')
+    print(res1.resid.var())
     #Note weighted mean is zero:
     #(res1.model.weights * res1.resid).mean()
 
@@ -111,7 +112,7 @@
     #changed z0 contains dummy and constant
     mod2 = GLSHet(y2, X2, exog_var=z0)
     res2 = mod2.iterative_fit(3)
-    print res2.params
+    print(res2.params)
 
     import statsmodels.api as sm
     #z = sm.add_constant(w, prepend=True)
@@ -119,21 +120,21 @@
     mod3 = GLSHet(y2, X2, exog_var=z1)#, link=sm.families.links.log())
     res3 = mod3.iterative_fit(20)
     error_var_3 = res3.mse_resid/res3.model.weights
-    print res3.params
-    print "np.array(res3.model.history['ols_params'])"
+    print(res3.params)
+    print("np.array(res3.model.history['ols_params'])")
 
-    print np.array(res3.model.history['ols_params'])
-    print "np.array(res3.model.history['self_params'])"
-    print np.array(res3.model.history['self_params'])
+    print(np.array(res3.model.history['ols_params']))
+    print("np.array(res3.model.history['self_params'])")
+    print(np.array(res3.model.history['self_params']))
 
     #Models 2 and 3 are equivalent with different parameterization of Z
-    print np.unique(res2.model.weights) #for discrete z only, only a few uniques
-    print np.unique(res3.model.weights)
+    print(np.unique(res2.model.weights)) #for discrete z only, only a few uniques
+    print(np.unique(res3.model.weights))
 
-    print res3.summary()
-    print '\n\nResults of estimation of weights'
-    print     '--------------------------------'
-    print res3.results_residual_regression.summary()
+    print(res3.summary())
+    print('\n\nResults of estimation of weights')
+    print('--------------------------------')
+    print(res3.results_residual_regression.summary())
 
     if doplots:
         plt.figure()
diff --git a/statsmodels/examples/ex_generic_mle.py b/statsmodels/examples/ex_generic_mle.py
index 5822a7eff1a..5548b457941 100644
--- a/statsmodels/examples/ex_generic_mle.py
+++ b/statsmodels/examples/ex_generic_mle.py
@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 import statsmodels.api as sm
@@ -26,12 +27,12 @@ def probitloglike(params, endog, exog):
 
 mod = GenericLikelihoodModel(data.endog, data.exog, loglike=probitloglike)
 res = mod.fit(method="nm", fargs=(data.endog,data.exog), maxiter=500)
-print res
+print(res)
 
 
 #np.allclose(res.params, probit_res.params)
 
-print res.params, probit_res.params
+print(res.params, probit_res.params)
 
 #datal = sm.datasets.longley.load()
 datal = sm.datasets.ccard.load()
@@ -57,7 +58,7 @@ def loglike_norm_xb(self, params):
     mod_norm = GenericLikelihoodModel(datal.endog, datal.exog, loglike_norm_xb)
     res_norm = mod_norm.fit(method="nm", maxiter = 500)
 
-    print res_norm.params
+    print(res_norm.params)
 
 if show_error2:
     def loglike_norm_xb(params, endog, exog):
@@ -73,7 +74,7 @@ def loglike_norm_xb(params, endog, exog):
                             method="nm", maxiter = 5000,
                             fargs=(datal.endog, datal.exog))
 
-    print res_norm.params
+    print(res_norm.params)
 
 class MygMLE(GenericLikelihoodModel):
     # just for testing
@@ -92,17 +93,17 @@ def loglikeobs(self, params):
 mod_norm2 = MygMLE(datal.endog, datal.exog)
 #res_norm = mod_norm.fit(start_params=np.ones(datal.exog.shape[1]+1), method="nm", maxiter = 500)
 res_norm2 = mod_norm2.fit(start_params=[1.]*datal.exog.shape[1]+[1], method="nm", maxiter = 500)
-print res_norm2.params
+print(res_norm2.params)
 
 res2 = sm.OLS(datal.endog, datal.exog).fit()
 start_params = np.hstack((res2.params, np.sqrt(res2.mse_resid)))
 res_norm3 = mod_norm2.fit(start_params=start_params, method="nm", maxiter = 500,
                           retall=0)
-print start_params
-print res_norm3.params
-print res2.bse
+print(start_params)
+print(res_norm3.params)
+print(res2.bse)
 #print res_norm3.bse   # not available
-print 'llf', res2.llf, res_norm3.llf
+print('llf', res2.llf, res_norm3.llf)
 
 bse = np.sqrt(np.diag(np.linalg.inv(res_norm3.model.hessian(res_norm3.params))))
 res_norm3.model.score(res_norm3.params)
@@ -144,25 +145,25 @@ def loglikeobs(self, params):
 hb=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
 hf=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
 hh = (hf+hb)/2.
-print np.linalg.eigh(hh)
+print(np.linalg.eigh(hh))
 
 grad = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
-print grad
+print(grad)
 gradb = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
 gradf = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
-print (gradb+gradf)/2.
+print((gradb+gradf)/2.)
 
-print res_norm3.model.score(res_norm3.params)
-print res_norm3.model.score(start_params)
+print(res_norm3.model.score(res_norm3.params))
+print(res_norm3.model.score(start_params))
 mod_norm2.loglike(start_params/2.)
-print np.linalg.inv(-1*mod_norm2.hessian(res_norm3.params))
-print np.sqrt(np.diag(res_bfgs.cov_params()))
-print res_norm3.bse
+print(np.linalg.inv(-1*mod_norm2.hessian(res_norm3.params)))
+print(np.sqrt(np.diag(res_bfgs.cov_params())))
+print(res_norm3.bse)
 
-print "MLE - OLS parameter estimates"
-print res_norm3.params[:-1] - res2.params
-print "bse diff in percent"
-print (res_norm3.bse[:-1] / res2.bse)*100. - 100
+print("MLE - OLS parameter estimates")
+print(res_norm3.params[:-1] - res2.params)
+print("bse diff in percent")
+print((res_norm3.bse[:-1] / res2.bse)*100. - 100)
 
 '''
 C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect;
@@ -372,12 +373,12 @@ def loglikeobs(self, params):
 Is scale a misnomer, actually scale squared, i.e. variance of error term ?
 '''
 
-print res_norm3.model.jac(res_norm3.params).shape
+print(res_norm3.model.jac(res_norm3.params).shape)
 
 jac = res_norm3.model.jac(res_norm3.params)
-print np.sqrt(np.diag(np.dot(jac.T, jac)))/start_params
+print(np.sqrt(np.diag(np.dot(jac.T, jac)))/start_params)
 jac2 = res_norm3.model.jac(res_norm3.params, centered=True)
 
-print np.sqrt(np.diag(np.linalg.inv(np.dot(jac.T, jac))))
-print res_norm3.bse
-print res2.bse
+print(np.sqrt(np.diag(np.linalg.inv(np.dot(jac.T, jac)))))
+print(res_norm3.bse)
+print(res2.bse)
diff --git a/statsmodels/examples/ex_generic_mle_t.py b/statsmodels/examples/ex_generic_mle_t.py
index 2c5487c4d0e..d18b61ecbff 100644
--- a/statsmodels/examples/ex_generic_mle_t.py
+++ b/statsmodels/examples/ex_generic_mle_t.py
@@ -6,6 +6,7 @@
 """
 
 
+from __future__ import print_function
 import numpy as np
 
 from scipy import stats, special
@@ -88,14 +89,14 @@ def nloglikeobs(self, params):
 modp.start_value[-2] = 10
 modp.start_params = modp.start_value
 resp = modp.fit(start_params = modp.start_value)
-print resp.params
-print resp.bse
+print(resp.params)
+print(resp.bse)
 
 from statsmodels.tools.numdiff import approx_fprime, approx_hess
 
 hb=-approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4)
 tmp = modp.loglike(modp.start_value)
-print tmp.shape
+print(tmp.shape)
 
 
 '''
diff --git a/statsmodels/examples/ex_generic_mle_tdist.py b/statsmodels/examples/ex_generic_mle_tdist.py
index dc74add707f..c00df484655 100644
--- a/statsmodels/examples/ex_generic_mle_tdist.py
+++ b/statsmodels/examples/ex_generic_mle_tdist.py
@@ -6,6 +6,8 @@
 """
 
 
+from __future__ import print_function
+from statsmodels.compat.python import zip
 import numpy as np
 
 from scipy import stats, special, optimize
@@ -90,16 +92,16 @@ def nloglikeobs(self, params):
 data_exog = sm.add_constant(rvs, prepend=False)
 xbeta = 0.9 + 0.1*rvs.sum(1)
 data_endog = xbeta + 0.1*np.random.standard_t(df, size=nobs)
-print data_endog.var()
+print(data_endog.var())
 
 res_ols = sm.OLS(data_endog, data_exog).fit()
-print res_ols.scale
-print np.sqrt(res_ols.scale)
-print res_ols.params
+print(res_ols.scale)
+print(np.sqrt(res_ols.scale))
+print(res_ols.params)
 kurt = stats.kurtosis(res_ols.resid)
 df_fromkurt = 6./kurt + 4
-print stats.t.stats(df_fromkurt, moments='mvsk')
-print stats.t.stats(df, moments='mvsk')
+print(stats.t.stats(df_fromkurt, moments='mvsk'))
+print(stats.t.stats(df, moments='mvsk'))
 
 modp = MyT(data_endog, data_exog)
 start_value = 0.1*np.ones(data_exog.shape[1]+2)
@@ -127,24 +129,24 @@ def nloglikeobs(self, params):
 
 resp = modp.fit(start_params = modp.start_params, disp=1, method='nm')#'newton')
 #resp = modp.fit(start_params = modp.start_params, disp=1, method='newton')
-print '\nestimation results t-dist'
-print resp.params
-print resp.bse
+print('\nestimation results t-dist')
+print(resp.params)
+print(resp.bse)
 resp2 = modp.fit(start_params = resp.params, method='Newton')
-print 'using Newton'
-print resp2.params
-print resp2.bse
+print('using Newton')
+print(resp2.params)
+print(resp2.bse)
 
 from statsmodels.tools.numdiff import approx_fprime, approx_hess
 
 hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)
 tmp = modp.loglike(modp.start_params)
-print tmp.shape
+print(tmp.shape)
 #np.linalg.eigh(np.linalg.inv(hb))[0]
 
 pp=np.array(store_params)
-print pp.min(0)
-print pp.max(0)
+print(pp.min(0))
+print(pp.max(0))
 
 
 
@@ -245,7 +247,7 @@ def pareto_ks(loc, rvs):
             res.append([trimidx, pareto_ks(loc-1e-10, rvs[trimidx:])])
         res = np.array(res)
         bestidx = res[np.argmin(res[:,1]),0].astype(int)
-        print bestidx
+        print(bestidx)
         locest = rvs[bestidx]
 
         est = stats.pareto.fit_fr(rvs[bestidx:], 1., frozen=[np.nan, locest, np.nan])
@@ -307,11 +309,11 @@ def pareto_ks(loc, rvs):
 
 res_parks = mod_par.fit_ks1()
 
-print res_par.params
+print(res_par.params)
 #print res_par2.params
-print res_parks
+print(res_parks)
 
-print res_par.params[1:].sum(), sum(res_parks[1:]), mod_par.endog.min()
+print(res_par.params[1:].sum(), sum(res_parks[1:]), mod_par.endog.min())
 
 #start new model, so we don't get two result instances with the same model instance
 mod_par = MyPareto(y)
@@ -329,8 +331,8 @@ def pareto_ks(loc, rvs):
 ##res_parkst = mod_par.fit_ks1_trim()
 ##print res_parkst
 
-print res5.summary()
-print res5.t_test([[1,0]])
+print(res5.summary())
+print(res5.t_test([[1,0]]))
 
 '''
 C:\Programs\Python25\lib\site-packages\matplotlib-0.99.1-py2.5-win32.egg\matplotlib\rcsetup.py:117: UserWarning: rcParams key "numerix" is obsolete and has no effect;
@@ -559,7 +561,7 @@ def pareto_ks(loc, rvs):
 #    if Narg == 0 and hasattr(self, '_fitstart'):
 #        x0 = self._fitstart(data)
 #    elif Narg > self.numargs:
-#        raise ValueError, "Too many input arguments."
+#        raise ValueError("Too many input arguments.")
 #    else:
 #        args += (1.0,)*(self.numargs-Narg)
 #        # location and scale are at the end
@@ -568,7 +570,7 @@ def pareto_ks(loc, rvs):
 #    if 'frozen' in kwds:
 #        frmask = np.array(kwds['frozen'])
 #        if len(frmask) != self.numargs+2:
-#            raise ValueError, "Incorrect number of frozen arguments."
+#            raise ValueError("Incorrect number of frozen arguments.")
 #        else:
 #            # keep starting values for not frozen parameters
 #            x0  = np.array(x0)[np.isnan(frmask)]
@@ -669,7 +671,7 @@ def pareto_ks(loc, rvs):
 #        loc,scale=map(kwds.get,['loc','scale'])
 #        args, loc, scale = self._fix_loc_scale(args, loc, scale)
 #        x,loc,scale = map(arr,(x,loc,scale))
-#        args = tuple(map(arr,args))
+#        args = tuple(lmap(arr,args))
 #        x = arr((x-loc)*1.0/scale)
 #        cond0 = self._argcheck(*args) & (scale > 0)
 #        cond1 = (scale > 0) & (x >= self.a) & (x <= self.b)
diff --git a/statsmodels/examples/ex_grangercausality.py b/statsmodels/examples/ex_grangercausality.py
index caaaa5928d3..d312aa75c61 100644
--- a/statsmodels/examples/ex_grangercausality.py
+++ b/statsmodels/examples/ex_grangercausality.py
@@ -6,6 +6,8 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
+from statsmodels.compat.python import iteritems
 import numpy as np
 from numpy.testing import assert_almost_equal
 from statsmodels.datasets import macrodata
@@ -26,15 +28,15 @@
                     decimal=7)
 
 lag = 2
-print '\nTest Results for %d lags' % lag
-print
-print '\n'.join(['%-20s statistic: %f6.4   p-value: %f6.4' % (k, res[0], res[1])
-                 for k, res in gr[lag][0].items() ])
-
-print '\n Results for auxiliary restricted regression with two lags'
-print
-print gr[lag][1][0].summary()
-
-print '\n Results for auxiliary unrestricted regression with two lags'
-print
-print gr[lag][1][1].summary()
+print('\nTest Results for %d lags' % lag)
+print()
+print('\n'.join(['%-20s statistic: %f6.4   p-value: %f6.4' % (k, res[0], res[1])
+                 for k, res in iteritems(gr[lag][0]) ]))
+
+print('\n Results for auxiliary restricted regression with two lags')
+print()
+print(gr[lag][1][0].summary())
+
+print('\n Results for auxiliary unrestricted regression with two lags')
+print()
+print(gr[lag][1][1].summary())
diff --git a/statsmodels/examples/ex_inter_rater.py b/statsmodels/examples/ex_inter_rater.py
index ac9220daa19..57abc001431 100644
--- a/statsmodels/examples/ex_inter_rater.py
+++ b/statsmodels/examples/ex_inter_rater.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -31,23 +32,23 @@
 table1 = table0[:, 1:-1]
 
 
-print fleiss_kappa(table1)
+print(fleiss_kappa(table1))
 table4 = np.array([[20,5], [10, 15]])
-print 'res', cohens_kappa(table4), 0.4 #wikipedia
+print('res', cohens_kappa(table4), 0.4) #wikipedia
 
 table5 = np.array([[45, 15], [25, 15]])
-print 'res', cohens_kappa(table5), 0.1304 #wikipedia
+print('res', cohens_kappa(table5), 0.1304) #wikipedia
 
 table6 = np.array([[25, 35], [5, 35]])
-print 'res', cohens_kappa(table6), 0.2593  #wikipedia
-print 'res', cohens_kappa(table6, weights=np.arange(2)), 0.2593  #wikipedia
+print('res', cohens_kappa(table6), 0.2593)  #wikipedia
+print('res', cohens_kappa(table6, weights=np.arange(2)), 0.2593)  #wikipedia
 t7 = np.array([[16, 18, 28],
                [10, 27, 13],
                [28, 20, 24]])
-print cohens_kappa(t7, weights=[0, 1, 2])
+print(cohens_kappa(t7, weights=[0, 1, 2]))
 
 table8 = np.array([[25, 35], [5, 35]])
-print 'res', cohens_kappa(table8)
+print('res', cohens_kappa(table8))
 
 #SAS example from http://www.john-uebersax.com/stat/saskappa.htm
 '''
@@ -60,8 +61,8 @@
       [5, 16,  3],
       [8, 12, 28]]
 res9 = cohens_kappa(t9)
-print 'res', res9
-print 'res', cohens_kappa(t9, weights=[0, 1, 2])
+print('res', res9)
+print('res', cohens_kappa(t9, weights=[0, 1, 2]))
 
 
 #check max kappa, constructed by hand, same marginals
@@ -69,14 +70,14 @@
 res = cohens_kappa(table6a)
 assert res.kappa == res.kappa_max
 #print np.divide(*cohens_kappa(table6)[:2])
-print res.kappa / res.kappa_max
+print(res.kappa / res.kappa_max)
 
 
 table10 = [[0, 4, 1],
            [0, 8, 0],
            [0, 1, 5]]
 res10 = cohens_kappa(table10)
-print 'res10', res10
+print('res10', res10)
 
 
 '''SAS result for table10
diff --git a/statsmodels/examples/ex_kde_confint.py b/statsmodels/examples/ex_kde_confint.py
new file mode 100644
index 00000000000..e064472431b
--- /dev/null
+++ b/statsmodels/examples/ex_kde_confint.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Mon Dec 16 11:02:59 2013
+
+Author: Josef Perktold
+"""
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+import statsmodels.nonparametric.api as npar
+from statsmodels.sandbox.nonparametric import kernels
+from statsmodels.distributions.mixture_rvs import mixture_rvs
+
+# example from test_kde.py mixture of two normal distributions
+np.random.seed(12345)
+x = mixture_rvs([.25,.75], size=200, dist=[stats.norm, stats.norm],
+                kwargs = (dict(loc=-1, scale=.5),dict(loc=1, scale=.5)))
+
+x.sort() # not needed
+
+kde = npar.KDEUnivariate(x)
+kde.fit('gau')
+ci = kde.kernel.density_confint(kde.density, len(x))
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.hist(x, bins=15, normed=True, alpha=0.25)
+ax.plot(kde.support, kde.density, lw=2, color='red')
+ax.fill_between(kde.support, ci[:,0], ci[:,1],
+                    color='grey', alpha='0.7')
+ax.set_title('Kernel Density Gaussian (bw = %4.2f)' % kde.bw)
+
+
+# use all kernels directly
+
+x_grid = np.linspace(np.min(x), np.max(x), 51)
+x_grid = np.linspace(-3, 3, 51)
+
+kernel_names = ['Biweight', 'Cosine', 'Epanechnikov', 'Gaussian',
+                'Triangular', 'Triweight', #'Uniform',
+                ]
+
+fig = plt.figure()
+for ii, kn in enumerate(kernel_names):
+    ax = fig.add_subplot(2, 3, ii+1)   # without uniform
+    ax.hist(x, bins=10, normed=True, alpha=0.25)
+    #reduce bandwidth for Gaussian and Uniform which are to large in example
+    if kn in ['Gaussian', 'Uniform']:
+        args = (0.5,)
+    else:
+        args = ()
+    kernel = getattr(kernels, kn)(*args)
+
+    kde_grid = [kernel.density(x, xi) for xi in x_grid]
+    confint_grid = kernel.density_confint(kde_grid, len(x))
+
+    ax.plot(x_grid, kde_grid, lw=2, color='red', label=kn)
+    ax.fill_between(x_grid, confint_grid[:,0], confint_grid[:,1],
+                    color='grey', alpha='0.7')
+    ax.legend(loc='upper left')
+
+plt.show()
diff --git a/statsmodels/examples/ex_kde_normalreference.py b/statsmodels/examples/ex_kde_normalreference.py
new file mode 100644
index 00000000000..ed5ea2c131f
--- /dev/null
+++ b/statsmodels/examples/ex_kde_normalreference.py
@@ -0,0 +1,59 @@
+# -*- coding: utf-8 -*-
+"""
+Author: Padarn Wilson
+
+Performance of normal reference plug-in estimator vs silverman. Sample is drawn
+from a mixture of gaussians. Distribution has been chosen to be reasoanbly close
+to normal.
+"""
+
+from __future__ import print_function
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+import statsmodels.nonparametric.api as npar
+from statsmodels.sandbox.nonparametric import kernels
+from statsmodels.distributions.mixture_rvs import mixture_rvs
+
+# example from test_kde.py mixture of two normal distributions
+np.random.seed(12345)
+x = mixture_rvs([.1, .9], size=200, dist=[stats.norm, stats.norm],
+                kwargs=(dict(loc=0, scale=.5), dict(loc=1, scale=.5)))
+
+kde = npar.KDEUnivariate(x)
+
+
+kernel_names = ['Gaussian', 'Epanechnikov', 'Biweight',
+                'Triangular', 'Triweight', 'Cosine'
+                ]
+
+kernel_switch = ['gau', 'epa', 'tri', 'biw',
+                 'triw', 'cos'
+                 ]
+
+
+def true_pdf(x):
+    pdf = 0.1 * stats.norm.pdf(x, loc=0, scale=0.5)
+    pdf += 0.9 * stats.norm.pdf(x, loc=1, scale=0.5)
+    return pdf
+
+fig = plt.figure()
+for ii, kn in enumerate(kernel_switch):
+
+    ax = fig.add_subplot(2, 3, ii + 1)   # without uniform
+    ax.hist(x, bins=20, normed=True, alpha=0.25)
+
+    kde.fit(kernel=kn, bw='silverman', fft=False)
+    ax.plot(kde.support, kde.density)
+
+    kde.fit(kernel=kn, bw='normal_reference', fft=False)
+    ax.plot(kde.support, kde.density)
+
+    ax.plot(kde.support, true_pdf(kde.support), color='black', linestyle='--')
+
+    ax.set_title(kernel_names[ii])
+
+
+ax.legend(['silverman', 'normal reference', 'true pdf'], loc='lower right')
+ax.set_title('200 points')
+plt.show()
diff --git a/statsmodels/examples/ex_kernel_regression.py b/statsmodels/examples/ex_kernel_regression.py
index 406841013da..9f8a119c05d 100644
--- a/statsmodels/examples/ex_kernel_regression.py
+++ b/statsmodels/examples/ex_kernel_regression.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold based on test file by George Panterov
 """
 
+from __future__ import print_function
 import numpy as np
 import numpy.testing as npt
 
diff --git a/statsmodels/examples/ex_kernel_regression2.py b/statsmodels/examples/ex_kernel_regression2.py
index df6f3615ed6..eaac45b2937 100644
--- a/statsmodels/examples/ex_kernel_regression2.py
+++ b/statsmodels/examples/ex_kernel_regression2.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 import numpy.testing as npt
 import statsmodels.nonparametric.api as nparam
@@ -40,9 +41,9 @@
 
     mean2, mfx2 = model2.fit()
 
-    print model.bw
-    print model1.bw
-    print model2.bw
+    print(model.bw)
+    print(model1.bw)
+    print(model2.bw)
 
     import matplotlib.pyplot as plt
     fig = plt.figure()
diff --git a/statsmodels/examples/ex_kernel_regression3.py b/statsmodels/examples/ex_kernel_regression3.py
index 1168906f08b..281533d35c4 100644
--- a/statsmodels/examples/ex_kernel_regression3.py
+++ b/statsmodels/examples/ex_kernel_regression3.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.nonparametric.api as nparam
 
@@ -54,9 +55,9 @@
 
     mean2, mfx2 = model2.fit()
 
-    print model.bw
+    print(model.bw)
     #print model1.bw
-    print model2.bw
+    print(model2.bw)
 
     ix = np.argsort(y_cens)
     ix_rev = np.zeros(nobs, int)
diff --git a/statsmodels/examples/ex_kernel_regression_censored2.py b/statsmodels/examples/ex_kernel_regression_censored2.py
index 6a9540eb506..c93f7e41032 100644
--- a/statsmodels/examples/ex_kernel_regression_censored2.py
+++ b/statsmodels/examples/ex_kernel_regression_censored2.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.nonparametric.api as nparam
 
diff --git a/statsmodels/examples/ex_kernel_regression_dgp.py b/statsmodels/examples/ex_kernel_regression_dgp.py
index 420aab60529..43df3fd5bb2 100644
--- a/statsmodels/examples/ex_kernel_regression_dgp.py
+++ b/statsmodels/examples/ex_kernel_regression_dgp.py
@@ -6,6 +6,8 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
+
 if __name__ == '__main__':
 
     import numpy as np
@@ -16,7 +18,7 @@
 
     seed = np.random.randint(999999)
     seed = 430973
-    print seed
+    print(seed)
     np.random.seed(seed)
 
     funcs = [dgp.UnivariateFanGijbels1(),
diff --git a/statsmodels/examples/ex_kernel_regression_sigtest.py b/statsmodels/examples/ex_kernel_regression_sigtest.py
index 10e5db121a4..985c3216f90 100644
--- a/statsmodels/examples/ex_kernel_regression_sigtest.py
+++ b/statsmodels/examples/ex_kernel_regression_sigtest.py
@@ -10,7 +10,7 @@
 results - this version
 ----------------------
 
->>> execfile('ex_kernel_regression_censored1.py')
+>>> exec(open('ex_kernel_regression_censored1.py').read())
 bw
 [ 0.3987821   0.50933458]
 [0.39878209999999997, 0.50933457999999998]
@@ -33,6 +33,7 @@
 
 """
 
+from __future__ import print_function
 import time
 
 import numpy as np
@@ -61,37 +62,37 @@
     #R_bw = [0.4017893, 0.4943397]  # Bandwidth obtained in R
     bw_expected = [0.3987821, 0.50933458]
     #npt.assert_allclose(model.bw, bw_expected, rtol=1e-3)
-    print 'bw'
-    print model.bw
-    print bw_expected
+    print('bw')
+    print(model.bw)
+    print(bw_expected)
 
-    print '\nsig_test - default'
-    print model.sig_test([1], nboot=100)
+    print('\nsig_test - default')
+    print(model.sig_test([1], nboot=100))
     t1 = time.time()
     res0 = smkr.TestRegCoefC(model, [1])
-    print 'pvalue'
-    print (res0.t_dist >= res0.test_stat).mean()
-    print 'test statistic', res0.test_stat
-    print 'bootstrap critical values'
+    print('pvalue')
+    print((res0.t_dist >= res0.test_stat).mean())
+    print('test statistic', res0.test_stat)
+    print('bootstrap critical values')
     probs = np.array([0.9, 0.95, 0.975, 0.99])
     bsort0 = np.sort(res0.t_dist)
     nrep0 = len(bsort0)
-    print bsort0[(probs * nrep0).astype(int)]
+    print(bsort0[(probs * nrep0).astype(int)])
 
     t2 = time.time()
-    print '\nsig_test - pivot=True, nboot=200, nested_res=50'
+    print('\nsig_test - pivot=True, nboot=200, nested_res=50')
     res1 = smkr.TestRegCoefC(model, [1], pivot=True, nboot=200, nested_res=50)
-    print 'pvalue'
-    print (res1.t_dist >= res1.test_stat).mean()
-    print 'test statistic', res1.test_stat
-    print 'bootstrap critical values'
+    print('pvalue')
+    print((res1.t_dist >= res1.test_stat).mean())
+    print('test statistic', res1.test_stat)
+    print('bootstrap critical values')
     probs = np.array([0.9, 0.95, 0.975, 0.99])
     bsort1 = np.sort(res1.t_dist)
     nrep1 = len(bsort1)
-    print bsort1[(probs * nrep1).astype(int)]
+    print(bsort1[(probs * nrep1).astype(int)])
     t3 = time.time()
 
-    print 'times:', t1-t0, t2-t1, t3-t2
+    print('times:', t1-t0, t2-t1, t3-t2)
 
 
 #    import matplotlib.pyplot as plt
diff --git a/statsmodels/examples/ex_kernel_semilinear_dgp.py b/statsmodels/examples/ex_kernel_semilinear_dgp.py
index 5a875268368..33aba7d5f05 100644
--- a/statsmodels/examples/ex_kernel_semilinear_dgp.py
+++ b/statsmodels/examples/ex_kernel_semilinear_dgp.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 
 
 if __name__ == '__main__':
@@ -25,7 +26,7 @@ def het_scale(self, x):
     #seed = 430973
     #seed = 47829
     seed = 648456 #good seed for het_scale = 0.5
-    print seed
+    print(seed)
     np.random.seed(seed)
 
     nobs, k_vars = 300, 3
@@ -59,23 +60,23 @@ def het_scale(self, x):
         ax.legend(loc='upper left')
         res.append((model, mean, mfx))
 
-    print 'beta', model.b
-    print 'scale - est', (y - (xb_est+mean)).std()
-    print 'scale - dgp realised, true', (y - (f.y_true + x2.sum(1))).std(), \
-                                        2 * f.het_scale(1)
+    print('beta', model.b)
+    print('scale - est', (y - (xb_est+mean)).std())
+    print('scale - dgp realised, true', (y - (f.y_true + x2.sum(1))).std(), \
+                                        2 * f.het_scale(1))
     fittedvalues = xb_est + mean
     resid = np.squeeze(model.endog) - fittedvalues
-    print 'corrcoef(fittedvalues, resid)', np.corrcoef(fittedvalues, resid)[0,1]
-    print 'variance of components, var and as fraction of var(y)'
-    print 'fitted values', fittedvalues.var(), fittedvalues.var() / y.var()
-    print 'linear       ', xb_est.var(), xb_est.var() / y.var()
-    print 'nonparametric', mean.var(), mean.var() / y.var()
-    print 'residual     ', resid.var(), resid.var() / y.var()
-    print '\ncovariance decomposition fraction of var(y)'
-    print np.cov(fittedvalues, resid) / model.endog.var(ddof=1)
-    print 'sum', (np.cov(fittedvalues, resid) / model.endog.var(ddof=1)).sum()
-    print '\ncovariance decomposition, xb, m, resid as fraction of var(y)'
-    print np.cov(np.column_stack((xb_est, mean, resid)), rowvar=False) / model.endog.var(ddof=1)
+    print('corrcoef(fittedvalues, resid)', np.corrcoef(fittedvalues, resid)[0,1])
+    print('variance of components, var and as fraction of var(y)')
+    print('fitted values', fittedvalues.var(), fittedvalues.var() / y.var())
+    print('linear       ', xb_est.var(), xb_est.var() / y.var())
+    print('nonparametric', mean.var(), mean.var() / y.var())
+    print('residual     ', resid.var(), resid.var() / y.var())
+    print('\ncovariance decomposition fraction of var(y)')
+    print(np.cov(fittedvalues, resid) / model.endog.var(ddof=1))
+    print('sum', (np.cov(fittedvalues, resid) / model.endog.var(ddof=1)).sum())
+    print('\ncovariance decomposition, xb, m, resid as fraction of var(y)')
+    print(np.cov(np.column_stack((xb_est, mean, resid)), rowvar=False) / model.endog.var(ddof=1))
 
     fig.suptitle('Kernel Regression')
     fig.show()
diff --git a/statsmodels/examples/ex_kernel_singleindex_dgp.py b/statsmodels/examples/ex_kernel_singleindex_dgp.py
index 2b88ec8d292..f6eb9e3fae6 100644
--- a/statsmodels/examples/ex_kernel_singleindex_dgp.py
+++ b/statsmodels/examples/ex_kernel_singleindex_dgp.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 
 
 if __name__ == '__main__':
@@ -25,7 +26,7 @@ def het_scale(self, x):
     #seed = 430973
     #seed = 47829
     seed = 648456 #good seed for het_scale = 0.5
-    print seed
+    print(seed)
     np.random.seed(seed)
 
     nobs, k_vars = 300, 3
diff --git a/statsmodels/examples/ex_kernel_test_functional.py b/statsmodels/examples/ex_kernel_test_functional.py
index 9636dc4eb8a..b05e0b4a36c 100644
--- a/statsmodels/examples/ex_kernel_test_functional.py
+++ b/statsmodels/examples/ex_kernel_test_functional.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 
 
 if __name__ == '__main__':
@@ -18,7 +19,7 @@
 
     seed = np.random.randint(999999)
     #seed = 661176
-    print seed
+    print(seed)
     np.random.seed(seed)
 
     sig_e = 0.5 #0.1
@@ -33,8 +34,8 @@
     y = y_true + sig_e * np.random.normal(size=nobs)
     endog = y
 
-    print 'DGP'
-    print 'nobs=%d, beta=%r, sig_e=%3.1f' % (nobs, beta, sig_e)
+    print('DGP')
+    print('nobs=%d, beta=%r, sig_e=%3.1f' % (nobs, beta, sig_e))
 
     mod_ols = OLS(endog, exog[:,:2])
     res_ols = mod_ols.fit()
@@ -44,17 +45,17 @@
                          estimator=lambda y,x: OLS(y,x).fit().params,
                          nboot=1000)
 
-    print 'bw', tst.bw
-    print 'tst.test_stat', tst.test_stat
-    print tst.sig
-    print 'tst.boots_results mean, min, max', (tst.boots_results.mean(),
+    print('bw', tst.bw)
+    print('tst.test_stat', tst.test_stat)
+    print(tst.sig)
+    print('tst.boots_results mean, min, max', (tst.boots_results.mean(),
                                                tst.boots_results.min(),
-                                               tst.boots_results.max())
-    print 'lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean()
-    print 'upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean()
+                                               tst.boots_results.max()))
+    print('lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean())
+    print('upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean())
     from scipy import stats
-    print 'aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2
-    print 'aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat)
+    print('aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2)
+    print('aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat))
 
     do_plot=True
     if do_plot:
diff --git a/statsmodels/examples/ex_kernel_test_functional_li_wang.py b/statsmodels/examples/ex_kernel_test_functional_li_wang.py
index d7b8a053bab..fab9365743d 100644
--- a/statsmodels/examples/ex_kernel_test_functional_li_wang.py
+++ b/statsmodels/examples/ex_kernel_test_functional_li_wang.py
@@ -59,6 +59,7 @@
 
 """
 
+from __future__ import print_function
 
 
 if __name__ == '__main__':
@@ -74,7 +75,7 @@
 
     seed = np.random.randint(999999)
     #seed = 661176
-    print seed
+    print(seed)
     np.random.seed(seed)
 
     sig_e = 0.1 #0.5 #0.1
@@ -108,21 +109,21 @@
     t1 = time.time()
     b_res = np.asarray(b_res)
 
-    print 'time', (t1 - t0) / 60.
-    print b_res.mean(0)
-    print b_res.std(0)
-    print 'reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap)'
-    print (b_res[:,1:,None] >= [0.2, 0.1, 0.05]).mean(0)
-
-    print 'bw', tst.bw
-    print 'tst.test_stat', tst.test_stat
-    print tst.sig
-    print 'tst.boots_results min, max', tst.boots_results.min(), tst.boots_results.max()
-    print 'lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean()
-    print 'upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean()
+    print('time', (t1 - t0) / 60.)
+    print(b_res.mean(0))
+    print(b_res.std(0))
+    print('reject at [0.2, 0.1, 0.05] (row 1: normal, row 2: bootstrap)')
+    print((b_res[:,1:,None] >= [0.2, 0.1, 0.05]).mean(0))
+
+    print('bw', tst.bw)
+    print('tst.test_stat', tst.test_stat)
+    print(tst.sig)
+    print('tst.boots_results min, max', tst.boots_results.min(), tst.boots_results.max())
+    print('lower tail bootstrap p-value', (tst.boots_results < tst.test_stat).mean())
+    print('upper tail bootstrap p-value', (tst.boots_results >= tst.test_stat).mean())
     from scipy import stats
-    print 'aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2
-    print 'aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat)
+    print('aymp.normal p-value (2-sided)', stats.norm.sf(np.abs(tst.test_stat))*2)
+    print('aymp.normal p-value (upper)', stats.norm.sf(tst.test_stat))
 
     res_ols = mod_ols.fit()
 
diff --git a/statsmodels/examples/ex_lowess.py b/statsmodels/examples/ex_lowess.py
index e575f2fa3d8..40d92756958 100644
--- a/statsmodels/examples/ex_lowess.py
+++ b/statsmodels/examples/ex_lowess.py
@@ -7,6 +7,7 @@
 extracted from test suite by josef-pktd
 """
 
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 import statsmodels.api as sm
@@ -52,8 +53,8 @@
                            [ 19.        ,  18.0466769 ]])
 
 actual_lowess = lowess(y, x)
-print actual_lowess
-print np.max(np.abs(actual_lowess-expected_lowess))
+print(actual_lowess)
+print(np.max(np.abs(actual_lowess-expected_lowess)))
 
 plt.plot(y, 'o')
 plt.plot(actual_lowess[:,1])
diff --git a/statsmodels/examples/ex_misc_tarma.py b/statsmodels/examples/ex_misc_tarma.py
index 921c182d020..aefd8d80abb 100644
--- a/statsmodels/examples/ex_misc_tarma.py
+++ b/statsmodels/examples/ex_misc_tarma.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 
 from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess
@@ -25,15 +26,15 @@
 res = mod.fit(order=order)
 res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm')
 
-print res[0]
+print(res[0])
 proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]])
 
-print ar, ma
+print(ar, ma)
 proc.nobs = nobs
 # TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__
-print proc.ar, proc.ma
+print(proc.ar, proc.ma)
 
-print proc.ar_roots(), proc.ma_roots()
+print(proc.ar_roots(), proc.ma_roots())
 
 from statsmodels.tsa.arma_mle import Arma
 modn = Arma(x)
@@ -42,26 +43,26 @@
 moda = ARMA(x, order=order)
 resa = moda.fit( trend='nc')
 
-print '\nparameter estimates'
-print 'ls  ', res[0]
-print 'norm', resn.params
-print 't   ', res2.params
-print 'A   ', resa.params
+print('\nparameter estimates')
+print('ls  ', res[0])
+print('norm', resn.params)
+print('t   ', res2.params)
+print('A   ', resa.params)
 
-print '\nstandard deviation of parameter estimates'
+print('\nstandard deviation of parameter estimates')
 #print 'ls  ', res[0]  #TODO: not available yet
-print 'norm', resn.bse
-print 't   ', res2.bse
-print 'A   ', resa.bse
-print 'A/t-1', resa.bse / res2.bse[:3] - 1
-
-print 'other bse'
-print resn.bsejac
-print resn.bsejhj
-print res2.bsejac
-print res2.bsejhj
-
-print res2.t_test(np.eye(len(res2.params)))
+print('norm', resn.bse)
+print('t   ', res2.bse)
+print('A   ', resa.bse)
+print('A/t-1', resa.bse / res2.bse[:3] - 1)
+
+print('other bse')
+print(resn.bsejac)
+print(resn.bsejhj)
+print(res2.bsejac)
+print(res2.bsejhj)
+
+print(res2.t_test(np.eye(len(res2.params))))
 
 # TArma has no fittedvalues and resid
 # TODO: check if lag is correct or if fitted `x-resid` is shifted
diff --git a/statsmodels/examples/ex_misc_tmodel.py b/statsmodels/examples/ex_misc_tmodel.py
index e4f848d174b..93fbad342a4 100644
--- a/statsmodels/examples/ex_misc_tmodel.py
+++ b/statsmodels/examples/ex_misc_tmodel.py
@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import numpy as np
 
 from scipy import stats, special, optimize
@@ -14,21 +15,21 @@
 data_exog = sm.add_constant(rvs, prepend=False)
 xbeta = 0.9 + 0.1*rvs.sum(1)
 data_endog = xbeta + 0.1*np.random.standard_t(df, size=nobs)
-print 'variance of endog:', data_endog.var()
-print 'true parameters:', [0.1]*nvars + [0.9]
+print('variance of endog:', data_endog.var())
+print('true parameters:', [0.1]*nvars + [0.9])
 
 res_ols = sm.OLS(data_endog, data_exog).fit()
-print '\nResults with ols'
-print   '----------------'
-print res_ols.scale
-print np.sqrt(res_ols.scale)
-print res_ols.params
-print res_ols.bse
+print('\nResults with ols')
+print('----------------')
+print(res_ols.scale)
+print(np.sqrt(res_ols.scale))
+print(res_ols.params)
+print(res_ols.bse)
 kurt = stats.kurtosis(res_ols.resid)
 df_fromkurt = 6./kurt + 4
-print 'df_fromkurt from ols residuals', df_fromkurt
-print stats.t.stats(df_fromkurt, moments='mvsk')
-print stats.t.stats(df, moments='mvsk')
+print('df_fromkurt from ols residuals', df_fromkurt)
+print(stats.t.stats(df_fromkurt, moments='mvsk'))
+print(stats.t.stats(df, moments='mvsk'))
 
 modp = TLinearModel(data_endog, data_exog)
 start_value = 0.1*np.ones(data_exog.shape[1]+2)
@@ -54,27 +55,27 @@
     modp.fixed_paramsmask = None
 
 
-print '\nResults with TLinearModel'
-print   '-------------------------'
+print('\nResults with TLinearModel')
+print('-------------------------')
 resp = modp.fit(start_params = modp.start_params, disp=1, method='nm',
                 maxfun=10000, maxiter=5000)#'newton')
 #resp = modp.fit(start_params = modp.start_params, disp=1, method='newton')
 
-print 'using Nelder-Mead'
-print resp.params
-print resp.bse
+print('using Nelder-Mead')
+print(resp.params)
+print(resp.bse)
 resp2 = modp.fit(start_params = resp.params, method='Newton')
-print 'using Newton'
-print resp2.params
-print resp2.bse
+print('using Newton')
+print(resp2.params)
+print(resp2.bse)
 
 from statsmodels.tools.numdiff import approx_fprime, approx_hess
 
 hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)
 tmp = modp.loglike(modp.start_params)
-print tmp.shape
-print 'eigenvalues of numerical Hessian'
-print np.linalg.eigh(np.linalg.inv(hb))[0]
+print(tmp.shape)
+print('eigenvalues of numerical Hessian')
+print(np.linalg.eigh(np.linalg.inv(hb))[0])
 
 #store_params is only available in original test script
 ##pp=np.array(store_params)
diff --git a/statsmodels/examples/ex_multivar_kde.py b/statsmodels/examples/ex_multivar_kde.py
index 963a0c992df..0606b1e7a0b 100644
--- a/statsmodels/examples/ex_multivar_kde.py
+++ b/statsmodels/examples/ex_multivar_kde.py
@@ -1,3 +1,5 @@
+
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import cm
diff --git a/statsmodels/examples/ex_nearest_corr.py b/statsmodels/examples/ex_nearest_corr.py
index c6dac1315e8..3fcf413b36d 100644
--- a/statsmodels/examples/ex_nearest_corr.py
+++ b/statsmodels/examples/ex_nearest_corr.py
@@ -16,6 +16,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 from statsmodels.stats.correlation_tools import (
                  corr_nearest, corr_clipped, cov_nearest)
@@ -31,21 +32,21 @@
 
     n_fact = 2
 
-    print 'evals original', np.linalg.eigvalsh(x)
+    print('evals original', np.linalg.eigvalsh(x))
     y = corr_nearest(x, n_fact=100)
-    print 'evals nearest', np.linalg.eigvalsh(y)
-    print y
+    print('evals nearest', np.linalg.eigvalsh(y))
+    print(y)
 
     y = corr_nearest(x, n_fact=100, threshold=1e-16)
-    print 'evals nearest', np.linalg.eigvalsh(y)
-    print y
+    print('evals nearest', np.linalg.eigvalsh(y))
+    print(y)
 
     y = corr_clipped(x, threshold=1e-16)
-    print 'evals clipped', np.linalg.eigvalsh(y)
-    print y
+    print('evals clipped', np.linalg.eigvalsh(y))
+    print(y)
 
     np.set_printoptions(precision=4)
-    print '\nMini Monte Carlo'
+    print('\nMini Monte Carlo')
     # we are simulating a uniformly distributed symmetric matrix
     #     and find close positive definite matrix
     # original can be far away from positive definite,
@@ -55,20 +56,20 @@
     k_vars = 5
     diag_idx = np.arange(k_vars)
     for ii in range(10):
-        print
+        print()
         x = np.random.uniform(-1, 1, size=(k_vars, k_vars))
         x = (x + x.T) * 0.5
         x[diag_idx, diag_idx] = 1
         #x_std = np.sqrt(np.diag(x))
         #x = x / x_std / x_std[:,None]
-        print
-        print np.sort(np.linalg.eigvals(x)), 'original'
+        print()
+        print(np.sort(np.linalg.eigvals(x)), 'original')
 
         yn = corr_nearest(x, threshold=1e-12, n_fact=200)
-        print np.sort(np.linalg.eigvals(yn)), ((yn - x)**2).sum(), 'nearest'
+        print(np.sort(np.linalg.eigvals(yn)), ((yn - x)**2).sum(), 'nearest')
 
         yc = corr_clipped(x, threshold=1e-12)
-        print np.sort(np.linalg.eigvals(yc)), ((yc - x)**2).sum(), 'clipped'
+        print(np.sort(np.linalg.eigvals(yc)), ((yc - x)**2).sum(), 'clipped')
 
     import time
     t0 = time.time()
@@ -80,7 +81,7 @@
         corr_clipped(x, threshold=1e-15)
     t2 = time.time()
 
-    print '\ntime (nearest, clipped):', t1 - t0, t2 - t1
+    print('\ntime (nearest, clipped):', t1 - t0, t2 - t1)
 
 if 'all' in examples:
     # example for test case against R
@@ -93,7 +94,7 @@
 
     y1 = corr_nearest(x2, threshold=1e-15, n_fact=200)
     y2 = corr_clipped(x2, threshold=1e-15)
-    print '\nmatrix 2'
-    print np.sort(np.linalg.eigvals(x2)), 'original'
-    print np.sort(np.linalg.eigvals(y1)), ((y1 - x2)**2).sum(), 'nearest'
-    print np.sort(np.linalg.eigvals(y1)), ((y2 - x2)**2).sum(), 'clipped'
+    print('\nmatrix 2')
+    print(np.sort(np.linalg.eigvals(x2)), 'original')
+    print(np.sort(np.linalg.eigvals(y1)), ((y1 - x2)**2).sum(), 'nearest')
+    print(np.sort(np.linalg.eigvals(y1)), ((y2 - x2)**2).sum(), 'clipped')
diff --git a/statsmodels/examples/ex_ols_robustcov.py b/statsmodels/examples/ex_ols_robustcov.py
new file mode 100644
index 00000000000..04554e2e4d9
--- /dev/null
+++ b/statsmodels/examples/ex_ols_robustcov.py
@@ -0,0 +1,42 @@
+
+from __future__ import print_function
+import numpy as np
+from statsmodels.regression.linear_model import OLS, GLSAR
+from statsmodels.tools.tools import add_constant
+from statsmodels.datasets import macrodata
+import statsmodels.regression.tests.results.results_macro_ols_robust as res
+
+
+d2 = macrodata.load().data
+g_gdp = 400*np.diff(np.log(d2['realgdp']))
+g_inv = 400*np.diff(np.log(d2['realinv']))
+exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
+res_olsg = OLS(g_inv, exogg).fit()
+
+
+
+print(res_olsg.summary())
+res_hc0 = res_olsg.get_robustcov_results('HC1')
+print('\n\n')
+print(res_hc0.summary())
+print('\n\n')
+res_hac4 = res_olsg.get_robustcov_results('HAC', maxlags=4, use_correction=True)
+print(res_hac4.summary())
+
+
+print('\n\n')
+tt = res_hac4.t_test(np.eye(len(res_hac4.params)))
+print(tt.summary())
+print('\n\n')
+print(tt.summary_frame())
+
+res_hac4.use_t = False
+
+print('\n\n')
+tt = res_hac4.t_test(np.eye(len(res_hac4.params)))
+print(tt.summary())
+print('\n\n')
+print(tt.summary_frame())
+
+print(vars(res_hac4.f_test(np.eye(len(res_hac4.params))[:-1], use_f=True)))
+print(vars(res_hac4.f_test(np.eye(len(res_hac4.params))[:-1], use_f=False)))
diff --git a/statsmodels/examples/ex_outliers_influence.py b/statsmodels/examples/ex_outliers_influence.py
index 779af962923..91d10a44954 100644
--- a/statsmodels/examples/ex_outliers_influence.py
+++ b/statsmodels/examples/ex_outliers_influence.py
@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import numpy as np
 
 import statsmodels.stats.outliers_influence as oi
@@ -64,13 +65,13 @@
     from scipy import stats
     alpha = 0.1
     #df looks wrong
-    print stats.f.isf(1-alpha, n_params, res.df_resid)
-    print stats.f.sf(cooks_d, n_params, res.df_resid)
+    print(stats.f.isf(1-alpha, n_params, res.df_resid))
+    print(stats.f.sf(cooks_d, n_params, res.df_resid))
 
 
-    print 'Cooks Distance'
-    print cooks_d
-    print cooks_d2
+    print('Cooks Distance')
+    print(cooks_d)
+    print(cooks_d2)
 
     doplot = 0
     if doplot:
@@ -91,17 +92,17 @@
         plt.setp(ltext, fontsize='small') # the legend text fontsize
 
 
-    print oi.reset_ramsey(res, degree=3)
+    print(oi.reset_ramsey(res, degree=3))
 
     #note, constant in last column
     for i in range(1):
-        print oi.variance_inflation_factor(res.model.exog, i)
+        print(oi.variance_inflation_factor(res.model.exog, i))
 
     infl = oi.OLSInfluence(res_ols)
-    print infl.resid_studentized_external
-    print infl.resid_studentized_internal
-    print infl.summary_table()
-    print oi.summary_table(res, alpha=0.05)[0]
+    print(infl.resid_studentized_external)
+    print(infl.resid_studentized_internal)
+    print(infl.summary_table())
+    print(oi.summary_table(res, alpha=0.05)[0])
 
 '''
 >>> res.resid
diff --git a/statsmodels/examples/ex_pairwise.py b/statsmodels/examples/ex_pairwise.py
index 3b81758e11b..1bb5cb81778 100644
--- a/statsmodels/examples/ex_pairwise.py
+++ b/statsmodels/examples/ex_pairwise.py
@@ -6,7 +6,9 @@
 Author: Josef Perktold
 """
 
-from statsmodels.compatnp.py3k import BytesIO, asbytes
+
+from __future__ import print_function
+from statsmodels.compat.python import BytesIO, asbytes, StringIO
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_equal
 
@@ -129,15 +131,15 @@
 
     mc = multi.MultiComparison(dta['Rust'], dta['Brand'])
     res = mc.tukeyhsd()
-    print res[0]
+    print(res[0])
 
     mc2 = multi.MultiComparison(dta2['StressReduction'], dta2['Treatment'])
     res2 = mc2.tukeyhsd()
-    print res2[0]
+    print(res2[0])
 
     mc2s = multi.MultiComparison(dta2['StressReduction'][3:29], dta2['Treatment'][3:29])
     res2s = mc2s.tukeyhsd()
-    print res2s[0]
+    print(res2s[0])
     res2s_001 = mc2s.tukeyhsd(alpha=0.01)
     #R result
     tukeyhsd2s = np.array([1.888889,0.8888889,-1,0.2658549,-0.5908785,-2.587133,3.511923,2.368656,0.5871331,0.002837638,0.150456,0.1266072]).reshape(3,4, order='F')
@@ -145,13 +147,13 @@
 
     mc3 = multi.MultiComparison(dta3['Relief'], dta3['Brand'])
     res3 = mc3.tukeyhsd()
-    print res3[0]
+    print(res3[0])
 
 #    for mci in [mc, mc2, mc3]:
 #        get_thsd(mci)
 
     from scipy import stats
-    print mc2.allpairtest(stats.ttest_ind, method='b')[0]
+    print(mc2.allpairtest(stats.ttest_ind, method='b')[0])
 
     '''same as SAS:
     >>> np.var(mci.groupstats.groupdemean(), ddof=3)
@@ -188,8 +190,7 @@
     1 - 2	-4.600	-8.249	-0.951	***
     1 - 3	-0.260	-3.909	 3.389	'''
 
-    import StringIO
-    dta5 = np.recfromtxt(StringIO.StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t')
+    dta5 = np.recfromtxt(StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t')
 
     sas_ = dta5[[1,3,2]]
     confint1 = res3[1][4]
diff --git a/statsmodels/examples/ex_pandas.py b/statsmodels/examples/ex_pandas.py
index 1f5d4f3b795..dc3cb0f392d 100644
--- a/statsmodels/examples/ex_pandas.py
+++ b/statsmodels/examples/ex_pandas.py
@@ -4,6 +4,8 @@
 """
 
 
+from __future__ import print_function
+from statsmodels.compat.python import zip
 from datetime import datetime
 
 import numpy as np
@@ -22,22 +24,22 @@
 #Example: OLS
 model = sm.OLS(Y, X)
 results = model.fit()
-print results.summary()
+print(results.summary())
 
-print results.params
-print results.cov_params()
+print(results.params)
+print(results.cov_params())
 
 infl = results.get_influence()
-print infl.summary_table()
+print(infl.summary_table())
 
 #raise
 
 #Example RLM
 huber_t = sm.RLM(Y, X, M=sm.robust.norms.HuberT())
 hub_results = huber_t.fit()
-print hub_results.params
-print hub_results.bcov_scaled
-print hub_results.summary()
+print(hub_results.params)
+print(hub_results.bcov_scaled)
+print(hub_results.summary())
 
 
 import matplotlib.pyplot as plt
@@ -88,11 +90,11 @@ def plot_acf_multiple(ys, lags=20):
 #Example TSA VAR
 
 model = tsa.VAR(log_difference, freq='D')
-print model.select_order()
+print(model.select_order())
 
 res = model.fit(2)
-print res.summary()
-print res.is_stable()
+print(res.summary())
+print(res.is_stable())
 
 irf = res.irf(20)
 irf.plot()
@@ -101,7 +103,7 @@ def plot_acf_multiple(ys, lags=20):
 fevd.plot()
 
 #print res.test_whiteness()
-print res.test_causality('m1', 'realgdp')
+print(res.test_causality('m1', 'realgdp'))
 #print res.test_normality() # exception
 '''
 Traceback (most recent call last):
diff --git a/statsmodels/examples/ex_pareto_plot.py b/statsmodels/examples/ex_pareto_plot.py
index 4568f7717b0..24f4682d48f 100644
--- a/statsmodels/examples/ex_pareto_plot.py
+++ b/statsmodels/examples/ex_pareto_plot.py
@@ -6,6 +6,7 @@
 """
 
 
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 import matplotlib.pyplot as plt
@@ -29,6 +30,6 @@
 rsf = nobs-rsind.argsort()
 plt.figure()
 plt.loglog(rs, nobs-np.arange(nobs), '-o')
-print stats.linregress(np.log(rs), np.log(nobs-np.arange(nobs)))
+print(stats.linregress(np.log(rs), np.log(nobs-np.arange(nobs))))
 
 plt.show()
diff --git a/statsmodels/examples/ex_proportion.py b/statsmodels/examples/ex_proportion.py
index 79a5db4b794..9b61f27b6fd 100644
--- a/statsmodels/examples/ex_proportion.py
+++ b/statsmodels/examples/ex_proportion.py
@@ -6,6 +6,8 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
+from statsmodels.compat.python import lmap
 import numpy as np
 import statsmodels.stats.proportion as sms
 import statsmodels.stats.weightstats as smw
@@ -26,7 +28,7 @@
 2 brown medium 53  2 brown dark   54  2 brown black  13'''
 
 dta0 = np.array(ss.split()).reshape(-1,4)
-dta = np.array(map(tuple, dta0.tolist()), dtype=[('Region', int), ('Eyes', 'S6'), ('Hair', 'S6'), ('Count', int)])
+dta = np.array(lmap(tuple, dta0.tolist()), dtype=[('Region', int), ('Eyes', 'S6'), ('Hair', 'S6'), ('Count', int)])
 
 xfair = np.repeat([1,0], [228, 762-228])
 
diff --git a/statsmodels/examples/ex_regressionplots.py b/statsmodels/examples/ex_regressionplots.py
index 58910062df5..672382fe31a 100644
--- a/statsmodels/examples/ex_regressionplots.py
+++ b/statsmodels/examples/ex_regressionplots.py
@@ -5,6 +5,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
diff --git a/statsmodels/examples/ex_rootfinding.py b/statsmodels/examples/ex_rootfinding.py
index 0dba116f8ec..c1a662e28a2 100644
--- a/statsmodels/examples/ex_rootfinding.py
+++ b/statsmodels/examples/ex_rootfinding.py
@@ -5,7 +5,7 @@
 
 Author: Josef Perktold
 """
-
+from __future__ import print_function
 import numpy as np
 from statsmodels.tools.rootfinding import brentq_expanding
 
@@ -20,86 +20,86 @@
 
 def func(x, a):
     f = (x - a)**3
-    if DEBUG: print 'evaluating at %g, fval = %f' % (x, f)
+    if DEBUG: print('evaluating at %g, fval = %f' % (x, f))
     return f
 
 def func_nan(x, a, b):
     x = np.atleast_1d(x)
     f = (x - 1.*a)**3
     f[x < b] = np.nan
-    if DEBUG: print 'evaluating at %f, fval = %f' % (x, f)
+    if DEBUG: print('evaluating at %f, fval = %f' % (x, f))
     return f
 
 
 
 def funcn(x, a):
     f = -(x - a)**3
-    if DEBUG: print 'evaluating at %g, fval = %g' % (x, f)
+    if DEBUG: print('evaluating at %g, fval = %g' % (x, f))
     return f
 
 def func2(x, a):
     f = (x - a)**3
-    print 'evaluating at %g, fval = %f' % (x, f)
+    print('evaluating at %g, fval = %f' % (x, f))
     return f
 
 if __name__ == '__main__':
     run_all = False
     if run_all:
-        print brentq_expanding(func, args=(0,), increasing=True)
+        print(brentq_expanding(func, args=(0,), increasing=True))
 
-        print brentq_expanding(funcn, args=(0,), increasing=False)
-        print brentq_expanding(funcn, args=(-50,), increasing=False)
+        print(brentq_expanding(funcn, args=(0,), increasing=False))
+        print(brentq_expanding(funcn, args=(-50,), increasing=False))
 
-        print brentq_expanding(func, args=(20,))
-        print brentq_expanding(funcn, args=(20,))
-        print brentq_expanding(func, args=(500000,))
+        print(brentq_expanding(func, args=(20,)))
+        print(brentq_expanding(funcn, args=(20,)))
+        print(brentq_expanding(func, args=(500000,)))
 
         # one bound
-        print brentq_expanding(func, args=(500000,), low=10000)
-        print brentq_expanding(func, args=(-50000,), upp=-1000)
+        print(brentq_expanding(func, args=(500000,), low=10000))
+        print(brentq_expanding(func, args=(-50000,), upp=-1000))
 
-        print brentq_expanding(funcn, args=(500000,), low=10000)
-        print brentq_expanding(funcn, args=(-50000,), upp=-1000)
+        print(brentq_expanding(funcn, args=(500000,), low=10000))
+        print(brentq_expanding(funcn, args=(-50000,), upp=-1000))
 
         # both bounds
         # hits maxiter in brentq if bounds too wide
-        print brentq_expanding(func, args=(500000,), low=300000, upp=700000)
-        print brentq_expanding(func, args=(-50000,), low= -70000, upp=-1000)
-        print brentq_expanding(funcn, args=(500000,), low=300000, upp=700000)
-        print brentq_expanding(funcn, args=(-50000,), low= -70000, upp=-10000)
+        print(brentq_expanding(func, args=(500000,), low=300000, upp=700000))
+        print(brentq_expanding(func, args=(-50000,), low= -70000, upp=-1000))
+        print(brentq_expanding(funcn, args=(500000,), low=300000, upp=700000))
+        print(brentq_expanding(funcn, args=(-50000,), low= -70000, upp=-10000))
 
-        print brentq_expanding(func, args=(1.234e30,), xtol=1e10,
-                               increasing=True, maxiter_bq=200)
+        print(brentq_expanding(func, args=(1.234e30,), xtol=1e10,
+                               increasing=True, maxiter_bq=200))
 
 
-    print brentq_expanding(func, args=(-50000,), start_low=-10000)
+    print(brentq_expanding(func, args=(-50000,), start_low=-10000))
     try:
-        print brentq_expanding(func, args=(-500,), start_upp=-100)
+        print(brentq_expanding(func, args=(-500,), start_upp=-100))
     except ValueError:
-        print 'raised ValueError start_upp needs to be positive'
+        print('raised ValueError start_upp needs to be positive')
 
     ''' it still works
     raise ValueError('start_upp needs to be positive')
     -499.999996336
     '''
     ''' this doesn't work
-    >>> print brentq_expanding(func, args=(-500,), start_upp=-1000)
+    >>> print(brentq_expanding(func, args=(-500,), start_upp=-1000)
     raise ValueError('start_upp needs to be positive')
     OverflowError: (34, 'Result too large')
     '''
 
     try:
-        print brentq_expanding(funcn, args=(-50000,), low= -40000, upp=-10000)
-    except Exception, e:
-        print e
+        print(brentq_expanding(funcn, args=(-50000,), low= -40000, upp=-10000))
+    except Exception as e:
+        print(e)
 
     val, info = brentq_expanding(func, args=(500,), full_output=True)
-    print val
-    print vars(info)
+    print(val)
+    print(vars(info))
 
     #
-    print brentq_expanding(func_nan, args=(20,0), increasing=True)
-    print brentq_expanding(func_nan, args=(20,0))
+    print(brentq_expanding(func_nan, args=(20,0), increasing=True))
+    print(brentq_expanding(func_nan, args=(20,0)))
     # In the next point 0 is minumum, below is nan
-    print brentq_expanding(func_nan, args=(-20,0), increasing=True)
-    print brentq_expanding(func_nan, args=(-20,0))
+    print(brentq_expanding(func_nan, args=(-20,0), increasing=True))
+    print(brentq_expanding(func_nan, args=(-20,0)))
diff --git a/statsmodels/examples/ex_sandwich.py b/statsmodels/examples/ex_sandwich.py
index 818593ded87..d0d28771b6d 100644
--- a/statsmodels/examples/ex_sandwich.py
+++ b/statsmodels/examples/ex_sandwich.py
@@ -4,7 +4,7 @@
 Author: Josef Perktold
 
 """
-
+from statsmodels.compat.python import lzip
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -25,30 +25,30 @@
 
 self = sm.OLS(endog, exog).fit()
 
-print self.HC3_se
-print sw.se_cov(sw.cov_hc3(self))
+print(self.HC3_se)
+print(sw.se_cov(sw.cov_hc3(self)))
 #test standalone refactoring
 assert_almost_equal(sw.se_cov(sw.cov_hc0(self)), self.HC0_se, 15)
 assert_almost_equal(sw.se_cov(sw.cov_hc1(self)), self.HC1_se, 15)
 assert_almost_equal(sw.se_cov(sw.cov_hc2(self)), self.HC2_se, 15)
 assert_almost_equal(sw.se_cov(sw.cov_hc3(self)), self.HC3_se, 15)
-print self.HC0_se
-print sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=False))
+print(self.HC0_se)
+print(sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=False)))
 #test White as HAC with nlags=0, same as nlags=1 ?
 bse_hac0 = sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=False))
 assert_almost_equal(bse_hac0, self.HC0_se, 15)
-print bse_hac0
+print(bse_hac0)
 #test White as HAC with nlags=0, same as nlags=1 ?
 bse_hac0c = sw.se_cov(sw.cov_hac_simple(self, nlags=0, use_correction=True))
 assert_almost_equal(bse_hac0c, self.HC1_se, 15)
 
 bse_w = sw.se_cov(sw.cov_white_simple(self, use_correction=False))
-print bse_w
+print(bse_w)
 #test White
 assert_almost_equal(bse_w, self.HC0_se, 15)
 
 bse_wc = sw.se_cov(sw.cov_white_simple(self, use_correction=True))
-print bse_wc
+print(bse_wc)
 #test White
 assert_almost_equal(bse_wc, self.HC1_se, 15)
 
@@ -56,19 +56,19 @@
 groups = np.repeat(np.arange(5), 20)
 
 idx = np.nonzero(np.diff(groups))[0].tolist()
-groupidx = zip([0]+idx, idx+[len(groups)])
+groupidx = lzip([0]+idx, idx+[len(groups)])
 ngroups = len(groupidx)
 
-print sw.se_cov(sw.cov_cluster(self, groups))
+print(sw.se_cov(sw.cov_cluster(self, groups)))
 #two strange looking corner cases BUG?
-print sw.se_cov(sw.cov_cluster(self, np.ones(len(endog), int), use_correction=False))
-print sw.se_cov(sw.cov_crosssection_0(self, np.arange(len(endog))))
+print(sw.se_cov(sw.cov_cluster(self, np.ones(len(endog), int), use_correction=False)))
+print(sw.se_cov(sw.cov_crosssection_0(self, np.arange(len(endog)))))
 #these results are close to simple (no group) white, 50 groups 2 obs each
 groups = np.repeat(np.arange(50), 100//50)
-print sw.se_cov(sw.cov_cluster(self, groups))
+print(sw.se_cov(sw.cov_cluster(self, groups)))
 #2 groups with 50 obs each, what was the interpretation again?
 groups = np.repeat(np.arange(2), 100//2)
-print sw.se_cov(sw.cov_cluster(self, groups))
+print(sw.se_cov(sw.cov_cluster(self, groups)))
 
 "http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt"
 '''
diff --git a/statsmodels/examples/ex_sandwich2.py b/statsmodels/examples/ex_sandwich2.py
index b852ea97a73..1565f45d5b9 100644
--- a/statsmodels/examples/ex_sandwich2.py
+++ b/statsmodels/examples/ex_sandwich2.py
@@ -5,6 +5,7 @@
 Author: Josef Perktold
 """
 
+from statsmodels.compat.python import urlretrieve
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -12,15 +13,15 @@
 import statsmodels.stats.sandwich_covariance as sw
 
 #http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs.dta
+
 import statsmodels.iolib.foreign as dta
 
 try:
     srs = dta.genfromdta("srs.dta")
-    print 'using local file'
+    print('using local file')
 except IOError:
-    import urllib
-    urllib.urlretrieve('http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs.dta', 'srs.dta')
-    print 'downloading file'
+    urlretrieve('http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs.dta', 'srs.dta')
+    print('downloading file')
     srs = dta.genfromdta("srs.dta")
 #    from statsmodels.tools.tools import webuse
 #    srs = webuse('srs', 'http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/')
@@ -46,14 +47,14 @@
 #run OLS
 
 res_srs = sm.OLS(y, xx).fit()
-print 'params    ', res_srs.params
-print 'bse_OLS   ', res_srs.bse
+print('params    ', res_srs.params)
+print('bse_OLS   ', res_srs.bse)
 
 #get cluster robust standard errors and compare with STATA
 
 cov_cr = sw.cov_cluster(res_srs, group.astype(int))
 bse_cr = sw.se_cov(cov_cr)
-print 'bse_rob   ', bse_cr
+print('bse_rob   ', bse_cr)
 
 res_stata = np.rec.array(
      [ ('growth', '|', -0.1027121, 0.22917029999999999, -0.45000000000000001, 0.65500000000000003, -0.55483519999999997, 0.34941109999999997),
@@ -64,9 +65,9 @@
              ('bse', 'float'), ('tvalues', 'float'), ('pvalues', 'float'),
              ('cilow', 'float'), ('ciupp', 'float')])
 
-print 'diff Stata', bse_cr - res_stata.bse
+print('diff Stata', bse_cr - res_stata.bse)
 assert_almost_equal(bse_cr, res_stata.bse, decimal=6)
 
 #We see that in this case the robust standard errors of the parameter estimates
 #are larger than those of OLS by 8 to 35 %
-print 'reldiff to OLS', bse_cr/res_srs.bse - 1
+print('reldiff to OLS', bse_cr/res_srs.bse - 1)
diff --git a/statsmodels/examples/ex_sandwich3.py b/statsmodels/examples/ex_sandwich3.py
index 0c9becca92e..931febbd968 100644
--- a/statsmodels/examples/ex_sandwich3.py
+++ b/statsmodels/examples/ex_sandwich3.py
@@ -5,6 +5,7 @@
 
 Author: Josef Perktold
 """
+from statsmodels.compat.python import urlretrieve
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -16,12 +17,11 @@
 #http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt
 try:
     pet = np.genfromtxt("test_data.txt")
-    print 'using local file'
+    print('using local file')
 except IOError:
-    import urllib
-    urllib.urlretrieve('http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt',
+    urlretrieve('http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt',
                        'test_data.txt')
-    print 'downloading file'
+    print('downloading file')
     pet = np.genfromtxt("test_data.txt")
 
 
@@ -45,14 +45,14 @@
 bse_1 = sw.se_cov(covt)
 bse_01 = sw.se_cov(cov01)
 
-print 'OLS            ', res.bse
-print 'het HC0        ', res.HC0_se, bse_petw - res.HC0_se
-print 'het firm       ', bse_0, bse_0 - bse_pet0
-print 'het year       ', bse_1, bse_1 - bse_pet1
-print 'het firm & year', bse_01, bse_01 - bse_pet01
+print('OLS            ', res.bse)
+print('het HC0        ', res.HC0_se, bse_petw - res.HC0_se)
+print('het firm       ', bse_0, bse_0 - bse_pet0)
+print('het year       ', bse_1, bse_1 - bse_pet1)
+print('het firm & year', bse_01, bse_01 - bse_pet01)
 
-print 'relative difference standard error het firm & year to OLS'
-print '               ', bse_01 / res.bse
+print('relative difference standard error het firm & year to OLS')
+print('               ', bse_01 / res.bse)
 
 #From the last line we see that the cluster and year robust standard errors
 #are approximately twice those of OLS
diff --git a/statsmodels/examples/ex_scatter_ellipse.py b/statsmodels/examples/ex_scatter_ellipse.py
index 3efbdc7a12a..1735f6505e4 100644
--- a/statsmodels/examples/ex_scatter_ellipse.py
+++ b/statsmodels/examples/ex_scatter_ellipse.py
@@ -6,7 +6,7 @@
 '''
 
 
-
+from statsmodels.compat.python import lrange
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -23,7 +23,7 @@
 r = (r + r.T) / 2.
 assert np.allclose(r, r.T)
 mcorr = r
-mcorr[range(nvars), range(nvars)] = 1
+mcorr[lrange(nvars), lrange(nvars)] = 1
 #dcorr = np.array([[1, 0.5, 0.1],[0.5, 1, -0.2], [0.1, -0.2, 1]])
 mstd = np.arange(1.,nvars+1)/nvars
 mcov = mcorr * np.outer(mstd, mstd)
@@ -34,11 +34,11 @@
 data = np.random.multivariate_normal(mmean, mcov, size=nobs)
 dmean = data.mean(0)
 dcov = np.cov(data, rowvar=0)
-print dmean
-print dcov
+print(dmean)
+print(dcov)
 dcorr = np.corrcoef(data, rowvar=0)
 dcorr[np.triu_indices(nvars)] = 0
-print dcorr
+print(dcorr)
 
 #default
 #fig = scatter_ellipse(data, level=[0.5, 0.75, 0.95])
diff --git a/statsmodels/examples/ex_shrink_pickle.py b/statsmodels/examples/ex_shrink_pickle.py
index 4528a028588..11e4a0a682d 100644
--- a/statsmodels/examples/ex_shrink_pickle.py
+++ b/statsmodels/examples/ex_shrink_pickle.py
@@ -5,7 +5,7 @@
 
 Author: Josef Perktold
 """
-
+from statsmodels.compat.python import StringIO
 import numpy as np
 import statsmodels.api as sm
 
@@ -26,23 +26,23 @@
 
 
 #print results.predict(xf)
-print results.model.predict(results.params, xf)
+print(results.model.predict(results.params, xf))
 results.summary()
 
 shrinkit = 1
 if shrinkit:
     results.remove_data()
 
-import pickle
+from statsmodels.compat.python import cPickle
 fname = 'try_shrink%d_ols.pickle' % shrinkit
 fh = open(fname, 'w')
-pickle.dump(results._results, fh)  #pickling wrapper doesn't work
+cPickle.dump(results._results, fh)  #pickling wrapper doesn't work
 fh.close()
 fh = open(fname, 'r')
-results2 = pickle.load(fh)
+results2 = cPickle.load(fh)
 fh.close()
-print results2.predict(xf)
-print results2.model.predict(results.params, xf)
+print(results2.predict(xf))
+print(results2.model.predict(results.params, xf))
 
 
 y_count = np.random.poisson(np.exp(x.sum(1)-x.mean()))
@@ -51,7 +51,7 @@
 
 results.summary()
 
-print results.model.predict(results.params, xf, exposure=1, offset=0)
+print(results.model.predict(results.params, xf, exposure=1, offset=0))
 
 if shrinkit:
     results.remove_data()
@@ -62,21 +62,20 @@
 import pickle
 fname = 'try_shrink%d_poisson.pickle' % shrinkit
 fh = open(fname, 'w')
-pickle.dump(results._results, fh)  #pickling wrapper doesn't work
+cPickle.dump(results._results, fh)  #pickling wrapper doesn't work
 fh.close()
 fh = open(fname, 'r')
-results3 = pickle.load(fh)
+results3 = cPickle.load(fh)
 fh.close()
-print results3.predict(xf, exposure=1, offset=0)
-print results3.model.predict(results.params, xf, exposure=1, offset=0)
+print(results3.predict(xf, exposure=1, offset=0))
+print(results3.model.predict(results.params, xf, exposure=1, offset=0))
 
 def check_pickle(obj):
-    import StringIO
-    fh = StringIO.StringIO()
-    pickle.dump(obj, fh)
+    fh =StringIO()
+    cPickle.dump(obj, fh)
     plen = fh.pos
     fh.seek(0,0)
-    res = pickle.load(fh)
+    res = cPickle.load(fh)
     fh.close()
     return res, plen
 
diff --git a/statsmodels/examples/ex_univar_kde.py b/statsmodels/examples/ex_univar_kde.py
index 35e46f2905d..d8f91007038 100644
--- a/statsmodels/examples/ex_univar_kde.py
+++ b/statsmodels/examples/ex_univar_kde.py
@@ -14,6 +14,7 @@
 """
 
 
+from __future__ import print_function
 import numpy as np
 import scipy.stats as stats
 import matplotlib.pyplot as plt
diff --git a/statsmodels/examples/example_discrete_mnl.py b/statsmodels/examples/example_discrete_mnl.py
index 245fb26d76b..cd5c39fa983 100644
--- a/statsmodels/examples/example_discrete_mnl.py
+++ b/statsmodels/examples/example_discrete_mnl.py
@@ -1,6 +1,8 @@
 """Example: statsmodels.discretemod
 """
 
+from __future__ import print_function
+from statsmodels.compat.python import lrange
 import numpy as np
 import statsmodels.api as sm
 
@@ -21,11 +23,11 @@
 from statsmodels.iolib.summary import (
                         summary_params_2d, summary_params_2dflat)
 
-exog_names = [anes_data.exog_name[i] for i in [0, 2]+range(5,8)] + ['const']
+exog_names = [anes_data.exog_name[i] for i in [0, 2]+lrange(5,8)] + ['const']
 endog_names = [anes_data.endog_name+'_%d' % i for i in np.unique(mlogit_res.model.endog)[1:]]
-print '\n\nMultinomial'
-print  summary_params_2d(mlogit_res, extras=['bse','tvalues'],
-                         endog_names=endog_names, exog_names=exog_names)
+print('\n\nMultinomial')
+print(summary_params_2d(mlogit_res, extras=['bse','tvalues'],
+                         endog_names=endog_names, exog_names=exog_names))
 tables, table_all = summary_params_2dflat(mlogit_res,
                                           endog_names=endog_names,
                                           exog_names=exog_names,
@@ -34,32 +36,33 @@
                                           endog_names=endog_names,
                                           exog_names=exog_names,
                                           keep_headers=False)
-print '\n\n'
-print table_all
-print '\n\n'
-print '\n'.join((str(t) for t in tables))
+print('\n\n')
+print(table_all)
+print('\n\n')
+print('\n'.join((str(t) for t in tables)))
 
 from statsmodels.iolib.summary import table_extend
 at = table_extend(tables)
-print at
+print(at)
 
-print '\n\n'
-print mlogit_res.summary()
-print mlogit_res.summary(yname='PID')
+print('\n\n')
+print(mlogit_res.summary())
+print(mlogit_res.summary(yname='PID'))
 #the following is supposed to raise ValueError
 #mlogit_res.summary(yname=['PID'])
 
 endog_names = [anes_data.endog_name+'=%d' % i for i in np.unique(mlogit_res.model.endog)[1:]]
-print mlogit_res.summary(yname='PID', yname_list=endog_names, xname=exog_names)
+print(mlogit_res.summary(yname='PID', yname_list=endog_names, xname=exog_names))
 
-''' #trying pickle
-import pickle #, copy
+
+''' #trying cPickle
+from statsmodels.compat.python import cPickle #, copy
 
 #copy.deepcopy(mlogit_res)  #raises exception: AttributeError: 'ResettableCache' object has no attribute '_resetdict'
 mnl_res = mlogit_mod.fit(method='bfgs', maxiter=100)
 mnl_res.cov_params()
 #mnl_res.model.endog = None
 #mnl_res.model.exog = None
-pickle.dump(mnl_res, open('mnl_res.dump', 'w'))
-mnl_res_l = pickle.load(open('mnl_res.dump', 'r'))
+cPickle.dump(mnl_res, open('mnl_res.dump', 'w'))
+mnl_res_l = cPickle.load(open('mnl_res.dump', 'r'))
 '''
diff --git a/statsmodels/examples/example_enhanced_boxplots.py b/statsmodels/examples/example_enhanced_boxplots.py
index c0ee9628fbd..ccaa90ca6ad 100644
--- a/statsmodels/examples/example_enhanced_boxplots.py
+++ b/statsmodels/examples/example_enhanced_boxplots.py
@@ -1,3 +1,5 @@
+
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 
diff --git a/statsmodels/examples/example_functional_plots.py b/statsmodels/examples/example_functional_plots.py
index 95dd37d654a..7c0f606a64b 100644
--- a/statsmodels/examples/example_functional_plots.py
+++ b/statsmodels/examples/example_functional_plots.py
@@ -7,6 +7,7 @@
 
 '''
 
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 import statsmodels.api as sm
diff --git a/statsmodels/examples/example_kde.py b/statsmodels/examples/example_kde.py
index 5adccf683e6..abdab0a0740 100644
--- a/statsmodels/examples/example_kde.py
+++ b/statsmodels/examples/example_kde.py
@@ -1,3 +1,5 @@
+
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 from statsmodels.distributions.mixture_rvs import mixture_rvs
diff --git a/statsmodels/examples/example_ols_minimal_comp.py b/statsmodels/examples/example_ols_minimal_comp.py
index 9b831329860..d7a0da822cb 100644
--- a/statsmodels/examples/example_ols_minimal_comp.py
+++ b/statsmodels/examples/example_ols_minimal_comp.py
@@ -4,6 +4,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 
@@ -15,13 +16,13 @@
 y = np.dot(X, beta) + np.random.normal(size=nsample)
 
 results = sm.OLS(y, X).fit()
-print results.summary()
+print(results.summary())
 
 results2 = sm.OLS(y, X[:,:2]).fit()
-print results.compare_f_test(results2)
-print results.f_test([0,0,1])
+print(results.compare_f_test(results2))
+print(results.f_test([0,0,1]))
 
-print results.compare_lr_test(results2)
+print(results.compare_lr_test(results2))
 
 '''
 (1.841903749875428, 0.1778775592033047)
diff --git a/statsmodels/examples/example_rpy.py b/statsmodels/examples/example_rpy.py
index fed9fb1aa72..c58bf0bc9cb 100644
--- a/statsmodels/examples/example_rpy.py
+++ b/statsmodels/examples/example_rpy.py
@@ -17,6 +17,8 @@
 some basic models for comparisons of results to statsmodels.
 '''
 
+from __future__ import print_function
+from statsmodels.compat.python import iterkeys
 from rpy import r
 import numpy as np
 import statsmodels.api as sm
@@ -31,8 +33,8 @@
     formula = r('y~%s-1' % '+'.join(des_cols))
     frame = r.data_frame(y=y, x=x)
     results = r.lm(formula, data=frame)
-    print results.keys()
-    print results['coefficients']
+    print(list(iterkeys(results)))
+    print(results['coefficients'])
 
 if 2 in examples:
     data2 = sm.datasets.star98.load()
@@ -45,6 +47,6 @@
     results2 = r.glm(formula2, data=frame2, family='binomial')
     params_est = [results2['coefficients'][k] for k
                     in sorted(results2['coefficients'])]
-    print params_est
-    print ', '.join(['%13.10f']*21) % tuple(params_est)
+    print(params_est)
+    print(', '.join(['%13.10f']*21) % tuple(params_est))
 
diff --git a/statsmodels/examples/l1_demo/demo.py b/statsmodels/examples/l1_demo/demo.py
index c69dc40df2a..9558a3c8b83 100644
--- a/statsmodels/examples/l1_demo/demo.py
+++ b/statsmodels/examples/l1_demo/demo.py
@@ -1,3 +1,5 @@
+from __future__ import print_function
+from statsmodels.compat.python import range
 from optparse import OptionParser
 import statsmodels.api as sm
 import scipy as sp
@@ -155,7 +157,7 @@ def run_demo(mode, base_alpha=0.01, N=500, get_l1_slsqp_results=False,
         Load exog/endog/true_params arrays from disk.
     """
     if mode != 'mnlogit':
-        print "Setting num_targets to 2 since mode != 'mnlogit'"
+        print("Setting num_targets to 2 since mode != 'mnlogit'")
         num_targets = 2
     models = {
             'logit': sm.Logit, 'mnlogit': sm.MNLogit, 'probit': sm.Probit}
@@ -187,7 +189,7 @@ def run_demo(mode, base_alpha=0.01, N=500, get_l1_slsqp_results=False,
     summary_str = get_summary_str(results, true_params, get_l1_slsqp_results,
             get_l1_cvxopt_results, print_summaries)
 
-    print summary_str
+    print(summary_str)
 
 
 def run_solvers(model, true_params, alpha, get_l1_slsqp_results,
@@ -297,7 +299,7 @@ def get_logit_endog(true_params, exog, noise_level):
     ### Create the endog
     cdf = class_probabilities.cumsum(axis=1)
     endog = sp.zeros(N)
-    for i in xrange(N):
+    for i in range(N):
         endog[i] = sp.searchsorted(cdf[i, :], sp.rand())
 
     return endog
@@ -317,7 +319,7 @@ def get_probit_endog(true_params, exog, noise_level):
     ### Create the endog
     cdf = stats.norm._cdf(-Xdotparams)
     endog = sp.zeros(N)
-    for i in xrange(N):
+    for i in range(N):
         endog[i] = sp.searchsorted(cdf[i, :], sp.rand())
 
     return endog
@@ -342,7 +344,7 @@ def get_exog(N, num_nonconst_covariates, cor_length):
     else:
         cov_matrix = sp.zeros((num_nonconst_covariates, num_nonconst_covariates))
         j = sp.arange(num_nonconst_covariates)
-        for i in xrange(num_nonconst_covariates):
+        for i in range(num_nonconst_covariates):
             cov_matrix[i,:] = sp.exp(-sp.fabs(i-j) / cor_length)
         chol = linalg.cholesky(cov_matrix)  # cov_matrix = sp.dot(chol.T, chol)
         exog = sp.dot(uncorrelated_exog, chol)
diff --git a/statsmodels/examples/l1_demo/short_demo.py b/statsmodels/examples/l1_demo/short_demo.py
index e475629ae94..6febb83e522 100644
--- a/statsmodels/examples/l1_demo/short_demo.py
+++ b/statsmodels/examples/l1_demo/short_demo.py
@@ -17,6 +17,8 @@
 The l1_cvxopt_cp solver is part of CVXOPT and this package needs to be
     installed separately.  It works well even for larger data sizes.
 """
+from __future__ import print_function
+from statsmodels.compat.python import range
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
 import numpy as np
@@ -47,12 +49,12 @@
         method='l1_cvxopt_cp', alpha=alpha)
 
 ## Print results
-print "============ Results for Logit ================="
-print "ML results"
-print logit_res.summary()
-print "l1 results"
-print logit_l1_res.summary()
-print logit_l1_cvxopt_res.summary()
+print("============ Results for Logit =================")
+print("ML results")
+print(logit_res.summary())
+print("l1 results")
+print(logit_l1_res.summary())
+print(logit_l1_cvxopt_res.summary())
 
 ### Multinomial Logit Example using American National Election Studies Data
 anes_data = sm.datasets.anes96.load()
@@ -67,18 +69,18 @@
 # Don't regularize the constant
 alpha[-1,:] = 0
 mlogit_l1_res = mlogit_mod.fit_regularized(method='l1', alpha=alpha)
-print mlogit_l1_res.params
+print(mlogit_l1_res.params)
 
 #mlogit_l1_res = mlogit_mod.fit_regularized(
 #        method='l1_cvxopt_cp', alpha=alpha, abstol=1e-10, trim_tol=1e-6)
 #print mlogit_l1_res.params
 
 ## Print results
-print "============ Results for MNLogit ================="
-print "ML results"
-print mlogit_res.summary()
-print "l1 results"
-print mlogit_l1_res.summary()
+print("============ Results for MNLogit =================")
+print("ML results")
+print(mlogit_res.summary())
+print("l1 results")
+print(mlogit_l1_res.summary())
 #
 #
 #### Logit example with many params, sweeping alpha
@@ -109,7 +111,7 @@
 plt.title('Regularization Path');
 plt.xlabel('alpha');
 plt.ylabel('Parameter value');
-for i in xrange(K):
+for i in range(K):
     plt.plot(alphas, coeff[:,i], label='X'+str(i), lw=3)
 plt.legend(loc='best')
 plt.show()
diff --git a/statsmodels/examples/l1_demo/sklearn_compare.py b/statsmodels/examples/l1_demo/sklearn_compare.py
index 9ec7e08de1e..4826cb3d737 100644
--- a/statsmodels/examples/l1_demo/sklearn_compare.py
+++ b/statsmodels/examples/l1_demo/sklearn_compare.py
@@ -16,6 +16,8 @@
     finding the reparameterization is non-trivial since the coefficient paths
     are NOT monotonic.  As a result, the paths don't match up perfectly.
 """
+from __future__ import print_function
+from statsmodels.compat.python import range, lrange
 from sklearn import linear_model
 from sklearn import datasets
 import statsmodels.api as sm
@@ -91,11 +93,11 @@
 for coeff, name in [(sm_coeff, 'sm'), (sk_coeff, 'sk')]:
     if name == 'sk':
         ltype = 'x'  # linetype
-        t = range(N)  # The 'time' parameter
+        t = lrange(N)  # The 'time' parameter
     else:
         ltype = 'o'
         t = s
-    for i in xrange(K):
+    for i in range(K):
         plt.plot(t, coeff[:,i], ltype+colors[i], label=name+'-X'+str(i))
 plt.legend(loc='best')
 plt.show()
diff --git a/statsmodels/examples/run_all.py b/statsmodels/examples/run_all.py
index 94f1c4b5e09..34053db80ff 100644
--- a/statsmodels/examples/run_all.py
+++ b/statsmodels/examples/run_all.py
@@ -7,6 +7,8 @@
 uncomment plt.show() to show all plot windows
 
 '''
+from __future__ import print_function
+from statsmodels.compat.python import lzip, input
 import matplotlib.pyplot as plt #matplotlib is required for many examples
 
 stop_on_error = True
@@ -24,7 +26,7 @@
     import glob
     filelist = glob.glob('*.py')
 
-print zip(range(len(filelist)), filelist)
+print(lzip(range(len(filelist)), filelist))
 
 for fname in ['run_all.py', 'example_rpy.py']:
     filelist.remove(fname)
@@ -39,27 +41,27 @@ def noop(*args):
     pass
 plt.show = noop
 
-cont = raw_input("""Are you sure you want to run all of the examples?
+cont = input("""Are you sure you want to run all of the examples?
 This is done mainly to check that they are up to date.
 (y/n) >>> """)
+has_errors = []
 if 'y' in cont.lower():
-    has_errors = []
     for run_all_f in filelist:
         try:
-            print "\n\nExecuting example file", run_all_f
-            print "-----------------------" + "-"*len(run_all_f)
-            execfile(run_all_f)
+            print("\n\nExecuting example file", run_all_f)
+            print("-----------------------" + "-"*len(run_all_f))
+            exec(open(run_all_f).read())
         except:
             #f might be overwritten in the executed file
-            print "**********************" + "*"*len(run_all_f)
-            print "ERROR in example file", run_all_f
-            print "**********************" + "*"*len(run_all_f)
+            print("**********************" + "*"*len(run_all_f))
+            print("ERROR in example file", run_all_f)
+            print("**********************" + "*"*len(run_all_f))
             has_errors.append(run_all_f)
             if stop_on_error:
                 raise
 
-print '\nModules that raised exception:'
-print has_errors
+print('\nModules that raised exception:')
+print(has_errors)
 
 #reenable show after closing windows
 plt.close('all')
diff --git a/statsmodels/examples/t_est_rlm.py b/statsmodels/examples/t_est_rlm.py
index 0581c1432a6..9554521ae28 100644
--- a/statsmodels/examples/t_est_rlm.py
+++ b/statsmodels/examples/t_est_rlm.py
@@ -6,6 +6,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 RLM = sm.RLM
@@ -33,12 +34,12 @@
 from statsmodels.robust.tests.results.results_rlm import Huber
 res2 = Huber()
 
-print "res2.h1"
-print res2.h1
-print "results.bcov_scaled"
-print results.bcov_scaled
-print "res2.h1 - results.bcov_scaled"
-print res2.h1 - results.bcov_scaled
+print("res2.h1")
+print(res2.h1)
+print("results.bcov_scaled")
+print(results.bcov_scaled)
+print("res2.h1 - results.bcov_scaled")
+print(res2.h1 - results.bcov_scaled)
 
 from numpy.testing import assert_almost_equal
 assert_almost_equal(res2.h1, results.bcov_scaled, 4)
diff --git a/statsmodels/examples/try_2regress.py b/statsmodels/examples/try_2regress.py
index 93176810314..4901ddc12d6 100644
--- a/statsmodels/examples/try_2regress.py
+++ b/statsmodels/examples/try_2regress.py
@@ -7,6 +7,7 @@
 Author: josef-pktd
 """
 
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 import statsmodels.api as sm
@@ -20,10 +21,10 @@
 x1 = sm.add_constant(x1, prepend=False)
 assert_almost_equal(x1, np.vander(x1[:,0],2), 16)
 res1 = sm.OLS(y1, x1).fit()
-print res1.params
-print np.polyfit(x1[:,0], y1, 1)
+print(res1.params)
+print(np.polyfit(x1[:,0], y1, 1))
 assert_almost_equal(res1.params, np.polyfit(x1[:,0], y1, 1), 14)
-print res1.summary(xname=['x1','const1'])
+print(res1.summary(xname=['x1','const1']))
 
 #regression 2
 x2 = np.random.randn(nobs)
@@ -34,10 +35,10 @@
 assert_almost_equal(x2, np.vander(x2[:,0],2), 16)
 
 res2 = sm.OLS(y2, x2).fit()
-print res2.params
-print np.polyfit(x2[:,0], y2, 1)
+print(res2.params)
+print(np.polyfit(x2[:,0], y2, 1))
 assert_almost_equal(res2.params, np.polyfit(x2[:,0], y2, 1), 14)
-print res2.summary(xname=['x2','const2'])
+print(res2.summary(xname=['x2','const2']))
 
 
 # joint regression
@@ -48,15 +49,15 @@
 x = np.column_stack((x,x*dummy[:,None]))
 
 res = sm.OLS(y, x).fit()
-print res.summary(xname=['x','const','x2','const2'])
+print(res.summary(xname=['x','const','x2','const2']))
 
-print '\nF test for equal coefficients in 2 regression equations'
+print('\nF test for equal coefficients in 2 regression equations')
 #effect of dummy times second regression is zero
 #is equivalent to 3rd and 4th coefficient are both zero
-print res.f_test([[0,0,1,0],[0,0,0,1]])
+print(res.f_test([[0,0,1,0],[0,0,0,1]]))
 
-print '\nchecking coefficients individual versus joint'
-print res1.params, res2.params
-print res.params[:2], res.params[:2]+res.params[2:]
+print('\nchecking coefficients individual versus joint')
+print(res1.params, res2.params)
+print(res.params[:2], res.params[:2]+res.params[2:])
 assert_almost_equal(res1.params, res.params[:2], 13)
 assert_almost_equal(res2.params, res.params[:2]+res.params[2:], 13)
diff --git a/statsmodels/examples/try_fit_constrained.py b/statsmodels/examples/try_fit_constrained.py
new file mode 100644
index 00000000000..13b6d1ff40a
--- /dev/null
+++ b/statsmodels/examples/try_fit_constrained.py
@@ -0,0 +1,191 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri May 30 22:56:57 2014
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_raises
+
+from statsmodels.base._constraints import (TransformRestriction,
+                   transform_params_constraint, fit_constrained)
+
+
+if __name__ == '__main__':
+
+
+
+    R = np.array([[1, 1, 0, 0, 0], [0, 0, 1, -1, 0]])
+
+
+
+    k_constr, k_vars = R.shape
+
+    m = np.eye(k_vars) - R.T.dot(np.linalg.pinv(R).T)
+    evals, evecs = np.linalg.eigh(m)
+
+    L = evecs[:, :k_constr]
+    T = evecs[:, k_constr:]
+
+    print T.T.dot(np.eye(k_vars))
+
+    tr = np.column_stack((T, R.T))
+
+    q = [2, 0]
+    tr0 = TransformRestriction(R, q)
+
+    p_reduced = [1,1,1]
+    #round trip test
+    assert_allclose(tr0.reduce(tr0.expand(p_reduced)), p_reduced, rtol=1e-14)
+
+
+    p = tr0.expand(p_reduced)
+    assert_allclose(R.dot(p), q, rtol=1e-14)
+
+    # inconsistent restrictions
+    #Ri = np.array([[1, 1, 0, 0, 0], [0, 0, 1, -1, 0], [0, 0, 1, -2, 0]])
+    R = np.array([[1, 1, 0, 0, 0], [0, 0, 1, -1, 0], [0, 0, 1, 0, -1]])
+    q = np.zeros(R.shape[0])
+    tr1 = TransformRestriction(R, q)  # bug raises error with q
+    p = tr1.expand([1,1])
+
+    # inconsistent restrictions that has a solution with p3=0
+    Ri = np.array([[1, 1, 0, 0, 0], [0, 0, 1, -1, 0], [0, 0, 1, -2, 0]])
+    tri = TransformRestriction(Ri, [0, 1, 1])
+    # Note: the only way this can hold is if variable 3 is zero
+    p = tri.expand([1,1])
+    print(p[[2,3]])
+    #array([  1.00000000e+00,  -1.34692639e-17])
+
+    # inconsistent without any possible solution
+    Ri2 = np.array([[0, 0, 0, 1, 0], [0, 0, 1, -1, 0], [0, 0, 1, -2, 0]])
+    q = [1, 1]
+    #tri2 = TransformRestriction(Ri2, q)
+    #p = tri.expand([1,1])
+    assert_raises(ValueError, TransformRestriction, Ri2, q)
+    # L doesn't have full row rank, calculating constant fails with Singular Matrix
+
+    # transform data xr = T x
+    np.random.seed(1)
+    x = np.random.randn(10, 5)
+    xr = tr1.reduce(x)
+    # roundtrip
+    x2 = tr1.expand(xr)
+    # this doesn't hold ? don't use constant? don't need it anyway ?
+    #assert_allclose(x2, x, rtol=1e-14)
+
+
+    from patsy import DesignInfo
+
+    names = 'a b c d'.split()
+    LC = DesignInfo(names).linear_constraint('a + b = 0')
+    LC = DesignInfo(names).linear_constraint(['a + b = 0', 'a + 2*c = 1', 'b-a', 'c-a', 'd-a'])
+    #LC = DesignInfo(self.model.exog_names).linear_constraint(r_matrix)
+    r_matrix, q_matrix = LC.coefs, LC.constants
+
+    np.random.seed(123)
+    nobs = 20
+    x = 1 + np.random.randn(nobs, 4)
+    exog = np.column_stack((np.ones(nobs), x))
+    endog = exog.sum(1) + np.random.randn(nobs)
+
+    from statsmodels.regression.linear_model import OLS
+    res2 = OLS(endog, exog).fit()
+    #transf = TransformRestriction(np.eye(exog.shape[1])[:2], res2.params[:2] / 2)
+    transf = TransformRestriction([[0, 0, 0,1,1]], res2.params[-2:].sum())
+    exog_st = transf.reduce(exog)
+    res1 = OLS(endog, exog_st).fit()
+    # need to correct for constant/offset in the optimization
+    res1 = OLS(endog - exog.dot(transf.constant.squeeze()), exog_st).fit()
+    params = transf.expand(res1.params).squeeze()
+    assert_allclose(params, res2.params, rtol=1e-13)
+    print(res2.params)
+    print(params)
+    print(res1.params)
+
+    res3_ols = OLS(endog - exog[:, -1], exog[:, :-2]).fit()
+    #transf = TransformRestriction(np.eye(exog.shape[1])[:2], res2.params[:2] / 2)
+    transf3 = TransformRestriction([[0, 0, 0, 1, 0],[0, 0, 0, 0, 1]], [0, 1])
+    exog3_st = transf3.reduce(exog)
+    res3 = OLS(endog, exog3_st).fit()
+    # need to correct for constant/offset in the optimization
+    res3 = OLS(endog - exog.dot(transf3.constant.squeeze()), exog3_st).fit()
+    params = transf3.expand(res3.params).squeeze()
+    assert_allclose(params[:-2], res3_ols.params, rtol=1e-13)
+    print(res3.params)
+    print(params)
+    print(res3_ols.params)
+    print(res3_ols.bse)
+    # the following raises `ValueError: can't test a constant constraint`
+    #tt = res3.t_test(transf3.transf_mat, transf3.constant.squeeze())
+    #print tt.sd
+    cov_params3 = transf3.transf_mat.dot(res3.cov_params()).dot(transf3.transf_mat.T)
+    bse3 = np.sqrt(np.diag(cov_params3))
+    print(bse3)
+
+    tp = transform_params_constraint(res2.params, res2.normalized_cov_params,
+                                     transf3.R, transf3.q)
+    tp = transform_params_constraint(res2.params, res2.cov_params(), transf3.R, transf3.q)
+
+
+    from statsmodels.discrete.discrete_model import Poisson
+    import statsmodels.api as sm
+    rand_data = sm.datasets.randhie.load()
+    rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)
+    rand_exog = sm.add_constant(rand_exog, prepend=False)
+
+
+    # Fit Poisson model:
+    poisson_mod0 = sm.Poisson(rand_data.endog, rand_exog)
+    poisson_res0 = poisson_mod0.fit(method="newton")
+
+    R = np.zeros((2, 10))
+    R[0, -2] = 1
+    R[1, -1] = 1
+    transfp = TransformRestriction(R, [0, 1])
+    poisson_mod = sm.Poisson(rand_data.endog, rand_exog[:, :-2])
+    # note wrong offset, why did I put offset in fit ? it's ignored
+    poisson_res = poisson_mod.fit(method="newton", offset=rand_exog.dot(transfp.constant.squeeze()))
+
+    exogp_st = transfp.reduce(rand_exog)
+    poisson_modr = sm.Poisson(rand_data.endog, exogp_st)
+    poisson_resr = poisson_modr.fit(method="newton")
+    paramsp = transfp.expand(poisson_resr.params).squeeze()
+    print('\nPoisson')
+    print(paramsp)
+    print(poisson_res.params)
+    # error because I don't use the unconstrained basic model
+#    tp = transform_params_constraint(poisson_res.params, poisson_res.cov_params(), transfp.R, transfp.q)
+#    cov_params3 = transf3.transf_mat.dot(res3.cov_params()).dot(transf3.transf_mat.T)
+#    bse3 = np.sqrt(np.diag(cov_params3))
+
+
+    poisson_mod0 = sm.Poisson(rand_data.endog, rand_exog)
+    poisson_res0 = poisson_mod0.fit(method="newton")
+    tp = transform_params_constraint(poisson_res0.params, poisson_res0.cov_params(), transfp.R, transfp.q)
+    cov_params3 = transf3.transf_mat.dot(res3.cov_params()).dot(transf3.transf_mat.T)
+    bse3 = np.sqrt(np.diag(cov_params3))
+
+    # try again same example as it was intended
+
+    poisson_mod = sm.Poisson(rand_data.endog, rand_exog[:, :-2], offset=rand_exog[:, -1])
+    poisson_res = poisson_mod.fit(method="newton")
+
+    exogp_st = transfp.reduce(rand_exog)
+    poisson_modr = sm.Poisson(rand_data.endog, exogp_st, offset=rand_exog.dot(transfp.constant.squeeze()))
+    poisson_resr = poisson_modr.fit(method="newton")
+    paramsp = transfp.expand(poisson_resr.params).squeeze()
+    print('\nPoisson')
+    print(paramsp)
+    print(poisson_resr.params)
+    tp = transform_params_constraint(poisson_res0.params, poisson_res0.cov_params(), transfp.R, transfp.q)
+    cov_paramsp = transfp.transf_mat.dot(poisson_resr.cov_params()).dot(transfp.transf_mat.T)
+    bsep = np.sqrt(np.diag(cov_paramsp))
+    print(bsep)
+    p, cov, res_r = fit_constrained(poisson_mod0, transfp.R, transfp.q)
+    se = np.sqrt(np.diag(cov))
+    print(p)
+    print(se)
diff --git a/statsmodels/examples/try_gee.py b/statsmodels/examples/try_gee.py
new file mode 100644
index 00000000000..1623b8b4d9d
--- /dev/null
+++ b/statsmodels/examples/try_gee.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Thu Jul 18 14:57:46 2013
+
+Author: Josef Perktold
+"""
+
+from __future__ import print_function
+import numpy as np
+
+from statsmodels.genmod.generalized_estimating_equations import GEE, GEEMargins
+
+from statsmodels.genmod.families import Gaussian, Binomial, Poisson
+from statsmodels.genmod.dependence_structures import (Exchangeable,
+    Independence, GlobalOddsRatio, Autoregressive, Nested)
+
+from statsmodels.genmod.tests import gee_gaussian_simulation_check as gees
+
+da,va = gees.gen_gendat_ar0(0.6)()
+ga = Gaussian()
+lhs = np.array([[0., 1, 1, 0, 0],])
+rhs = np.r_[0.,]
+
+example = []
+if 'constraint' in example:
+    md = GEE(da.endog, da.exog, da.group, da.time, ga, va,
+                     constraint=(lhs, rhs))
+    mdf = md.fit()
+    print(mdf.summary())
+
+
+md2 = GEE(da.endog, da.exog, da.group, da.time, ga, va,
+                 constraint=None)
+mdf2 = md2.fit()
+print('\n\n')
+print(mdf2.summary())
+
+
+mdf2.use_t = False
+mdf2.df_resid = np.diff(mdf2.model.exog.shape)
+tt2 = mdf2.t_test(np.eye(len(mdf2.params)))
+# need master to get wald_test
+#print mdf2.wald_test(np.eye(len(mdf2.params))[1:])
+
+'''
+>>> mdf2.predict(da.exog.mean(0))
+Traceback (most recent call last):
+  File "<pyshell#11>", line 1, in <module>
+    mdf2.predict(da.exog.mean(0))
+  File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all-new2_py27\statsmodels\statsmodels\base\model.py", line 963, in predict
+    return self.model.predict(self.params, exog, *args, **kwargs)
+  File "e:\josef\eclipsegworkspace\statsmodels-git\statsmodels-all-new2_py27\statsmodels\statsmodels\genmod\generalized_estimating_equations.py", line 621, in predict
+    fitted = offset + np.dot(exog, params)
+TypeError: unsupported operand type(s) for +: 'NoneType' and 'numpy.float64'
+'''
+mdf2.predict(da.exog.mean(0), offset=0)
+# -0.10867809062890971
+
+marg2 = GEEMargins(mdf2, ())
+print(marg2.summary())
+
+
+mdf_nc = md2.fit(covariance_type='naive')
+mdf_bc = md2.fit(covariance_type='bias_reduced')
+
+mdf_nc.use_t = False
+mdf_nc.df_resid = np.diff(mdf2.model.exog.shape)
+mdf_bc.use_t = False
+mdf_bc.df_resid = np.diff(mdf2.model.exog.shape)
+
+tt_nc = mdf_nc.t_test(np.eye(len(mdf2.params)))
+tt_bc = mdf_bc.t_test(np.eye(len(mdf2.params)))
+
+print('\nttest robust')
+print(tt2)
+print('\nttest naive')
+print(tt_nc)
+print('\nttest bias corrected')
+print(tt_bc)
+
+print("\nbse after fit option ")
+bse = np.column_stack((mdf2.bse, mdf2.bse, mdf_nc.bse, mdf_bc.bse))
+print(bse)
+
+print("\nimplemented `standard_errors`")
+bse2 = np.column_stack((mdf2.bse, mdf2.standard_errors(),
+                                 mdf2.standard_errors(covariance_type='naive'),
+                                 mdf2.standard_errors(covariance_type='bias_reduced')))
+print(bse2)
+print("bse and `standard_errors` agree:", np.allclose(bse, bse2))
+
+print("\nimplied standard errors in t_test")
+bse1 = np.column_stack((mdf2.bse, tt2.sd, tt_nc.sd, tt_bc.sd))
+print(bse1)
+print("t_test uses correct cov_params:", np.allclose(bse1, bse2))
diff --git a/statsmodels/examples/try_gof_chisquare.py b/statsmodels/examples/try_gof_chisquare.py
index 8e2fce3b952..93ece1a296d 100644
--- a/statsmodels/examples/try_gof_chisquare.py
+++ b/statsmodels/examples/try_gof_chisquare.py
@@ -6,6 +6,7 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 from statsmodels.stats.gof import (chisquare, chisquare_power,
@@ -21,7 +22,7 @@
 #nicer
 probs = np.round(probs, 2)
 probs[-1] = 1 - probs[:-1].sum()
-print "probs", probs
+print("probs", probs)
 probs_d = probs.copy()
 delta = 0.01
 probs_d[0] += delta
@@ -30,32 +31,32 @@
 #rvs = np.random.multinomial(n_bins, probs, size=10)
 #rvs = np.round(np.random.randn(10), 2)
 rvs = np.argmax(np.random.rand(nobs,1) < probs_cs, 1)
-print probs
-print np.bincount(rvs) * (1. / nobs)
+print(probs)
+print(np.bincount(rvs) * (1. / nobs))
 
 
 freq = np.bincount(rvs)
-print stats.chisquare(freq, nobs*probs)
-print 'null', chisquare(freq, nobs*probs)
-print 'delta', chisquare(freq, nobs*probs_d)
+print(stats.chisquare(freq, nobs*probs))
+print('null', chisquare(freq, nobs*probs))
+print('delta', chisquare(freq, nobs*probs_d))
 chisq_null, pval_null = chisquare(freq, nobs*probs)
 
 # effect size ?
 d_null = ((freq / float(nobs) - probs)**2 / probs).sum()
-print d_null
+print(d_null)
 d_delta = ((freq / float(nobs) - probs_d)**2 / probs_d).sum()
-print d_delta
+print(d_delta)
 d_null_alt = ((probs - probs_d)**2 / probs_d).sum()
-print d_null_alt
+print(d_null_alt)
 
-print '\nchisquare with value'
+print('\nchisquare with value')
 chisq, pval = chisquare(freq, nobs*probs_d)
-print stats.ncx2.sf(chisq_null, n_bins, 0.001 * nobs)
-print stats.ncx2.sf(chisq, n_bins, 0.001 * nobs)
-print stats.ncx2.sf(chisq, n_bins, d_delta * nobs)
-print chisquare(freq, nobs*probs_d, value=np.sqrt(d_delta))
-print chisquare(freq, nobs*probs_d, value=np.sqrt(chisq / nobs))
-print
+print(stats.ncx2.sf(chisq_null, n_bins, 0.001 * nobs))
+print(stats.ncx2.sf(chisq, n_bins, 0.001 * nobs))
+print(stats.ncx2.sf(chisq, n_bins, d_delta * nobs))
+print(chisquare(freq, nobs*probs_d, value=np.sqrt(d_delta)))
+print(chisquare(freq, nobs*probs_d, value=np.sqrt(chisq / nobs)))
+print()
 
 assert_almost_equal(stats.chi2.sf(d_delta * nobs, n_bins - 1),
                     chisquare(freq, nobs*probs_d)[1], decimal=13)
@@ -68,9 +69,9 @@
 effect_size = 0.001
 power = chisquare_power(effect_size, nobs, n_bins, alpha=0.05)
 assert_almost_equal(power, 0.05147563, decimal=7)
-print chisquare(freq, nobs*probs, value=0, ddof=0)
+print(chisquare(freq, nobs*probs, value=0, ddof=0))
 d_null_alt = ((probs - probs_d)**2 / probs).sum()
-print chisquare(freq, nobs*probs, value=np.sqrt(d_null_alt), ddof=0)
+print(chisquare(freq, nobs*probs, value=np.sqrt(d_null_alt), ddof=0))
 
 
 #Monte Carlo to check correct size and power of test
@@ -90,11 +91,11 @@
 alpha = np.array([0.01, 0.05, 0.1, 0.25, 0.5])
 chi2_power = chisquare_power(chisquare_effectsize(probs, probs_d), 3000, n_bins,
                              alpha=[0.01, 0.05, 0.1, 0.25, 0.5])
-print (res_boots[:, 3:] < 0.05).mean(0)
+print((res_boots[:, 3:] < 0.05).mean(0))
 reject_freq = (res_boots[:, 3:, None] < alpha).mean(0)
 reject = (res_boots[:, 3:, None] < alpha).sum(0)
 
 desired = np.column_stack((alpha, chi2_power, alpha)).T
 
-print 'relative difference Monte Carlo rejection and expected (in %)'
-print (reject_freq / desired - 1) * 100
+print('relative difference Monte Carlo rejection and expected (in %)')
+print((reject_freq / desired - 1) * 100)
diff --git a/statsmodels/examples/try_polytrend.py b/statsmodels/examples/try_polytrend.py
index 56ac8f859b4..6c8557252a7 100644
--- a/statsmodels/examples/try_polytrend.py
+++ b/statsmodels/examples/try_polytrend.py
@@ -1,5 +1,6 @@
 
 
+from __future__ import print_function
 import numpy as np
 #import statsmodels.linear_model.regression as smreg
 
@@ -25,7 +26,7 @@
 fitted = [sm.OLS(gdp, exog[:, :maxr]).fit().fittedvalues for maxr in
           range(2,maxorder)]
 
-print (np.corrcoef(exog[:,1:6], rowvar=0)*10000).astype(int)
+print((np.corrcoef(exog[:,1:6], rowvar=0)*10000).astype(int))
 
 import matplotlib.pyplot as plt
 
diff --git a/statsmodels/examples/try_power.py b/statsmodels/examples/try_power.py
index 3901025b6cc..06047271de4 100644
--- a/statsmodels/examples/try_power.py
+++ b/statsmodels/examples/try_power.py
@@ -6,63 +6,64 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 import numpy as np
 
 import statsmodels.stats.power as smp
 import statsmodels.stats.proportion as smpr
 
 sigma=1; d=0.3; nobs=80; alpha=0.05
-print smp.normal_power(d, nobs/2, 0.05)
-print smp.NormalIndPower().power(d, nobs, 0.05)
-print smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None)
-print 0.475100870572638, 'R'
+print(smp.normal_power(d, nobs/2, 0.05))
+print(smp.NormalIndPower().power(d, nobs, 0.05))
+print(smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None))
+print(0.475100870572638, 'R')
 
 norm_pow = smp.normal_power(-0.01, nobs/2, 0.05)
 norm_pow_R = 0.05045832927039234
 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="two.sided")
-print 'norm_pow', norm_pow, norm_pow - norm_pow_R
+print('norm_pow', norm_pow, norm_pow - norm_pow_R)
 
 norm_pow = smp.NormalIndPower().power(0.01, nobs, 0.05, alternative="larger")
 norm_pow_R = 0.056869534873146124
 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="greater")
-print 'norm_pow', norm_pow, norm_pow - norm_pow_R
+print('norm_pow', norm_pow, norm_pow - norm_pow_R)
 
 # Note: negative effect size is same as switching one-sided alternative
 # TODO: should I switch to larger/smaller instead of "one-sided" options
 norm_pow = smp.NormalIndPower().power(-0.01, nobs, 0.05, alternative="larger")
 norm_pow_R = 0.0438089705093578
 #value from R: >pwr.2p.test(h=0.01,n=80,sig.level=0.05,alternative="less")
-print 'norm_pow', norm_pow, norm_pow - norm_pow_R
+print('norm_pow', norm_pow, norm_pow - norm_pow_R)
 
 
 #Note: I use n_bins and ddof instead of df
 # pwr.chisq.test(w=0.289,df=(4-1),N=100,sig.level=0.05)
 chi2_pow = smp.GofChisquarePower().power(0.289, 100, 4, 0.05)
 chi2_pow_R = 0.675077657003721
-print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R
+print('chi2_pow', chi2_pow, chi2_pow - chi2_pow_R)
 
 chi2_pow = smp.GofChisquarePower().power(0.01, 100, 4, 0.05)
 chi2_pow_R = 0.0505845519208533
-print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R
+print('chi2_pow', chi2_pow, chi2_pow - chi2_pow_R)
 
 chi2_pow = smp.GofChisquarePower().power(2, 100, 4, 0.05)
 chi2_pow_R = 1
-print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R
+print('chi2_pow', chi2_pow, chi2_pow - chi2_pow_R)
 
 chi2_pow = smp.GofChisquarePower().power(0.9, 100, 4, 0.05)
 chi2_pow_R = 0.999999999919477
-print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R, 'lower precision ?'
+print('chi2_pow', chi2_pow, chi2_pow - chi2_pow_R, 'lower precision ?')
 
 chi2_pow = smp.GofChisquarePower().power(0.8, 100, 4, 0.05)
 chi2_pow_R = 0.999999968205591
-print 'chi2_pow', chi2_pow, chi2_pow - chi2_pow_R
+print('chi2_pow', chi2_pow, chi2_pow - chi2_pow_R)
 
 def cohen_es(*args, **kwds):
-    print "You better check what's a meaningful effect size for your question."
+    print("You better check what's a meaningful effect size for your question.")
 
 
 #BUG: after fixing 2.sided option, 2 rejection areas
 tt_pow = smp.TTestPower().power(effect_size=0.01, nobs=nobs, alpha=0.05)
 tt_pow_R = 0.05089485285965
 # value from> pwr.t.test(d=0.01,n=80,sig.level=0.05,type="one.sample",alternative="two.sided")
-print 'tt_pow', tt_pow, tt_pow - tt_pow_R
+print('tt_pow', tt_pow, tt_pow - tt_pow_R)
diff --git a/statsmodels/examples/try_power2.py b/statsmodels/examples/try_power2.py
index de4212cff43..14b27edba40 100644
--- a/statsmodels/examples/try_power2.py
+++ b/statsmodels/examples/try_power2.py
@@ -6,52 +6,53 @@
 Author: Josef Perktold
 """
 
+from __future__ import print_function
 from statsmodels.stats.power import TTestPower, TTestIndPower, tt_solve_power
 
 if __name__ == '__main__':
     effect_size, alpha, power = 0.5, 0.05, 0.8
 
     ttest_pow = TTestPower()
-    print '\nroundtrip - root with respect to all variables'
-    print '\n       calculated, desired'
+    print('\nroundtrip - root with respect to all variables')
+    print('\n       calculated, desired')
 
     nobs_p = ttest_pow.solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power)
-    print 'nobs  ', nobs_p
-    print 'effect', ttest_pow.solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size
+    print('nobs  ', nobs_p)
+    print('effect', ttest_pow.solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size)
 
-    print 'alpha ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha
-    print 'power  ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power
+    print('alpha ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha)
+    print('power  ', ttest_pow.solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power)
 
-    print '\nroundtrip - root with respect to all variables'
-    print '\n       calculated, desired'
+    print('\nroundtrip - root with respect to all variables')
+    print('\n       calculated, desired')
 
-    print 'nobs  ', tt_solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power), nobs_p
-    print 'effect', tt_solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size
+    print('nobs  ', tt_solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power), nobs_p)
+    print('effect', tt_solve_power(effect_size=None, nobs=nobs_p, alpha=alpha, power=power), effect_size)
 
-    print 'alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha
-    print 'power  ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power
+    print('alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=None, power=power), alpha)
+    print('power  ', tt_solve_power(effect_size=effect_size, nobs=nobs_p, alpha=alpha, power=None), power)
 
-    print '\none sided'
+    print('\none sided')
     nobs_p1 = tt_solve_power(effect_size=effect_size, nobs=None, alpha=alpha, power=power, alternative='larger')
-    print 'nobs  ', nobs_p1
-    print 'effect', tt_solve_power(effect_size=None, nobs=nobs_p1, alpha=alpha, power=power, alternative='larger'), effect_size
-    print 'alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=None, power=power, alternative='larger'), alpha
-    print 'power  ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=alpha, power=None, alternative='larger'), power
+    print('nobs  ', nobs_p1)
+    print('effect', tt_solve_power(effect_size=None, nobs=nobs_p1, alpha=alpha, power=power, alternative='larger'), effect_size)
+    print('alpha ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=None, power=power, alternative='larger'), alpha)
+    print('power  ', tt_solve_power(effect_size=effect_size, nobs=nobs_p1, alpha=alpha, power=None, alternative='larger'), power)
 
     #start_ttp = dict(effect_size=0.01, nobs1=10., alpha=0.15, power=0.6)
 
     ttind_solve_power = TTestIndPower().solve_power
 
-    print '\nroundtrip - root with respect to all variables'
-    print '\n       calculated, desired'
+    print('\nroundtrip - root with respect to all variables')
+    print('\n       calculated, desired')
 
     nobs_p2 = ttind_solve_power(effect_size=effect_size, nobs1=None, alpha=alpha, power=power)
-    print 'nobs  ', nobs_p2
-    print 'effect', ttind_solve_power(effect_size=None, nobs1=nobs_p2, alpha=alpha, power=power), effect_size
-    print 'alpha ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=None, power=power), alpha
-    print 'power  ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=None), power
-    print 'ratio  ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=power, ratio=None), 1
-
-    print '\ncheck ratio'
-    print 'smaller power', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.7, ratio=None), '< 1'
-    print 'larger power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.9, ratio=None), '> 1'
+    print('nobs  ', nobs_p2)
+    print('effect', ttind_solve_power(effect_size=None, nobs1=nobs_p2, alpha=alpha, power=power), effect_size)
+    print('alpha ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=None, power=power), alpha)
+    print('power  ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=None), power)
+    print('ratio  ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=power, ratio=None), 1)
+
+    print('\ncheck ratio')
+    print('smaller power', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.7, ratio=None), '< 1')
+    print('larger power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=0.9, ratio=None), '> 1')
diff --git a/statsmodels/examples/try_tukey_hsd.py b/statsmodels/examples/try_tukey_hsd.py
index c19efaffb68..a5b9bab30de 100644
--- a/statsmodels/examples/try_tukey_hsd.py
+++ b/statsmodels/examples/try_tukey_hsd.py
@@ -6,7 +6,8 @@
 Author: Josef Perktold
 """
 
-import StringIO
+from __future__ import print_function
+from statsmodels.compat.python import StringIO
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_equal
 
@@ -117,9 +118,9 @@
     'Germany', 'Japan', 'Japan', 'USA', 'USA', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'Japan', 'USA', 
     'USA', 'USA', 'USA', 'Japan', 'USA', 'USA', 'USA', 'Germany', 'USA', 'USA', 'USA'])
 
-dta = np.recfromtxt(StringIO.StringIO(ss), names=("Rust","Brand","Replication"))
-dta2 = np.recfromtxt(StringIO.StringIO(ss2), names = ("idx", "Treatment", "StressReduction"))
-dta3 = np.recfromtxt(StringIO.StringIO(ss3), names = ("Brand", "Relief"))
+dta = np.recfromtxt(StringIO(ss), names=("Rust","Brand","Replication"))
+dta2 = np.recfromtxt(StringIO(ss2), names = ("idx", "Treatment", "StressReduction"))
+dta3 = np.recfromtxt(StringIO(ss3), names = ("Brand", "Relief"))
 
 from statsmodels.sandbox.stats.multicomp import tukeyhsd
 import statsmodels.sandbox.stats.multicomp as multi
@@ -130,7 +131,7 @@ def get_thsd(mci):
     means = mci.groupstats.groupmean
     nobs = mci.groupstats.groupnobs
     resi = tukeyhsd(means, nobs, var_, df=None, alpha=0.05, q_crit=qsturng(0.95, len(means), (nobs-1).sum()))
-    print resi[4]
+    print(resi[4])
     var2 = (mci.groupstats.groupvarwithin() * (nobs - 1)).sum() \
                                                         / (nobs - 1).sum()
     assert_almost_equal(var_, var2, decimal=14)
@@ -138,15 +139,15 @@ def get_thsd(mci):
 
 mc = multi.MultiComparison(dta['Rust'], dta['Brand'])
 res = mc.tukeyhsd()
-print res
+print(res)
 
 mc2 = multi.MultiComparison(dta2['StressReduction'], dta2['Treatment'])
 res2 = mc2.tukeyhsd()
-print res2
+print(res2)
 
 mc2s = multi.MultiComparison(dta2['StressReduction'][3:29], dta2['Treatment'][3:29])
 res2s = mc2s.tukeyhsd()
-print res2s
+print(res2s)
 res2s_001 = mc2s.tukeyhsd(alpha=0.01)
 #R result
 tukeyhsd2s = np.array([1.888889,0.8888889,-1,0.2658549,-0.5908785,-2.587133,3.511923,2.368656,0.5871331,0.002837638,0.150456,0.1266072]).reshape(3,4, order='F')
@@ -154,23 +155,23 @@ def get_thsd(mci):
 
 mc3 = multi.MultiComparison(dta3['Relief'], dta3['Brand'])
 res3 = mc3.tukeyhsd()
-print res3
+print(res3)
 
 tukeyhsd4 = multi.MultiComparison(cylinders, cyl_labels, group_order=["Sweden", "Japan", "Germany", "France", "USA"])
 res4 = tukeyhsd4.tukeyhsd()
-print res4
+print(res4)
 try:
     import matplotlib.pyplot as plt
     fig = res4.plot_simultaneous("USA")
     plt.show()
 except Exception as e:
-    print e
+    print(e)
 
 for mci in [mc, mc2, mc3]:
     get_thsd(mci)
 
 from scipy import stats
-print mc2.allpairtest(stats.ttest_ind, method='b')[0]
+print(mc2.allpairtest(stats.ttest_ind, method='b')[0])
 
 '''same as SAS:
 >>> np.var(mci.groupstats.groupdemean(), ddof=3)
@@ -207,7 +208,7 @@ def get_thsd(mci):
 1 - 2	-4.600	-8.249	-0.951	***
 1 - 3	-0.260	-3.909	3.389	'''
 
-dta5 = np.recfromtxt(StringIO.StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t')
+dta5 = np.recfromtxt(StringIO(ss5), names = ('pair', 'mean', 'lower', 'upper', 'sig'), delimiter='\t')
 
 sas_ = dta5[[1,3,2]]
 confint1 = res3.confint
diff --git a/statsmodels/examples/tsa/ar1cholesky.py b/statsmodels/examples/tsa/ar1cholesky.py
index 2a2fa1534d3..1751c5769d7 100644
--- a/statsmodels/examples/tsa/ar1cholesky.py
+++ b/statsmodels/examples/tsa/ar1cholesky.py
@@ -5,6 +5,7 @@
 Author: josef-pktd
 """
 
+from __future__ import print_function
 import numpy as np
 from scipy import linalg
 
@@ -29,14 +30,14 @@ def tiny2zero(x, eps = 1e-15):
 c = linalg.cholesky(sigma, lower=True)
 ci = linalg.cholesky(sigmainv, lower=True)
 
-print sigma
-print tiny2zero(ci/ci.max())
+print(sigma)
+print(tiny2zero(ci/ci.max()))
 
 "this is the text book transformation"
-print 'coefficient for first observation', np.sqrt(1-autocov[1]**2)
+print('coefficient for first observation', np.sqrt(1-autocov[1]**2))
 ci2 = ci[::-1,::-1].T
-print tiny2zero(ci2/ci2.max())
+print(tiny2zero(ci2/ci2.max()))
 
-print np.dot(ci/ci.max(), np.ones(nobs))
+print(np.dot(ci/ci.max(), np.ones(nobs)))
 
-print np.dot(ci2/ci2.max(), np.ones(nobs))
+print(np.dot(ci2/ci2.max(), np.ones(nobs)))
diff --git a/statsmodels/examples/tsa/arma_plots.py b/statsmodels/examples/tsa/arma_plots.py
index 41442a19a21..7bfddd1b377 100644
--- a/statsmodels/examples/tsa/arma_plots.py
+++ b/statsmodels/examples/tsa/arma_plots.py
@@ -3,6 +3,7 @@
 '''
 
 
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 import statsmodels.tsa.arima_process as tsp
diff --git a/statsmodels/examples/tsa/compare_arma.py b/statsmodels/examples/tsa/compare_arma.py
index da8b7c39a42..3b26f8182bd 100644
--- a/statsmodels/examples/tsa/compare_arma.py
+++ b/statsmodels/examples/tsa/compare_arma.py
@@ -1,23 +1,24 @@
-print "Battle of the dueling ARMAs"
-
+from __future__ import print_function
 from time import time
 from statsmodels.tsa.arma_mle import Arma
 from statsmodels.tsa.api import ARMA
 import numpy as np
 
+print("Battle of the dueling ARMAs")
+
 y_arma22 = np.loadtxt(r'C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\tsa\y_arma22.txt')
 
 arma1 = Arma(y_arma22)
 arma2 = ARMA(y_arma22)
 
-print "The actual results from gretl exact mle are"
+print("The actual results from gretl exact mle are")
 params_mle = np.array([.826990, -.333986, .0362419, -.792825])
 sigma_mle = 1.094011
 llf_mle = -1510.233
-print "params: ", params_mle
-print "sigma: ", sigma_mle
-print "llf: ", llf_mle
-print "The actual results from gretl css are"
+print("params: ", params_mle)
+print("sigma: ", sigma_mle)
+print("llf: ", llf_mle)
+print("The actual results from gretl css are")
 params_css = np.array([.824810, -.337077, .0407222, -.789792])
 sigma_css = 1.095688
 llf_css = -1507.301
@@ -27,51 +28,51 @@
 results += ["gretl css", params_css, sigma_css, llf_css]
 
 t0 = time()
-print "Exact MLE - Kalman filter version using l_bfgs_b"
+print("Exact MLE - Kalman filter version using l_bfgs_b")
 arma2.fit(order=(2,2), trend='nc')
 t1 = time()
-print "params: ", arma2.params
-print "sigma: ", arma2.sigma2**.5
+print("params: ", arma2.params)
+print("sigma: ", arma2.sigma2**.5)
 arma2.llf = arma2.loglike(arma2._invtransparams(arma2.params))
 results += ["exact mle kalmanf", arma2.params, arma2.sigma2**.5, arma2.llf]
-print 'time used:', t1-t0
+print('time used:', t1-t0)
 
 t1=time()
-print "CSS MLE - ARMA Class"
+print("CSS MLE - ARMA Class")
 arma2.fit(order=(2,2), trend='nc', method="css")
 t2=time()
 arma2.llf = arma2.loglike_css(arma2._invtransparams(arma2.params))
-print "params: ", arma2.params
-print "sigma: ", arma2.sigma2**.5
+print("params: ", arma2.params)
+print("sigma: ", arma2.sigma2**.5)
 results += ["css kalmanf", arma2.params, arma2.sigma2**.5, arma2.llf]
-print 'time used:', t2-t1
+print('time used:', t2-t1)
 
-print "Arma.fit_mle results"
+print("Arma.fit_mle results")
 # have to set nar and nma manually
 arma1.nar = 2
 arma1.nma = 2
 t2=time()
 ret = arma1.fit_mle()
 t3=time()
-print "params, first 4, sigma, last 1 ", ret.params
+print("params, first 4, sigma, last 1 ", ret.params)
 results += ["Arma.fit_mle ", ret.params[:4], ret.params[-1], ret.llf]
-print 'time used:', t3-t2
+print('time used:', t3-t2)
 
-print "Arma.fit method = \"ls\""
+print("Arma.fit method = \"ls\"")
 t3=time()
 ret2 = arma1.fit(order=(2,0,2), method="ls")
 t4=time()
-print ret2[0]
+print(ret2[0])
 results += ["Arma.fit ls", ret2[0]]
-print 'time used:', t4-t3
+print('time used:', t4-t3)
 
-print "Arma.fit method = \"CLS\""
+print("Arma.fit method = \"CLS\"")
 t4=time()
 ret3 = arma1.fit(order=(2,0,2), method="None")
 t5=time()
-print ret3
+print(ret3)
 results += ["Arma.fit other", ret3[0]]
-print 'time used:', t5-t4
+print('time used:', t5-t4)
 
-for i in results: print i
+for i in results: print(i)
 
diff --git a/statsmodels/examples/tsa/ex_arma.py b/statsmodels/examples/tsa/ex_arma.py
index 8f557a4be9b..0bcf0b5edc1 100644
--- a/statsmodels/examples/tsa/ex_arma.py
+++ b/statsmodels/examples/tsa/ex_arma.py
@@ -4,6 +4,7 @@
 works ok if noise variance is large
 '''
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 from statsmodels.tsa.arima_process import arma_generate_sample
@@ -15,7 +16,7 @@
 
 
 
-print "\nExample 1"
+print("\nExample 1")
 ar = [1.0,  -0.6, 0.1]
 ma = [1.0,  0.5, 0.3]
 nobs = 1000
@@ -24,7 +25,7 @@
 start_params = [0.1, 0.1, 0.1, 0.1]
 start_params_lhs = [-0.1, -0.1, 0.1, 0.1]
 
-print 'truelhs', np.r_[ar[1:], ma[1:]]
+print('truelhs', np.r_[ar[1:], ma[1:]])
 
 
 
@@ -37,7 +38,7 @@
 ##res = arma22.fit(start_params=start_params)
 ##print res.params
 
-print '\nARIMA new'
+print('\nARIMA new')
 arest2 = Arma(y22)
 
 naryw = 4  #= 30
@@ -50,8 +51,8 @@
 yt = x[:,0]
 xt = x[:,1:]
 res_ols = sm.OLS(yt, xt).fit()
-print 'hannan_rissannen'
-print res_ols.params
+print('hannan_rissannen')
+print(res_ols.params)
 start_params = res_ols.params
 start_params_mle = np.r_[-res_ols.params[:2],
                           res_ols.params[2:],
@@ -64,38 +65,38 @@
                          * np.minimum(np.abs(start_params_mle[:-1]),0.75))
 
 
-print 'conditional least-squares'
+print('conditional least-squares')
 
 #print rhohat2
-print 'with mle'
+print('with mle')
 arest2.nar = 2
 arest2.nma = 2
 #
 res = arest2.fit_mle(start_params=start_params_mle, method='nm') #no order in fit
-print res.params
+print(res.params)
 rhohat2, cov_x2a, infodict, mesg, ier = arest2.fit((2,2))
-print '\nARIMA_old'
+print('\nARIMA_old')
 arest = ARIMA_old(y22)
 rhohat1, cov_x1, infodict, mesg, ier = arest.fit((2,0,2))
-print rhohat1
-print np.sqrt(np.diag(cov_x1))
+print(rhohat1)
+print(np.sqrt(np.diag(cov_x1)))
 err1 = arest.errfn(x=y22)
-print np.var(err1)
-print 'bse ls, formula  not checked'
-print np.sqrt(np.diag(cov_x1))*err1.std()
-print 'bsejac for mle'
+print(np.var(err1))
+print('bse ls, formula  not checked')
+print(np.sqrt(np.diag(cov_x1))*err1.std())
+print('bsejac for mle')
 #print arest2.bsejac
 #TODO:check bsejac raises singular matrix linalg error
 #in model.py line620: return np.linalg.inv(np.dot(jacv.T, jacv))
 
-print '\nyule-walker'
-print sm.regression.yule_walker(y22, order=2, inv=True)
+print('\nyule-walker')
+print(sm.regression.yule_walker(y22, order=2, inv=True))
 
-print '\nArmamle_old'
+print('\nArmamle_old')
 arma1 = Armamle_old(y22)
 arma1.nar = 2
 arma1.nma = 2
 #arma1res = arma1.fit(start_params=np.r_[-0.5, -0.1, 0.1, 0.1, 0.5], method='fmin')
                      #maxfun=1000)
 arma1res = arma1.fit(start_params=res.params*0.7, method='fmin')
-print arma1res.params
+print(arma1res.params)
diff --git a/statsmodels/examples/tsa/ex_arma_all.py b/statsmodels/examples/tsa/ex_arma_all.py
index aacfb580768..1cc1c843d04 100644
--- a/statsmodels/examples/tsa/ex_arma_all.py
+++ b/statsmodels/examples/tsa/ex_arma_all.py
@@ -1,5 +1,6 @@
 
 
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 import matplotlib.pyplot as plt
@@ -13,13 +14,13 @@
 
 #d.fit(order=(1,1))
 d.fit((1,1), trend='nc')
-print d.res.params
+print(d.res.params)
 
 modc = Arma(x)
 resls = modc.fit(order=(1,1))
-print resls[0]
+print(resls[0])
 rescm = modc.fit_mle(order=(1,1), start_params=[-0.4,0.4, 1.])
-print rescm.params
+print(rescm.params)
 
 #decimal 1 corresponds to threshold of 5% difference
 assert_almost_equal(resls[0] / d.res.params, 1, decimal=1)
@@ -37,10 +38,10 @@
 
 modct = TArma(x)
 reslst = modc.fit(order=(1,1))
-print reslst[0]
+print(reslst[0])
 rescmt = modct.fit_mle(order=(1,1), start_params=[-0.4,0.4, 10, 1.],maxiter=500,
                        maxfun=500)
-print rescmt.params
+print(rescmt.params)
 
 
 from statsmodels.tsa.arima_model import ARMA
@@ -48,7 +49,7 @@
 ##rkf = mkf.fit((1,1))
 ##rkf.params
 rkf = mkf.fit((1,1), trend='nc')
-print rkf.params
+print(rkf.params)
 
 from statsmodels.tsa.arima_process import arma_generate_sample
 np.random.seed(12345)
@@ -60,14 +61,14 @@
 ##print 'css', res22css.params
 mod22 = Arma(y_arma22)
 resls22 = mod22.fit(order=(2,2))
-print 'ls ', resls22[0]
+print('ls ', resls22[0])
 resmle22 = mod22.fit_mle(order=(2,2), maxfun=2000)
-print 'mle', resmle22.params
+print('mle', resmle22.params)
 
 f = mod22.forecast()
 f3 = mod22.forecast3(start=900)[-20:]
 
-print y_arma22[-10:]
-print f[-20:]
-print f3[-109:-90]
+print(y_arma22[-10:])
+print(f[-20:])
+print(f3[-109:-90])
 plt.show()
\ No newline at end of file
diff --git a/statsmodels/examples/tsa/ex_coint.py b/statsmodels/examples/tsa/ex_coint.py
index 6fd7c487348..6685604f351 100644
--- a/statsmodels/examples/tsa/ex_coint.py
+++ b/statsmodels/examples/tsa/ex_coint.py
@@ -1,7 +1,9 @@
+
+from __future__ import print_function
 from statsmodels.tsa.tests.test_stattools import CheckCoint, TestCoint_t
 
 
 #test whether t-test for cointegration equals that produced by Stata
 
 tst = TestCoint_t()
-print tst.test_tstat()
+print(tst.test_tstat())
diff --git a/statsmodels/examples/tsa/ex_var.py b/statsmodels/examples/tsa/ex_var.py
index 331dc921551..a8a1a6e917a 100644
--- a/statsmodels/examples/tsa/ex_var.py
+++ b/statsmodels/examples/tsa/ex_var.py
@@ -1,4 +1,5 @@
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 from statsmodels.tsa.api import VAR
@@ -22,14 +23,14 @@
 fc_in = np.array([np.squeeze(res.forecast(model.y[t-20:t], 1))
                   for t in range(nobs_all-6,nobs_all)])
 
-print fc_in - res.fittedvalues[-6:]
+print(fc_in - res.fittedvalues[-6:])
 
 #out-of-sample 1-step ahead forecasts
 fc_out = np.array([np.squeeze(VAR(data[:t]).fit(2).forecast(data[t-20:t], 1))
                    for t in range(nobs_all-6,nobs_all)])
 
-print fc_out - data[nobs_all-6:nobs_all]
-print fc_out - res.fittedvalues[-6:]
+print(fc_out - data[nobs_all-6:nobs_all])
+print(fc_out - res.fittedvalues[-6:])
 
 
 #out-of-sample h-step ahead forecasts
@@ -37,8 +38,8 @@
 fc_out = np.array([VAR(data[:t]).fit(2).forecast(data[t-20:t], h)[-1]
                    for t in range(nobs_all-6-h+1,nobs_all-h+1)])
 
-print fc_out - data[nobs_all-6:nobs_all]  #out-of-sample forecast error
-print fc_out - res.fittedvalues[-6:]
+print(fc_out - data[nobs_all-6:nobs_all])  #out-of-sample forecast error
+print(fc_out - res.fittedvalues[-6:])
 
 import matplotlib.pyplot as plt
 res.plot_forecast(20)
diff --git a/statsmodels/examples/tsa/ex_var_reorder.py b/statsmodels/examples/tsa/ex_var_reorder.py
index b0ba1f24021..bdba3dfa9bb 100644
--- a/statsmodels/examples/tsa/ex_var_reorder.py
+++ b/statsmodels/examples/tsa/ex_var_reorder.py
@@ -1,3 +1,5 @@
+
+from __future__ import print_function
 import statsmodels.api as sm
 from statsmodels.tsa.vector_ar.tests.test_var import TestVARResults
 
diff --git a/statsmodels/examples/tsa/lagpolynomial.py b/statsmodels/examples/tsa/lagpolynomial.py
index 63637a8fa2c..e6690e612cf 100644
--- a/statsmodels/examples/tsa/lagpolynomial.py
+++ b/statsmodels/examples/tsa/lagpolynomial.py
@@ -6,6 +6,7 @@
 License: BSD (3-clause)
 """
 
+from __future__ import print_function
 import numpy as np
 from numpy import polynomial as npp
 
diff --git a/statsmodels/examples/tsa/try_ar.py b/statsmodels/examples/tsa/try_ar.py
index 8c67ba4c340..c29db274fa7 100644
--- a/statsmodels/examples/tsa/try_ar.py
+++ b/statsmodels/examples/tsa/try_ar.py
@@ -5,6 +5,7 @@
 Author: josef-pktd
 """
 
+from __future__ import print_function
 import numpy as np
 from scipy import signal
 
@@ -40,7 +41,7 @@ def armaloop(arcoefs, macoefs, x):
     arlag = arcoefs_r.shape[0]
     malag = macoefs_r.shape[0]
     maxlag = max(arlag, malag)
-    print maxlag
+    print(maxlag)
     y = np.zeros(x.shape, float)
     e = np.zeros(x.shape, float)
     y[:maxlag] = x[:maxlag]
@@ -58,23 +59,23 @@ def armaloop(arcoefs, macoefs, x):
     return y, e
 
 arcoefs, macoefs = -np.array([1, -0.8, 0.2])[1:], np.array([1., 0.5, 0.1])[1:]
-print armaloop(arcoefs, macoefs, np.ones(10))
-print armaloop([0.8], [], np.ones(10))
-print armaloop([0.8], [], np.arange(2,10))
+print(armaloop(arcoefs, macoefs, np.ones(10)))
+print(armaloop([0.8], [], np.ones(10)))
+print(armaloop([0.8], [], np.arange(2,10)))
 y, e = armaloop([0.1], [0.8], np.arange(2,10))
-print e
-print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.arange(2,10))
+print(e)
+print(signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.arange(2,10)))
 
 y, e = armaloop([], [0.8], np.ones(10))
-print e
-print signal.lfilter(np.array([1, -0.]), np.array([1., 0.8]), np.ones(10))
+print(e)
+print(signal.lfilter(np.array([1, -0.]), np.array([1., 0.8]), np.ones(10)))
 
 ic=signal.lfiltic(np.array([1, -0.1]), np.array([1., 0.8]), np.ones([0]), np.array([1]))
-print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=ic)
+print(signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=ic))
 
 zi = signal.lfilter_zi(np.array([1, -0.8, 0.2]), np.array([1., 0, 0]))
-print signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=zi)
-print signal.filtfilt(np.array([1, -0.8]), np.array([1.]), np.ones(10))
+print(signal.lfilter(np.array([1, -0.1]), np.array([1., 0.8]), np.ones(10), zi=zi))
+print(signal.filtfilt(np.array([1, -0.8]), np.array([1.]), np.ones(10)))
 
 #todo write examples/test across different versions
 
diff --git a/statsmodels/examples/tut_ols_ancova.py b/statsmodels/examples/tut_ols_ancova.py
index 9d715dd5bff..2940bf74124 100644
--- a/statsmodels/examples/tut_ols_ancova.py
+++ b/statsmodels/examples/tut_ols_ancova.py
@@ -33,6 +33,7 @@
 
 '''
 
+from __future__ import print_function
 import numpy as np
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
@@ -67,9 +68,9 @@
 
 res2 = sm.OLS(y, X).fit()
 #print "estimated parameters: x d1-d0 d2-d0 constant"
-print res2.params
+print(res2.params)
 #print "standard deviation of parameter estimates"
-print res2.bse
+print(res2.bse)
 prstd, iv_l, iv_u = wls_prediction_std(res2)
 #print res.summary()
 
@@ -93,8 +94,8 @@
 
 # F test joint hypothesis R * beta = 0
 # i.e. coefficient on both dummy variables equal zero
-print "Test hypothesis that all groups have same intercept"
-print res2.f_test(R)
+print("Test hypothesis that all groups have same intercept")
+print(res2.f_test(R))
 
 
 
diff --git a/statsmodels/examples/tut_ols_rlm_short.py b/statsmodels/examples/tut_ols_rlm_short.py
index 762d80aa158..21cdc165644 100644
--- a/statsmodels/examples/tut_ols_rlm_short.py
+++ b/statsmodels/examples/tut_ols_rlm_short.py
@@ -8,6 +8,7 @@
 Note: uncomment plt.show() to display graphs
 '''
 
+from __future__ import print_function
 import numpy as np
 #from scipy import stats
 import statsmodels.api as sm
@@ -35,10 +36,10 @@
 
 
 res2 = sm.OLS(y2, X).fit()
-print "OLS: parameter estimates: slope, constant"
-print res2.params
-print "standard deviation of parameter estimates"
-print res2.bse
+print("OLS: parameter estimates: slope, constant")
+print(res2.params)
+print("standard deviation of parameter estimates")
+print(res2.bse)
 prstd, iv_l, iv_u = wls_prediction_std(res2)
 plt.plot(x1, res2.fittedvalues, 'r-')
 plt.plot(x1, iv_u, 'r--')
@@ -48,10 +49,10 @@
 #compare with robust estimator
 
 resrlm2 = sm.RLM(y2, X).fit()
-print "\nRLM: parameter estimates: slope, constant"
-print resrlm2.params
-print "standard deviation of parameter estimates"
-print resrlm2.bse
+print("\nRLM: parameter estimates: slope, constant")
+print(resrlm2.params)
+print("standard deviation of parameter estimates")
+print(resrlm2.bse)
 plt.plot(x1, resrlm2.fittedvalues, 'g.-')
 plt.title('Data with Outliers; blue: true, red: OLS, green: RLM')
 
diff --git a/statsmodels/formula/__init__.py b/statsmodels/formula/__init__.py
index 1cf94599cb7..d3690aa11aa 100644
--- a/statsmodels/formula/__init__.py
+++ b/statsmodels/formula/__init__.py
@@ -1,4 +1,4 @@
 from statsmodels import NoseWrapper as Tester
 test = Tester().test
 
-from formulatools import handle_formula_data
+from .formulatools import handle_formula_data
diff --git a/statsmodels/formula/api.py b/statsmodels/formula/api.py
index e9e9aed85e4..219e1367a81 100644
--- a/statsmodels/formula/api.py
+++ b/statsmodels/formula/api.py
@@ -6,6 +6,8 @@
 ols = OLS.from_formula
 from statsmodels.regression.linear_model import GLSAR
 glsar = GLSAR.from_formula
+from statsmodels.regression.mixed_linear_model import MixedLM
+mixedlm = MixedLM.from_formula
 from statsmodels.genmod.generalized_linear_model import GLM
 glm = GLM.from_formula
 from statsmodels.robust.robust_linear_model import RLM
@@ -22,4 +24,7 @@
 negativebinomial = NegativeBinomial.from_formula
 from statsmodels.regression.quantile_regression import QuantReg
 quantreg = QuantReg.from_formula
-
+from statsmodels.duration.hazard_regression import PHReg
+phreg = PHReg.from_formula
+from statsmodels.genmod.generalized_estimating_equations import GEE
+gee = GEE.from_formula
diff --git a/statsmodels/formula/formulatools.py b/statsmodels/formula/formulatools.py
index d4acc9819c7..08cac58dcfa 100644
--- a/statsmodels/formula/formulatools.py
+++ b/statsmodels/formula/formulatools.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import iterkeys
 import statsmodels.tools.data as data_util
 from patsy import dmatrices
 
@@ -32,7 +33,7 @@ def handle_formula_data(Y, X, formula, depth=0):
         Should preserve the input type of Y,X. Could be None.
     """
     # half ass attempt to handle other formula objects
-    if isinstance(formula, tuple(formula_handler.keys())):
+    if isinstance(formula, tuple(iterkeys(formula_handler))):
         return formula_handler[type(formula)]
 
     if X is not None:
diff --git a/statsmodels/formula/tests/test_formula.py b/statsmodels/formula/tests/test_formula.py
index 9be595dd6d9..236dfe3fde0 100644
--- a/statsmodels/formula/tests/test_formula.py
+++ b/statsmodels/formula/tests/test_formula.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import iteritems, StringIO
 import warnings
 
 from statsmodels.formula.api import ols
@@ -53,7 +54,7 @@ def setupClass(cls):
 class TestFormulaDict(CheckFormulaOLS):
     @classmethod
     def setupClass(cls):
-        data = dict((k, v.tolist()) for k, v in load_pandas().data.iteritems())
+        data = dict((k, v.tolist()) for k, v in iteritems(load_pandas().data))
         cls.model = ols(longley_formula, data)
         super(TestFormulaDict, cls).setupClass()
 
@@ -83,7 +84,6 @@ def test_tests():
 def test_formula_labels():
     # make sure labels pass through patsy as expected
     # data(Duncan) from car in R
-    from StringIO import StringIO
     dta = StringIO(""""type" "income" "education" "prestige"\n"accountant" "prof" 62 86 82\n"pilot" "prof" 72 76 83\n"architect" "prof" 75 92 90\n"author" "prof" 55 90 76\n"chemist" "prof" 64 86 90\n"minister" "prof" 21 84 87\n"professor" "prof" 64 93 93\n"dentist" "prof" 80 100 90\n"reporter" "wc" 67 87 52\n"engineer" "prof" 72 86 88\n"undertaker" "prof" 42 74 57\n"lawyer" "prof" 76 98 89\n"physician" "prof" 76 97 97\n"welfare.worker" "prof" 41 84 59\n"teacher" "prof" 48 91 73\n"conductor" "wc" 76 34 38\n"contractor" "prof" 53 45 76\n"factory.owner" "prof" 60 56 81\n"store.manager" "prof" 42 44 45\n"banker" "prof" 78 82 92\n"bookkeeper" "wc" 29 72 39\n"mail.carrier" "wc" 48 55 34\n"insurance.agent" "wc" 55 71 41\n"store.clerk" "wc" 29 50 16\n"carpenter" "bc" 21 23 33\n"electrician" "bc" 47 39 53\n"RR.engineer" "bc" 81 28 67\n"machinist" "bc" 36 32 57\n"auto.repairman" "bc" 22 22 26\n"plumber" "bc" 44 25 29\n"gas.stn.attendant" "bc" 15 29 10\n"coal.miner" "bc" 7 7 15\n"streetcar.motorman" "bc" 42 26 19\n"taxi.driver" "bc" 9 19 10\n"truck.driver" "bc" 21 15 13\n"machine.operator" "bc" 21 20 24\n"barber" "bc" 16 26 20\n"bartender" "bc" 16 28 7\n"shoe.shiner" "bc" 9 17 3\n"cook" "bc" 14 22 16\n"soda.clerk" "bc" 12 30 6\n"watchman" "bc" 17 25 11\n"janitor" "bc" 7 20 8\n"policeman" "bc" 34 47 41\n"waiter" "bc" 8 32 10""")
     from pandas import read_table
     dta = read_table(dta, sep=" ")
diff --git a/statsmodels/genmod/dependence_structures/__init__.py b/statsmodels/genmod/dependence_structures/__init__.py
new file mode 100644
index 00000000000..618b1767415
--- /dev/null
+++ b/statsmodels/genmod/dependence_structures/__init__.py
@@ -0,0 +1,7 @@
+'''
+This module contains dependence structures for fitting models using
+generalized estimating equations (GEE).
+'''
+
+from .covstruct import Independence, Exchangeable, GlobalOddsRatio,\
+    Autoregressive, Nested, CovStruct
diff --git a/statsmodels/genmod/dependence_structures/covstruct.py b/statsmodels/genmod/dependence_structures/covstruct.py
new file mode 100644
index 00000000000..072e23f7b67
--- /dev/null
+++ b/statsmodels/genmod/dependence_structures/covstruct.py
@@ -0,0 +1,899 @@
+from statsmodels.compat.python import iterkeys, itervalues, zip, range
+from statsmodels.stats.correlation_tools import cov_nearest
+import numpy as np
+from scipy import linalg as spl
+from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
+                                             IterationLimitWarning)
+import warnings
+
+
+class CovStruct(object):
+    """
+    A base class for correlation and covariance structures of grouped
+    data.
+
+    Each implementation of this class takes the residuals from a
+    regression model that has been fitted to grouped data, and uses
+    them to estimate the within-group dependence structure of the
+    random errors in the model.
+
+    The state of the covariance structure is represented through the
+    value of the class variable `dep_params`.  The default state of a
+    newly-created instance should correspond to the identity
+    correlation matrix.
+    """
+
+    def __init__(self):
+
+        # Parameters describing the dependency structure
+        self.dep_params = None
+
+        self.cov_adjust = 0
+
+
+
+    def initialize(self, model):
+        """
+        Called by GEE, used by implementations that need additional
+        setup prior to running `fit`.
+
+        Parameters
+        ----------
+        model : GEE class
+            A reference to the parent GEE class instance.
+        """
+        self.model = model
+
+    def update(self, params):
+        """
+        Updates the association parameter values based on the current
+        regression coefficients.
+
+        Parameters
+        ----------
+        params : array-like
+            Working values for the regression parameters.
+        """
+        raise NotImplementedError
+
+    def covariance_matrix(self, endog_expval, index):
+        """
+        Returns the working covariance or correlation matrix for a
+        given cluster of data.
+
+        Parameters
+        ----------
+        endog_expval: array-like
+           The expected values of endog for the cluster for which the
+           covariance or correlation matrix will be returned
+        index: integer
+           The index of the cluster for which the covariane or
+           correlation matrix will be returned
+
+        Returns
+        -------
+        M: matrix
+            The covariance or correlation matrix of endog
+        is_cor: bool
+            True if M is a correlation matrix, False if M is a
+            covariance matrix
+        """
+        raise NotImplementedError
+
+    def covariance_matrix_solve(self, expval, index, stdev, rhs):
+        """
+        Solves matrix equations of the form `covmat * soln = rhs` and
+        returns the values of `soln`, where `covmat` is the covariance
+        matrix represented by this class.
+
+        Parameters
+        ----------
+        expval: array-like
+           The expected value of endog for each observed value in the
+           group.
+        index: integer
+           The group index.
+        stdev : array-like
+            The standard deviation of endog for each observation in
+            the group.
+        rhs : list/tuple of array-like
+            A set of right-hand sides; each defines a matrix equation
+            to be solved.
+
+        Returns
+        -------
+        soln : list/tuple of array-like
+            The solutions to the matrix equations.
+
+        Notes
+        -----
+        Returns None if the solver fails.
+
+        Some dependence structures do not use `expval` and/or `index`
+        to determine the correlation matrix.  Some families
+        (e.g. binomial) do not use the `stdev` parameter when forming
+        the covariance matrix.
+
+        If the covariance matrix is singular or not SPD, it is
+        projected to the nearest such matrix.  These projection events
+        are recorded in the fit_history member of the GEE model.
+
+        Systems of linear equations with the covariance matrix as the
+        left hand side (LHS) are solved for different right hand sides
+        (RHS); the LHS is only factorized once to save time.
+
+        This is a default implementation, it can be reimplemented in
+        subclasses to optimize the linear algebra according to the
+        struture of the covariance matrix.
+        """
+
+        vmat, is_cor = self.covariance_matrix(expval, index)
+
+        if is_cor:
+            vmat *= np.outer(stdev, stdev)
+
+        # Factor, the covariance matrix.  If the factorization fails,
+        # attempt to condition it into a factorizable matrix.
+        threshold = 1e-2
+        success = False
+        cov_adjust = 0
+        for itr in range(10):
+            try:
+                vco = spl.cho_factor(vmat)
+                success = True
+                break
+            except np.linalg.LinAlgError:
+                vmat = cov_nearest(vmat, method="nearest", threshold=threshold)
+                threshold *= 2
+                cov_adjust = 1
+
+        self.cov_adjust += cov_adjust
+        if success == False:
+            warnings.warn("Unable to condition covariance matrix to an SPD matrix",
+                          ConvergenceWarning)
+            return None
+
+        soln = [spl.cho_solve(vco, x) for x in rhs]
+        return soln
+
+    def summary(self):
+        """
+        Returns a text summary of the current estimate of the
+        dependence structure.
+        """
+        raise NotImplementedError
+
+
+class Independence(CovStruct):
+    """
+    An independence working dependence structure.
+    """
+
+    # Nothing to update
+    def update(self, params):
+        return
+
+    def covariance_matrix(self, expval, index):
+        dim = len(expval)
+        return np.eye(dim, dtype=np.float64), True
+
+    def covariance_matrix_solve(self, expval, index, stdev, rhs):
+        v = stdev**2
+        rslt = []
+        for x in rhs:
+            if x.ndim == 1:
+                rslt.append(x / v)
+            else:
+                rslt.append(x / v[:, None])
+        return rslt
+
+    update.__doc__ = CovStruct.update.__doc__
+    covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
+    covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
+
+    def summary(self):
+        return "Observations within a cluster are modeled as being independent."
+
+
+class Exchangeable(CovStruct):
+    """
+    An exchangeable working dependence structure.
+    """
+
+    def __init__(self):
+
+        super(Exchangeable, self).__init__()
+
+        # The correlation between any two values in the same cluster
+        self.dep_params = 0.
+
+    def update(self, params):
+
+        endog = self.model.endog_li
+
+        nobs = self.model.nobs
+        dim = len(params)
+
+        varfunc = self.model.family.variance
+
+        cached_means = self.model.cached_means
+
+        residsq_sum, scale, nterm = 0, 0, 0
+        for i in range(self.model.num_group):
+
+            expval, _ = cached_means[i]
+            stdev = np.sqrt(varfunc(expval))
+            resid = (endog[i] - expval) / stdev
+
+            ngrp = len(resid)
+            residsq = np.outer(resid, resid)
+            scale += np.trace(residsq)
+            residsq = np.tril(residsq, -1)
+            residsq_sum += residsq.sum()
+            nterm += 0.5 * ngrp * (ngrp - 1)
+
+        scale /= (nobs - dim)
+        self.dep_params = residsq_sum / (scale * (nterm - dim))
+
+    def covariance_matrix(self, expval, index):
+        dim = len(expval)
+        dp = self.dep_params * np.ones((dim, dim), dtype=np.float64)
+        return  dp + (1. - self.dep_params) * np.eye(dim), True
+
+    def covariance_matrix_solve(self, expval, index, stdev, rhs):
+
+        k = len(expval)
+        c = self.dep_params / (1. - self.dep_params)
+        c /= 1. + self.dep_params * (k - 1)
+
+        rslt = []
+        for x in rhs:
+            if x.ndim == 1:
+                x1 = x / stdev
+                y = x1 / (1. - self.dep_params)
+                y -= c * sum(x1)
+                y /= stdev
+            else:
+                x1 = x / stdev[:, None]
+                y = x1 / (1. - self.dep_params)
+                y -= c * x1.sum(0)
+                y /= stdev[:, None]
+            rslt.append(y)
+
+        return rslt
+
+    update.__doc__ = CovStruct.update.__doc__
+    covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
+    covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
+
+    def summary(self):
+        return ("The correlation between two observations in the " +
+                "same cluster is %.3f" % self.dep_params)
+
+
+class Nested(CovStruct):
+    """
+    A nested working dependence structure.
+
+    A working dependence structure that captures a nested hierarchy of
+    groups, each level of which contributes to the random error term
+    of the model.
+
+    When using this working covariance structure, `dep_data` of the
+    GEE instance should contain a n_obs x k matrix of 0/1 indicators,
+    corresponding to the k subgroups nested under the top-level
+    `groups` of the GEE instance.  These subgroups should be nested
+    from left to right, so that two observations with the same value
+    for column j of `dep_data` should also have the same value for all
+    columns j' < j (this only applies to observations in the same
+    top-level cluster given by the `groups` argument to GEE).
+
+    Example
+    -------
+    Suppose our data are student test scores, and the students are in
+    classrooms, nested in schools, nested in school districts.  The
+    school district is the highest level of grouping, so the school
+    district id would be provided to GEE as `groups`, and the school
+    and classroom id's would be provided to the Nested class as the
+    `dep_data` argument, e.g.
+
+        0 0  # School 0, classroom 0, student 0
+        0 0  # School 0, classroom 0, student 1
+        0 1  # School 0, classroom 1, student 0
+        0 1  # School 0, classroom 1, student 1
+        1 0  # School 1, classroom 0, student 0
+        1 0  # School 1, classroom 0, student 1
+        1 1  # School 1, classroom 1, student 0
+        1 1  # School 1, classroom 1, student 1
+
+    Labels lower in the hierarchy are recycled, so that student 0 in
+    classroom 0 is different fro student 0 in classroom 1, etc.
+
+    Notes
+    -----
+    The calculations for this dependence structure involve all pairs
+    of observations within a group (that is, within the top level
+    `group` structure passed to GEE).  Large group sizes will result
+    in slow iterations.
+
+    The variance components are estimated using least squares
+    regression of the products r*r', for standardized residuals r and
+    r' in the same group, on a vector of indicators defining which
+    variance components are shared by r and r'.
+    """
+
+    def initialize(self, model):
+        """
+        Called on the first call to update
+
+        `ilabels` is a list of n_i x n_i matrices containing integer
+        labels that correspond to specific correlation parameters.
+        Two elements of ilabels[i] with the same label share identical
+        variance components.
+
+        `designx` is a matrix, with each row containing dummy
+        variables indicating which variance components are associated
+        with the corresponding element of QY.
+        """
+
+        super(Nested, self).initialize(model)
+
+        # A bit of processing of the nest data
+        id_matrix = np.asarray(self.model.dep_data)
+        if id_matrix.ndim == 1:
+            id_matrix = id_matrix[:,None]
+        self.id_matrix = id_matrix
+
+        endog = self.model.endog_li
+        designx, ilabels = [], []
+
+        # The number of layers of nesting
+        n_nest = self.id_matrix.shape[1]
+
+        for i in range(self.model.num_group):
+            ngrp = len(endog[i])
+            glab = self.model.group_labels[i]
+            rix = self.model.group_indices[glab]
+
+            # Determine the number of common variance components
+            # shared by each pair of observations.
+            ix1, ix2 = np.tril_indices(ngrp, -1)
+            ncm = (self.id_matrix[rix[ix1], :] ==
+                   self.id_matrix[rix[ix2], :]).sum(1)
+
+            # This is used to construct the working correlation
+            # matrix.
+            ilabel = np.zeros((ngrp, ngrp), dtype=np.int32)
+            ilabel[[ix1, ix2]] = ncm + 1
+            ilabel[[ix2, ix1]] = ncm + 1
+            ilabels.append(ilabel)
+
+            # This is used to estimate the variance components.
+            dsx = np.zeros((len(ix1), n_nest+1), dtype=np.float64)
+            dsx[:,0] = 1
+            for k in np.unique(ncm):
+                ii = np.flatnonzero(ncm == k)
+                dsx[ii, 1:k+1] = 1
+            designx.append(dsx)
+
+        self.designx = np.concatenate(designx, axis=0)
+        self.ilabels = ilabels
+
+        svd = np.linalg.svd(self.designx, 0)
+        self.designx_u = svd[0]
+        self.designx_s = svd[1]
+        self.designx_v = svd[2].T
+
+    def update(self, params):
+
+        endog = self.model.endog_li
+        offset = self.model.offset_li
+
+        nobs = self.model.nobs
+        dim = len(params)
+
+        if self.designx is None:
+            self._compute_design(self.model)
+
+        cached_means = self.model.cached_means
+
+        varfunc = self.model.family.variance
+
+        dvmat = []
+        scale = 0.
+        for i in range(self.model.num_group):
+
+            expval, _ = cached_means[i]
+
+            stdev = np.sqrt(varfunc(expval))
+            resid = (endog[i] - offset[i] - expval) / stdev
+
+            ix1, ix2 = np.tril_indices(len(resid), -1)
+            dvmat.append(resid[ix1] * resid[ix2])
+
+            scale += np.sum(resid**2)
+
+        dvmat = np.concatenate(dvmat)
+        scale /= (nobs - dim)
+
+        # Use least squares regression to estimate the variance
+        # components
+        vcomp_coeff = np.dot(self.designx_v, np.dot(self.designx_u.T,
+                                dvmat) / self.designx_s)
+
+        self.vcomp_coeff = np.clip(vcomp_coeff, 0, np.inf)
+        self.scale = scale
+
+        self.dep_params = self.vcomp_coeff.copy()
+
+    def covariance_matrix(self, expval, index):
+
+        dim = len(expval)
+
+        # First iteration
+        if self.dep_params is None:
+            return np.eye(dim, dtype=np.float64), True
+
+        ilabel = self.ilabels[index]
+
+        c = np.r_[self.scale, np.cumsum(self.vcomp_coeff)]
+        vmat = c[ilabel]
+        vmat /= self.scale
+        return vmat, True
+
+    update.__doc__ = CovStruct.update.__doc__
+    covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
+
+    def summary(self):
+        """
+        Returns a summary string describing the state of the
+        dependence structure.
+        """
+
+        msg = "Variance estimates\n------------------\n"
+        for k in range(len(self.vcomp_coeff)):
+            msg += "Component %d: %.3f\n" % (k+1, self.vcomp_coeff[k])
+        msg += "Residual: %.3f\n" % (self.scale -
+                                     np.sum(self.vcomp_coeff))
+        return msg
+
+
+
+class Autoregressive(CovStruct):
+    """
+    An autoregressive working dependence structure.
+
+    The dependence is defined in terms of the `time` component of the
+    parent GEE class.  Time represents a potentially multidimensional
+    index from which distances between pairs of observations can be
+    determined.  The correlation between two observations in the same
+    cluster is dep_params^distance, where `dep_params` is the
+    autocorrelation parameter to be estimated, and `distance` is the
+    distance between the two observations, calculated from their
+    corresponding time values.  `time` is stored as an n_obs x k
+    matrix, where `k` represents the number of dimensions in the time
+    index.
+
+    The autocorrelation parameter is estimated using weighted
+    nonlinear least squares, regressing each value within a cluster on
+    each preceeding value in the same cluster.
+
+    Parameters
+    ----------
+    dist_func: function from R^k x R^k to R^+, optional
+       A function that computes the distance between the two
+       observations based on their `time` values.
+
+    Reference
+    ---------
+    B Rosner, A Munoz.  Autoregressive modeling for the analysis of
+    longitudinal data with unequally spaced examinations.  Statistics
+    in medicine. Vol 7, 59-71, 1988.
+    """
+
+    def __init__(self, dist_func=None):
+
+        super(Autoregressive, self).__init__()
+
+        # The function for determining distances based on time
+        if dist_func is None:
+            self.dist_func = lambda x, y: np.abs(x - y).sum()
+        else:
+            self.dist_func = dist_func
+
+        self.designx = None
+
+        # The autocorrelation parameter
+        self.dep_params = 0.
+
+    def update(self, params):
+
+        endog = self.model.endog_li
+        time = self.model.time_li
+
+        # Only need to compute this once
+        if self.designx is not None:
+            designx = self.designx
+        else:
+            designx = []
+            for i in range(self.model.num_group):
+
+                ngrp = len(endog[i])
+                if ngrp == 0:
+                    continue
+
+                # Loop over pairs of observations within a cluster
+                for j1 in range(ngrp):
+                    for j2 in range(j1):
+                        designx.append(self.dist_func(time[i][j1, :],
+                                                      time[i][j2, :]))
+
+            designx = np.array(designx)
+            self.designx = designx
+
+        scale = self.model.estimate_scale()
+        varfunc = self.model.family.variance
+        cached_means = self.model.cached_means
+
+        # Weights
+        var = 1. - self.dep_params**(2*designx)
+        var /= 1. - self.dep_params**2
+        wts = 1. / var
+        wts /= wts.sum()
+
+        residmat = []
+        for i in range(self.model.num_group):
+
+            expval, _ = cached_means[i]
+            stdev = np.sqrt(scale * varfunc(expval))
+            resid = (endog[i] - expval) / stdev
+
+            ngrp = len(resid)
+            for j1 in range(ngrp):
+                for j2 in range(j1):
+                    residmat.append([resid[j1], resid[j2]])
+
+        residmat = np.array(residmat)
+
+        # Need to minimize this
+        def fitfunc(a):
+            dif = residmat[:, 0] - (a**designx)*residmat[:, 1]
+            return np.dot(dif**2, wts)
+
+        # Left bracket point
+        b_lft, f_lft = 0., fitfunc(0.)
+
+        # Center bracket point
+        b_ctr, f_ctr = 0.5, fitfunc(0.5)
+        while f_ctr > f_lft:
+            b_ctr /= 2
+            f_ctr = fitfunc(b_ctr)
+            if b_ctr < 1e-8:
+                self.dep_params = 0
+                return
+
+        # Right bracket point
+        b_rgt, f_rgt = 0.75, fitfunc(0.75)
+        while f_rgt < f_ctr:
+            b_rgt = b_rgt + (1. - b_rgt) / 2
+            f_rgt = fitfunc(b_rgt)
+            if b_rgt > 1. - 1e-6:
+                raise ValueError(
+                    "Autoregressive: unable to find right bracket")
+
+        from scipy.optimize import brent
+        self.dep_params = brent(fitfunc, brack=[b_lft, b_ctr, b_rgt])
+
+    def covariance_matrix(self, endog_expval, index):
+        ngrp = len(endog_expval)
+        if self.dep_params == 0:
+            return np.eye(ngrp, dtype=np.float64), True
+        idx = np.arange(ngrp)
+        cmat = self.dep_params**np.abs(idx[:, None] - idx[None, :])
+        return cmat, True
+
+    def covariance_matrix_solve(self, expval, index, stdev, rhs):
+        # The inverse of an AR(1) covariance matrix is tri-diagonal.
+
+        k = len(expval)
+        soln = []
+
+        # LHS has 1 column
+        if k == 1:
+            return [x / stdev**2 for x in rhs]
+
+        # LHS has 2 columns
+        if k == 2:
+            mat = np.array([[1, -self.dep_params], [-self.dep_params, 1]])
+            mat /= (1. - self.dep_params**2)
+            for x in rhs:
+                if x.ndim == 1:
+                    x1 = x / stdev
+                else:
+                    x1 = x / stdev[:, None]
+                x1 = np.dot(mat, x1)
+                if x.ndim == 1:
+                    x1 /= stdev
+                else:
+                    x1 /= stdev[:, None]
+                soln.append(x1)
+            return soln
+
+        # LHS has >= 3 columns: values c0, c1, c2 defined below give
+        # the inverse.  c0 is on the diagonal, except for the first
+        # and last position.  c1 is on the first and last position of
+        # the diagonal.  c2 is on the sub/super diagonal.
+        c0 = (1. + self.dep_params**2) / (1. - self.dep_params**2)
+        c1 = 1. / (1. - self.dep_params**2)
+        c2 = -self.dep_params / (1. - self.dep_params**2)
+        soln = []
+        for x in rhs:
+            flatten = False
+            if x.ndim == 1:
+                x = x[:, None]
+                flatten = True
+            x1 = x / stdev[:, None]
+
+            z0 = np.zeros((1, x.shape[1]))
+            rhs1 = np.concatenate((x[1:,:], z0), axis=0)
+            rhs2 = np.concatenate((z0, x[0:-1,:]), axis=0)
+
+            y = c0*x + c2*rhs1 + c2*rhs2
+            y[0, :] = c1*x[0, :] + c2*x[1, :]
+            y[-1, :] = c1*x[-1, :] + c2*x[-2, :]
+
+            y /= stdev[:, None]
+
+            if flatten:
+                y = np.squeeze(y)
+
+            soln.append(y)
+
+        return soln
+
+    update.__doc__ = CovStruct.update.__doc__
+    covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
+    covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
+
+    def summary(self):
+
+        return ("Autoregressive(1) dependence parameter: %.3f\n" %
+                self.dep_params)
+
+
+class GlobalOddsRatio(CovStruct):
+    """
+    Estimate the global odds ratio for a GEE with ordinal or nominal
+    data.
+
+    References
+    ----------
+    PJ Heagerty and S Zeger. "Marginal Regression Models for Clustered
+    Ordinal Measurements". Journal of the American Statistical
+    Association Vol. 91, Issue 435 (1996).
+
+    Thomas Lumley. Generalized Estimating Equations for Ordinal Data:
+    A Note on Working Correlation Structures. Biometrics Vol. 52,
+    No. 1 (Mar., 1996), pp. 354-361
+    http://www.jstor.org/stable/2533173
+
+    Notes:
+    ------
+    The following data structures are calculated in the class:
+
+    'ibd' is a list whose i^th element ibd[i] is a sequence of integer
+    pairs (a,b), where endog_li[i][a:b] is the subvector of binary
+    indicators derived from the same ordinal value.
+
+    `cpp` is a dictionary where cpp[group] is a map from cut-point
+    pairs (c,c') to the indices of all between-subject pairs derived
+    from the given cut points.
+    """
+
+    def __init__(self, endog_type):
+        super(GlobalOddsRatio, self).__init__()
+        self.endog_type = endog_type
+        self.dep_params = 0.
+
+    def initialize(self, model):
+
+        super(GlobalOddsRatio, self).initialize(model)
+
+        self.nlevel = len(model.endog_values)
+        self.ncut = self.nlevel - 1
+
+        ibd = []
+        for v in model.endog_li:
+            jj = np.arange(0, len(v) + 1, self.ncut)
+            ibd1 = np.hstack((jj[0:-1][:, None], jj[1:][:, None]))
+            ibd1 = [(jj[k], jj[k + 1]) for k in range(len(jj) - 1)]
+            ibd.append(ibd1)
+        self.ibd = ibd
+
+        # Need to restrict to between-subject pairs
+        cpp = []
+        for v in model.endog_li:
+
+            # Number of subjects in this group
+            m = int(len(v) / self.ncut)
+
+            cpp1 = {}
+            # Loop over distinct subject pairs
+            for i1 in range(m):
+                for i2 in range(i1):
+                    # Loop over cut point pairs
+                    for k1 in range(self.ncut):
+                        for k2 in range(k1+1):
+                            if (k2, k1) not in cpp1:
+                                cpp1[(k2, k1)] = []
+                            j1 = i1*self.ncut + k1
+                            j2 = i2*self.ncut + k2
+                            cpp1[(k2, k1)].append([j2, j1])
+
+            for k in cpp1.keys():
+                cpp1[k] = np.asarray(cpp1[k])
+            cpp.append(cpp1)
+
+        self.cpp = cpp
+
+        # Initialize the dependence parameters
+        self.crude_or = self.observed_crude_oddsratio()
+        self.dep_params = self.crude_or
+
+
+    def pooled_odds_ratio(self, tables):
+        """
+        Returns the pooled odds ratio for a list of 2x2 tables.
+
+        The pooled odds ratio is the inverse variance weighted average
+        of the sample odds ratios of the tables.
+        """
+
+        if len(tables) == 0:
+            return 1.
+
+        # Get the sampled odds ratios and variances
+        log_oddsratio, var = [], []
+        for table in tables:
+            lor = np.log(table[1, 1]) + np.log(table[0, 0]) -\
+                  np.log(table[0, 1]) - np.log(table[1, 0])
+            log_oddsratio.append(lor)
+            var.append((1 / table.astype(np.float64)).sum())
+
+        # Calculate the inverse variance weighted average
+        wts = [1 / v for v in var]
+        wtsum = sum(wts)
+        wts = [w / wtsum for w in wts]
+        log_pooled_or = sum([w*e for w, e in zip(wts, log_oddsratio)])
+
+        return np.exp(log_pooled_or)
+
+    def covariance_matrix(self, expected_value, index):
+
+        vmat = self.get_eyy(expected_value, index)
+        vmat -= np.outer(expected_value, expected_value)
+        return vmat, False
+
+    def observed_crude_oddsratio(self):
+        """
+        To obtain the crude (global) odds ratio, first pool all binary
+        indicators corresponding to a given pair of cut points (c,c'),
+        then calculate the odds ratio for this 2x2 table.  The crude
+        odds ratio is the inverse variance weighted average of these
+        odds ratios.  Since the covariate effects are ignored, this OR
+        will generally be greater than the stratified OR.
+        """
+
+        cpp = self.cpp
+        endog = self.model.endog_li
+
+        # Storage for the contingency tables for each (c,c')
+        tables = {}
+        for ii in iterkeys(cpp[0]):
+            tables[ii] = np.zeros((2, 2), dtype=np.float64)
+
+        # Get the observed crude OR
+        for i in range(len(endog)):
+
+            # The observed joint values for the current cluster
+            yvec = endog[i]
+            endog_11 = np.outer(yvec, yvec)
+            endog_10 = np.outer(yvec, 1. - yvec)
+            endog_01 = np.outer(1. - yvec, yvec)
+            endog_00 = np.outer(1. - yvec, 1. - yvec)
+
+            cpp1 = cpp[i]
+            for ky in iterkeys(cpp1):
+                ix = cpp1[ky]
+                tables[ky][1, 1] += endog_11[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][1, 0] += endog_10[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][0, 1] += endog_01[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][0, 0] += endog_00[ix[:, 0], ix[:, 1]].sum()
+
+        return self.pooled_odds_ratio(list(itervalues(tables)))
+
+    def get_eyy(self, endog_expval, index):
+        """
+        Returns a matrix V such that V[i,j] is the joint probability
+        that endog[i] = 1 and endog[j] = 1, based on the marginal
+        probabilities of endog and the global odds ratio `current_or`.
+        """
+
+        current_or = self.dep_params
+        ibd = self.ibd[index]
+
+        # The between-observation joint probabilities
+        if current_or == 1.0:
+            vmat = np.outer(endog_expval, endog_expval)
+        else:
+            psum = endog_expval[:, None] + endog_expval[None, :]
+            pprod = endog_expval[:, None] * endog_expval[None, :]
+            pfac = np.sqrt((1. + psum * (current_or - 1.))**2 +
+                           4 * current_or * (1. - current_or) * pprod)
+            vmat = 1. +  psum * (current_or - 1.) - pfac
+            vmat /= 2. * (current_or - 1)
+
+        # Fix E[YY'] for elements that belong to same observation
+        for bdl in ibd:
+            evy = endog_expval[bdl[0]:bdl[1]]
+            if self.endog_type == "ordinal":
+                eyr = np.outer(evy, np.ones(len(evy)))
+                eyc = np.outer(np.ones(len(evy)), evy)
+                vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = \
+                    np.where(eyr < eyc, eyr, eyc)
+            else:
+                vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = np.diag(evy)
+
+        return vmat
+
+    def update(self, params):
+        """
+        Update the global odds ratio based on the current value of
+        params.
+        """
+
+        endog = self.model.endog_li
+        cpp = self.cpp
+        cached_means = self.model.cached_means
+
+        # This will happen if all the clusters have only
+        # one observation
+        if len(cpp[0]) == 0:
+            return
+
+        tables = {}
+        for ii in cpp[0]:
+            tables[ii] = np.zeros((2, 2), dtype=np.float64)
+
+        for i in range(self.model.num_group):
+
+            endog_expval, _ = cached_means[i]
+
+            emat_11 = self.get_eyy(endog_expval, i)
+            emat_10 = endog_expval[:, None] - emat_11
+            emat_01 = -emat_11 + endog_expval
+            emat_00 = 1. - (emat_11 + emat_10 + emat_01)
+
+            cpp1 = cpp[i]
+            for ky in iterkeys(cpp1):
+                ix = cpp1[ky]
+                tables[ky][1, 1] += emat_11[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][1, 0] += emat_10[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][0, 1] += emat_01[ix[:, 0], ix[:, 1]].sum()
+                tables[ky][0, 0] += emat_00[ix[:, 0], ix[:, 1]].sum()
+
+        cor_expval = self.pooled_odds_ratio(list(itervalues(tables)))
+
+        self.dep_params *= self.crude_or / cor_expval
+        if not np.isfinite(self.dep_params):
+            self.dep_params = 1.
+            warnings.warn("dep_params became inf, resetting to 1",
+                          ConvergenceWarning)
+
+
+    update.__doc__ = CovStruct.update.__doc__
+    covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
+
+    def summary(self):
+
+        return "Global odds ratio: %.3f\n" % self.dep_params
diff --git a/statsmodels/genmod/families/__init__.py b/statsmodels/genmod/families/__init__.py
index 754b9f3ed66..c0ebee5f48a 100644
--- a/statsmodels/genmod/families/__init__.py
+++ b/statsmodels/genmod/families/__init__.py
@@ -12,5 +12,5 @@
 
 #from statsmodels.family.family import Gaussian, Family, Poisson, Gamma, \
 #    InverseGaussian, Binomial, NegativeBinomial
-from family import Gaussian, Family, Poisson, Gamma, \
+from .family import Gaussian, Family, Poisson, Gamma, \
     InverseGaussian, Binomial, NegativeBinomial
diff --git a/statsmodels/genmod/families/family.py b/statsmodels/genmod/families/family.py
index 947cd613fd0..b7bf8fd79cf 100644
--- a/statsmodels/genmod/families/family.py
+++ b/statsmodels/genmod/families/family.py
@@ -8,12 +8,11 @@
 import numpy as np
 from scipy import special
 from scipy.stats import ss
-import links as L
-import varfuncs as V
+from . import links as L
+from . import varfuncs as V
 FLOAT_EPS = np.finfo(float).eps
 
 class Family(object):
-
     """
     The parent class for one-parameter exponential families.
 
@@ -25,6 +24,11 @@ class Family(object):
     variance : a variance function
         Measures the variance as a function of the mean probabilities.
         See the individual families for the default variance function.
+
+    See Also
+    --------
+    :ref:`links`
+
     """
 #TODO: change these class attributes, use valid somewhere...
     valid = [-np.inf, np.inf]
@@ -57,7 +61,7 @@ def _setlink(self, link):
             validlink = max([isinstance(link, _) for _ in self.links])
             if not validlink:
                 errmsg = "Invalid link for family, should be in %s. (got %s)"
-                raise ValueError(errmsg % (`self.links`, link))
+                raise ValueError(errmsg % (repr(self.links), link))
 
 
     def _getlink(self):
@@ -118,15 +122,15 @@ def weights(self, mu):
         """
         return 1. / (self.link.deriv(mu)**2 * self.variance(mu))
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         """
-        Deviance of (Y,mu) pair.
+        Deviance of (endog,mu) pair.
 
         Deviance is usually defined as twice the loglikelihood ratio.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             The endogenous response variable
         mu : array-like
             The inverse of the link function at the linear predicted values.
@@ -140,19 +144,19 @@ def deviance(self, Y, mu, scale=1.):
 
         Notes
         -----
-        DEV = (sum_i(2*loglike(Y_i,Y_i) - 2*loglike(Y_i,mu_i)) / scale
+        DEV = (sum_i(2*loglike(endog_i,endog_i) - 2*loglike(endog_i,mu_i)) / scale
 
         The deviance functions are analytically defined for each family.
         """
         raise NotImplementedError
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """
         The deviance residuals
 
         Parameters
         ----------
-        Y : array
+        endog : array
             The endogenous response variable
         mu : array
             The inverse of the link function at the linear predicted values.
@@ -169,13 +173,13 @@ def resid_dev(self, Y, mu, scale=1.):
         """
         raise NotImplementedError
 
-    def fitted(self, eta):
+    def fitted(self, lin_pred):
         """
-        Fitted values based on linear predictors eta.
+        Fitted values based on linear predictors lin_pred.
 
         Parameters
         -----------
-        eta : array
+        lin_pred : array
             Values of the linear predictor of the model.
             dot(X,beta) in a classical linear model.
 
@@ -185,7 +189,7 @@ def fitted(self, eta):
             The mean response variables given by the inverse of the link
             function.
         """
-        return self.link.inverse(eta)
+        return self.link.inverse(lin_pred)
 
     def predict(self, mu):
         """
@@ -198,19 +202,19 @@ def predict(self, mu):
 
         Returns
         -------
-        eta : array
+        lin_pred : array
             Linear predictors based on the mean response variables.  The value
             of the link function at the given mu.
         """
         return self.link(mu)
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         The loglikelihood function.
 
         Parameters
         ----------
-        `Y` : array
+        `endog` : array
             Usually the endogenous response variable.
         `mu` : array
             Usually but not always the fitted mean response variable.
@@ -218,16 +222,16 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood evaluated at (Y,mu).
+            The value of the loglikelihood evaluated at (endog,mu).
         Notes
         -----
-        This is defined for each family.  Y and mu are not restricted to
-        `Y` and `mu` respectively.  For instance, the deviance function calls
-        both loglike(Y,Y) and loglike(Y,mu) to get the likelihood ratio.
+        This is defined for each family.  endog and mu are not restricted to
+        `endog` and `mu` respectively.  For instance, the deviance function calls
+        both loglike(endog,endog) and loglike(endog,mu) to get the likelihood ratio.
         """
         raise NotImplementedError
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         The Anscome residuals.
 
@@ -260,6 +264,8 @@ class Poisson(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
+
     """
 
     links = [L.log, L.identity, L.sqrt]
@@ -270,12 +276,12 @@ def __init__(self, link=L.log):
         self.variance = Poisson.variance
         self.link = link()
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """Poisson deviance residual
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -289,17 +295,17 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         -----
-        resid_dev = sign(Y-mu)*sqrt(2*Y*log(Y/mu)-2*(Y-mu))
+        resid_dev = sign(endog-mu)*sqrt(2*endog*log(endog/mu)-2*(endog-mu))
         """
-        return np.sign(Y-mu) * np.sqrt(2*Y*np.log(Y/mu)-2*(Y-mu))/scale
+        return np.sign(endog-mu) * np.sqrt(2*endog*np.log(endog/mu)-2*(endog-mu))/scale
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         '''
         Poisson deviance function
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -309,7 +315,7 @@ def deviance(self, Y, mu, scale=1.):
         Returns
         -------
         deviance : float
-            The deviance function at (Y,mu) as defined below.
+            The deviance function at (endog,mu) as defined below.
 
         Notes
         -----
@@ -317,24 +323,24 @@ def deviance(self, Y, mu, scale=1.):
 
         :math:`deviance = 2*\\sum_{i}(Y*\\log(Y/\\mu))`
         '''
-        if np.any(Y==0):
-            retarr = np.zeros(Y.shape)
-            Ymu = Y/mu
-            mask = Ymu != 0
-            YmuMasked = Ymu[mask]
-            Ymasked = Y[mask]
-            np.putmask(retarr, mask, Ymasked*np.log(YmuMasked)/scale)
+        if np.any(endog==0):
+            retarr = np.zeros(endog.shape)
+            endog_mu = endog/mu
+            mask = endog_mu != 0
+            endog_mu_Masked = endog_mu[mask]
+            endog_masked = endog[mask]
+            np.putmask(retarr, mask, endog_masked*np.log(endog_mu_Masked)/scale)
             return 2*np.sum(retarr)
         else:
-            return 2*np.sum(Y*np.log(Y/mu))/scale
+            return 2*np.sum(endog*np.log(endog/mu))/scale
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         Loglikelihood function for Poisson exponential family distribution.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -344,23 +350,23 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
         -----
-        llf = scale * sum(-mu + Y*log(mu) - gammaln(Y+1))
+        llf = scale * sum(-mu + endog*log(mu) - gammaln(endog+1))
         where gammaln is the log gamma function
         """
-        return scale * np.sum(-mu + Y*np.log(mu)-special.gammaln(Y+1))
+        return scale * np.sum(-mu + endog*np.log(mu)-special.gammaln(endog+1))
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         Anscombe residuals for the Poisson exponential family distribution
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -372,12 +378,11 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         -----
-        resid_anscombe = :math:`(3/2.)*(Y^{2/3.} - \\mu**(2/3.))/\\mu^{1/6.}`
+        resid_anscombe = :math:`(3/2.)*(endog^{2/3.} - \\mu**(2/3.))/\\mu^{1/6.}`
         """
-        return (3/2.)*(Y**(2/3.)-mu**(2/3.))/mu**(1/6.)
+        return (3/2.)*(endog**(2/3.)-mu**(2/3.))/mu**(1/6.)
 
 class Gaussian(Family):
-
     """
     Gaussian exponential family distribution.
 
@@ -398,6 +403,8 @@ class Gaussian(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
+
     """
 
     links = [L.log, L.identity, L.inverse_power]
@@ -407,13 +414,13 @@ def __init__(self, link=L.identity):
         self.variance = Gaussian.variance
         self.link = link()
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """
         Gaussian deviance residuals
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -427,18 +434,18 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         --------
-        `resid_dev` = (`Y` - `mu`)/sqrt(variance(`mu`))
+        `resid_dev` = (`endog` - `mu`)/sqrt(variance(`mu`))
         """
 
-        return (Y - mu) / np.sqrt(self.variance(mu))/scale
+        return (endog - mu) / np.sqrt(self.variance(mu))/scale
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         """
         Gaussian deviance function
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -448,21 +455,21 @@ def deviance(self, Y, mu, scale=1.):
         Returns
         -------
         deviance : float
-            The deviance function at (Y,mu) as defined below.
+            The deviance function at (endog,mu) as defined below.
 
         Notes
         --------
-        `deviance` = sum((Y-mu)**2)
+        `deviance` = sum((endog-mu)**2)
         """
-        return np.sum((Y-mu)**2)/scale
+        return np.sum((endog-mu)**2)/scale
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         Loglikelihood function for Gaussian exponential family distribution.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -472,7 +479,7 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
@@ -480,32 +487,32 @@ def loglike(self, Y, mu, scale=1.):
         If the link is the identity link function then the
         loglikelihood function is the same as the classical OLS model.
         llf = -(nobs/2)*(log(SSR) + (1 + log(2*pi/nobs)))
-        where SSR = sum((Y-link^(-1)(mu))**2)
+        where SSR = sum((endog-link^(-1)(mu))**2)
 
         If the links is not the identity link then the loglikelihood
         function is defined as
-        llf = sum((`Y`*`mu`-`mu`**2/2)/`scale` - `Y`**2/(2*`scale`) - \
+        llf = sum((`endog`*`mu`-`mu`**2/2)/`scale` - `endog`**2/(2*`scale`) - \
             (1/2.)*log(2*pi*`scale`))
         """
         if isinstance(self.link, L.Power) and self.link.power == 1:
         # This is just the loglikelihood for classical OLS
-            nobs2 = Y.shape[0]/2.
-            SSR = ss(Y-self.fitted(mu))
+            nobs2 = endog.shape[0]/2.
+            SSR = ss(endog-self.fitted(mu))
             llf = -np.log(SSR) * nobs2
             llf -= (1+np.log(np.pi/nobs2))*nobs2
             return llf
         else:
         # Return the loglikelihood for Gaussian GLM
-            return np.sum((Y*mu-mu**2/2)/scale-Y**2/(2*scale)-\
+            return np.sum((endog*mu-mu**2/2)/scale-endog**2/(2*scale)-\
                     .5*np.log(2*np.pi*scale))
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         The Anscombe residuals for the Gaussian exponential family distribution
 
         Parameters
         ----------
-        Y : array
+        endog : array
             Endogenous response variable
         mu : array
             Fitted mean response variable
@@ -517,12 +524,11 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         --------
-        `resid_anscombe` = `Y` - `mu`
+        `resid_anscombe` = `endog` - `mu`
         """
-        return Y-mu
+        return endog-mu
 
 class Gamma(Family):
-
     """
     Gamma exponential family distribution.
 
@@ -543,6 +549,8 @@ class Gamma(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
+
     """
 
     links = [L.log, L.identity, L.inverse_power]
@@ -565,13 +573,13 @@ def _clean(self, x):
         """
         return np.clip(x, FLOAT_EPS, np.inf)
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         """
         Gamma deviance function
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -585,18 +593,18 @@ def deviance(self, Y, mu, scale=1.):
 
         Notes
         -----
-        `deviance` = 2*sum((Y - mu)/mu - log(Y/mu))
+        `deviance` = 2*sum((endog - mu)/mu - log(endog/mu))
         """
-        Y_mu = self._clean(Y/mu)
-        return 2 * np.sum((Y - mu)/mu - np.log(Y_mu))
+        endog_mu = self._clean(endog/mu)
+        return 2 * np.sum((endog - mu)/mu - np.log(endog_mu))
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """
         Gamma deviance residuals
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -610,18 +618,18 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         -----
-        `resid_dev` = sign(Y - mu) * sqrt(-2*(-(Y-mu)/mu + log(Y/mu)))
+        `resid_dev` = sign(endog - mu) * sqrt(-2*(-(endog-mu)/mu + log(endog/mu)))
         """
-        Y_mu = self._clean(Y/mu)
-        return np.sign(Y-mu) * np.sqrt(-2*(-(Y-mu)/mu + np.log(Y_mu)))
+        endog_mu = self._clean(endog/mu)
+        return np.sign(endog-mu) * np.sqrt(-2*(-(endog-mu)/mu + np.log(endog_mu)))
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         Loglikelihood function for Gamma exponential family distribution.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -631,28 +639,28 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
         --------
-        llf = -1/scale * sum(Y/mu + log(mu) + (scale-1)*log(Y) + log(scale) +\
+        llf = -1/scale * sum(endog/mu + log(mu) + (scale-1)*log(endog) + log(scale) +\
             scale*gammaln(1/scale))
         where gammaln is the log gamma function.
         """
-        return - 1./scale * np.sum(Y/mu+np.log(mu)+(scale-1)*np.log(Y)\
+        return - 1./scale * np.sum(endog/mu+np.log(mu)+(scale-1)*np.log(endog)\
                 +np.log(scale)+scale*special.gammaln(1./scale))
 # in Stata scale is set to equal 1 for reporting llf
 # in R it's the dispersion, though there is a loss of precision vs. our
 # results due to an assumed difference in implementation
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         The Anscombe residuals for Gamma exponential family distribution
 
         Parameters
         ----------
-        Y : array
+        endog : array
             Endogenous response variable
         mu : array
             Fitted mean response variable
@@ -664,12 +672,11 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         -----
-        resid_anscombe = 3*(Y**(1/3.)-mu**(1/3.))/mu**(1/3.)
+        resid_anscombe = 3*(endog**(1/3.)-mu**(1/3.))/mu**(1/3.)
         """
-        return 3*(Y**(1/3.)-mu**(1/3.))/mu**(1/3.)
+        return 3*(endog**(1/3.)-mu**(1/3.))/mu**(1/3.)
 
 class Binomial(Family):
-
     """
     Binomial exponential family distribution.
 
@@ -690,10 +697,12 @@ class Binomial(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
 
     Notes
     -----
     endog for Binomial can be specified in one of three ways.
+
     """
 
     links = [L.logit, L.probit, L.cauchy, L.log, L.cloglog, L.identity]
@@ -704,7 +713,7 @@ def __init__(self, link=L.logit):  #, n=1.):
 # equal to n
         self.n = 1 # overwritten by initialize if needed but
                    # always used to initialize variance
-                   # since Y is assumed/forced to be (0,1)
+                   # since endog is assumed/forced to be (0,1)
         self.variance = V.Binomial(n=self.n)
         self.link = link()
 
@@ -718,38 +727,38 @@ def starting_mu(self, y):
         """
         return (y + .5)/2
 
-    def initialize(self, Y):
+    def initialize(self, endog):
         '''
         Initialize the response variable.
 
         Parameters
         ----------
-        Y : array
+        endog : array
             Endogenous response variable
 
         Returns
         --------
-        If `Y` is binary, returns `Y`
+        If `endog` is binary, returns `endog`
 
-        If `Y` is a 2d array, then the input is assumed to be in the format
+        If `endog` is a 2d array, then the input is assumed to be in the format
         (successes, failures) and
         successes/(success + failures) is returned.  And n is set to
         successes + failures.
         '''
-        if (Y.ndim > 1 and Y.shape[1] > 1):
-            y = Y[:,0]
-            self.n = Y.sum(1) # overwrite self.n for deviance below
+        if (endog.ndim > 1 and endog.shape[1] > 1):
+            y = endog[:,0]
+            self.n = endog.sum(1) # overwrite self.n for deviance below
             return y*1./self.n
         else:
-            return Y
+            return endog
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         '''
         Deviance function for either Bernoulli or Binomial data.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable (already transformed to a probability
             if appropriate).
         mu : array
@@ -768,28 +777,28 @@ def deviance(self, Y, mu, scale=1.):
 
         `deviance` = -2*sum(I_one * log(mu) + (I_zero)*log(1-mu))
 
-        where I_one is an indicator function that evalueates to 1 if Y_i == 1.
-        and I_zero is an indicator function that evaluates to 1 if Y_i == 0.
+        where I_one is an indicator function that evalueates to 1 if endog_i == 1.
+        and I_zero is an indicator function that evaluates to 1 if endog_i == 0.
 
         If the model is ninomial:
 
-        `deviance` = 2*sum(log(Y/mu) + (n-Y)*log((n-Y)/(n-mu)))
-        where Y and n are as defined in Binomial.initialize.
+        `deviance` = 2*sum(log(endog/mu) + (n-endog)*log((n-endog)/(n-mu)))
+        where endog and n are as defined in Binomial.initialize.
         '''
         if np.shape(self.n) == () and self.n == 1:
-            one = np.equal(Y,1)
+            one = np.equal(endog,1)
             return -2 * np.sum(one * np.log(mu+1e-200) + (1-one) * np.log(1-mu+1e-200))
 
         else:
-            return 2*np.sum(self.n*(Y*np.log(Y/mu+1e-200)+(1-Y)*np.log((1-Y)/(1-mu)+1e-200)))
+            return 2*np.sum(self.n*(endog*np.log(endog/mu+1e-200)+(1-endog)*np.log((1-endog)/(1-mu)+1e-200)))
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """
         Binomial deviance residuals
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -803,36 +812,36 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         -----
-        If `Y` is binary:
+        If `endog` is binary:
 
-        resid_dev = sign(Y-mu)*sqrt(-2*log(I_one*mu + I_zero*(1-mu)))
+        resid_dev = sign(endog-mu)*sqrt(-2*log(I_one*mu + I_zero*(1-mu)))
 
-        where I_one is an indicator function that evaluates as 1 if Y == 1
-        and I_zero is an indicator function that evaluates as 1 if Y == 0.
+        where I_one is an indicator function that evaluates as 1 if endog == 1
+        and I_zero is an indicator function that evaluates as 1 if endog == 0.
 
-        If `Y` is binomial:
+        If `endog` is binomial:
 
-        resid_dev = sign(Y-mu)*sqrt(2*n*(Y*log(Y/mu)+(1-Y)*log((1-Y)/(1-mu))))
+        resid_dev = sign(endog-mu)*sqrt(2*n*(endog*log(endog/mu)+(1-endog)*log((1-endog)/(1-mu))))
 
-        where Y and n are as defined in Binomial.initialize.
+        where endog and n are as defined in Binomial.initialize.
         """
 
         mu = self.link._clean(mu)
         if np.shape(self.n) == () and self.n == 1:
-            one = np.equal(Y,1)
-            return np.sign(Y-mu)*np.sqrt(-2*np.log(one*mu+(1-one)*(1-mu)))\
+            one = np.equal(endog,1)
+            return np.sign(endog-mu)*np.sqrt(-2*np.log(one*mu+(1-one)*(1-mu)))\
                     /scale
         else:
-            return np.sign(Y-mu) * np.sqrt(2*self.n*(Y*np.log(Y/mu+1e-200)+(1-Y)*\
-                        np.log((1-Y)/(1-mu)+1e-200)))/scale
+            return np.sign(endog-mu) * np.sqrt(2*self.n*(endog*np.log(endog/mu+1e-200)+(1-endog)*\
+                        np.log((1-endog)/(1-mu)+1e-200)))/scale
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         Loglikelihood function for Binomial exponential family distribution.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -842,38 +851,38 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
         --------
-        If `Y` is binary:
-        `llf` = scale*sum(Y*log(mu/(1-mu))+log(1-mu))
+        If `endog` is binary:
+        `llf` = scale*sum(endog*log(mu/(1-mu))+log(1-mu))
 
-        If `Y` is binomial:
+        If `endog` is binomial:
         `llf` = scale*sum(gammaln(n+1) - gammaln(y+1) - gammaln(n-y+1) +\
                 y*log(mu/(1-mu)) + n*log(1-mu)
 
-        where gammaln is the log gamma function and y = Y*n with Y and n
+        where gammaln is the log gamma function and y = endog*n with endog and n
         as defined in Binomial initialize.  This simply makes y the original
         number of successes.
         """
 
         if np.shape(self.n) == () and self.n == 1:
-            return scale*np.sum(Y*np.log(mu/(1-mu)+1e-200)+np.log(1-mu))
+            return scale*np.sum(endog*np.log(mu/(1-mu)+1e-200)+np.log(1-mu))
         else:
-            y=Y*self.n  #convert back to successes
+            y=endog*self.n  #convert back to successes
             return scale * np.sum(special.gammaln(self.n+1)-\
                 special.gammaln(y+1)-special.gammaln(self.n-y+1)\
                 +y*np.log(mu/(1-mu))+self.n*np.log(1-mu))
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         '''
         The Anscombe residuals
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -885,7 +894,7 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         -----
-        sqrt(n)*(cox_snell(Y)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.))
+        sqrt(n)*(cox_snell(endog)-cox_snell(mu))/(mu**(1/6.)*(1-mu)**(1/6.))
 
         where cox_snell is defined as
         cox_snell(x) = betainc(2/3., 2/3., x)*betainc(2/3.,2/3.)
@@ -910,11 +919,10 @@ def resid_anscombe(self, Y, mu):
         '''
         cox_snell = lambda x: special.betainc(2/3., 2/3., x)\
                             *special.beta(2/3.,2/3.)
-        return np.sqrt(self.n)*(cox_snell(Y)-cox_snell(mu))/\
+        return np.sqrt(self.n)*(cox_snell(endog)-cox_snell(mu))/\
                         (mu**(1/6.)*(1-mu)**(1/6.))
 
 class InverseGaussian(Family):
-
     """
     InverseGaussian exponential family.
 
@@ -936,11 +944,13 @@ class InverseGaussian(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
 
     Notes
     -----
     The inverse Guassian distribution is sometimes referred to in the
     literature as the wald distribution.
+
     """
 
     links = [L.inverse_squared, L.inverse_power, L.identity, L.log]
@@ -950,13 +960,13 @@ def __init__(self, link=L.inverse_squared):
         self.variance = InverseGaussian.variance
         self.link = link()
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         """
         Returns the deviance residuals for the inverse Gaussian family.
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -970,17 +980,17 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         -----
-        `dev_resid` = sign(Y-mu)*sqrt((Y-mu)**2/(Y*mu**2))
+        `dev_resid` = sign(endog-mu)*sqrt((endog-mu)**2/(endog*mu**2))
         """
-        return np.sign(Y-mu) * np.sqrt((Y-mu)**2/(Y*mu**2))/scale
+        return np.sign(endog-mu) * np.sqrt((endog-mu)**2/(endog*mu**2))/scale
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         """
         Inverse Gaussian deviance function
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -994,17 +1004,17 @@ def deviance(self, Y, mu, scale=1.):
 
         Notes
         -----
-        `deviance` = sum((Y=mu)**2/(Y*mu**2))
+        `deviance` = sum((endog=mu)**2/(endog*mu**2))
         """
-        return np.sum((Y-mu)**2/(Y*mu**2))/scale
+        return np.sum((endog-mu)**2/(endog*mu**2))/scale
 
-    def loglike(self, Y, mu, scale=1.):
+    def loglike(self, endog, mu, scale=1.):
         """
         Loglikelihood function for inverse Gaussian distribution.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -1014,24 +1024,24 @@ def loglike(self, Y, mu, scale=1.):
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
         -----
-        `llf` = -(1/2.)*sum((Y-mu)**2/(Y*mu**2*scale) + log(scale*Y**3)\
+        `llf` = -(1/2.)*sum((endog-mu)**2/(endog*mu**2*scale) + log(scale*endog**3)\
                  + log(2*pi))
         """
-        return -.5 * np.sum((Y-mu)**2/(Y*mu**2*scale)\
-                + np.log(scale*Y**3) + np.log(2*np.pi))
+        return -.5 * np.sum((endog-mu)**2/(endog*mu**2*scale)\
+                + np.log(scale*endog**3) + np.log(2*np.pi))
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         The Anscombe residuals for the inverse Gaussian distribution
 
         Parameters
         ----------
-        Y : array
+        endog : array
             Endogenous response variable
         mu : array
             Fitted mean response variable
@@ -1044,9 +1054,9 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         -----
-        `resid_anscombe` =  log(Y/mu)/sqrt(mu)
+        `resid_anscombe` =  log(endog/mu)/sqrt(mu)
         """
-        return np.log(Y/mu)/np.sqrt(mu)
+        return np.log(endog/mu)/np.sqrt(mu)
 
 class NegativeBinomial(Family):
     """
@@ -1074,10 +1084,12 @@ class NegativeBinomial(Family):
     See also
     --------
     statsmodels.genmod.families.family.Family
+    :ref:`links`
 
     Notes
     -----
-    Support for Power link functions is not yet supported.
+    Power link functions are not yet supported.
+
     """
     links = [L.log, L.cloglog, L.identity, L.nbinom, L.Power]
 #TODO: add the ability to use the power links with an if test
@@ -1104,13 +1116,13 @@ def _clean(self, x):
         """
         return np.clip(x, FLOAT_EPS, np.inf)
 
-    def deviance(self, Y, mu, scale=1.):
+    def deviance(self, endog, mu, scale=1.):
         """
         Returns the value of the deviance function.
 
         Parameters
         -----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -1136,23 +1148,23 @@ def deviance(self, Y, mu, scale=1.):
 
         piecewise_i = :math:`2 Y \\log(Y/\\mu)-2/\\alpha(1+\\alpha Y)*\\log((1+\\alpha Y)/(1+\\alpha\\mu))`
         """
-        iszero = np.equal(Y,0)
+        iszero = np.equal(endog,0)
         notzero = 1 - iszero
-        tmp = np.zeros(len(Y))
-        Y_mu = self._clean(Y/mu)
+        tmp = np.zeros(len(endog))
+        endog_mu = self._clean(endog/mu)
         tmp = iszero*2*np.log(1+self.alpha*mu)/self.alpha
-        tmp += notzero*(2*Y*np.log(Y_mu)-2/self.alpha*(1+self.alpha*Y)*\
-                np.log((1+self.alpha*Y)/(1+self.alpha*mu)))
+        tmp += notzero*(2*endog*np.log(endog_mu)-2/self.alpha*(1+self.alpha*endog)*\
+                np.log((1+self.alpha*endog)/(1+self.alpha*mu)))
         return np.sum(tmp)/scale
 
-    def resid_dev(self, Y, mu, scale=1.):
+    def resid_dev(self, endog, mu, scale=1.):
         '''
         Negative Binomial Deviance Residual
 
         Parameters
         ----------
-        Y : array-like
-            `Y` is the response variable
+        endog : array-like
+            `endog` is the response variable
         mu : array-like
             `mu` is the fitted value of the model
         scale : float, optional
@@ -1165,7 +1177,7 @@ def resid_dev(self, Y, mu, scale=1.):
 
         Notes
         -----
-        `resid_dev` = sign(Y-mu) * sqrt(piecewise)
+        `resid_dev` = sign(endog-mu) * sqrt(piecewise)
 
         where piecewise is defined as
         if :math:`Y_i = 0`:
@@ -1174,58 +1186,60 @@ def resid_dev(self, Y, mu, scale=1.):
         if :math:`Y_i > 0`:
         :math:`piecewise_i = 2*Y*log(Y/\\mu)-2/\\alpha*(1+\\alpha*Y)*log((1+\\alpha*Y)/(1+\\alpha*\\mu))`
         '''
-        iszero = np.equal(Y,0)
+        iszero = np.equal(endog,0)
         notzero = 1 - iszero
-        tmp=np.zeros(len(Y))
+        tmp=np.zeros(len(endog))
         tmp = iszero*2*np.log(1+self.alpha*mu)/self.alpha
-        tmp += notzero*(2*Y*np.log(Y/mu)-2/self.alpha*(1+self.alpha*Y)*\
-                np.log((1+self.alpha*Y)/(1+self.alpha*mu)))
-        return np.sign(Y-mu)*np.sqrt(tmp)/scale
+        tmp += notzero*(2*endog*np.log(endog/mu)-2/self.alpha*(1+self.alpha*endog)*\
+                np.log((1+self.alpha*endog)/(1+self.alpha*mu)))
+        return np.sign(endog-mu)*np.sqrt(tmp)/scale
 
-    def loglike(self, Y, fittedvalues=None):
+    def loglike(self, endog, lin_pred=None):
         """
         The loglikelihood function for the negative binomial family.
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
-        fittedvalues : array-like
-            The linear fitted values of the model.  This is dot(exog,params).
+        lin_pred : array-like
+            The linear predictor of the model.  This is dot(exog,params),
+            plus the offset if present.
 
         Returns
         -------
         llf : float
-            The value of the loglikelihood function evaluated at (Y,mu,scale)
+            The value of the loglikelihood function evaluated at (endog,mu,scale)
             as defined below.
 
         Notes
         -----
-        sum(Y*log(alpha*exp(fittedvalues)/(1+alpha*exp(fittedvalues))) -\
-                log(1+alpha*exp(fittedvalues))/alpha + constant)
+        sum(endog*log(alpha*exp(lin_pred)/(1+alpha*exp(lin_pred))) -\
+                log(1+alpha*exp(lin_pred))/alpha + constant)
 
         where constant is defined as
-        constant = gammaln(Y + 1/alpha) - gammaln(Y + 1) - gammaln(1/alpha)
+        constant = gammaln(endog + 1/alpha) - gammaln(endog + 1) - gammaln(1/alpha)
         """
         # don't need to specify mu
-        if fittedvalues is None:
+        if lin_pred is None:
             raise AttributeError('The loglikelihood for the negative binomial \
-requires that the fitted values be provided via the `fittedvalues` keyword \
+requires that the fitted values be provided via the `lin_pred` keyword \
 argument.')
-        constant = special.gammaln(Y + 1/self.alpha) - special.gammaln(Y+1)\
+        constant = special.gammaln(endog + 1/self.alpha) - special.gammaln(endog+1)\
                     -special.gammaln(1/self.alpha)
-        return np.sum(Y*np.log(self.alpha*np.exp(fittedvalues)/\
-            (1 + self.alpha*np.exp(fittedvalues))) - \
-            np.log(1+self.alpha*np.exp(fittedvalues))/self.alpha\
+        exp_lin_pred = np.exp(lin_pred)
+        return np.sum(endog*np.log(self.alpha*exp_lin_pred/\
+            (1 + self.alpha*exp_lin_pred)) - \
+            np.log(1+self.alpha*exp_lin_pred)/self.alpha\
             + constant)
 
-    def resid_anscombe(self, Y, mu):
+    def resid_anscombe(self, endog, mu):
         """
         The Anscombe residuals for the negative binomial family
 
         Parameters
         ----------
-        Y : array-like
+        endog : array-like
             Endogenous response variable
         mu : array-like
             Fitted mean response variable
@@ -1237,13 +1251,13 @@ def resid_anscombe(self, Y, mu):
 
         Notes
         -----
-        `resid_anscombe` = (hyp2f1(-alpha*Y)-hyp2f1(-alpha*mu)+\
-                1.5*(Y**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.)
+        `resid_anscombe` = (hyp2f1(-alpha*endog)-hyp2f1(-alpha*mu)+\
+                1.5*(endog**(2/3.)-mu**(2/3.)))/(mu+alpha*mu**2)**(1/6.)
 
         where hyp2f1 is the hypergeometric 2f1 function parameterized as
         hyp2f1(x) = hyp2f1(2/3.,1/3.,5/3.,x)
         """
 
         hyp2f1 = lambda x : special.hyp2f1(2/3.,1/3.,5/3.,x)
-        return (hyp2f1(-self.alpha*Y)-hyp2f1(-self.alpha*mu)+1.5*(Y**(2/3.)-\
+        return (hyp2f1(-self.alpha*endog)-hyp2f1(-self.alpha*mu)+1.5*(endog**(2/3.)-\
                 mu**(2/3.)))/(mu+self.alpha*mu**2)**(1/6.)
diff --git a/statsmodels/genmod/families/links.py b/statsmodels/genmod/families/links.py
index 1f7de4684b5..afb1b38041a 100644
--- a/statsmodels/genmod/families/links.py
+++ b/statsmodels/genmod/families/links.py
@@ -1,5 +1,5 @@
 '''
-Defines the link functions to be used with GLM families.
+Defines the link functions to be used with GLM and GEE families.
 '''
 
 import numpy as np
@@ -64,6 +64,39 @@ def deriv(self, p):
         """
         return NotImplementedError
 
+
+    def deriv2(self, p):
+        """Second derivative of the link function g''(p)
+
+        implemented through numerical differentiation
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(p, self.deriv))
+
+
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse link function g^(-1)(z).
+
+        Notes
+        -----
+        This reference implementation gives the correct result but it inefficient,
+        so it can be overriden in subclasses.
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        The value of the derivative of the inverse of the link function
+
+        """
+        return 1/self.deriv(self.inverse(z))
+
+
 class Logit(Link):
     """
     The logit transform
@@ -160,6 +193,24 @@ def deriv(self, p):
         p = self._clean(p)
         return 1. / (p * (1 - p))
 
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the logit transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        The value of the derivative of the inverse of the logit function
+
+        """
+        t = np.exp(z)
+        return t/(1+t)**2
+
+
 #logit = Logit()
 class logit(Logit):
     pass
@@ -247,6 +298,22 @@ def deriv(self, p):
         """
         return self.power * np.power(p, self.power - 1)
 
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the power transform
+
+        Parameters
+        ----------
+        z : array-like
+            `z` is usually the linear predictor for a GLM or GEE model.
+
+        Returns
+        -------
+        The value of the derivative of the inverse of the power transform function
+
+        """
+        return np.power(z, (1 - self.power)/self.power) / self.power
+
 #inverse = Power(power=-1.)
 class inverse_power(Power):
     """
@@ -377,6 +444,21 @@ def deriv(self, p):
         p = self._clean(p)
         return 1. / p
 
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the log transform link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        The value of the derivative of the inverse of the logit function
+        """
+        return np.exp(z)
+
 class log(Log):
     """
     The log transform
@@ -470,6 +552,23 @@ def deriv(self, p):
         p = self._clean(p)
         return 1. / self.dbn.pdf(self.dbn.ppf(p))
 
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the CDF transformation link function
+
+        Parameters
+        ----------
+        z : array
+            The inverse of the link function at `p`
+
+        Returns
+        -------
+        The value of the derivative of the inverse of the logit function
+        """
+        return 1/self.deriv(self.inverse(z))
+
+
+
 #probit = CDFLink()
 class probit(CDFLink):
     """
@@ -552,7 +651,7 @@ def inverse(self, z):
 
     def deriv(self, p):
         """
-        Derivatve of C-Log-Log transform link function
+        Derivative of C-Log-Log transform link function
 
         Parameters
         ----------
@@ -571,6 +670,21 @@ def deriv(self, p):
         p = self._clean(p)
         return 1. / ((p-1)*(np.log(1-p)))
 
+    def inverse_deriv(self, z):
+        """
+        Derivative of the inverse of the C-Log-Log transform link function
+
+        Parameters
+        ----------
+        z : array-like
+            The value of the inverse of the CLogLog link function at `p`
+
+        Returns
+        -------
+        The derivative of the inverse of the CLogLog link function
+        """
+        return np.exp(z - np.exp(z))
+
 class cloglog(CLogLog):
     """
     The CLogLog transform link function.
@@ -602,7 +716,7 @@ def __init__(self, alpha=1.):
     def _clean(self, x):
         return np.clip(x, FLOAT_EPS, np.inf)
 
-    def __call__(self, x):
+    def __call__(self, p):
         '''
         Negative Binomial transform link function
 
@@ -662,6 +776,22 @@ def deriv(self,p):
         '''
         return 1/(p+self.alpha*p**2)
 
+    def inverse_deriv(self, z):
+        '''
+        Derivative of the inverse of the negative binomial transform
+
+        Parameters
+        -----------
+        z : array-like
+            Usually the linear predictor for a GLM or GEE model
+
+        Returns
+        -------
+        The value of the inverse of the derivative of the negative binomial link
+        '''
+        t = np.exp(z)
+        return t / (self.alpha * (1-t)**2)
+
 class nbinom(NegativeBinomial):
     """
     The negative binomial link function.
diff --git a/statsmodels/genmod/families/tests/__init__.py b/statsmodels/genmod/families/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/statsmodels/genmod/families/tests/test_link.py b/statsmodels/genmod/families/tests/test_link.py
new file mode 100644
index 00000000000..1d496305aa5
--- /dev/null
+++ b/statsmodels/genmod/families/tests/test_link.py
@@ -0,0 +1,52 @@
+"""
+Test functions for genmod.families.links
+"""
+from statsmodels.compat.python import range
+import numpy as np
+from numpy.testing import assert_allclose
+import statsmodels.genmod.families as families
+
+# Family instances
+links = families.links
+logit = links.Logit()
+inverse_power = links.inverse_power()
+sqrt = links.sqrt()
+inverse_squared = links.inverse_squared()
+identity = links.identity()
+log = links.log()
+probit = links.probit()
+cauchy = links.cauchy()
+cloglog = links.CLogLog()
+negbinom = links.NegativeBinomial()
+
+Links = [logit, inverse_power, sqrt, inverse_squared, identity, log, probit, cauchy,
+         cloglog, negbinom]
+
+
+def test_inverse():
+
+    ## Logic check that link.inverse(link) is the identity
+    for link in Links:
+        for k in range(10):
+            p = np.random.uniform() # In domain for all families
+            d = p - link.inverse(link(p))
+            assert_allclose(d, 0, atol=1e-8)
+
+
+def test_inverse_deriv():
+
+    ## Logic check that inverse_deriv equals 1/link.deriv(link.inverse)
+    for link in Links:
+        for k in range(10):
+            z = -np.log(np.random.uniform()) # In domain for all families
+            d = link.inverse_deriv(z)
+            f = 1 / link.deriv(link.inverse(z))
+            assert_allclose(d, f, rtol=1e-8, atol=1e-10)
+
+
+if __name__=="__main__":
+    #run_module_suite()
+    #taken from Fernando Perez:
+    import nose
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'],
+                       exit=False)
diff --git a/statsmodels/genmod/families/varfuncs.py b/statsmodels/genmod/families/varfuncs.py
index fd32f70462b..9b8f897cd44 100644
--- a/statsmodels/genmod/families/varfuncs.py
+++ b/statsmodels/genmod/families/varfuncs.py
@@ -45,9 +45,19 @@ def __call__(self, mu):
         mu = np.asarray(mu)
         return np.ones(mu.shape, np.float64)
 
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
 constant = VarianceFunction()
 constant.__doc__ = """
-The call method of constnat returns a constant variance, ie., a vector of ones.
+The call method of constant returns a constant variance, i.e., a vector of ones.
 
 constant is an alias of VarianceFunction()
 """
@@ -97,6 +107,17 @@ def __call__(self, mu):
         """
         return np.power(np.fabs(mu), self.power)
 
+
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        #return approx_fprime_cs(mu, self)  # TODO fix breaks in `fabs
+        # TODO: diag is workaround problem with numdiff for 1d
+        return np.diag(approx_fprime(mu, self))
+
+
 mu = Power()
 mu.__doc__ = """
 Returns np.fabs(mu)
@@ -175,6 +196,16 @@ def __call__(self, mu):
         p = self._clean(mu / self.n)
         return p * (1 - p) * self.n
 
+    #TODO: inherit from super
+    def deriv(self, mu):
+        """
+        Derivative of the variance function v'(mu)
+        """
+        from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
+        # TODO: diag workaround proplem with numdiff for 1d
+        return np.diag(approx_fprime_cs(mu, self))
+
+
 binary = Binomial()
 binary.__doc__ = """
 The binomial variance function for n = 1
diff --git a/statsmodels/genmod/generalized_estimating_equations.py b/statsmodels/genmod/generalized_estimating_equations.py
new file mode 100644
index 00000000000..67b487861bc
--- /dev/null
+++ b/statsmodels/genmod/generalized_estimating_equations.py
@@ -0,0 +1,2246 @@
+"""
+Procedures for fitting marginal regression models to dependent data
+using Generalized Estimating Equations.
+
+References
+----------
+KY Liang and S Zeger. "Longitudinal data analysis using
+generalized linear models". Biometrika (1986) 73 (1): 13-22.
+
+S Zeger and KY Liang. "Longitudinal Data Analysis for Discrete and
+Continuous Outcomes". Biometrics Vol. 42, No. 1 (Mar., 1986),
+pp. 121-130
+
+A Rotnitzky and NP Jewell (1990). "Hypothesis testing of regression
+parameters in semiparametric generalized linear models for cluster
+correlated data", Biometrika, 77, 485-497.
+
+Xu Guo and Wei Pan (2002). "Small sample performance of the score
+test in GEE".
+http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf
+
+LA Mancl LA, TA DeRouen (2001). A covariance estimator for GEE with
+improved small-sample properties.  Biometrics. 2001 Mar;57(1):126-34.
+"""
+from statsmodels.compat.python import iterkeys, range, lrange, lzip, zip
+import numpy as np
+from scipy import stats
+import pandas as pd
+
+from statsmodels.tools.decorators import (cache_readonly,
+    resettable_cache)
+import statsmodels.base.model as base
+from statsmodels.genmod import families
+from statsmodels.genmod import dependence_structures
+from statsmodels.genmod.dependence_structures import CovStruct
+import statsmodels.genmod.families.varfuncs as varfuncs
+from statsmodels.genmod.families.links import Link
+from statsmodels.genmod.families import Family
+
+from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
+                                             IterationLimitWarning)
+import warnings
+
+
+# Workaround for block_diag, not available until scipy version
+# 0.11. When the statsmodels scipy dependency moves to version 0.11,
+# we can remove this function and use:
+# from scipy.sparse import block_diag
+def block_diag(dblocks, format=None):
+
+    from scipy.sparse import bmat
+
+    n = len(dblocks)
+    blocks = []
+    for i in range(n):
+        b = [None,] * n
+        b[i] = dblocks[i]
+        blocks.append(b)
+
+    return bmat(blocks, format)
+
+
+class ParameterConstraint(object):
+    """
+    A class for managing linear equality constraints for a parameter
+    vector.
+    """
+
+    def __init__(self, lhs, rhs, exog):
+        """
+        Parameters:
+        ----------
+        lhs : ndarray
+           A q x p matrix which is the left hand side of the
+           constraint lhs * param = rhs.  The number of constraints is
+           q >= 1 and p is the dimension of the parameter vector.
+        rhs : ndarray
+          A 1-dimensional vector of length q which is the right hand
+          side of the constraint equation.
+        exog : ndarray
+          The n x p exognenous data for the full model.
+        """
+
+        # In case a row or column vector is passed (patsy linear
+        # constraints passes a column vector).
+        rhs = np.atleast_1d(rhs.squeeze())
+
+        if rhs.ndim > 1:
+            raise ValueError("The right hand side of the constraint "
+                             "must be a vector.")
+
+        if len(rhs) != lhs.shape[0]:
+            raise ValueError("The number of rows of the left hand "
+                             "side constraint matrix L must equal "
+                             "the length of the right hand side "
+                             "constraint vector R.")
+
+        self.lhs = lhs
+        self.rhs = rhs
+
+        # The columns of lhs0 are an orthogonal basis for the
+        # orthogonal complement to row(lhs), the columns of lhs1 are
+        # an orthogonal basis for row(lhs).  The columns of lhsf =
+        # [lhs0, lhs1] are mutually orthogonal.
+        lhs_u, lhs_s, lhs_vt = np.linalg.svd(lhs.T, full_matrices=1)
+        self.lhs0 = lhs_u[:, len(lhs_s):]
+        self.lhs1 = lhs_u[:, 0:len(lhs_s)]
+        self.lhsf = np.hstack((self.lhs0, self.lhs1))
+
+        # param0 is one solution to the underdetermined system
+        # L * param = R.
+        self.param0 = np.dot(self.lhs1, np.dot(lhs_vt, self.rhs) /
+                             lhs_s)
+
+        self._offset_increment = np.dot(exog, self.param0)
+
+        self.orig_exog = exog
+        self.exog_fulltrans = np.dot(exog, self.lhsf)
+
+    def offset_increment(self):
+        """
+        Returns a vector that should be added to the offset vector to
+        accommodate the constraint.
+
+        Parameters:
+        -----------
+        exog : array-like
+           The exogeneous data for the model.
+        """
+
+        return self._offset_increment
+
+    def reduced_exog(self):
+        """
+        Returns a linearly transformed exog matrix whose columns span
+        the constrained model space.
+
+        Parameters:
+        -----------
+        exog : array-like
+           The exogeneous data for the model.
+        """
+        return self.exog_fulltrans[:, 0:self.lhs0.shape[1]]
+
+    def restore_exog(self):
+        """
+        Returns the full exog matrix before it was reduced to
+        satisfy the constraint.
+        """
+        return self.orig_exog
+
+    def unpack_param(self, params):
+        """
+        Converts the parameter vector `params` from reduced to full
+        coordinates.
+        """
+
+        return self.param0 + np.dot(self.lhs0, params)
+
+    def unpack_cov(self, bcov):
+        """
+        Converts the covariance matrix `bcov` from reduced to full
+        coordinates.
+        """
+
+        return np.dot(self.lhs0, np.dot(bcov, self.lhs0.T))
+
+
+_gee_init_doc = """
+    GEE can be used to fit Generalized Linear Models (GLMs) when the
+    data have a grouped structure, and the observations are possibly
+    correlated within groups but not between groups.
+
+    Parameters
+    ----------
+    endog : array-like
+        1d array of endogenous values (i.e. responses, outcomes,
+        dependent variables, or 'Y' values).
+    exog : array-like
+        2d array of exogeneous values (i.e. covariates, predictors,
+        independent variables, regressors, or 'X' values). A nobs x k
+        array where `nobs` is the number of observations and `k` is
+        the number of regressors. An intercept is not included by
+        default and should be added by the user. See
+        `statsmodels.tools.add_constant`.
+    groups : array-like
+        A 1d array of length `nobs` containing the group labels.
+    time : array-like
+        A 2d array of time (or other index) values, used by some
+        dependence structures to define similarity relationships among
+        observations within a cluster.
+    family : family class instance
+        The default is Gaussian.  To specify the binomial
+        distribution family = sm.family.Binomial(). Each family can
+        take a link instance as an argument.  See
+        statsmodels.family.family for more information.
+    cov_struct : CovStruct class instance
+        The default is Independence.  To specify an exchangeable
+        structure use cov_struct = Exchangeable().  See
+        statsmodels.genmod.dependence_structures.CovStruct for more
+        information.
+    offset : array-like
+        An offset to be included in the fit.  If provided, must be
+        an array whose length is the number of rows in exog.
+    dep_data : array-like
+        Additional data passed to the dependence structure.
+    constraint : (ndarray, ndarray)
+        If provided, the constraint is a tuple (L, R) such that the
+        model parameters are estimated under the constraint L *
+        param = R, where L is a q x p matrix and R is a
+        q-dimensional vector.  If constraint is provided, a score
+        test is performed to compare the constrained model to the
+        unconstrained model.
+    %(extra_params)s
+
+    See Also
+    --------
+    statsmodels.genmod.families.family
+    :ref:`families`
+    :ref:`links`
+
+    Notes
+    -----
+    Only the following combinations make sense for family and link ::
+
+                   + ident log logit probit cloglog pow opow nbinom loglog logc
+      Gaussian     |   x    x                        x
+      inv Gaussian |   x    x                        x
+      binomial     |   x    x    x     x       x     x    x           x      x
+      Poission     |   x    x                        x
+      neg binomial |   x    x                        x          x
+      gamma        |   x    x                        x
+
+    Not all of these link functions are currently available.
+
+    Endog and exog are references so that if the data they refer
+    to are already arrays and these arrays are changed, endog and
+    exog will change.
+
+    The "robust" covariance type is the standard "sandwich estimator"
+    (e.g. Liang and Zeger (1986)).  It is the default here and in most
+    other packages.  The "naive" estimator gives smaller standard
+    errors, but is only correct if the working correlation structure
+    is correctly specified.  The "bias reduced" estimator of Mancl and
+    DeRouen (Biometrics, 2001) reduces the downard bias of the robust
+    estimator.
+
+    Examples
+    --------
+    %(example)s
+"""
+
+_gee_fit_doc = """
+    Fits a marginal regression model using generalized estimating
+    equations (GEE).
+
+    Parameters
+    ----------
+    maxiter : integer
+        The maximum number of iterations
+    ctol : float
+        The convergence criterion for stopping the Gauss-Seidel
+        iterations
+    start_params : array-like
+        A vector of starting values for the regression
+        coefficients.  If None, a default is chosen.
+    params_niter : integer
+        The number of Gauss-Seidel updates of the mean structure
+        parameters that take place prior to each update of the
+        dependence structure.
+    first_dep_update : integer
+        No dependence structure updates occur before this
+        iteration number.
+    covariance_type : string
+        One of "robust", "naive", or "bias_reduced".
+
+    Returns
+    -------
+    An instance of the GEEResults class or subclass
+
+    Notes
+    -----
+    If convergence difficulties occur, increase the values of
+    `first_dep_update` and/or `params_niter`.  Setting
+    `first_dep_update` to a greater value (e.g. ~10-20) causes the
+    algorithm to move close to the GLM solution before attempting
+    to identify the dependence structure.
+
+    For the Gaussian family, there is no benefit to setting
+    `params_niter` to a value greater than 1, since the mean
+    structure parameters converge in one step.
+"""
+
+_gee_results_doc = """
+    Returns
+    -------
+    **Attributes**
+
+    naive_covariance : ndarray
+        covariance of the parameter estimates that is not robust to
+        correlation or variance misspecification
+    robust_covariance_bc : ndarray
+        covariance of the parameter estimates that is robust and bias
+        reduced
+    converged : bool
+        indicator for convergence of the optimization.
+        True if the norm of the score is smaller than a threshold
+    covariance_type : string
+        string indicating whether a "robust", "naive" or "bias_
+        reduced" covariance is used as default
+    fit_history : dict
+        Contains information about the iterations.
+    fittedvalues : array
+        Linear predicted values for the fitted model.
+        dot(exog, params)
+    model : class instance
+        Pointer to GEE model instance that called `fit`.
+    normalized_cov_params : array
+        See GEE docstring
+    params : array
+        The coefficients of the fitted model.  Note that
+        interpretation of the coefficients often depends on the
+        distribution family and the data.
+    scale : float
+        The estimate of the scale / dispersion for the model fit.
+        See GEE.fit for more information.
+    score_norm : float
+        norm of the score at the end of the iterative estimation.
+    bse : array
+        The standard errors of the fitted GEE parameters.
+"""
+
+_gee_example = """
+    Logistic regression with autoregressive working dependence:
+
+    >>> family = Binomial()
+    >>> va = Autoregressive()
+    >>> mod = GEE(endog, exog, group, family=family, cov_struct=va)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+
+    Use formulas to fit a Poisson GLM with independent working
+    dependence:
+
+    >>> fam = Poisson()
+    >>> ind = Independence()
+    >>> mod = GEE.from_formula("y ~ age + trt + base", "subject",
+                               data, cov_struct=ind, family=fam)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+"""
+
+_gee_ordinal_example = """
+    >>> family = Binomial()
+    >>> gor = GlobalOddsRatio("ordinal")
+    >>> mod = OrdinalGEE(endog, exog, groups, None, family, gor)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+
+    Use formulas:
+
+    >>> mod = GEE.from_formula("y ~ x1 + x2", groups, data,
+                               cov_struct=gor, family=family)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+"""
+
+_gee_nominal_example = """
+    >>> family = Multinomial(3)
+    >>> gor = GlobalOddsRatio("nominal")
+    >>> mod = NominalGEE(endog, exog, groups, None, family, gor)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+
+    Use formulas:
+
+    >>> mod = GEE.from_formula("y ~ x1 + x2", groups, data,
+                               cov_struct=gor, family=family)
+    >>> rslt = mod.fit()
+    >>> print rslt.summary()
+"""
+
+
+class GEE(base.Model):
+
+    __doc__ = (
+        "    Estimation of marginal regression models using Generalized\n"
+        "    Estimating Equations (GEE).\n" + _gee_init_doc %
+        {'extra_params': base._missing_param_doc,
+         'example': _gee_example})
+
+    fit_history = None
+    cached_means = None
+
+    def __init__(self, endog, exog, groups, time=None, family=None,
+                 cov_struct=None, missing='none', offset=None,
+                 dep_data=None, constraint=None):
+
+        self.init(endog, exog, groups, time=time, family=family,
+                  cov_struct=cov_struct, missing=missing,
+                  offset=offset, dep_data=dep_data,
+                  constraint=constraint)
+
+
+    # All the actions of __init__ should go here
+    def init(self, endog, exog, groups, time=None, family=None,
+             cov_struct=None, missing='none', offset=None,
+             dep_data=None, constraint=None):
+
+        self.missing = missing
+        self.dep_data = dep_data
+        self.constraint = constraint
+
+        groups = np.array(groups) # in case groups is pandas
+        # Pass groups, time, offset, and dep_data so they are
+        # processed for missing data along with endog and exog.
+        # Calling super creates self.exog, self.endog, etc. as
+        # ndarrays and the original exog, endog, etc. are
+        # self.data.endog, etc.
+        super(GEE, self).__init__(endog, exog, groups=groups,
+                                  time=time, offset=offset,
+                                  dep_data=dep_data, missing=missing)
+
+        # Handle the family argument
+        if family is None:
+            family = families.Gaussian()
+        else:
+            if not issubclass(family.__class__, families.Family):
+                raise ValueError("GEE: `family` must be a genmod "
+                                 "family instance")
+        self.family = family
+
+        # Handle the cov_struct argument
+        if cov_struct is None:
+            cov_struct = dependence_structures.Independence()
+        else:
+            if not issubclass(cov_struct.__class__, CovStruct):
+                raise ValueError("GEE: `cov_struct` must be a genmod "
+                                 "cov_struct instance")
+
+        self.cov_struct = cov_struct
+
+        if offset is None:
+            self.offset = np.zeros(self.exog.shape[0],
+                                   dtype=np.float64)
+        else:
+            self.offset = offset
+
+        # Handle the constraint
+        self.constraint = None
+        if constraint is not None:
+            if len(constraint) != 2:
+                raise ValueError("GEE: `constraint` must be a 2-tuple.")
+            if constraint[0].shape[1] != self.exog.shape[1]:
+                raise ValueError("GEE: the left hand side of the "
+                   "constraint must have the same number of columns "
+                   "as the exog matrix.")
+            self.constraint = ParameterConstraint(constraint[0],
+                                                  constraint[1],
+                                                  self.exog)
+
+            self.offset += self.constraint.offset_increment()
+            self.exog = self.constraint.reduced_exog()
+
+        # Convert the data to the internal representation, which is a
+        # list of arrays, corresponding to the clusters.
+        group_labels = sorted(set(groups))
+        group_indices = dict((s, []) for s in group_labels)
+        for i in range(len(self.endog)):
+            group_indices[groups[i]].append(i)
+        for k in iterkeys(group_indices):
+            group_indices[k] = np.asarray(group_indices[k])
+        self.group_indices = group_indices
+        self.group_labels = group_labels
+
+        self.endog_li = self.cluster_list(self.endog)
+        self.exog_li = self.cluster_list(self.exog)
+
+        self.num_group = len(self.endog_li)
+
+        # Time defaults to a 1d grid with equal spacing
+        if self.time is not None:
+            self.time = np.asarray(self.time, np.float64)
+            if self.time.ndim == 1:
+                self.time = self.time[:,None]
+            self.time_li = self.cluster_list(self.time)
+        else:
+            self.time_li = \
+                [np.arange(len(y), dtype=np.float64)[:, None]
+                 for y in self.endog_li]
+            self.time = np.concatenate(self.time_li)
+
+        self.offset_li = self.cluster_list(self.offset)
+        if constraint is not None:
+            self.constraint.exog_fulltrans_li = \
+                self.cluster_list(self.constraint.exog_fulltrans)
+
+        self.family = family
+
+        self.cov_struct.initialize(self)
+
+        # Total sample size
+        group_ns = [len(y) for y in self.endog_li]
+        self.nobs = sum(group_ns)
+
+        # mean_deriv is the derivative of E[endog|exog] with respect
+        # to params
+        try:
+            # This custom mean_deriv is currently only used for the
+            # multinomial logit model
+            self.mean_deriv = self.family.link.mean_deriv
+        except AttributeError:
+            # Otherwise it can be obtained easily from inverse_deriv
+            mean_deriv_lpr = self.family.link.inverse_deriv
+
+            def mean_deriv(exog, lpr):
+                dmat = exog * mean_deriv_lpr(lpr)[:, None]
+                return dmat
+
+            self.mean_deriv = mean_deriv
+
+        # mean_deriv_exog is the derivative of E[endog|exog] with
+        # respect to exog
+        try:
+            # This custom mean_deriv_exog is currently only used for
+            # the multinomial logit model
+            self.mean_deriv_exog = self.family.link.mean_deriv_exog
+        except AttributeError:
+            # Otherwise it can be obtained easily from inverse_deriv
+            mean_deriv_lpr = self.family.link.inverse_deriv
+
+            def mean_deriv_exog(exog, params):
+                lpr = np.dot(exog, params)
+                dmat = np.outer(mean_deriv_lpr(lpr), params)
+                return dmat
+
+            self.mean_deriv_exog = mean_deriv_exog
+
+        # Skip the covariance updates if all groups have a single
+        # observation (reduces to fitting a GLM).
+        self._do_cov_update = True
+        if max([len(x) for x in self.endog_li]) == 1:
+            self._do_cov_update = False
+
+    # Override to allow groups and time to be passed as variable
+    # names.
+    @classmethod
+    def from_formula(cls, formula, groups, data, subset=None,
+                     *args, **kwargs):
+
+        if type(groups) == str:
+            groups = data[groups]
+
+        if "time" in kwargs and type(kwargs["time"]) == str:
+            kwargs["time"] = data[kwargs["time"]]
+
+        mod = super(GEE, cls).from_formula(formula, data, subset,
+                                           groups, *args, **kwargs)
+        return mod
+
+    def cluster_list(self, array):
+        """
+        Returns `array` split into subarrays corresponding to the
+        cluster structure.
+        """
+
+        if array.ndim == 1:
+            return [np.array(array[self.group_indices[k]])
+                    for k in self.group_labels]
+        else:
+            return [np.array(array[self.group_indices[k], :])
+                    for k in self.group_labels]
+
+    def estimate_scale(self):
+        """
+        Returns an estimate of the scale parameter `phi` at the
+        current parameter value.
+        """
+
+        endog = self.endog_li
+        exog = self.exog_li
+        offset = self.offset_li
+
+        cached_means = self.cached_means
+
+        nobs = self.nobs
+        exog_dim = exog[0].shape[1]
+
+        varfunc = self.family.variance
+
+        scale = 0.
+        for i in range(self.num_group):
+
+            if len(endog[i]) == 0:
+                continue
+
+            expval, _ = cached_means[i]
+
+            sdev = np.sqrt(varfunc(expval))
+            resid = (endog[i] - offset[i] - expval) / sdev
+
+            scale += np.sum(resid**2)
+
+        scale /= (nobs - exog_dim)
+
+        return scale
+
+    def _update_mean_params(self):
+        """
+        Returns
+        -------
+        update : array-like
+            The update vector such that params + update is the next
+            iterate when solving the score equations.
+        score : array-like
+            The current value of the score equations, not
+            incorporating the scale parameter.  If desired,
+            multiply this vector by the scale parameter to
+            incorporate the scale.
+        """
+
+        endog = self.endog_li
+        exog = self.exog_li
+
+        cached_means = self.cached_means
+
+        varfunc = self.family.variance
+
+        self.cov_struct.cov_adjust = 0
+
+        bmat, score = 0, 0
+        for i in range(self.num_group):
+
+            expval, lpr = cached_means[i]
+            resid = endog[i] - expval
+            dmat = self.mean_deriv(exog[i], lpr)
+            sdev = np.sqrt(varfunc(expval))
+
+            rslt = self.cov_struct.covariance_matrix_solve(expval, i,
+                                                sdev, (dmat, resid))
+            if rslt is None:
+                return None, None
+            vinv_d, vinv_resid = tuple(rslt)
+
+            bmat += np.dot(dmat.T, vinv_d)
+            score += np.dot(dmat.T, vinv_resid)
+
+        update = np.linalg.solve(bmat, score)
+
+        self.fit_history["cov_adjust"].append(
+                                   self.cov_struct.cov_adjust)
+
+        return update, score
+
+    def update_cached_means(self, mean_params):
+        """
+        cached_means should always contain the most recent calculation
+        of the group-wise mean vectors.  This function should be
+        called every time the regression parameters are changed, to
+        keep the cached means up to date.
+        """
+
+        endog = self.endog_li
+        exog = self.exog_li
+        offset = self.offset_li
+
+        linkinv = self.family.link.inverse
+
+        self.cached_means = []
+
+        for i in range(self.num_group):
+
+            if len(endog[i]) == 0:
+                continue
+
+            lpr = offset[i] + np.dot(exog[i], mean_params)
+            expval = linkinv(lpr)
+
+            self.cached_means.append((expval, lpr))
+
+    def _covmat(self):
+        """
+        Returns the sampling covariance matrix of the regression
+        parameters and related quantities.
+
+        Returns
+        -------
+        robust_covariance : array-like
+           The robust, or sandwich estimate of the covariance, which
+           is meaningful even if the working covariance structure is
+           incorrectly specified.
+        naive_covariance : array-like
+           The model-based estimate of the covariance, which is
+           meaningful if the covariance structure is correctly
+           specified.
+        robust_covariance_bc : array-like
+           The "bias corrected" robust covariance of Mancl and
+           DeRouen.
+        cmat : array-like
+           The center matrix of the sandwich expression, used in
+           obtaining score test results.
+        """
+
+        endog = self.endog_li
+        exog = self.exog_li
+
+        varfunc = self.family.variance
+        cached_means = self.cached_means
+
+        # Calculate the naive (model-based) and robust (sandwich)
+        # covariances.
+        bmat, cmat = 0, 0
+        for i in range(self.num_group):
+
+            expval, lpr = cached_means[i]
+            resid = endog[i] - expval
+            dmat = self.mean_deriv(exog[i], lpr)
+            sdev = np.sqrt(varfunc(expval))
+
+            rslt = self.cov_struct.covariance_matrix_solve(expval, i,
+                                                sdev, (dmat, resid))
+            if rslt is None:
+                return None, None, None, None
+            vinv_d, vinv_resid = tuple(rslt)
+
+            bmat += np.dot(dmat.T, vinv_d)
+            dvinv_resid = np.dot(dmat.T, vinv_resid)
+            cmat += np.outer(dvinv_resid, dvinv_resid)
+
+        scale = self.estimate_scale()
+
+        bmati = np.linalg.inv(bmat)
+        naive_covariance = bmati * scale
+        robust_covariance = np.dot(bmati, np.dot(cmat, bmati))
+
+        # Calculate the bias-corrected sandwich estimate of Mancl and
+        # DeRouen (requires naive_covariance so cannot be calculated
+        # in the previous loop).
+        bcm = 0
+        for i in range(self.num_group):
+
+            expval, lpr = cached_means[i]
+            resid = endog[i] - expval
+            dmat = self.mean_deriv(exog[i], lpr)
+            sdev = np.sqrt(varfunc(expval))
+
+            rslt = self.cov_struct.covariance_matrix_solve(expval,
+                                                  i, sdev, (dmat,))
+            if rslt is None:
+                return None, None, None, None
+            vinv_d = rslt[0]
+            vinv_d /= scale
+
+            hmat = np.dot(vinv_d, naive_covariance)
+            hmat = np.dot(hmat, dmat.T).T
+
+            aresid = np.linalg.solve(np.eye(len(resid)) - hmat, resid)
+            rslt = self.cov_struct.covariance_matrix_solve(expval, i,
+                                                     sdev, (aresid,))
+            if rslt is None:
+                return None, None, None, None
+            srt = rslt[0]
+            srt = np.dot(dmat.T, srt) / scale
+            bcm += np.outer(srt, srt)
+
+        robust_covariance_bc = np.dot(naive_covariance,
+                                      np.dot(bcm, naive_covariance))
+
+        return (robust_covariance, naive_covariance,
+            robust_covariance_bc, cmat)
+
+    def predict(self, params, exog=None, offset=None, linear=False):
+        """
+        Return predicted values for a design matrix
+
+        Parameters
+        ----------
+        params : array-like
+            Parameters / coefficients of a GLM.
+        exog : array-like, optional
+            Design / exogenous data. If exog is None, model exog is
+            used.
+        offset : array-like, optional
+            Offset for exog if provided.  If offset is None, model
+            offset is used.
+        linear : bool
+            If True, returns the linear predicted values.  If False,
+            returns the value of the inverse of the model's link
+            function at the linear predicted values.
+
+        Returns
+        -------
+        An array of fitted values
+        """
+
+        if exog is None:
+            exog = self.exog
+            offset = self.offset
+        else:
+            if offset is None:
+                offset = 0
+
+        fitted = offset + np.dot(exog, params)
+
+        if not linear:
+            fitted = self.family.link(fitted)
+
+        return fitted
+
+    def _starting_params(self):
+        """
+        Returns a starting value for the mean parameters and a list of
+        variable names.
+        """
+
+        from statsmodels.genmod.dependence_structures import (
+            GlobalOddsRatio, Independence)
+
+        dm = self.exog.shape[1]
+
+        if isinstance(self.cov_struct, GlobalOddsRatio):
+
+            ind = Independence()
+            md = GEE(self.endog, self.exog, self.groups,
+                     time=self.time, family=self.family,
+                     offset=self.offset)
+            mdf = md.fit()
+            return mdf.params
+
+        else:
+            return np.zeros(dm, dtype=np.float64)
+
+    def fit(self, maxiter=60, ctol=1e-6, start_params=None,
+            params_niter=1, first_dep_update=0,
+            covariance_type='robust'):
+
+        self.fit_history = {'params': [],
+                            'score': [],
+                            'dep_params': [],
+                            'cov_adjust': []}
+
+        if start_params is None:
+            mean_params = self._starting_params()
+        else:
+            mean_params = start_params.copy()
+
+        self.update_cached_means(mean_params)
+
+        del_params = -1.
+        num_assoc_updates = 0
+        for itr in range(maxiter):
+
+            update, score = self._update_mean_params()
+            if update is None:
+                warnings.warn("Singular matrix encountered in GEE update",
+                              ConvergenceWarning)
+                break
+            mean_params += update
+            self.update_cached_means(mean_params)
+
+            # L2 norm of the change in mean structure parameters at
+            # this iteration.
+            del_params = np.sqrt(np.sum(score**2))
+
+            self.fit_history['params'].append(mean_params.copy())
+            self.fit_history['score'].append(score)
+            self.fit_history['dep_params'].append(
+                self.cov_struct.dep_params)
+
+            # Don't exit until the association parameters have been
+            # updated at least once.
+            if del_params < ctol and num_assoc_updates > 0:
+                break
+
+            if self._do_cov_update and (itr % params_niter) == 0\
+                   and (itr >= first_dep_update):
+                self._update_assoc(mean_params)
+                num_assoc_updates += 1
+
+        if del_params >= ctol:
+            warnings.warn("Iteration limit reached prior to convergence",
+                          IterationLimitWarning)
+
+        if mean_params is None:
+            warnings.warn("Unable to estimate GEE parameters.",
+                          ConvergenceWarning)
+            return None
+
+        bcov, ncov, bc_cov, _ = self._covmat()
+        if bcov is None:
+            warnings.warn("Estimated covariance structure for GEE "
+                          "estimates is singular", ConvergenceWarning)
+            return None
+
+        if self.constraint is not None:
+            mean_params, bcov = self._handle_constraint(mean_params, bcov)
+            if mean_params is None:
+                warnings.warn("Unable to estimate constrained GEE "
+                              "parameters.", ConvergenceWarning)
+                return None
+
+        scale = self.estimate_scale()
+
+        # The superclass constructor will multiply the covariance
+        # matrix argument bcov by scale, which we don't want, so we
+        # divide bcov by the scale parameter here
+        results = GEEResults(self, mean_params, bcov / scale, scale)
+
+        results.covariance_type = covariance_type
+        results.fit_history = self.fit_history
+        results.naive_covariance = ncov
+        results.robust_covariance_bc = bc_cov
+        results.score_norm = del_params
+        results.converged = (del_params < ctol)
+        results.cov_struct = self.cov_struct
+
+        return results
+
+    fit.__doc__ = _gee_fit_doc
+
+    def _handle_constraint(self, mean_params, bcov):
+        """
+        Expand the parameter estimate `mean_params` and covariance matrix
+        `bcov` to the coordinate system of the unconstrained model.
+
+        Parameters:
+        -----------
+        mean_params : array-like
+            A parameter vector estimate for the reduced model.
+        bcov : array-like
+            The covariance matrix of mean_params.
+
+        Returns:
+        --------
+        mean_params : array-like
+            The input parameter vector mean_params, expanded to the
+            coordinate system of the full model
+        bcov : array-like
+            The input covariance matrix bcov, expanded to the
+            coordinate system of the full model
+        """
+
+        # The number of variables in the full model
+        red_p = len(mean_params)
+        full_p = self.constraint.lhs.shape[1]
+        mean_params0 = np.r_[mean_params, np.zeros(full_p - red_p)]
+
+        # Get the score vector under the full model.
+        save_exog_li = self.exog_li
+        self.exog_li = self.constraint.exog_fulltrans_li
+        import copy
+        save_cached_means = copy.deepcopy(self.cached_means)
+        self.update_cached_means(mean_params0)
+        _, score = self._update_mean_params()
+
+        if score is None:
+            warnings.warn("Singular matrix encountered in GEE score test",
+                          ConvergenceWarning)
+            return None, None
+
+        _, ncov1, _, cmat = self._covmat()
+        scale = self.estimate_scale()
+        cmat = cmat / scale**2
+        score2 = score[red_p:] / scale
+
+        amat = np.linalg.inv(ncov1)
+
+        bmat_11 = cmat[0:red_p, 0:red_p]
+        bmat_22 = cmat[red_p:, red_p:]
+        bmat_12 = cmat[0:red_p, red_p:]
+        amat_11 = amat[0:red_p, 0:red_p]
+        amat_12 = amat[0:red_p, red_p:]
+
+        score_cov = bmat_22 - \
+            np.dot(amat_12.T, np.linalg.solve(amat_11, bmat_12))
+        score_cov -= np.dot(bmat_12.T,
+                        np.linalg.solve(amat_11, amat_12))
+        score_cov += np.dot(amat_12.T,
+                            np.dot(np.linalg.solve(amat_11, bmat_11),
+                                   np.linalg.solve(amat_11, amat_12)))
+
+        from scipy.stats.distributions import chi2
+        score_statistic = np.dot(score2,
+                                 np.linalg.solve(score_cov, score2))
+        score_df = len(score2)
+        score_pvalue = 1 - chi2.cdf(score_statistic, score_df)
+        self.score_test_results = {"statistic": score_statistic,
+                                   "df": score_df,
+                                   "p-value": score_pvalue}
+
+        mean_params = self.constraint.unpack_param(mean_params)
+        bcov = self.constraint.unpack_cov(bcov)
+
+        self.exog_li = save_exog_li
+        self.cached_means = save_cached_means
+        self.exog = self.constraint.restore_exog()
+
+        return mean_params, bcov
+
+    def _update_assoc(self, params):
+        """
+        Update the association parameters
+        """
+
+        self.cov_struct.update(params)
+
+    def _derivative_exog(self, params, exog=None, transform='dydx',
+            dummy_idx=None, count_idx=None):
+        """
+        For computing marginal effects returns dF(XB) / dX where F(.) is
+        the predicted probabilities
+
+        transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.
+
+        Not all of these make sense in the presence of discrete regressors,
+        but checks are done in the results in get_margeff.
+        """
+        #note, this form should be appropriate for
+        ## group 1 probit, logit, logistic, cloglog, heckprob, xtprobit
+        if exog is None:
+            exog = self.exog
+        margeff = self.mean_deriv_exog(exog, params)
+#        lpr = np.dot(exog, params)
+#        margeff = (self.mean_deriv(exog, lpr) / exog) * params
+#        margeff = np.dot(self.pdf(np.dot(exog, params))[:, None],
+#                                                          params[None,:])
+
+        if 'ex' in transform:
+            margeff *= exog
+        if 'ey' in transform:
+            margeff /= self.predict(params, exog)[:, None]
+        if count_idx is not None:
+            from statsmodels.discrete.discrete_margins import (
+                    _get_count_effects)
+            margeff = _get_count_effects(margeff, exog, count_idx, transform,
+                    self, params)
+        if dummy_idx is not None:
+            from statsmodels.discrete.discrete_margins import (
+                    _get_dummy_effects)
+            margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform,
+                    self, params)
+        return margeff
+
+
+class GEEResults(base.LikelihoodModelResults):
+
+    __doc__ = (
+        "This class summarizes the fit of a marginal regression model using GEE.\n"
+        + _gee_results_doc)
+
+    # Default covariance type
+    covariance_type = "robust"
+
+    def __init__(self, model, params, cov_params, scale):
+
+        super(GEEResults, self).__init__(model, params,
+                normalized_cov_params=cov_params, scale=scale)
+
+    def standard_errors(self, covariance_type="robust"):
+        """
+        This is a convenience function that returns the standard
+        errors for any covariance type.  The value of `bse` is the
+        standard errors for whichever covariance type is specified as
+        an argument to `fit` (defaults to "robust").
+
+        Arguments:
+        ----------
+        covariance_type : string
+            One of "robust", "naive", or "bias_reduced".  Determines
+            the covariance used to compute standard errors.  Defaults
+            to "robust".
+        """
+
+        # Check covariance_type
+        covariance_type = covariance_type.lower()
+        allowed_covariances = ["robust", "naive", "bias_reduced"]
+        if covariance_type not in allowed_covariances:
+            msg = "GEE: `covariance_type` must be one of " +\
+                ", ".join(allowed_covariances)
+            raise ValueError(msg)
+
+        if covariance_type == "robust":
+            return np.sqrt(np.diag(self.cov_params()))
+        elif covariance_type == "naive":
+            return np.sqrt(np.diag(self.naive_covariance))
+        elif covariance_type == "bias_reduced":
+            return np.sqrt(np.diag(self.robust_covariance_bc))
+
+    # Need to override to allow for different covariance types.
+    @cache_readonly
+    def bse(self):
+        return self.standard_errors(self.covariance_type)
+
+    @cache_readonly
+    def resid(self):
+        """
+        Returns the residuals, the endogeneous data minus the fitted
+        values from the model.
+        """
+        return self.model.endog - self.fittedvalues
+
+    @cache_readonly
+    def split_resid(self):
+        """
+        Returns the residuals, the endogeneous data minus the fitted
+        values from the model.  The residuals are returned as a list
+        of arrays containing the residuals for each cluster.
+        """
+        sresid = []
+        for v in self.model.group_labels:
+            ii = self.model.group_indices[v]
+            sresid.append(self.resid[ii])
+        return sresid
+
+    @cache_readonly
+    def centered_resid(self):
+        """
+        Returns the residuals centered within each group.
+        """
+        cresid = self.resid.copy()
+        for v in self.model.group_labels:
+            ii = self.model.group_indices[v]
+            cresid[ii] -= cresid[ii].mean()
+        return cresid
+
+    @cache_readonly
+    def split_centered_resid(self):
+        """
+        Returns the residuals centered within each group.  The
+        residuals are returned as a list of arrays containing the
+        centered residuals for each cluster.
+        """
+        sresid = []
+        for v in self.model.group_labels:
+            ii = self.model.group_indices[v]
+            sresid.append(self.centered_resid[ii])
+        return sresid
+
+    @cache_readonly
+    def fittedvalues(self):
+        """
+        Returns the fitted values from the model.
+        """
+        return self.model.family.link.inverse(np.dot(self.model.exog,
+                                                     self.params))
+
+    def conf_int(self, alpha=.05, cols=None,
+                 covariance_type="robust"):
+        """
+        Returns confidence intervals for the fitted parameters.
+
+        Parameters
+        ----------
+        alpha : float, optional
+             The `alpha` level for the confidence interval.  i.e., The
+             default `alpha` = .05 returns a 95% confidence interval.
+        cols : array-like, optional
+             `cols` specifies which confidence intervals to return
+        covariance_type : string
+             The covariance type used for computing standard errors;
+             must be one of 'robust', 'naive', and 'bias reduced'.
+             See `GEE` for details.
+
+        Notes
+        -----
+        The confidence interval is based on the Gaussian distribution.
+        """
+        bse = self.standard_errors(covariance_type=covariance_type)
+        params = self.params
+        dist = stats.norm
+        q = dist.ppf(1 - alpha / 2)
+
+        if cols is None:
+            lower = self.params - q * bse
+            upper = self.params + q * bse
+        else:
+            cols = np.asarray(cols)
+            lower = params[cols] - q * bse[cols]
+            upper = params[cols] + q * bse[cols]
+        return np.asarray(lzip(lower, upper))
+
+    def summary(self, yname=None, xname=None, title=None, alpha=.05,
+                covariance_type="robust"):
+        """
+        Summarize the GEE regression results
+
+        Parameters
+        -----------
+        yname : string, optional
+            Default is `y`
+        xname : list of strings, optional
+            Default is `var_##` for ## in p the number of regressors
+        title : string, optional
+            Title for the top table. If not None, then this replaces
+            the default title
+        alpha : float
+            significance level for the confidence intervals
+        covariance_type : string
+            The covariance type used to compute the standard errors;
+            one of 'robust' (the usual robust sandwich-type covariance
+            estimate), 'naive' (ignores dependence), and 'bias
+            reduced' (the Mancl/DeRouen estimate).
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be
+            printed or converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary
+            results
+
+        """
+
+        self.covariance_type = covariance_type
+
+        top_left = [('Dep. Variable:', None),
+                    ('Model:', None),
+                    ('Method:', ['Generalized']),
+                    ('', ['Estimating Equations']),
+                    ('Family:', [self.model.family.__class__.__name__]),
+                    ('Dependence structure:',
+                     [self.model.cov_struct.__class__.__name__]),
+                    ('Date:', None),
+                    ('Covariance type: ', [covariance_type,])
+                   ]
+
+        NY = [len(y) for y in self.model.endog_li]
+
+        top_right = [('No. Observations:', [sum(NY)]),
+                     ('No. clusters:', [len(self.model.endog_li)]),
+                     ('Min. cluster size:', [min(NY)]),
+                     ('Max. cluster size:', [max(NY)]),
+                     ('Mean cluster size:', ["%.1f" % np.mean(NY)]),
+                     ('Num. iterations:', ['%d' %
+                           len(self.model.fit_history['params'])]),
+                     ('Scale:', ["%.3f" % self.scale]),
+                     ('Time:', None),
+                 ]
+
+        # The skew of the residuals
+        skew1 = stats.skew(self.resid)
+        kurt1 = stats.kurtosis(self.resid)
+        skew2 = stats.skew(self.centered_resid)
+        kurt2 = stats.kurtosis(self.centered_resid)
+
+        diagn_left = [('Skew:', ["%12.4f" % skew1]),
+                      ('Centered skew:', ["%12.4f" % skew2])]
+
+        diagn_right = [('Kurtosis:', ["%12.4f" % kurt1]),
+                       ('Centered kurtosis:', ["%12.4f" % kurt2])
+                   ]
+
+        if title is None:
+            title = self.model.__class__.__name__ + ' ' +\
+                    "Regression Results"
+
+        # Override the dataframe names if xname is provided as an
+        # argument.
+        if xname is not None:
+            xna = xname
+        else:
+            xna = self.model.exog_names
+
+        # Create summary table instance
+        from statsmodels.iolib.summary import Summary
+        smry = Summary()
+        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
+                             yname=self.model.endog_names, xname=xna,
+                             title=title)
+        smry.add_table_params(self, yname=yname, xname=xna,
+                              alpha=alpha, use_t=False)
+        smry.add_table_2cols(self, gleft=diagn_left,
+                             gright=diagn_right, yname=yname,
+                             xname=xna, title="")
+
+        return smry
+
+    def plot_isotropic_dependence(self, ax=None, xpoints=10,
+                                  min_n=50):
+        """
+        Create a plot of the pairwise products of within-group
+        residuals against the corresponding time differences.  This
+        plot can be used to assess the possible form of an isotropic
+        covariance structure.
+
+        Arguments:
+        ----------
+        ax : Matplotlib axes instance
+            An axes on which to draw the graph.  If None, new
+            figure and axes objects are created
+        xpoints : scalar or array-like
+            If scalar, the number of points equally spaced points on
+            the time difference axis used to define bins for
+            calculating local means.  If an array, the specific points
+            that define the bins.
+        min_n : integer
+            The minimum sample size in a bin for the mean residual
+            product to be included on the plot.
+        """
+
+        from statsmodels.graphics import utils as gutils
+
+        resid = self.model.cluster_list(self.resid)
+        time = self.model.cluster_list(self.model.time)
+
+        # All within-group pairwise time distances (xdt) and the
+        # corresponding products of scaled residuals (xre).
+        xre, xdt = [], []
+        for re, ti in zip(resid, time):
+            ix = np.tril_indices(re.shape[0], 0)
+            re = re[ix[0]] * re[ix[1]] / self.scale**2
+            xre.append(re)
+            dists = np.sqrt(((ti[ix[0],:] - ti[ix[1],:])**2).sum(1))
+            xdt.append(dists)
+
+        xre = np.concatenate(xre)
+        xdt = np.concatenate(xdt)
+
+        if ax is None:
+            fig, ax = gutils.create_mpl_ax(ax)
+        else:
+            fig = ax.get_figure()
+
+        # Convert to a correlation
+        ii = np.flatnonzero(xdt == 0)
+        v0 = np.mean(xre[ii])
+        xre /= v0
+
+        # Use the simple average to smooth, since fancier smoothers
+        # that trim and downweight outliers give biased results (we
+        # need the actual mean of a skewed distribution).
+        if np.isscalar(xpoints):
+            xpoints = np.linspace(0, max(xdt), xpoints)
+        dg = np.digitize(xdt, xpoints)
+        dgu = np.unique(dg)
+        hist = np.asarray([np.sum(dg==k) for k in dgu])
+        ii = np.flatnonzero(hist >= min_n)
+        dgu = dgu[ii]
+        dgy = np.asarray([np.mean(xre[dg==k]) for k in dgu])
+        dgx = np.asarray([np.mean(xdt[dg==k]) for k in dgu])
+
+        ax.plot(dgx, dgy, '-', color='orange', lw=5)
+        ax.set_xlabel("Time difference")
+        ax.set_ylabel("Product of scaled residuals")
+
+        return fig
+
+    def params_sensitivity(self, dep_params_first,
+                           dep_params_last, num_steps):
+        """
+        Refits the GEE model using a sequence of values for the
+        dependence parameters.
+
+        Parameters
+        ----------
+        dep_params_first : array-like
+            The first dep_params in the sequence
+        dep_params_last : array-like
+            The last dep_params in the sequence
+        num_steps : int
+            The number of dep_params in the sequence
+
+        Returns
+        -------
+        dep_params : array-like
+            The sequence of dependence parameter values.
+        results : array-like
+            The GEEResults objects resulting from the fits.
+        """
+
+        dep_params = []
+        results = []
+        for x in np.linspace(0, 1, num_steps):
+
+            dp = x * dep_params_last + (1 - x) * dep_params_first
+            dep_params.append(dp)
+
+            self.model.cov_struct.dep_params = dp
+            rslt = self.model.fit(start_params=self.params,
+                                  first_dep_update=1e50)
+            results.append(rslt)
+
+        return dep_params, results
+
+
+
+class OrdinalGEE(GEE):
+
+    __doc__ = (
+        "    Estimation of ordinal response marginal regression models\n"
+        "    using Generalized Estimating Equations (GEE).\n" +
+        _gee_init_doc % {'extra_params': base._missing_param_doc,
+                         'example': _gee_ordinal_example})
+
+    def __init__(self, endog, exog, groups, time=None, family=None,
+                       cov_struct=None, missing='none', offset=None,
+                       dep_data=None, constraint=None):
+
+        endog, exog, groups, time, offset = self.setup_ordinal(endog,
+                                       exog, groups, time, offset)
+
+        super(OrdinalGEE, self).__init__(endog, exog, groups, time,
+                                         family, cov_struct, missing,
+                                         offset, dep_data, constraint)
+
+    def setup_ordinal(self, endog, exog, groups, time, offset):
+        """
+        Restructure ordinal data as binary indicators so that they can
+        be analysed using Generalized Estimating Equations.
+        """
+
+        self.endog_orig = endog.copy()
+        self.exog_orig = exog.copy()
+        self.groups_orig = groups.copy()
+        if offset is not None:
+            self.offset_orig = offset.copy()
+        else:
+            self.offset_orig = None
+            offset = np.zeros(len(endog))
+        if time is not None:
+            self.time_orig = time.copy()
+        else:
+            self.time_orig = None
+            time = np.zeros((len(endog),1))
+
+        exog = np.asarray(exog)
+        endog = np.asarray(endog)
+        groups = np.asarray(groups)
+        time = np.asarray(time)
+        offset = np.asarray(offset)
+
+        # The unique outcomes, except the greatest one.
+        self.endog_values = np.unique(endog)
+        endog_cuts = self.endog_values[0:-1]
+        ncut = len(endog_cuts)
+
+        nrows = ncut * len(endog)
+        exog_out = np.zeros((nrows, exog.shape[1]),
+                            dtype=np.float64)
+        endog_out = np.zeros(nrows, dtype=np.float64)
+        intercepts = np.zeros((nrows, ncut), dtype=np.float64)
+        groups_out = np.zeros(nrows, dtype=groups.dtype)
+        time_out = np.zeros((nrows, time.shape[1]),
+                            dtype=np.float64)
+        offset_out = np.zeros(nrows, dtype=np.float64)
+
+        jrow = 0
+        zipper = zip(exog, endog, groups, time, offset)
+        for (exog_row, endog_value, group_value, time_value,
+             offset_value) in zipper:
+
+            # Loop over thresholds for the indicators
+            for thresh_ix, thresh in enumerate(endog_cuts):
+
+                exog_out[jrow, :] = exog_row
+                endog_out[jrow] = (int(endog_value > thresh))
+                intercepts[jrow, thresh_ix] = 1
+                groups_out[jrow] = group_value
+                time_out[jrow] = time_value
+                offset_out[jrow] = offset_value
+                jrow += 1
+
+        exog_out = np.concatenate((intercepts, exog_out), axis=1)
+
+        # exog column names, including intercepts
+        xnames = ["I(y>%.1f)" % v for v in endog_cuts]
+        if type(self.exog_orig) == pd.DataFrame:
+            xnames.extend(self.exog_orig.columns)
+        else:
+            xnames.extend(["x%d" % k for k in range(1, exog.shape[1]+1)])
+        exog_out = pd.DataFrame(exog_out, columns=xnames)
+
+        # Preserve the endog name if there is one
+        if type(self.endog_orig) == pd.Series:
+            endog_out = pd.Series(endog_out, name=self.endog_orig.name)
+
+        return endog_out, exog_out, groups_out, time_out, offset_out
+
+    def fit(self, maxiter=60, ctol=1e-6, start_params=None,
+            params_niter=1, first_dep_update=0,
+            covariance_type='robust'):
+
+        rslt = super(OrdinalGEE, self).fit(maxiter, ctol, start_params,
+                                           params_niter, first_dep_update,
+                                           covariance_type)
+        return OrdinalGEEResults(self, rslt.params,
+                                 rslt.cov_params() / rslt.scale,
+                                 rslt.scale)
+
+    fit.__doc__ = _gee_fit_doc
+
+class OrdinalGEEResults(GEEResults):
+
+    __doc__ = (
+        "This class summarizes the fit of a marginal regression model"
+        "for an ordinal response using GEE.\n"
+        + _gee_results_doc)
+
+    def __init__(self, model, params, cov_params, scale):
+
+        super(OrdinalGEEResults, self).__init__(model, params,
+                                      cov_params, scale)
+
+
+    def plot_distribution(self, ax=None, exog_values=None):
+        """
+        Plot the fitted probabilities of endog in an ordinal model,
+        for specifed values of the predictors.
+
+        Arguments:
+        ----------
+        ax : Matplotlib axes instance
+            An axes on which to draw the graph.  If None, new
+            figure and axes objects are created
+        exog_values : array-like
+            A list of dictionaries, with each dictionary mapping
+            variable names to values at which the variable is held
+            fixed.  The values P(endog=y | exog) are plotted for all
+            possible values of y, at the given exog value.  Variables
+            not included in a dictionary are held fixed at the mean
+            value.
+
+        Example:
+        --------
+        We have a model with covariates 'age' and 'sex', and wish to
+        plot the probabilities P(endog=y | exog) for males (sex=0) and
+        for females (sex=1), as separate paths on the plot.  Since
+        'age' is not included below in the map, it is held fixed at
+        its mean value.
+
+        >> ev = [{"sex": 1}, {"sex": 0}]
+        >> rslt.distribution_plot(exog_values=ev)
+        """
+
+        from statsmodels.graphics import utils as gutils
+
+        if ax is None:
+            fig, ax = gutils.create_mpl_ax(ax)
+        else:
+            fig = ax.get_figure()
+
+        # If no covariate patterns are specified, create one with all
+        # variables set to their mean values.
+        if exog_values is None:
+            exog_values = [{},]
+
+        exog_means = self.model.exog.mean(0)
+        ix_icept = [i for i,x in enumerate(self.model.exog_names) if
+                    x.startswith("I(")]
+
+        for ev in exog_values:
+
+            for k in ev.keys():
+                if k not in self.model.exog_names:
+                    raise ValueError("%s is not a variable in the model"
+                                     % k)
+
+            # Get the fitted probability for each level, at the given
+            # covariate values.
+            pr = []
+            for j in ix_icept:
+
+                xp = np.zeros_like(self.params)
+                xp[j] = 1.
+                for i,vn in enumerate(self.model.exog_names):
+                    if i in ix_icept:
+                        continue
+                    # User-specified value
+                    if vn in ev:
+                        xp[i] = ev[vn]
+                    # Mean value
+                    else:
+                        xp[i] = exog_means[i]
+
+                p = 1 / (1 + np.exp(-np.dot(xp, self.params)))
+                pr.append(p)
+
+            pr.insert(0, 1)
+            pr.append(0)
+            pr = np.asarray(pr)
+            prd = -np.diff(pr)
+
+            ax.plot(self.model.endog_values, prd, 'o-')
+
+        ax.set_xlabel("Response value")
+        ax.set_ylabel("Probability")
+        ax.set_ylim(0, 1)
+
+        return fig
+
+
+class NominalGEE(GEE):
+
+    __doc__ = (
+        "    Estimation of nominal response marginal regression models\n"
+        "    using Generalized Estimating Equations (GEE).\n" +
+        _gee_init_doc % {'extra_params': base._missing_param_doc,
+                         'example': _gee_nominal_example})
+
+    def __init__(self, endog, exog, groups, time=None, family=None,
+                       cov_struct=None, missing='none', offset=None,
+                       dep_data=None, constraint=None):
+
+        endog, exog, groups, time, offset = self.setup_nominal(endog,
+                                       exog, groups, time, offset)
+
+        super(NominalGEE, self).__init__(endog, exog, groups,
+                 time, family, cov_struct, missing, offset, dep_data,
+                 constraint)
+
+    def setup_nominal(self, endog, exog, groups, time, offset):
+        """
+        Restructure nominal data as binary indicators so that they can
+        be analysed using Generalized Estimating Equations.
+        """
+
+        self.endog_orig = endog.copy()
+        self.exog_orig = exog.copy()
+        self.groups_orig = groups.copy()
+        if offset is not None:
+            self.offset_orig = offset.copy()
+        else:
+            self.offset_orig = None
+            offset = np.zeros(len(endog))
+        if time is not None:
+            self.time_orig = time.copy()
+        else:
+            self.time_orig = None
+            time = np.zeros((len(endog),1))
+
+        # The unique outcomes, except the greatest one.
+        self.endog_values = np.unique(endog)
+        endog_cuts = self.endog_values[0:-1]
+        ncut = len(endog_cuts)
+
+        nrows = len(endog_cuts) * exog.shape[0]
+        ncols = len(endog_cuts) * exog.shape[1]
+        exog_out = np.zeros((nrows, ncols), dtype=np.float64)
+        endog_out = np.zeros(nrows, dtype=np.float64)
+        groups_out = np.zeros(nrows, dtype=np.float64)
+        time_out = np.zeros((nrows, time.shape[1]),
+                        dtype=np.float64)
+        offset_out = np.zeros(nrows, dtype=np.float64)
+
+        jrow = 0
+        zipper = zip(exog, endog, groups, time, offset)
+        for (exog_row, endog_value, group_value, time_value,
+             offset_value) in zipper:
+
+            # Loop over thresholds for the indicators
+            for thresh_ix, thresh in enumerate(endog_cuts):
+
+                u = np.zeros(len(endog_cuts), dtype=np.float64)
+                u[thresh_ix] = 1
+                exog_out[jrow, :] = np.kron(u, exog_row)
+                endog_out[jrow] = (int(endog_value == thresh))
+                groups_out[jrow] = group_value
+                time_out[jrow] = time_value
+                offset_out[jrow] = offset_value
+                jrow += 1
+
+        # exog names
+        if type(self.exog_orig) == pd.DataFrame:
+            xnames_in = self.exog_orig.columns
+        else:
+            xnames_in = ["x%d" % k for k in range(1, exog.shape[1]+1)]
+        xnames = []
+        for tr in endog_cuts:
+            xnames.extend(["%s[%.1f]" % (v, tr) for v in xnames_in])
+        exog_out = pd.DataFrame(exog_out, columns=xnames)
+        exog_out = pd.DataFrame(exog_out, columns=xnames)
+
+        # Preserve endog name if there is one
+        if type(self.endog_orig) == pd.Series:
+            endog_out = pd.Series(endog_out, name=self.endog_orig.names)
+
+        return endog_out, exog_out, groups_out, time_out, offset_out
+
+    def fit(self, maxiter=60, ctol=1e-6, start_params=None,
+            params_niter=1, first_dep_update=0,
+            covariance_type='robust'):
+        rslt = super(NominalGEE, self).fit(maxiter, ctol, start_params,
+                                           params_niter, first_dep_update,
+                                           covariance_type)
+        if rslt is None:
+            warnings.warn("GEE updates did not converge",
+                          ConvergenceWarning)
+            return None
+        return NominalGEEResults(self, rslt.params,
+                                 rslt.cov_params() / rslt.scale,
+                                 rslt.scale)
+
+    fit.__doc__ = _gee_fit_doc
+
+class NominalGEEResults(GEEResults):
+
+    __doc__ = (
+        "This class summarizes the fit of a marginal regression model"
+        "for a nominal response using GEE.\n"
+        + _gee_results_doc)
+
+    def __init__(self, model, params, cov_params, scale):
+
+        super(NominalGEEResults, self).__init__(model, params,
+                                      cov_params, scale)
+
+    def plot_distribution(self, ax=None, exog_values=None):
+        """
+        Plot the fitted probabilities of endog in an nominal model,
+        for specifed values of the predictors.
+
+        Arguments:
+        ----------
+        ax : Matplotlib axes instance
+            An axes on which to draw the graph.  If None, new
+            figure and axes objects are created
+        exog_values : array-like
+            A list of dictionaries, with each dictionary mapping
+            variable names to values at which the variable is held
+            fixed.  The values P(endog=y | exog) are plotted for all
+            possible values of y, at the given exog value.  Variables
+            not included in a dictionary are held fixed at the mean
+            value.
+
+        Example:
+        --------
+        We have a model with covariates 'age' and 'sex', and wish to
+        plot the probabilities P(endog=y | exog) for males (sex=0) and
+        for females (sex=1), as separate paths on the plot.  Since
+        'age' is not included below in the map, it is held fixed at
+        its mean value.
+
+        >>> ex = [{"sex": 1}, {"sex": 0}]
+        >>> rslt.distribution_plot(exog_values=ex)
+        """
+
+        from statsmodels.graphics import utils as gutils
+
+        if ax is None:
+            fig, ax = gutils.create_mpl_ax(ax)
+        else:
+            fig = ax.get_figure()
+
+        # If no covariate patterns are specified, create one with all
+        # variables set to their mean values.
+        if exog_values is None:
+            exog_values = [{},]
+
+        link = self.model.family.link.inverse
+        ncut = self.model.family.ncut
+
+        k = self.model.exog.shape[1] / ncut
+        exog_means = self.model.exog.mean(0)[0:k]
+        exog_names = self.model.exog_names[0:k]
+        exog_names = [x.split("[")[0] for x in exog_names]
+
+        params = np.reshape(self.params,
+                            (ncut, len(self.params) / ncut))
+
+        for ev in exog_values:
+
+            exog = exog_means.copy()
+
+            for k in ev.keys():
+                if k not in exog_names:
+                    raise ValueError("%s is not a variable in the model"
+                                     % k)
+
+                ii = exog_names.index(k)
+                exog[ii] = ev[k]
+
+            lpr = np.dot(params, exog)
+            pr = link(lpr)
+            pr = np.r_[pr, 1-pr.sum()]
+
+            ax.plot(self.model.endog_values, pr, 'o-')
+
+        ax.set_xlabel("Response value")
+        ax.set_ylabel("Probability")
+        ax.set_xticks(self.model.endog_values)
+        ax.set_xticklabels(self.model.endog_values)
+        ax.set_ylim(0, 1)
+
+        return fig
+
+
+
+class MultinomialLogit(Link):
+    """
+    The multinomial logit transform, only for use with GEE.
+
+    Notes
+    -----
+    The data are assumed coded as binary indicators, where each
+    observed multinomial value y is coded as I(y == S[0]), ..., I(y ==
+    S[-1]), where S is the set of possible response labels, excluding
+    the largest one.  Thererefore functions in this class should only
+    be called using vector argument whose length is a multiple of |S|
+    = ncut, which is an argument to be provided when initializing the
+    class.
+
+    call and derivative use a private method _clean to trim p by 1e-10
+    so that p is in (0, 1)
+    """
+
+    def __init__(self, ncut):
+        self.ncut = ncut
+
+    def inverse(self, lpr):
+        """
+        Inverse of the multinomial logit transform, which gives the
+        expected values of the data as a function of the linear
+        predictors.
+
+        Parameters
+        ----------
+        lpr : array-like (length must be divisible by `ncut`)
+            The linear predictors
+
+        Returns
+        -------
+        prob : array
+            Probabilities, or expected values
+        """
+
+        expval = np.exp(lpr)
+
+        denom = 1 + np.reshape(expval, (len(expval) / self.ncut,
+                                        self.ncut)).sum(1)
+        denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))
+
+        prob = expval / denom
+
+        return prob
+
+    def mean_deriv(self, exog, lpr):
+        """
+        Derivative of the expected endog with respect to param.
+
+        Parameters
+        ----------
+        exog : array-like
+           The exogeneous data at which the derivative is computed,
+           number of rows must be a multiple of `ncut`.
+        lpr : array-like
+           The linear predictor values, length must be multiple of
+           `ncut`.
+
+        Returns
+        -------
+        The value of the derivative of the expected endog with respect
+        to param
+        """
+
+        expval = np.exp(lpr)
+
+        expval_m = np.reshape(expval, (len(expval) / self.ncut,
+                                       self.ncut))
+
+        denom = 1 + expval_m.sum(1)
+        denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))
+
+        dmat = expval[:, None] * exog / denom[:, None]
+
+        ones = np.ones(self.ncut, dtype=np.float64)
+        cmat = block_diag([np.outer(ones, x) for x in expval_m], "csr")
+        rmat = cmat.dot(exog)
+        dmat -= expval[:, None] * rmat / denom[:, None]**2
+
+        return dmat
+
+    # Minimally tested
+    def mean_deriv_exog(self, exog, params):
+        """
+        Derivative of the expected endog with respect to exog for the
+        multinomial model, used in analyzing marginal effects.
+
+        Parameters
+        ----------
+        exog : array-like
+           The exogeneous data at which the derivative is computed,
+           number of rows must be a multiple of `ncut`.
+        lpr : array-like
+           The linear predictor values, length must be multiple of
+           `ncut`.
+
+        Returns
+        -------
+        The value of the derivative of the expected endog with respect
+        to exog.
+        """
+
+        lpr = np.dot(exog, params)
+        expval = np.exp(lpr)
+
+        expval_m = np.reshape(expval, (len(expval) / self.ncut,
+                                       self.ncut))
+
+        denom = 1 + expval_m.sum(1)
+        denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))
+
+        bmat0 = np.outer(np.ones(exog.shape[0]), params)
+
+        # Masking matrix
+        qmat = []
+        for j in range(self.ncut):
+            ee = np.zeros(self.ncut, dtype=np.float64)
+            ee[j] = 1
+            qmat.append(np.kron(ee, np.ones(len(params) / self.ncut)))
+        qmat = np.array(qmat)
+        qmat = np.kron(np.ones((exog.shape[0]/self.ncut, 1)), qmat)
+        bmat = bmat0 * qmat
+
+        dmat = expval[:, None] * bmat / denom[:, None]
+
+        expval_mb = np.kron(expval_m, np.ones((self.ncut, 1)))
+        expval_mb = np.kron(expval_mb, np.ones((1, self.ncut)))
+
+        dmat -= expval[:, None] * (bmat * expval_mb) / denom[:, None]**2
+
+        return dmat
+
+
+class Multinomial(Family):
+    """
+    Pseudo-link function for fitting nominal multinomial models with
+    GEE.  Not for use outside the GEE class.
+    """
+
+    links = [MultinomialLogit,]
+    variance = varfuncs.binary
+
+    def __init__(self, nlevels):
+        """
+        Arguments:
+        ----------
+        nlevels : integer
+            The number of distinct categories for the multinomial
+            distribution.
+        """
+
+        self.ncut = nlevels - 1
+        self.link = MultinomialLogit(self.ncut)
+
+
+
+
+from statsmodels.discrete.discrete_margins import \
+    _get_margeff_exog, _get_const_index, _check_margeff_args, \
+    _effects_at, margeff_cov_with_se, _check_at_is_all, \
+    _transform_names, \
+    _check_discrete_args, _get_dummy_index, _get_count_index
+
+
+
+class GEEMargins(object):
+    """Estimate the marginal effects of a model fit using generalized
+    estimating equations.
+
+    Parameters
+    ----------
+    results : GEEResults instance
+        The results instance of a fitted discrete choice model
+    args : tuple
+        Args are passed to `get_margeff`. This is the same as
+        results.get_margeff. See there for more information.
+    kwargs : dict
+        Keyword args are passed to `get_margeff`. This is the same as
+        results.get_margeff. See there for more information.
+    """
+    def __init__(self, results, args, kwargs={}):
+        self._cache = resettable_cache()
+        self.results = results
+        self.get_margeff(*args, **kwargs)
+
+    def _reset(self):
+        self._cache = resettable_cache()
+
+    @cache_readonly
+    def tvalues(self):
+        _check_at_is_all(self.margeff_options)
+        return self.margeff / self.margeff_se
+
+    def summary_frame(self, alpha=.05):
+        """
+        Returns a DataFrame summarizing the marginal effects.
+
+        Parameters
+        ----------
+        alpha : float
+            Number between 0 and 1. The confidence intervals have the
+            probability 1-alpha.
+
+        Returns
+        -------
+        frame : DataFrames
+            A DataFrame summarizing the marginal effects.
+        """
+        _check_at_is_all(self.margeff_options)
+        from pandas import DataFrame
+        names = [_transform_names[self.margeff_options['method']],
+                                  'Std. Err.', 'z', 'Pr(>|z|)',
+                                  'Conf. Int. Low', 'Cont. Int. Hi.']
+        ind = self.results.model.exog.var(0) != 0 # True if not a constant
+        exog_names = self.results.model.exog_names
+        var_names = [name for i, name in enumerate(exog_names) if ind[i]]
+        table = np.column_stack((self.margeff, self.margeff_se, self.tvalues,
+                                 self.pvalues, self.conf_int(alpha)))
+        return DataFrame(table, columns=names, index=var_names)
+
+    @cache_readonly
+    def pvalues(self):
+        _check_at_is_all(self.margeff_options)
+        return stats.norm.sf(np.abs(self.tvalues)) * 2
+
+    def conf_int(self, alpha=.05):
+        """
+        Returns the confidence intervals of the marginal effects
+
+        Parameters
+        ----------
+        alpha : float
+            Number between 0 and 1. The confidence intervals have the
+            probability 1-alpha.
+
+        Returns
+        -------
+        conf_int : ndarray
+            An array with lower, upper confidence intervals for the marginal
+            effects.
+        """
+        _check_at_is_all(self.margeff_options)
+        me_se = self.margeff_se
+        q = stats.norm.ppf(1 - alpha / 2)
+        lower = self.margeff - q * me_se
+        upper = self.margeff + q * me_se
+        return np.asarray(lzip(lower, upper))
+
+    def summary(self, alpha=.05):
+        """
+        Returns a summary table for marginal effects
+
+        Parameters
+        ----------
+        alpha : float
+            Number between 0 and 1. The confidence intervals have the
+            probability 1-alpha.
+
+        Returns
+        -------
+        Summary : SummaryTable
+            A SummaryTable instance
+        """
+        _check_at_is_all(self.margeff_options)
+        results = self.results
+        model = results.model
+        title = model.__class__.__name__ + " Marginal Effects"
+        method = self.margeff_options['method']
+        top_left = [('Dep. Variable:', [model.endog_names]),
+                ('Method:', [method]),
+                ('At:', [self.margeff_options['at']]),]
+
+        from statsmodels.iolib.summary import (Summary, summary_params,
+                                                table_extend)
+        exog_names = model.exog_names[:] # copy
+        smry = Summary()
+
+        # sigh, we really need to hold on to this in _data...
+        _, const_idx = _get_const_index(model.exog)
+        if const_idx is not None:
+            exog_names.pop(const_idx)
+
+        J = int(getattr(model, "J", 1))
+        if J > 1:
+            yname, yname_list = results._get_endog_name(model.endog_names,
+                                                None, all=True)
+        else:
+            yname = model.endog_names
+            yname_list = [yname]
+
+        smry.add_table_2cols(self, gleft=top_left, gright=[],
+                yname=yname, xname=exog_names, title=title)
+
+        #NOTE: add_table_params is not general enough yet for margeff
+        # could use a refactor with getattr instead of hard-coded params
+        # tvalues etc.
+        table = []
+        conf_int = self.conf_int(alpha)
+        margeff = self.margeff
+        margeff_se = self.margeff_se
+        tvalues = self.tvalues
+        pvalues = self.pvalues
+        if J > 1:
+            for eq in range(J):
+                restup = (results, margeff[:, eq], margeff_se[:, eq],
+                          tvalues[:, eq], pvalues[:, eq], conf_int[:, :, eq])
+                tble = summary_params(restup, yname=yname_list[eq],
+                              xname=exog_names, alpha=alpha, use_t=False,
+                              skip_header=True)
+                tble.title = yname_list[eq]
+                # overwrite coef with method name
+                header = ['', _transform_names[method], 'std err', 'z',
+                        'P>|z|', '[%3.1f%% Conf. Int.]' % (100-alpha*100)]
+                tble.insert_header_row(0, header)
+                #from IPython.core.debugger import Pdb; Pdb().set_trace()
+                table.append(tble)
+
+            table = table_extend(table, keep_headers=True)
+        else:
+            restup = (results, margeff, margeff_se, tvalues, pvalues, conf_int)
+            table = summary_params(restup, yname=yname, xname=exog_names,
+                    alpha=alpha, use_t=False, skip_header=True)
+            header = ['', _transform_names[method], 'std err', 'z',
+                        'P>|z|', '[%3.1f%% Conf. Int.]' % (100-alpha*100)]
+            table.insert_header_row(0, header)
+
+        smry.tables.append(table)
+        return smry
+
+    def get_margeff(self, at='overall', method='dydx', atexog=None,
+                          dummy=False, count=False):
+        """Get marginal effects of the fitted model.
+
+        Parameters
+        ----------
+        at : str, optional
+            Options are:
+
+            - 'overall', The average of the marginal effects at each
+              observation.
+            - 'mean', The marginal effects at the mean of each regressor.
+            - 'median', The marginal effects at the median of each regressor.
+            - 'zero', The marginal effects at zero for each regressor.
+            - 'all', The marginal effects at each observation. If `at` is all
+              only margeff will be available.
+
+            Note that if `exog` is specified, then marginal effects for all
+            variables not specified by `exog` are calculated using the `at`
+            option.
+        method : str, optional
+            Options are:
+
+            - 'dydx' - dy/dx - No transformation is made and marginal effects
+              are returned.  This is the default.
+            - 'eyex' - estimate elasticities of variables in `exog` --
+              d(lny)/d(lnx)
+            - 'dyex' - estimate semielasticity -- dy/d(lnx)
+            - 'eydx' - estimate semeilasticity -- d(lny)/dx
+
+            Note that tranformations are done after each observation is
+            calculated.  Semi-elasticities for binary variables are computed
+            using the midpoint method. 'dyex' and 'eyex' do not make sense
+            for discrete variables.
+        atexog : array-like, optional
+            Optionally, you can provide the exogenous variables over which to
+            get the marginal effects.  This should be a dictionary with the key
+            as the zero-indexed column number and the value of the dictionary.
+            Default is None for all independent variables less the constant.
+        dummy : bool, optional
+            If False, treats binary variables (if present) as continuous.  This
+            is the default.  Else if True, treats binary variables as
+            changing from 0 to 1.  Note that any variable that is either 0 or 1
+            is treated as binary.  Each binary variable is treated separately
+            for now.
+        count : bool, optional
+            If False, treats count variables (if present) as continuous.  This
+            is the default.  Else if True, the marginal effect is the
+            change in probabilities when each observation is increased by one.
+
+        Returns
+        -------
+        effects : ndarray
+            the marginal effect corresponding to the input options
+
+        Notes
+        -----
+        When using after Poisson, returns the expected number of events
+        per period, assuming that the model is loglinear.
+        """
+        self._reset() # always reset the cache when this is called
+        #TODO: if at is not all or overall, we can also put atexog values
+        # in summary table head
+        method = method.lower()
+        at = at.lower()
+        _check_margeff_args(at, method)
+        self.margeff_options = dict(method=method, at=at)
+        results = self.results
+        model = results.model
+        params = results.params
+        exog = model.exog.copy() # copy because values are changed
+        effects_idx, const_idx = _get_const_index(exog)
+
+        if dummy:
+            _check_discrete_args(at, method)
+            dummy_idx, dummy = _get_dummy_index(exog, const_idx)
+        else:
+            dummy_idx = None
+
+        if count:
+            _check_discrete_args(at, method)
+            count_idx, count = _get_count_index(exog, const_idx)
+        else:
+            count_idx = None
+
+        # get the exogenous variables
+        exog = _get_margeff_exog(exog, at, atexog, effects_idx)
+
+        # get base marginal effects, handled by sub-classes
+        effects = model._derivative_exog(params, exog, method,
+                                                    dummy_idx, count_idx)
+
+        J = getattr(model, 'J', 1)
+        effects_idx = np.tile(effects_idx, J) # adjust for multi-equation.
+
+        effects = _effects_at(effects, at)
+
+        if at == 'all':
+            if J > 1:
+                K = model.K - np.any(~effects_idx) # subtract constant
+                self.margeff = effects[:, effects_idx].reshape(-1, K, J,
+                                                                order='F')
+            else:
+                self.margeff = effects[:, effects_idx]
+        else:
+            # Set standard error of the marginal effects by Delta method.
+            margeff_cov, margeff_se = margeff_cov_with_se(model, params, exog,
+                                                results.cov_params(), at,
+                                                model._derivative_exog,
+                                                dummy_idx, count_idx,
+                                                method, J)
+
+            # reshape for multi-equation
+            if J > 1:
+                K = model.K - np.any(~effects_idx) # subtract constant
+                self.margeff = effects[effects_idx].reshape(K, J, order='F')
+                self.margeff_se = margeff_se[effects_idx].reshape(K, J,
+                                                                  order='F')
+                self.margeff_cov = margeff_cov[effects_idx][:, effects_idx]
+            else:
+                # don't care about at constant
+                self.margeff_cov = margeff_cov[effects_idx][:, effects_idx]
+                self.margeff_se = margeff_se[effects_idx]
+                self.margeff = effects[effects_idx]
diff --git a/statsmodels/genmod/generalized_linear_model.py b/statsmodels/genmod/generalized_linear_model.py
index 0512080f912..7ffaa888928 100644
--- a/statsmodels/genmod/generalized_linear_model.py
+++ b/statsmodels/genmod/generalized_linear_model.py
@@ -19,28 +19,28 @@
 """
 
 import numpy as np
-import families
-from statsmodels.tools.tools import rank
-from statsmodels.tools.decorators import (cache_readonly,
-        resettable_cache)
+from . import families
+from statsmodels.tools.decorators import cache_readonly, resettable_cache
 
 import statsmodels.base.model as base
 import statsmodels.regression.linear_model as lm
 import statsmodels.base.wrapper as wrap
+from statsmodels.compat.numpy import np_matrix_rank
 
 from statsmodels.tools.sm_exceptions import PerfectSeparationError
 
 __all__ = ['GLM']
 
-def _check_convergence(criterion, iteration, tol, maxiter):
-    return not ((np.fabs(criterion[iteration] - criterion[iteration-1]) > tol)
-            and iteration <= maxiter)
+
+def _check_convergence(criterion, iteration, tol):
+    return not (np.fabs(criterion[iteration] - criterion[iteration-1]) > tol)
+
 
 class GLM(base.LikelihoodModel):
-    __doc__ = '''
+    __doc__ = """
     Generalized Linear Models class
 
-    GLM inherits from statsmodels.LikelihoodModel
+    GLM inherits from statsmodels.base.model.LikelihoodModel
 
     Parameters
     -----------
@@ -50,8 +50,9 @@ class GLM(base.LikelihoodModel):
         supplied, each observation is expected to be [success, failure].
     exog : array-like
         A nobs x k array where `nobs` is the number of observations and `k`
-        is the number of regressors. An interecept is not included by default
-        and should be added by the user. See `statsmodels.tools.add_constant`.
+        is the number of regressors. An intercept is not included by default
+        and should be added by the user (models specified using a formula
+        include an intercept by default). See `statsmodels.tools.add_constant`.
     family : family class instance
         The default is Gaussian.  To specify the binomial distribution
         family = sm.family.Binomial()
@@ -110,7 +111,9 @@ class GLM(base.LikelihoodModel):
 
     See also
     --------
-    statsmodels.families.*
+    statsmodels.genmod.families.family
+    :ref:`families`
+    :ref:`links`
 
     Notes
     -----
@@ -144,7 +147,8 @@ class GLM(base.LikelihoodModel):
         data is already an array and it is changed, then `endog` changes
         as well.
     exposure : array-like
-        Include ln(exposure) in model with coefficient constrained to 1.
+        Include ln(exposure) in model with coefficient constrained to 1. Can
+        only be used if the link is the logarithm function.
     exog : array
         See above.  Note that endog is a reference to the data so that if
         data is already an array and it is changed, then `endog` changes
@@ -156,7 +160,7 @@ class GLM(base.LikelihoodModel):
         statsmodels.families.  Default is Gaussian.
     mu : array
         The mean response of the transformed variable.  `mu` is the value of
-        the inverse of the link function at eta, where eta is the linear
+        the inverse of the link function at lin_pred, where lin_pred is the linear
         predicted value of the WLS fit of the transformed variable.  `mu` is
         only available after fit is called.  See
         statsmodels.families.family.fitted of the distribution family for more
@@ -182,10 +186,10 @@ class GLM(base.LikelihoodModel):
         The value of the weights after the last iteration of fit.  Only
         available after fit is called.  See statsmodels.families.family for
         the specific distribution weighting functions.
-    ''' % {'extra_params' : base._missing_param_doc}
+    """ % {'extra_params' : base._missing_param_doc}
 
     def __init__(self, endog, exog, family=None, offset=None, exposure=None,
-                        missing='none'):
+                 missing='none'):
         self._check_inputs(family, offset, exposure, endog)
         super(GLM, self).__init__(endog, exog, missing=missing,
                                   offset=self.offset, exposure=self.exposure)
@@ -195,7 +199,9 @@ def __init__(self, endog, exog, family=None, offset=None, exposure=None,
             delattr(self, 'exposure')
         #things to remove_data
         self._data_attr.extend(['weights', 'pinv_wexog', 'mu', 'data_weights',
-                                ])
+                                '_offset_exposure'])
+        # register kwds for __init__, offset and exposure are added by super
+        self._init_keys.append('family')
 
     def initialize(self):
         """
@@ -208,16 +214,21 @@ def initialize(self):
 
         self.pinv_wexog = np.linalg.pinv(self.exog)
         self.normalized_cov_params = np.dot(self.pinv_wexog,
-                                        np.transpose(self.pinv_wexog))
+                                            np.transpose(self.pinv_wexog))
 
-        self.df_model = rank(self.exog)-1
-        self.df_resid = self.exog.shape[0] - rank(self.exog)
+        self.df_model = np_matrix_rank(self.exog)-1
+        self.df_resid = self.exog.shape[0] - np_matrix_rank(self.exog)
 
     def _check_inputs(self, family, offset, exposure, endog):
+
+        # Default family is Gaussian
         if family is None:
             family = families.Gaussian()
         self.family = family
 
+        if exposure is not None and not isinstance(self.family.link, families.links.Log):
+            raise ValueError("exposure can only be used with the log link function")
+
         if offset is not None:
             offset = np.asarray(offset)
             if offset.shape[0] != endog.shape[0]:
@@ -225,16 +236,12 @@ def _check_inputs(self, family, offset, exposure, endog):
         self.offset = offset
 
         if exposure is not None:
-            exposure = np.log(exposure)
+            exposure = np.asarray(exposure)
             if exposure.shape[0] != endog.shape[0]:
                 raise ValueError("exposure is not the same length as endog")
+            exposure = np.log(exposure)
         self.exposure = exposure
 
-    def score(self, params):
-        """
-        Score matrix.  Not yet implemeneted
-        """
-        raise NotImplementedError
 
     def loglike(self, *args):
         """
@@ -245,11 +252,257 @@ def loglike(self, *args):
         """
         return self.family.loglike(*args)
 
-    def information(self, params):
+
+    def score_obs(self, params, scale=None):
+        """score first derivative of the loglikelihood for each observation.
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+        scale : None or float
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score_obs : ndarray, 2d
+            The first derivative of the loglikelihood function evaluated at
+            params for each observation.
+
+        """
+
+        score_factor = self.score_factor(params, scale=scale)
+        return score_factor[:, None] * self.exog
+
+
+    def score(self, params, scale=None):
+        """score, first derivative of the loglikelihood function
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which score is evaluated
+        scale : None or float
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score : ndarray_1d
+            The first derivative of the loglikelihood function calculated as
+            the sum of `score_obs`
+
+        """
+        return self.score_obs(params, scale=scale).sum(0)
+
+
+    def score_factor(self, params, scale=None):
+        """weights for score for each observation
+
+        This can be considered as score residuals.
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which Hessian is evaluated
+        scale : None or float
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+
+        Returns
+        -------
+        score_factor : ndarray_1d
+            A 1d weight vector used in the calculation of the score_obs.
+            The score_obs are obtained by `score_factor[:, None] * exog`
+
+        """
+        mu = self.predict(params)
+        if scale is None:
+            scale = self.estimate_scale(mu)
+
+        score_factor = (self.endog - mu) / self.family.link.deriv(mu)
+        score_factor /= self.family.variance(mu)
+        score_factor *= self.data_weights
+
+        if not scale == 1:
+            score_factor /= scale
+
+        return score_factor
+
+
+    def hessian_factor(self, params, scale=None, observed=True):
+        """Weights for calculating Hessian
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which Hessian is evaluated
+        scale : None or float
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+        observed : bool
+            If True, then the observed Hessian is returned. If false then the
+            expected information matrix is returned.
+
+        Returns
+        -------
+        hessian_factor : ndarray, 1d
+            A 1d weight vector used in the calculation of the Hessian.
+            The hessian is obtained by `(exog.T * hessian_factor).dot(exog)`
+
+
+        """
+
+        # calculating eim_factor
+        mu = self.predict(params)
+        if scale is None:
+            scale = self.estimate_scale(mu)
+
+        eim_factor = 1 / (self.family.link.deriv(mu)**2 *
+                            self.family.variance(mu))
+        eim_factor *= self.data_weights
+
+        if not observed:
+            if not scale == 1:
+                eim_factor /= scale
+            return eim_factor
+
+        # calculating oim_factor, eim_factor is with scale=1
+
+        score_factor = self.score_factor(params, scale=1.)
+        if eim_factor.ndim > 1 or score_factor.ndim > 1:
+            raise RuntimeError('something wrong')
+
+        tmp = self.family.variance(mu) * self.family.link.deriv2(mu)
+        tmp += self.family.variance.deriv(mu) * self.family.link.deriv(mu)
+
+        tmp = score_factor * eim_factor * tmp
+        # correct for duplicatee data_weights in oim_factor and score_factor
+        tmp /= self.data_weights
+        oim_factor = eim_factor * (1 + tmp)
+
+        if tmp.ndim > 1:
+            raise RuntimeError('something wrong')
+
+        if not scale == 1:
+            oim_factor /= scale
+
+        return oim_factor
+
+
+    def hessian(self, params, scale=None, observed=True):
+        """Hessian, second derivative of loglikelihood function
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which Hessian is evaluated
+        scale : None or float
+            If scale is None, then the default scale will be calculated.
+            Default scale is defined by `self.scaletype` and set in fit.
+            If scale is not None, then it is used as a fixed scale.
+        observed : bool
+            If True, then the observed Hessian is returned. If false then the
+            expected information matrix is returned.
+
+        Returns
+        -------
+        hessian : ndarray
+            Hessian, i.e. observed information, or expected information matrix.
+
+
         """
-        Fisher information matrix.  Not yet implemented.
+
+        factor = self.hessian_factor(params, scale=scale, observed=observed)
+        hess = -np.dot(self.exog.T * factor, self.exog)
+        return hess
+
+
+    def information(self, params, scale=None):
+        """
+        Fisher information matrix.
+        """
+        return self.hessian(params, scale=scale, observed=False)
+
+
+    def score_test(self, params_constrained, k_constraints=None,
+                   exog_extra=None, observed=True):
+        """score test for restrictions or for omitted variables
+
+        The covariance matrix for the score is based on the Hessian, i.e.
+        observed information matrix or optionally on the expected information
+        matrix..
+
+        Parameters
+        ----------
+        params_constrained : array_like
+            estimated parameter of the restricted model. This can be the
+            parameter estimate for the current when testing for omitted
+            variables.
+        k_constraints : int or None
+            Number of constraints that were used in the estimation of params
+            restricted relative to the number of exog in the model.
+            This must be provided if no exog_extra are given. If exog_extra is
+            not None, then k_constraints is assumed to be zero if it is None.
+        exog_extra : None or array_like
+            Explanatory variables that are jointly tested for inclusion in the
+            model, i.e. omitted variables.
+        observed : bool
+            If True, then the observed Hessian is used in calculating the
+            covariance matrix of the score. If false then the expected
+            information matrix is used.
+
+        Returns
+        -------
+        chi2_stat : float
+            chisquare statistic for the score test
+        p-value : float
+            P-value of the score test based on the chisquare distribution.
+        df : int
+            Degrees of freedom used in the p-value calculation. This is equal
+            to the number of constraints.
+
+        Notes
+        -----
+        not yet verified for case with scale not equal to 1.
+
         """
-        raise NotImplementedError
+
+        if exog_extra is None:
+            if k_constraints is None:
+                raise ValueError('if exog_extra is None, then k_constraints'
+                                 'needs to be given')
+
+            score = self.score(params_constrained)
+            hessian = self.hessian(params_constrained, observed=observed)
+
+        else:
+            #exog_extra = np.asarray(exog_extra)
+            if k_constraints is None:
+                k_constraints = 0
+
+            ex = np.column_stack((self.exog, exog_extra))
+            k_constraints += ex.shape[1] - self.exog.shape[1]
+
+            score_factor = self.score_factor(params_constrained)
+            score = (score_factor[:, None] * ex).sum(0)
+            hessian_factor = self.hessian_factor(params_constrained,
+                                                 observed=observed)
+            hessian = -np.dot(ex.T * hessian_factor, ex)
+
+
+        from scipy import stats
+        # TODO check sign, why minus?
+        chi2stat = -score.dot(np.linalg.solve(hessian, score[:, None]))
+        pval = stats.chi2.sf(chi2stat, k_constraints)
+        # return a stats results instance instead?  Contrast?
+        return chi2stat, pval, k_constraints
+
 
     def _update_history(self, tmp_result, mu, history):
         """
@@ -288,8 +541,8 @@ def estimate_scale(self, mu):
                 return 1.
             else:
                 resid = self.endog - mu
-                return ((np.power(resid, 2) / self.family.variance(mu)).sum() \
-                    / self.df_resid)
+                return ((np.power(resid, 2) / self.family.variance(mu)).sum()
+                        / self.df_resid)
 
         if isinstance(self.scaletype, float):
             return np.array(self.scaletype)
@@ -297,19 +550,20 @@ def estimate_scale(self, mu):
         if isinstance(self.scaletype, str):
             if self.scaletype.lower() == 'x2':
                 resid = self.endog - mu
-                return ((np.power(resid, 2) / self.family.variance(mu)).sum() \
-                    / self.df_resid)
+                return ((np.power(resid, 2) / self.family.variance(mu)).sum()
+                        / self.df_resid)
             elif self.scaletype.lower() == 'dev':
                 return self.family.deviance(self.endog, mu)/self.df_resid
             else:
-                raise ValueError("Scale %s with type %s not understood" %\
-                    (self.scaletype,type(self.scaletype)))
+                raise ValueError("Scale %s with type %s not understood" %
+                                 (self.scaletype, type(self.scaletype)))
 
         else:
-            raise ValueError("Scale %s with type %s not understood" %\
-                (self.scaletype, type(self.scaletype)))
+            raise ValueError("Scale %s with type %s not understood" %
+                             (self.scaletype, type(self.scaletype)))
 
-    def predict(self, params, exog=None, linear=False):
+    def predict(self, params, exog=None, exposure=None, offset=None,
+                linear=False):
         """
         Return predicted values for a design matrix
 
@@ -319,6 +573,11 @@ def predict(self, params, exog=None, linear=False):
             Parameters / coefficients of a GLM.
         exog : array-like, optional
             Design / exogenous data. Is exog is None, model exog is used.
+        exposure : array-like, optional
+            Exposure time values, only can be used with the log link
+            function.  See notes for details.
+        offset : array-like, optional
+            Offset values.  See notes for details.
         linear : bool
             If True, returns the linear predicted values.  If False,
             returns the value of the inverse of the model's link function at
@@ -327,19 +586,48 @@ def predict(self, params, exog=None, linear=False):
         Returns
         -------
         An array of fitted values
+
+        Notes
+        -----
+        Any `exposure` and `offset` provided here take precedence over
+        the `exposure` and `offset` used in the model fit.  If `exog`
+        is passed as an argument here, then any `exposure` and
+        `offset` values in the fit will be ignored.
+
+        Exposure values must be strictly positive.
         """
-        offset = getattr(self, 'offset', 0)
-        exposure = getattr(self, 'exposure', 0)
+
+        # Use fit offset if appropriate
+        if offset is None and exog is None and hasattr(self, 'offset'):
+            offset = getattr(self, 'offset')
+        elif offset is None:
+            offset = 0.
+
+        if exposure is not None and not isinstance(self.family.link,
+                                                   families.links.Log):
+            raise ValueError("exposure can only be used with the log link function")
+
+        # Use fit exposure if appropriate
+        if exposure is None and exog is None and hasattr(self, 'exposure'):
+            # Already logged
+            exposure = getattr(self, 'exposure')
+        elif exposure is None:
+            exposure = 0.
+        else:
+            exposure = np.log(exposure)
+
         if exog is None:
             exog = self.exog
+
+        linpred = np.dot(exog, params) + offset + exposure
         if linear:
-            return np.dot(exog, params) + offset + exposure
+            return linpred
         else:
-            return self.family.fitted(np.dot(exog, params) + exposure + \
-                                                             offset)
+            return self.family.fitted(linpred)
 
-    def fit(self, maxiter=100, method='IRLS', tol=1e-8, scale=None):
-        '''
+    def fit(self, start_params=None, maxiter=100, method='IRLS', tol=1e-8,
+            scale=None):
+        """
         Fits a generalized linear model for a given family.
 
         parameters
@@ -358,16 +646,20 @@ def fit(self, maxiter=100, method='IRLS', tol=1e-8, scale=None):
             `dev` is the deviance divided by df_resid
         tol : float
             Convergence tolerance.  Default is 1e-8.
-        '''
+        start_params : array-like, optional
+            Initial guess of the solution for the loglikelihood maximization.
+            The default is family-specific and is given by the
+            ``family.starting_mu(endog)``. If start_params is given then the
+            initial mean will be calculated as ``np.dot(exog, start_params)``.
+        """
         endog = self.endog
         if endog.ndim > 1 and endog.shape[1] == 2:
-            data_weights = endog.sum(1) # weights are total trials
+            data_weights = endog.sum(1)  # weights are total trials
         else:
             data_weights = np.ones((endog.shape[0]))
         self.data_weights = data_weights
-        if np.shape(self.data_weights) == () and self.data_weights>1:
-            self.data_weights = self.data_weights *\
-                    np.ones((endog.shape[0]))
+        if np.shape(self.data_weights) == () and self.data_weights > 1:
+            self.data_weights = self.data_weights * np.ones((endog.shape[0]))
         self.scaletype = scale
         if isinstance(self.family, families.Binomial):
         # this checks what kind of data is given for Binomial.
@@ -375,55 +667,126 @@ def fit(self, maxiter=100, method='IRLS', tol=1e-8, scale=None):
         # preprocessing
             self.endog = self.family.initialize(self.endog)
 
+        # Construct a combined offset/exposure term.  Note that
+        # exposure has already been logged if present.
+        offset_exposure = 0.
         if hasattr(self, 'offset'):
-            offset = self.offset
-        elif hasattr(self, 'exposure'):
-            offset = self.exposure
-        else:
-            offset = 0
-        #TODO: would there ever be both and exposure and an offset?
+            offset_exposure = self.offset
+        if hasattr(self, 'exposure'):
+            offset_exposure = offset_exposure + self.exposure
+        self._offset_exposure = offset_exposure
 
-        mu = self.family.starting_mu(self.endog)
         wlsexog = self.exog
-        eta = self.family.predict(mu)
+        if start_params is None:
+            mu = self.family.starting_mu(self.endog)
+            lin_pred = self.family.predict(mu)
+        else:
+            lin_pred = np.dot(wlsexog, start_params) + offset_exposure
+            mu = self.family.fitted(lin_pred)
         dev = self.family.deviance(self.endog, mu)
         if np.isnan(dev):
             raise ValueError("The first guess on the deviance function "
                              "returned a nan.  This could be a boundary "
                              " problem and should be reported.")
 
-
         # first guess on the deviance is assumed to be scaled by 1.
         # params are none to start, so they line up with the deviance
-        history = dict(params = [None, None], deviance=[np.inf,dev])
-        iteration = 0
-        converged = 0
+        history = dict(params=[None, start_params], deviance=[np.inf, dev])
+        converged = False
         criterion = history['deviance']
-        while not converged:
+        # This special case is used to get the likelihood for a specific
+        # params vector.
+        if maxiter == 0:
+            mu = self.family.fitted(lin_pred)
+            self.scale = self.estimate_scale(mu)
+            wls_results = lm.RegressionResults(self, start_params, None)
+            iteration = 0
+        for iteration in range(maxiter):
             self.weights = data_weights*self.family.weights(mu)
-            wlsendog = eta + self.family.link.deriv(mu) * (self.endog-mu) \
-                - offset
+            wlsendog = (lin_pred + self.family.link.deriv(mu) * (self.endog-mu)
+                        - offset_exposure)
             wls_results = lm.WLS(wlsendog, wlsexog, self.weights).fit()
-            eta = np.dot(self.exog, wls_results.params) + offset
-            mu = self.family.fitted(eta)
+            lin_pred = np.dot(self.exog, wls_results.params) + offset_exposure
+            mu = self.family.fitted(lin_pred)
             history = self._update_history(wls_results, mu, history)
             self.scale = self.estimate_scale(mu)
-            iteration += 1
             if endog.squeeze().ndim == 1 and np.allclose(mu - endog, 0):
                 msg = "Perfect separation detected, results not available"
                 raise PerfectSeparationError(msg)
-            converged = _check_convergence(criterion, iteration, tol,
-                                            maxiter)
+            converged = _check_convergence(criterion, iteration, tol)
+            if converged:
+                break
         self.mu = mu
         glm_results = GLMResults(self, wls_results.params,
                                  wls_results.normalized_cov_params,
                                  self.scale)
-        history['iteration'] = iteration
+        history['iteration'] = iteration + 1
         glm_results.fit_history = history
         return GLMResultsWrapper(glm_results)
 
+
+    def fit_constrained(self, constraints, start_params=None, **fit_kwds):
+        """fit the model subject to linear equality constraints
+
+        The constraints are of the form   `R params = q`
+        where R is the constraint_matrix and q is the vector of
+        constraint_values.
+
+        The estimation creates a new model with transformed design matrix,
+        exog, and converts the results back to the original parameterization.
+
+
+        Parameters
+        ----------
+        constraints : formula expression or tuple
+            If it is a tuple, then the constraint needs to be given by two
+            arrays (constraint_matrix, constraint_value), i.e. (R, q).
+            Otherwise, the constraints can be given as strings or list of
+            strings.
+            see t_test for details
+        start_params : None or array_like
+            starting values for the optimization. `start_params` needs to be
+            given in the original parameter space and are internally
+            transformed.
+        **fit_kwds : keyword arguments
+            fit_kwds are used in the optimization of the transformed model.
+
+        Returns
+        -------
+        results : Results instance
+
+        """
+
+        from patsy import DesignInfo
+        from statsmodels.base._constraints import fit_constrained
+
+        # same pattern as in base.LikelihoodModel.t_test
+        lc = DesignInfo(self.exog_names).linear_constraint(constraints)
+        R, q = lc.coefs, lc.constants
+
+        # TODO: add start_params option, need access to tranformation
+        #       fit_constrained needs to do the transformation
+        params, cov, res_constr = fit_constrained(self, R, q,
+                                                  start_params=start_params,
+                                                  fit_kwds=fit_kwds)
+        #create dummy results Instance, TODO: wire up properly
+        res = self.fit(start_params=params, maxiter=0) # we get a wrapper back
+        res._results.params = params
+        res._results.normalized_cov_params = cov
+        k_constr = len(q)
+        res._results.df_resid += k_constr
+        res._results.df_model -= k_constr
+        res._results.constraints = lc
+        res._results.k_constr = k_constr
+        res._results.results_constrained = res_constr
+        # TODO: the next is not the best. history should bin in results
+        res._results.model.history = res_constr.model.history
+        res._results.mu = res_constr.mu
+        return res
+
+
 class GLMResults(base.LikelihoodModelResults):
-    '''
+    """
     Class to contain GLM results.
 
     GLMResults inherits from statsmodels.LikelihoodModelResults
@@ -457,7 +820,8 @@ class GLMResults(base.LikelihoodModelResults):
         dot(exog, params)
     llf : float
         Value of the loglikelihood function evalued at params.
-        See statsmodels.families.family for distribution-specific loglikelihoods.
+        See statsmodels.families.family for distribution-specific
+        loglikelihoods.
     model : class instance
         Pointer to GLM model instance that called fit.
     mu : array
@@ -506,12 +870,13 @@ class GLMResults(base.LikelihoodModelResults):
 
     See Also
     --------
-    statsmodels.LikelihoodModelResults
-    '''
+    statsmodels.base.model.LikelihoodModelResults
+    """
 
     def __init__(self, model, params, normalized_cov_params, scale):
         super(GLMResults, self).__init__(model, params,
-                normalized_cov_params=normalized_cov_params, scale=scale)
+                                         normalized_cov_params=
+                                         normalized_cov_params, scale=scale)
         self.family = model.family
         self._endog = model.endog
         self.nobs = model.endog.shape[0]
@@ -524,14 +889,19 @@ def __init__(self, model, params, normalized_cov_params, scale):
         # are these intermediate results needed or can we just
         # call the model's attributes?
 
+        # for remove data and pickle without large arrays
+        self._data_attr.extend(['results_constrained'])
+        self.data_in_cache = getattr(self, 'data_in_cache', [])
+        self.data_in_cache.extend(['null'])
+
     @cache_readonly
     def resid_response(self):
         return self._data_weights * (self._endog-self.mu)
 
     @cache_readonly
     def resid_pearson(self):
-        return np.sqrt(self._data_weights) * (self._endog-self.mu)/\
-                        np.sqrt(self.family.variance(self.mu))
+        return (np.sqrt(self._data_weights) * (self._endog-self.mu) /
+                np.sqrt(self.family.variance(self.mu)))
 
     @cache_readonly
     def resid_working(self):
@@ -547,10 +917,9 @@ def resid_anscombe(self):
     def resid_deviance(self):
         return self.family.resid_dev(self._endog, self.mu)
 
-
     @cache_readonly
     def pearson_chi2(self):
-        chisq =  (self._endog- self.mu)**2 / self.family.variance(self.mu)
+        chisq = (self._endog - self.mu)**2 / self.family.variance(self.mu)
         chisq *= self._data_weights
         chisqsum = np.sum(chisq)
         return chisqsum
@@ -564,12 +933,13 @@ def null(self):
         endog = self._endog
         model = self.model
         exog = np.ones((len(endog), 1))
+        kwargs = {}
         if hasattr(model, 'offset'):
-            return GLM(endog, exog, offset=model.offset,
-                       family=self.family).fit().mu
-        elif hasattr(model, 'exposure'):
-            return GLM(endog, exog, exposure=model.exposure,
-                    family=self.family).fit().mu
+            kwargs['offset'] = model.offset
+        if hasattr(model, 'exposure'):
+            kwargs['exposure'] = model.exposure
+        if len(kwargs) > 0:
+            return GLM(endog, exog, family=self.family, **kwargs).fit().mu
         else:
             wls_model = lm.WLS(endog, exog, weights=self._data_weights)
             return wls_model.fit().fittedvalues
@@ -582,15 +952,20 @@ def deviance(self):
     def null_deviance(self):
         return self.family.deviance(self._endog, self.null)
 
+    @cache_readonly
+    def llnull(self):
+        return self.family.loglike(self._endog, self.null, scale=self.scale)
+
     @cache_readonly
     def llf(self):
         _modelfamily = self.family
         if isinstance(_modelfamily, families.NegativeBinomial):
+            lin_pred = np.dot(self.model.exog, self.params) +\
+                       self.model._offset_exposure
             val = _modelfamily.loglike(self.model.endog,
-                        fittedvalues = np.dot(self.model.exog,self.params))
+                                       lin_pred=lin_pred)
         else:
-            val = _modelfamily.loglike(self._endog, self.mu,
-                                    scale=self.scale)
+            val = _modelfamily.loglike(self._endog, self.mu, scale=self.scale)
         return val
 
     @cache_readonly
@@ -604,7 +979,7 @@ def bic(self):
     def remove_data(self):
         #GLM has alias/reference in result instance
         self._data_attr.extend([i for i in self.model._data_attr
-                                           if not '_data.' in i])
+                                if not '_data.' in i])
         super(self.__class__, self).remove_data()
 
         #TODO: what are these in results?
@@ -613,9 +988,9 @@ def remove_data(self):
 
     remove_data.__doc__ = base.LikelihoodModelResults.remove_data.__doc__
 
-
     def summary(self, yname=None, xname=None, title=None, alpha=.05):
-        """Summarize the Regression Results
+        """
+        Summarize the Regression Results
 
         Parameters
         -----------
@@ -643,14 +1018,15 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         """
 
         top_left = [('Dep. Variable:', None),
-                  ('Model:', None),
-                  ('Model Family:', [self.family.__class__.__name__]),
-                  ('Link Function:', [self.family.link.__class__.__name__]),
-                  ('Method:', ['IRLS']),
-                  ('Date:', None),
-                  ('Time:', None),
-                  ('No. Iterations:', ["%d" % self.fit_history['iteration']]),
-                  ]
+                    ('Model:', None),
+                    ('Model Family:', [self.family.__class__.__name__]),
+                    ('Link Function:', [self.family.link.__class__.__name__]),
+                    ('Method:', ['IRLS']),
+                    ('Date:', None),
+                    ('Time:', None),
+                    ('No. Iterations:',
+                     ["%d" % self.fit_history['iteration']]),
+                    ]
 
         top_right = [('No. Observations:', None),
                      ('Df Residuals:', None),
@@ -667,10 +1043,14 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         #create summary tables
         from statsmodels.iolib.summary import Summary
         smry = Summary()
-        smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
-                          yname=yname, xname=xname, title=title)
+        smry.add_table_2cols(self, gleft=top_left, gright=top_right,  # [],
+                             yname=yname, xname=xname, title=title)
         smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
-                             use_t=True)
+                              use_t=self.use_t)
+
+        if hasattr(self, 'constraints'):
+            smry.add_extra_txt(['Model has been estimated subject to linear '
+                          'equality constraints.'])
 
         #diagnostic table is not used yet:
         #smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
@@ -679,9 +1059,8 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
 
         return smry
 
-
     def summary2(self, yname=None, xname=None, title=None, alpha=.05,
-                float_format="%.4f"):
+                 float_format="%.4f"):
 
         """Experimental summary for regression Results
 
@@ -707,7 +1086,7 @@ def summary2(self, yname=None, xname=None, title=None, alpha=.05,
 
         See Also
         --------
-        statsmodels.iolib.summary.Summary : class to hold summary
+        statsmodels.iolib.summary2.Summary : class to hold summary
             results
 
         """
@@ -715,7 +1094,10 @@ def summary2(self, yname=None, xname=None, title=None, alpha=.05,
         from statsmodels.iolib import summary2
         smry = summary2.Summary()
         smry.add_base(results=self, alpha=alpha, float_format=float_format,
-                xname=xname, yname=yname, title=title)
+                      xname=xname, yname=yname, title=title)
+        if hasattr(self, 'constraints'):
+            smry.add_text('Model has been estimated subject to linear '
+                          'equality constraints.')
 
         return smry
 
@@ -734,7 +1116,6 @@ class GLMResultsWrapper(lm.RegressionResultsWrapper):
 
 if __name__ == "__main__":
     import statsmodels.api as sm
-    import numpy as np
     data = sm.datasets.longley.load()
     #data.exog = add_constant(data.exog)
     GLMmod = GLM(data.endog, data.exog).fit()
@@ -744,7 +1125,6 @@ class GLMResultsWrapper(lm.RegressionResultsWrapper):
 ##    print(GLMT[2])
     GLMTp = GLMmod.summary(title='Test GLM')
 
-
     """
 From Stata
 . webuse beetle
diff --git a/statsmodels/genmod/tests/gee_categorical_simulation_check.py b/statsmodels/genmod/tests/gee_categorical_simulation_check.py
new file mode 100644
index 00000000000..01b36d8fff1
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_categorical_simulation_check.py
@@ -0,0 +1,288 @@
+"""
+Assesment of Generalized Estimating Equations using simulation.
+
+This script checks ordinal and nominal models for multinomial data.
+
+See the generated file "gee_categorical_simulation_check.txt" for
+results.
+"""
+from statsmodels.compat.python import lrange
+import numpy as np
+from scipy import stats
+from statsmodels.genmod.generalized_estimating_equations import GEE,\
+    gee_setup_ordinal, gee_setup_nominal,\
+    gee_ordinal_starting_values, Multinomial
+from statsmodels.genmod.families import Binomial
+from statsmodels.genmod.dependence_structures import GlobalOddsRatio
+from .gee_gaussian_simulation_check import GEE_simulator
+
+
+class ordinal_simulator(GEE_simulator):
+
+    # The thresholds where the latent continuous process is cut to
+    # obtain the categorical values.
+    thresholds = None
+
+
+    def true_params(self):
+        return np.concatenate((self.thresholds, self.params))
+
+
+    def starting_values(self, nconstraints):
+        beta = gee_ordinal_starting_values(self.endog,
+                                           len(self.params))
+        if nconstraints > 0:
+            m = self.exog_ex.shape[1] - nconstraints
+            beta = beta[0:m]
+
+        return beta
+
+
+    def print_dparams(self, dparams_est):
+        OUT.write("Odds ratio estimate:   %8.4f\n" % dparams_est[0])
+        OUT.write("Odds ratio truth:      %8.4f\n" %
+                  self.dparams[0])
+        OUT.write("\n")
+
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize,2))
+            time.append(time1)
+
+            exog1 = np.random.normal(size=(gsize, len(self.params)))
+            exog.append(exog1)
+
+            lp = np.dot(exog1, self.params)
+
+            z = np.random.uniform(size=gsize)
+            z = np.log(z / (1 - z)) + lp
+            endog1 = np.array([np.sum(x > self.thresholds) for x in z])
+            endog.append(endog1)
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+        self.offset = np.zeros(len(self.endog), dtype=np.float64)
+
+
+class nominal_simulator(GEE_simulator):
+
+    def starting_values(self, nconstraints):
+        return None
+
+    def true_params(self):
+        return np.concatenate(self.params[:-1])
+
+    def print_dparams(self, dparams_est):
+        OUT.write("Odds ratio estimate:   %8.4f\n" % dparams_est[0])
+        OUT.write("Odds ratio truth:      %8.4f\n" % self.dparams[0])
+        OUT.write("\n")
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize,2))
+            time.append(time1)
+
+            exog1 = np.random.normal(size=(gsize, len(self.params[0])))
+            exog.append(exog1)
+
+            # Probabilities for each outcome
+            prob = [np.exp(np.dot(exog1, p)) for p in self.params]
+            prob = np.vstack(prob).T
+            prob /= prob.sum(1)[:, None]
+
+            m = len(self.params)
+            endog1 = []
+            for k in range(gsize):
+                pdist = stats.rv_discrete(values=(lrange(m),
+                                                  prob[k,:]))
+                endog1.append(pdist.rvs())
+
+            endog.append(np.asarray(endog1))
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog).astype(np.int32)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+        self.offset = np.zeros(len(self.endog), dtype=np.float64)
+
+
+
+def gendat_ordinal():
+
+    os = ordinal_simulator()
+    os.params = np.r_[0., 1]
+    os.ngroups = 200
+    os.thresholds = [1, 0, -1]
+    os.dparams = [1.,]
+    os.simulate()
+
+    data = np.concatenate((os.endog[:,None], os.exog,
+                           os.group[:,None]), axis=1)
+
+    os.endog_ex, os.exog_ex, os.intercepts, os.nthresh = \
+        gee_setup_ordinal(data, 0)
+
+    os.group_ex = os.exog_ex[:,-1]
+    os.exog_ex = os.exog_ex[:,0:-1]
+
+    os.exog_ex = np.concatenate((os.intercepts, os.exog_ex),
+                                axis=1)
+
+    va = GlobalOddsRatio(4, "ordinal")
+
+    lhs = np.array([[0., 0., 0, 1., 0.], [0., 0, 0, 0, 1]])
+    rhs = np.r_[0., 1]
+
+    return os, va, Binomial(), (lhs, rhs)
+
+
+def gendat_nominal():
+
+    ns = nominal_simulator()
+
+    # The last component of params must be identically zero
+    ns.params = [np.r_[0., 1], np.r_[-1., 0], np.r_[0., 0]]
+    ns.ngroups = 200
+    ns.dparams = [1., ]
+    ns.simulate()
+
+    data = np.concatenate((ns.endog[:,None], ns.exog,
+                           ns.group[:,None]), axis=1)
+
+    ns.endog_ex, ns.exog_ex, ns.exog_ne, ns.nlevel = \
+        gee_setup_nominal(data, 0, [3,])
+
+    ns.group_ex = ns.exog_ne[:,0]
+
+    va = GlobalOddsRatio(3, "nominal")
+
+    lhs = np.array([[0., 1., 1, 0],])
+    rhs = np.r_[0.,]
+
+    return ns, va, Multinomial(3), (lhs, rhs)
+
+
+if __name__ == '__main__':
+
+    nrep = 100
+
+    OUT = open("gee_categorical_simulation_check.txt", "w")
+
+    np.set_printoptions(formatter={'all': lambda x: "%8.3f" % x},
+                        suppress=True)
+
+    # Loop over data generating models
+    gendats = [gendat_nominal, gendat_ordinal]
+
+    for jg,gendat in enumerate(gendats):
+
+        dparams = []
+        params = []
+        std_errors = []
+        pvalues = []
+
+        for j in range(nrep):
+
+            da, va, mt, constraint = gendat()
+
+            beta = da.starting_values(0)
+
+            md = GEE(da.endog_ex, da.exog_ex, da.group_ex, None,
+                     mt, va)
+            mdf = md.fit(start_params = beta)
+
+            if mdf is None:
+                continue
+
+            scale_inv = 1 / md.estimate_scale()
+
+            dparams.append(np.r_[va.dparams, scale_inv])
+
+            params.append(np.asarray(mdf.params))
+            std_errors.append(np.asarray(mdf.standard_errors))
+
+            da, va, mt, constraint = gendat()
+
+            beta = da.starting_values(constraint[0].shape[0])
+
+            md = GEE(da.endog_ex, da.exog_ex, da.group_ex, None,
+                     mt, va, constraint=constraint)
+            mdf = md.fit(start_params = beta)
+
+            if mdf is None:
+                continue
+
+            score = md.score_test_results
+            pvalues.append(score["p-value"])
+
+        dparams_mean = np.array(sum(dparams) / len(dparams))
+
+        OUT.write("%s data.\n" % ("Nominal", "Ordinal")[jg])
+        OUT.write("%d runs converged successfully.\n" % len(pvalues))
+
+        OUT.write("Checking dependence parameters:\n")
+        da.print_dparams(dparams_mean)
+
+        params = np.array(params)
+        eparams = params.mean(0)
+        sdparams = params.std(0)
+        std_errors = np.array(std_errors)
+        std_errors = std_errors.mean(0)
+        true_params = da.true_params()
+
+        OUT.write("Checking parameter values:\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(eparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(true_params) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(eparams - true_params) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((eparams - true_params) / true_params)
+                  + "\n")
+        OUT.write("\n")
+
+        OUT.write("Checking standard errors:\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(sdparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(std_errors) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(sdparams - std_errors) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((sdparams - std_errors) / std_errors)
+                  + "\n")
+        OUT.write("\n")
+
+        OUT.write("Checking constrained estimation:\n")
+        OUT.write("Observed   Expected\n")
+
+        pvalues.sort()
+        for q in np.arange(0.1, 0.91, 0.1):
+            OUT.write("%10.3f %10.3f\n" %
+                      (pvalues[int(q*len(pvalues))], q))
+
+        OUT.write("=" * 80 + "\n\n")
+
+    OUT.close()
diff --git a/statsmodels/genmod/tests/gee_categorical_simulation_check.txt b/statsmodels/genmod/tests/gee_categorical_simulation_check.txt
new file mode 100644
index 00000000000..66ac0593999
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_categorical_simulation_check.txt
@@ -0,0 +1,5 @@
+Nominal data.
+99 runs converged successfully.
+Checking dependence parameters:
+Odds ratio estimate:     1.0028
+Odds ratio truth:        1.0000
diff --git a/statsmodels/genmod/tests/gee_gaussian_simulation_check.py b/statsmodels/genmod/tests/gee_gaussian_simulation_check.py
new file mode 100644
index 00000000000..40ffc294ee4
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_gaussian_simulation_check.py
@@ -0,0 +1,337 @@
+"""
+Assesment of Generalized Estimating Equations using simulation.
+
+This script checks Gaussian models.
+
+See the generated file "gee_gaussian_simulation_check.txt" for
+results.
+"""
+from statsmodels.compat.python import range, lrange, zip
+import scipy
+import numpy as np
+from itertools import product
+from statsmodels.genmod.families import Gaussian
+from statsmodels.genmod.generalized_estimating_equations import GEE
+from statsmodels.genmod.dependence_structures import Autoregressive, Nested
+
+class GEE_simulator(object):
+
+    #
+    # Parameters that must be defined
+    #
+
+    # Number of groups
+    ngroups = None
+
+    # Standard deviation of the pure errors
+    error_sd = None
+
+    # The regression coefficients
+    params = None
+
+    # The parameters defining the dependence structure
+    dep_params = None
+
+    # The true scale parameter
+    scale = None
+
+    #
+    # Output parameters
+    #
+
+    # Matrix of exogeneous data (rows are cases, columns are
+    # variables)
+    exog = None
+
+    # Matrix of endogeneous data (len(endog) = exog.shape[0])
+    endog = None
+
+    # Matrix of time information (time.shape[0] = len(endog))
+    time = None
+
+    # Group labels (len(groups) = len(endog))
+    group = None
+
+    # Group sizes are random within this range
+    group_size_range = [4, 11]
+
+    # dparams_est is dparams with scale_inv appended
+    def print_dparams(self, dparams_est):
+        raise NotImplementedError
+
+
+class AR_simulator(GEE_simulator):
+
+    # The distance function for determining AR correlations.
+    distfun = [lambda x, y: np.sqrt(np.sum((x-y)**2)),]
+
+
+    def print_dparams(self, dparams_est):
+        OUT.write("AR coefficient estimate:   %8.4f\n" %
+                  dparams_est[0])
+        OUT.write("AR coefficient truth:      %8.4f\n" %
+                  self.dep_params[0])
+        OUT.write("Error variance estimate:   %8.4f\n" %
+                  dparams_est[1])
+        OUT.write("Error variance truth:      %8.4f\n" %
+                  self.error_sd**2)
+        OUT.write("\n")
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize,2))
+            time.append(time1)
+
+            exog1 = np.random.normal(size=(gsize, 5))
+            exog1[:,0] = 1
+            exog.append(exog1)
+
+            # Pairwise distances within the cluster
+            distances = scipy.spatial.distance.cdist(time1, time1,
+                                                     self.distfun[0])
+
+            # Pairwise correlations within the cluster
+            correlations = self.dep_params[0]**distances
+            correlations_sr = np.linalg.cholesky(correlations)
+
+            errors = np.dot(correlations_sr, np.random.normal(size=gsize))
+
+            endog1 = np.dot(exog1, self.params) + errors * self.error_sd
+            endog.append(endog1)
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+
+
+
+class Nested_simulator(GEE_simulator):
+
+    # Vector containing list of nest sizes (used instead of
+    # group_size_range).
+    nest_sizes = None
+
+    # Matrix of nest id's (an output parameter)
+    id_matrix = None
+
+
+    def print_dparams(self, dparams_est):
+        for j in range(len(self.nest_sizes)):
+            OUT.write("Nest %d variance estimate:  %8.4f\n" % \
+                          (j+1, dparams_est[j]))
+            OUT.write("Nest %d variance truth:     %8.4f\n" % \
+                          (j+1, self.dep_params[j]))
+
+        OUT.write("Error variance estimate:   %8.4f\n" % \
+            (dparams_est[-1] - sum(dparams_est[0:-1])))
+        OUT.write("Error variance truth:      %8.4f\n" %
+                  self.error_sd**2)
+        OUT.write("\n")
+
+
+    def simulate(self):
+
+        group_effect_var = self.dep_params[0]
+
+        vcomp = self.dep_params[1:]
+        vcomp.append(0)
+
+        endog, exog, group, id_matrix = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            iterators = [lrange(n) for n in self.nest_sizes]
+
+            # The random effects
+            variances = [np.sqrt(v)*np.random.normal(size=n)
+                         for v,n in zip(vcomp, self.nest_sizes)]
+
+            gpe = np.random.normal() * np.sqrt(group_effect_var)
+
+            nest_all = []
+            for j in self.nest_sizes:
+                nest_all.append(set())
+
+            for nest in product(*iterators):
+
+                group.append(i)
+
+                # The sum of all random effects that apply to this
+                # unit
+                ref = gpe + sum([v[j] for v,j in zip(variances, nest)])
+
+                exog1 = np.random.normal(size=5)
+                exog1[0] = 1
+                exog.append(exog1)
+
+                error = ref + self.error_sd * np.random.normal()
+
+                endog1 = np.dot(exog1, self.params) + error
+                endog.append(endog1)
+
+                for j in range(len(nest)):
+                    nest_all[j].add(tuple(nest[0:j+1]))
+
+                nest1 = [len(x)-1 for x in nest_all]
+                id_matrix.append(nest1[0:-1])
+
+        self.exog = np.array(exog)
+        self.endog = np.array(endog)
+        self.group = np.array(group)
+        self.id_matrix = np.array(id_matrix)
+        self.time = np.zeros_like(self.endog)
+
+
+
+def gen_gendat_ar0(ar):
+    def gendat_ar0(msg = False):
+        ars = AR_simulator()
+        ars.ngroups = 200
+        ars.params = np.r_[0, -1, 1, 0, 0.5]
+        ars.error_sd = 2
+        ars.dep_params = [ar,]
+        ars.simulate()
+        return ars, Autoregressive()
+    return gendat_ar0
+
+def gen_gendat_ar1(ar):
+    def gendat_ar1():
+        ars = AR_simulator()
+        ars.ngroups = 200
+        ars.params = np.r_[0, -0.8, 1.2, 0, 0.5]
+        ars.error_sd = 2
+        ars.dep_params = [ar,]
+        ars.simulate()
+        return ars, Autoregressive()
+    return gendat_ar1
+
+def gendat_nested0():
+    ns = Nested_simulator()
+    ns.error_sd = 1.
+    ns.params = np.r_[0., 1, 1, -1, -1]
+    ns.ngroups = 50
+    ns.nest_sizes = [10, 5]
+    ns.dep_params = [2., 1.]
+    ns.simulate()
+    return ns, Nested(ns.id_matrix)
+
+def gendat_nested1():
+    ns = Nested_simulator()
+    ns.error_sd = 2.
+    ns.params = np.r_[0, 1, 1.3, -0.8, -1.2]
+    ns.ngroups = 50
+    ns.nest_sizes = [10, 5]
+    ns.dep_params = [1., 3.]
+    ns.simulate()
+    return ns, Nested(ns.id_matrix)
+
+
+if __name__ == "__main__":
+
+    try:
+        np.set_printoptions(formatter={'all': lambda x: "%8.3f" % x},
+                            suppress=True)
+    except TypeError:
+        # older numpy versions do not have formatter option
+        pass
+
+    OUT = open("gee_gaussian_simulation_check.txt", "w")
+
+    nrep = 100
+
+    gendats = [gen_gendat_ar0(ar) for ar in (0, 0.3, 0.6)]
+    gendats.extend([gen_gendat_ar1(ar) for ar in (0, 0.3, 0.6)])
+    gendats.extend([gendat_nested0, gendat_nested1])
+
+    lhs = np.array([[0., 1, 1, 0, 0],])
+    rhs = np.r_[0.,]
+
+    # Loop over data generating models
+    for gendat in gendats:
+
+        pvalues = []
+        params = []
+        std_errors = []
+        dep_params = []
+
+        for j in range(nrep):
+
+            da,va = gendat()
+            ga = Gaussian()
+
+            md = GEE(da.endog, da.exog, da.group, da.time, ga, va)
+            mdf = md.fit()
+
+            scale_inv = 1 / md.estimate_scale()
+            dep_params.append(np.r_[va.dep_params, scale_inv])
+            params.append(np.asarray(mdf.params))
+            std_errors.append(np.asarray(mdf.standard_errors()))
+
+            da,va = gendat()
+            ga = Gaussian()
+
+            md = GEE(da.endog, da.exog, da.group, da.time, ga, va,
+                     constraint=(lhs, rhs))
+            mdf = md.fit()
+            score = md.score_test_results
+            pvalue = score["p-value"]
+            pvalues.append(pvalue)
+
+        dparams_mean = np.array(sum(dep_params) / len(dep_params))
+        OUT.write("Checking dependence parameters:\n")
+        da.print_dparams(dparams_mean)
+
+        params = np.array(params)
+        eparams = params.mean(0)
+        sdparams = params.std(0)
+        std_errors = np.array(std_errors)
+        std_errors = std_errors.mean(0)
+
+        OUT.write("Checking parameter values:\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(eparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(da.params) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(eparams - da.params) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((eparams - da.params) / da.params)
+                  + "\n")
+        OUT.write("\n")
+
+        OUT.write("Checking standard errors\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(sdparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(std_errors) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(sdparams - std_errors) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((sdparams - std_errors) / std_errors)
+                  + "\n")
+        OUT.write("\n")
+
+        pvalues.sort()
+        OUT.write("Checking constrained estimation:\n")
+        OUT.write("Left hand side:\n")
+        OUT.write(np.array_str(lhs) + "\n")
+        OUT.write("Right hand side:\n")
+        OUT.write(np.array_str(rhs) + "\n")
+        OUT.write("Observed p-values   Expected Null p-values\n")
+        for q in np.arange(0.1, 0.91, 0.1):
+            OUT.write("%20.3f %20.3f\n" %
+                      (pvalues[int(q*len(pvalues))], q))
+
+        OUT.write("=" * 80 + "\n\n")
+
+    OUT.close()
diff --git a/statsmodels/genmod/tests/gee_gaussian_simulation_check.txt b/statsmodels/genmod/tests/gee_gaussian_simulation_check.txt
new file mode 100644
index 00000000000..620923094fe
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_gaussian_simulation_check.txt
@@ -0,0 +1,283 @@
+Checking dependence parameters:
+AR coefficient estimate:     0.0000
+AR coefficient truth:        0.0000
+Error variance estimate:     4.0006
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.005   -1.007    1.008   -0.003    0.494]
+Expected:            [   0.000   -1.000    1.000    0.000    0.500]
+Absolute difference: [  -0.005   -0.007    0.008   -0.003   -0.006]
+Relative difference: [    -inf    0.007    0.008     -inf   -0.012]
+
+Checking standard errors
+Observed:            [   0.054    0.056    0.056    0.052    0.053]
+Expected:            [   0.053    0.053    0.054    0.053    0.053]
+Absolute difference: [   0.001    0.003    0.002   -0.001   -0.001]
+Relative difference: [   0.023    0.052    0.042   -0.025   -0.012]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.169                0.100
+               0.284                0.200
+               0.406                0.300
+               0.488                0.400
+               0.600                0.500
+               0.679                0.600
+               0.732                0.700
+               0.810                0.800
+               0.910                0.900
+================================================================================
+
+Checking dependence parameters:
+AR coefficient estimate:     0.3766
+AR coefficient truth:        0.3000
+Error variance estimate:     3.9845
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.005   -1.002    0.998   -0.003    0.492]
+Expected:            [   0.000   -1.000    1.000    0.000    0.500]
+Absolute difference: [  -0.005   -0.002   -0.002   -0.003   -0.008]
+Relative difference: [    -inf    0.002   -0.002     -inf   -0.017]
+
+Checking standard errors
+Observed:            [   0.082    0.055    0.056    0.055    0.054]
+Expected:            [   0.080    0.054    0.053    0.053    0.053]
+Absolute difference: [   0.002    0.002    0.002    0.002    0.001]
+Relative difference: [   0.026    0.034    0.044    0.034    0.020]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.152                0.100
+               0.268                0.200
+               0.347                0.300
+               0.437                0.400
+               0.517                0.500
+               0.585                0.600
+               0.705                0.700
+               0.826                0.800
+               0.915                0.900
+================================================================================
+
+Checking dependence parameters:
+AR coefficient estimate:     0.6689
+AR coefficient truth:        0.6000
+Error variance estimate:     4.0118
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.009   -0.998    0.996   -0.002    0.493]
+Expected:            [   0.000   -1.000    1.000    0.000    0.500]
+Absolute difference: [  -0.009    0.002   -0.004   -0.002   -0.007]
+Relative difference: [    -inf   -0.002   -0.004     -inf   -0.014]
+
+Checking standard errors
+Observed:            [   0.102    0.050    0.046    0.050    0.044]
+Expected:            [   0.106    0.048    0.048    0.048    0.048]
+Absolute difference: [  -0.004    0.001   -0.002    0.002   -0.004]
+Relative difference: [  -0.040    0.031   -0.039    0.044   -0.075]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.064                0.100
+               0.126                0.200
+               0.191                0.300
+               0.329                0.400
+               0.474                0.500
+               0.609                0.600
+               0.676                0.700
+               0.840                0.800
+               0.934                0.900
+================================================================================
+
+Checking dependence parameters:
+AR coefficient estimate:     0.0000
+AR coefficient truth:        0.0000
+Error variance estimate:     3.9885
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.005   -0.798    1.195    0.005    0.505]
+Expected:            [   0.000   -0.800    1.200    0.000    0.500]
+Absolute difference: [  -0.005    0.002   -0.005    0.005    0.005]
+Relative difference: [    -inf   -0.002   -0.004      inf    0.011]
+
+Checking standard errors
+Observed:            [   0.044    0.047    0.054    0.059    0.057]
+Expected:            [   0.053    0.053    0.053    0.053    0.053]
+Absolute difference: [  -0.009   -0.005    0.001    0.006    0.004]
+Relative difference: [  -0.178   -0.098    0.014    0.106    0.071]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.000                0.100
+               0.000                0.200
+               0.000                0.300
+               0.000                0.400
+               0.000                0.500
+               0.000                0.600
+               0.000                0.700
+               0.000                0.800
+               0.000                0.900
+================================================================================
+
+Checking dependence parameters:
+AR coefficient estimate:     0.3810
+AR coefficient truth:        0.3000
+Error variance estimate:     3.9966
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.000   -0.797    1.200   -0.002    0.499]
+Expected:            [   0.000   -0.800    1.200    0.000    0.500]
+Absolute difference: [  -0.000    0.003    0.000   -0.002   -0.001]
+Relative difference: [    -inf   -0.004    0.000     -inf   -0.002]
+
+Checking standard errors
+Observed:            [   0.086    0.062    0.044    0.058    0.053]
+Expected:            [   0.080    0.053    0.053    0.054    0.054]
+Absolute difference: [   0.006    0.008   -0.009    0.004   -0.001]
+Relative difference: [   0.074    0.158   -0.165    0.081   -0.010]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.000                0.100
+               0.000                0.200
+               0.000                0.300
+               0.000                0.400
+               0.000                0.500
+               0.000                0.600
+               0.000                0.700
+               0.000                0.800
+               0.000                0.900
+================================================================================
+
+Checking dependence parameters:
+AR coefficient estimate:     0.6660
+AR coefficient truth:        0.6000
+Error variance estimate:     4.0002
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [  -0.007   -0.793    1.201   -0.006    0.501]
+Expected:            [   0.000   -0.800    1.200    0.000    0.500]
+Absolute difference: [  -0.007    0.007    0.001   -0.006    0.001]
+Relative difference: [    -inf   -0.009    0.001     -inf    0.003]
+
+Checking standard errors
+Observed:            [   0.109    0.048    0.046    0.047    0.049]
+Expected:            [   0.106    0.048    0.048    0.048    0.048]
+Absolute difference: [   0.002   -0.000   -0.001   -0.001    0.002]
+Relative difference: [   0.023   -0.007   -0.029   -0.018    0.034]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.000                0.100
+               0.000                0.200
+               0.000                0.300
+               0.000                0.400
+               0.000                0.500
+               0.000                0.600
+               0.000                0.700
+               0.000                0.800
+               0.000                0.900
+================================================================================
+
+Checking dependence parameters:
+Nest 1 variance estimate:    1.9752
+Nest 1 variance truth:       2.0000
+Nest 2 variance estimate:    1.0013
+Nest 2 variance truth:       1.0000
+Error variance estimate:     1.0089
+Error variance truth:        1.0000
+
+Checking parameter values:
+Observed:            [   0.024    1.002    0.999   -1.000   -1.001]
+Expected:            [   0.000    1.000    1.000   -1.000   -1.000]
+Absolute difference: [   0.024    0.002   -0.001   -0.000   -0.001]
+Relative difference: [     inf    0.002   -0.001    0.000    0.001]
+
+Checking standard errors
+Observed:            [   0.219    0.024    0.022    0.021    0.020]
+Expected:            [   0.204    0.021    0.022    0.021    0.022]
+Absolute difference: [   0.016    0.002    0.001   -0.001   -0.002]
+Relative difference: [   0.077    0.104    0.026   -0.041   -0.089]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.000                0.100
+               0.000                0.200
+               0.000                0.300
+               0.000                0.400
+               0.000                0.500
+               0.000                0.600
+               0.000                0.700
+               0.000                0.800
+               0.000                0.900
+================================================================================
+
+Checking dependence parameters:
+Nest 1 variance estimate:    0.9614
+Nest 1 variance truth:       1.0000
+Nest 2 variance estimate:    3.0497
+Nest 2 variance truth:       3.0000
+Error variance estimate:     4.0144
+Error variance truth:        4.0000
+
+Checking parameter values:
+Observed:            [   0.007    0.999    1.297   -0.799   -1.200]
+Expected:            [   0.000    1.000    1.300   -0.800   -1.200]
+Absolute difference: [   0.007   -0.001   -0.003    0.001    0.000]
+Relative difference: [     inf   -0.001   -0.002   -0.001   -0.000]
+
+Checking standard errors
+Observed:            [   0.142    0.047    0.044    0.044    0.040]
+Expected:            [   0.163    0.044    0.043    0.043    0.042]
+Absolute difference: [  -0.021    0.003    0.001    0.001   -0.002]
+Relative difference: [  -0.129    0.077    0.015    0.015   -0.052]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000    1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.000                0.100
+               0.000                0.200
+               0.000                0.300
+               0.000                0.400
+               0.000                0.500
+               0.000                0.600
+               0.000                0.700
+               0.000                0.800
+               0.000                0.900
+================================================================================
diff --git a/statsmodels/genmod/tests/gee_poisson_simulation_check.py b/statsmodels/genmod/tests/gee_poisson_simulation_check.py
new file mode 100644
index 00000000000..21c4b22af92
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_poisson_simulation_check.py
@@ -0,0 +1,284 @@
+"""
+Assesment of Generalized Estimating Equations using simulation.
+
+This script checks Poisson models.
+
+See the generated file "gee_poisson_simulation_check.txt" for results.
+"""
+
+from __future__ import print_function
+import numpy as np
+from statsmodels.genmod.families import Poisson
+from .gee_gaussian_simulation_check import GEE_simulator
+from statsmodels.genmod.generalized_estimating_equations import GEE
+from statsmodels.genmod.dependence_structures import Exchangeable,Independence
+
+
+class Exchangeable_simulator(GEE_simulator):
+    """
+    Simulate exchangeable Poisson data.
+
+    The data within a cluster are simulated as y_i = z_c + z_i.  The
+    z_c, and {z_i} are independent Poisson random variables with
+    expected values e_c and {e_i}, respectively.  In order for the
+    pairwise correlation to be equal to `f` for all pairs, we need
+
+         e_c / sqrt((e_c + e_i) * (e_c + e_j)) = f for all i, j.
+
+    By setting all e_i = e within a cluster, these equations can be
+    satisfied.  We thus need
+
+         e_c * (1 - f) = f * e,
+
+    which can be solved (non-uniquely) for e and e_c.
+    """
+
+    scale_inv = 1.
+
+    def print_dparams(self, dparams_est):
+        OUT.write("Estimated common pairwise correlation:   %8.4f\n" %
+                  dparams_est[0])
+        OUT.write("True common pairwise correlation:        %8.4f\n" %
+                  self.dparams[0])
+        OUT.write("Estimated inverse scale parameter:       %8.4f\n" %
+                  dparams_est[1])
+        OUT.write("True inverse scale parameter:            %8.4f\n" %
+                  self.scale_inv)
+        OUT.write("\n")
+
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        # Get a basis for the orthogonal complement to params.
+        f = np.sum(self.params**2)
+        u,s,vt = np.linalg.svd(np.eye(len(self.params)) -
+                               np.outer(self.params, self.params) / f)
+        params0 = u[:,np.flatnonzero(s > 1e-6)]
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize, 2))
+            time.append(time1)
+
+            e_c = np.random.uniform(low=1, high=10)
+            e = e_c * (1 - self.dparams[0]) / self.dparams[0]
+
+            common = np.random.poisson(e_c)
+            unique = np.random.poisson(e, gsize)
+            endog1 = common + unique
+            endog.append(endog1)
+
+            lpr = np.log(e_c + e) * np.ones(gsize)
+
+            # Create an exog matrix so that E[Y] = log(dot(exog1, params))
+            exog1 = np.outer(lpr, self.params) / np.sum(self.params**2)
+            emat = np.random.normal(size=(len(lpr), params0.shape[1]))
+            exog1 += np.dot(emat, params0.T)
+
+            exog.append(exog1)
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+
+
+class Overdispersed_simulator(GEE_simulator):
+    """
+    Use the negative binomial distribution to check GEE estimation
+    using the overdispered Poisson model with independent dependence.
+
+    Simulating
+        X = np.random.negative_binomial(n, p, size)
+    then EX = (1 - p) * n / p
+         Var(X) = (1 - p) * n / p**2
+
+    These equations can be inverted as follows:
+
+        p = E / V
+        n = E * p / (1 - p)
+
+    dparams[0] is the common correlation coefficient
+    """
+
+
+    def print_dparams(self, dparams_est):
+        OUT.write("Estimated inverse scale parameter:       %8.4f\n" %
+                  dparams_est[0])
+        OUT.write("True inverse scale parameter:            %8.4f\n" %
+                  self.scale_inv)
+        OUT.write("\n")
+
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        # Get a basis for the orthogonal complement to params.
+        f = np.sum(self.params**2)
+        u,s,vt = np.linalg.svd(np.eye(len(self.params)) -
+                               np.outer(self.params, self.params) / f)
+        params0 = u[:,np.flatnonzero(s > 1e-6)]
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize, 2))
+            time.append(time1)
+
+            exog1 = np.random.normal(size=(gsize, len(self.params)))
+            exog.append(exog1)
+
+            E = np.exp(np.dot(exog1, self.params))
+            V = E * self.scale_inv
+
+            p = E / V
+            n = E * p / (1 - p)
+
+            endog1 = np.random.negative_binomial(n, p, gsize)
+            endog.append(endog1)
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+
+
+
+def gendat_exchangeable():
+    exs = Exchangeable_simulator()
+    exs.params = np.r_[2., 0.2, 0.2, -0.1, -0.2]
+    exs.ngroups = 200
+    exs.dparams = [0.3,]
+    exs.simulate()
+    return exs, Exchangeable()
+
+def gendat_overdispersed():
+    exs = Overdispersed_simulator()
+    exs.params = np.r_[2., 0.2, 0.2, -0.1, -0.2]
+    exs.ngroups = 200
+    exs.scale_inv = 2.
+    exs.dparams = []
+    exs.simulate()
+    return exs, Independence()
+
+
+if __name__ == "__main__":
+
+    np.set_printoptions(formatter={'all': lambda x: "%8.3f" % x},
+                        suppress=True)
+
+    OUT = open("gee_poisson_simulation_check.txt", "w")
+
+    nrep = 100
+
+    gendats = [gendat_exchangeable, gendat_overdispersed]
+
+    lhs = np.array([[0., 1, -1, 0, 0],])
+    rhs = np.r_[0.0,]
+
+    # Loop over data generating models
+    for gendat in gendats:
+
+        pvalues = []
+        params = []
+        std_errors = []
+        dparams = []
+
+        for j in range(nrep):
+
+            da, va = gendat()
+            ga = Poisson()
+
+            # Poisson seems to be more sensitive to starting values,
+            # so we run the independence model first.
+            md = GEE(da.endog, da.exog, da.group, da.time, ga,
+                     Independence())
+            mdf = md.fit()
+
+            md = GEE(da.endog, da.exog, da.group, da.time, ga, va)
+            mdf = md.fit(start_params = mdf.params)
+            if mdf is None or (not mdf.converged):
+                print("Failed to converge")
+                continue
+
+            scale_inv = 1. / md.estimate_scale()
+            dparams.append(np.r_[va.dparams, scale_inv])
+            params.append(np.asarray(mdf.params))
+            std_errors.append(np.asarray(mdf.standard_errors))
+
+            da,va = gendat()
+            ga = Poisson()
+
+            md = GEE(da.endog, da.exog, da.group, da.time, ga, va,
+                     constraint=(lhs, rhs))
+            mdf = md.fit()
+            if mdf is None or (not mdf.converged):
+                print("Failed to converge")
+                continue
+
+            score = md.score_test_results
+            pvalue = score["p-value"]
+            pvalues.append(pvalue)
+
+        dparams_mean = np.array(sum(dparams) / len(dparams))
+        OUT.write("Results based on %d successful fits out of %d data sets.\n\n"
+                  % (len(dparams), nrep))
+        OUT.write("Checking dependence parameters:\n")
+        da.print_dparams(dparams_mean)
+
+        params = np.array(params)
+        eparams = params.mean(0)
+        sdparams = params.std(0)
+        std_errors = np.array(std_errors)
+        std_errors = std_errors.mean(0)
+
+        OUT.write("Checking parameter values:\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(eparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(da.params) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(eparams - da.params) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((eparams - da.params) / da.params)
+                  + "\n")
+        OUT.write("\n")
+
+        OUT.write("Checking standard errors\n")
+        OUT.write("Observed:            ")
+        OUT.write(np.array_str(sdparams) + "\n")
+        OUT.write("Expected:            ")
+        OUT.write(np.array_str(std_errors) + "\n")
+        OUT.write("Absolute difference: ")
+        OUT.write(np.array_str(sdparams - std_errors) + "\n")
+        OUT.write("Relative difference: ")
+        OUT.write(np.array_str((sdparams - std_errors) / std_errors)
+                  + "\n")
+        OUT.write("\n")
+
+        pvalues.sort()
+        OUT.write("Checking constrained estimation:\n")
+        OUT.write("Left hand side:\n")
+        OUT.write(np.array_str(lhs) + "\n")
+        OUT.write("Right hand side:\n")
+        OUT.write(np.array_str(rhs) + "\n")
+        OUT.write("Observed p-values   Expected Null p-values\n")
+        for q in np.arange(0.1, 0.91, 0.1):
+            OUT.write("%20.3f %20.3f\n" %
+                      (pvalues[int(q*len(pvalues))], q))
+
+        OUT.write("=" * 80 + "\n\n")
+
+    OUT.close()
diff --git a/statsmodels/genmod/tests/gee_poisson_simulation_check.txt b/statsmodels/genmod/tests/gee_poisson_simulation_check.txt
new file mode 100644
index 00000000000..069b1f829be
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_poisson_simulation_check.txt
@@ -0,0 +1,67 @@
+Results based on 100 successful fits out of 100 data sets.Checking dependence parameters:
+Estimated common pairwise correlation:     0.2993
+True common pairwise correlation:          0.3000
+Estimated inverse scale parameter:         1.0104
+True inverse scale parameter:              1.0000
+
+Checking parameter values:
+Observed:            [   1.999    0.198    0.200   -0.100   -0.200]
+Expected:            [   2.000    0.200    0.200   -0.100   -0.200]
+Absolute difference: [  -0.001   -0.002   -0.000    0.000    0.000]
+Relative difference: [  -0.000   -0.009   -0.000   -0.004   -0.002]
+
+Checking standard errors
+Observed:            [   0.007    0.006    0.006    0.006    0.006]
+Expected:            [   0.007    0.006    0.006    0.006    0.006]
+Absolute difference: [   0.000    0.000    0.000    0.000   -0.000]
+Relative difference: [   0.046    0.050    0.077    0.042   -0.007]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000   -1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.127                0.100
+               0.206                0.200
+               0.250                0.300
+               0.329                0.400
+               0.403                0.500
+               0.484                0.600
+               0.565                0.700
+               0.645                0.800
+               0.832                0.900
+================================================================================
+
+Results based on 98 successful fits out of 100 data sets.Checking dependence parameters:
+Estimated inverse scale parameter:         1.9792
+True inverse scale parameter:              2.0000
+
+Checking parameter values:
+Observed:            [   2.001    0.198    0.199   -0.100   -0.199]
+Expected:            [   2.000    0.200    0.200   -0.100   -0.200]
+Absolute difference: [   0.001   -0.002   -0.001    0.000    0.001]
+Relative difference: [   0.000   -0.010   -0.007   -0.004   -0.004]
+
+Checking standard errors
+Observed:            [   0.007    0.014    0.016    0.016    0.014]
+Expected:            [   0.007    0.014    0.014    0.013    0.013]
+Absolute difference: [   0.000    0.001    0.002    0.002    0.001]
+Relative difference: [   0.010    0.057    0.150    0.185    0.043]
+
+Checking constrained estimation:
+Left hand side:
+[[   0.000    1.000   -1.000    0.000    0.000]]
+Right hand side:
+[   0.000]
+Observed p-values   Expected Null p-values
+               0.096                0.100
+               0.147                0.200
+               0.241                0.300
+               0.281                0.400
+               0.459                0.500
+               0.530                0.600
+               0.649                0.700
+               0.849                0.800
+               0.879                0.900
+================================================================================
diff --git a/statsmodels/genmod/tests/gee_simulation_check.py b/statsmodels/genmod/tests/gee_simulation_check.py
new file mode 100644
index 00000000000..9a9139c5949
--- /dev/null
+++ b/statsmodels/genmod/tests/gee_simulation_check.py
@@ -0,0 +1,339 @@
+"""
+Assesment of Generalized Estimating Equations using simulation.
+
+Only Gaussian models are currently checked.
+
+See the generated file "gee_simulation_check.txt" for results.
+"""
+from statsmodels.compat.python import range, lrange, zip
+import scipy
+import numpy as np
+from itertools import product
+from statsmodels.genmod.families import Gaussian
+from statsmodels.genmod.generalized_estimating_equations import GEE
+from statsmodels.genmod.dependence_structures import Autoregressive, Nested
+
+np.set_printoptions(formatter={'all': lambda x: "%8.3f" % x},
+                    suppress=True)
+
+
+OUT = open("gee_simulation_check.txt", "w")
+
+class GEE_simulator(object):
+
+    #
+    # Parameters that must be defined
+    #
+
+    # Number of groups
+    ngroups = None
+
+    # Standard deviation of the pure errors
+    error_sd = None
+
+    # The regression coefficients
+    params = None
+
+    # The parameters defining the dependence structure
+    dparams = None
+
+
+    #
+    # Output parameters
+    #
+
+    # Matrix of exogeneous data (rows are cases, columns are
+    # variables)
+    exog = None
+
+    # Matrix of endogeneous data (len(endog) = exog.shape[0])
+    endog = None
+
+    # Matrix of time information (time.shape[0] = len(endog))
+    time = None
+
+    # Group labels (len(groups) = len(endog))
+    group = None
+
+    # Group sizes are random within this range
+    group_size_range = [4, 11]
+
+    # dparams_est is dparams with scale_inv appended
+    def print_dparams(self, dparams_est):
+        raise NotImplementedError
+
+
+class AR_simulator(GEE_simulator):
+
+    # The distance function for determining AR correlations.
+    distfun = [lambda x, y: np.sqrt(np.sum((x-y)**2)),]
+
+
+    def print_dparams(self, dparams_est):
+        OUT.write("AR coefficient estimate:   %8.4f\n" %
+                  dparams_est[0])
+        OUT.write("AR coefficient truth:      %8.4f\n" %
+                  self.dparams[0])
+        OUT.write("Error variance estimate:   %8.4f\n" %
+                  dparams_est[1])
+        OUT.write("Error variance truth:      %8.4f\n" %
+                  self.error_sd**2)
+        OUT.write("\n")
+
+    def simulate(self):
+
+        endog, exog, group, time = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            gsize = np.random.randint(self.group_size_range[0],
+                                      self.group_size_range[1])
+
+            group.append([i,] * gsize)
+
+            time1 = np.random.normal(size=(gsize,2))
+            time.append(time1)
+
+            exog1 = np.random.normal(size=(gsize, 5))
+            exog1[:,0] = 1
+            exog.append(exog1)
+
+            # Pairwise distances within the cluster
+            distances = scipy.spatial.distance.cdist(time1, time1,
+                                                     self.distfun[0])
+
+            # Pairwise correlations within the cluster
+            correlations = self.dparams[0]**distances
+            correlations_sr = np.linalg.cholesky(correlations)
+
+            errors = np.dot(correlations_sr, np.random.normal(size=gsize))
+
+            endog1 = np.dot(exog1, self.params) + errors * self.error_sd
+            endog.append(endog1)
+
+        self.exog = np.concatenate(exog, axis=0)
+        self.endog = np.concatenate(endog)
+        self.time = np.concatenate(time, axis=0)
+        self.group = np.concatenate(group)
+
+
+
+class Nested_simulator(GEE_simulator):
+
+    # Vector containing list of nest sizes (used instead of
+    # group_size_range).
+    nest_sizes = None
+
+    # Matrix of nest id's (an output parameter)
+    id_matrix = None
+
+
+    def print_dparams(self, dparams_est):
+        for j in range(len(self.nest_sizes)):
+            OUT.write("Nest %d variance estimate:  %8.4f\n" % \
+                          (j+1, dparams_est[j]))
+            OUT.write("Nest %d variance truth:     %8.4f\n" % \
+                          (j+1, self.dparams[j]))
+
+        OUT.write("Error variance estimate:   %8.4f\n" % \
+            (dparams_est[-1] - sum(dparams_est[0:-1])))
+        OUT.write("Error variance truth:      %8.4f\n" %
+                  self.error_sd**2)
+        OUT.write("\n")
+
+
+    def simulate(self):
+
+        group_effect_var = self.dparams[0]
+
+        vcomp = self.dparams[1:]
+        vcomp.append(0)
+
+        endog, exog, group, id_matrix = [], [], [], []
+
+        for i in range(self.ngroups):
+
+            iterators = [lrange(n) for n in self.nest_sizes]
+
+            # The random effects
+            variances = [np.sqrt(v)*np.random.normal(size=n)
+                         for v,n in zip(vcomp, self.nest_sizes)]
+
+            gpe = np.random.normal() * np.sqrt(group_effect_var)
+
+            nest_all = []
+            for j in self.nest_sizes:
+                nest_all.append(set())
+
+            for nest in product(*iterators):
+
+                group.append(i)
+
+                # The sum of all random effects that apply to this
+                # unit
+                ref = gpe + sum([v[j] for v,j in zip(variances, nest)])
+
+                exog1 = np.random.normal(size=5)
+                exog1[0] = 1
+                exog.append(exog1)
+
+                error = ref + self.error_sd * np.random.normal()
+
+                endog1 = np.dot(exog1, self.params) + error
+                endog.append(endog1)
+
+                for j in range(len(nest)):
+                    nest_all[j].add(tuple(nest[0:j+1]))
+
+                nest1 = [len(x)-1 for x in nest_all]
+                id_matrix.append(nest1[0:-1])
+
+        self.exog = np.array(exog)
+        self.endog = np.array(endog)
+        self.group = np.array(group)
+        self.id_matrix = np.array(id_matrix)
+        self.time = np.zeros_like(self.endog)
+
+
+
+
+
+
+
+def check_constraint(da, va, ga):
+    """
+    Check the score testing of the parameter constraints.
+    """
+
+
+
+
+
+def gen_gendat_ar0(ar):
+    def gendat_ar0(msg = False):
+        ars = AR_simulator()
+        ars.ngroups = 200
+        ars.params = np.r_[0, -1, 1, 0, 0.5]
+        ars.error_sd = 2
+        ars.dparams = [ar,]
+        ars.simulate()
+        return ars, Autoregressive()
+    return gendat_ar0
+
+def gen_gendat_ar1(ar):
+    def gendat_ar1():
+        ars = AR_simulator()
+        ars.ngroups = 200
+        ars.params = np.r_[0, -0.8, 1.2, 0, 0.5]
+        ars.error_sd = 2
+        ars.dparams = [ar,]
+        ars.simulate()
+        return ars, Autoregressive()
+    return gendat_ar1
+
+def gendat_nested0():
+    ns = Nested_simulator()
+    ns.error_sd = 1.
+    ns.params = np.r_[0., 1, 1, -1, -1]
+    ns.ngroups = 50
+    ns.nest_sizes = [10, 5]
+    ns.dparams = [2., 1.]
+    ns.simulate()
+    return ns, Nested(ns.id_matrix)
+
+def gendat_nested1():
+    ns = Nested_simulator()
+    ns.error_sd = 2.
+    ns.params = np.r_[0, 1, 1.3, -0.8, -1.2]
+    ns.ngroups = 50
+    ns.nest_sizes = [10, 5]
+    ns.dparams = [1., 3.]
+    ns.simulate()
+    return ns, Nested(ns.id_matrix)
+
+
+nrep = 100
+
+gendats = [gen_gendat_ar0(ar) for ar in (0, 0.3, 0.6)]
+gendats.extend([gen_gendat_ar1(ar) for ar in (0, 0.3, 0.6)])
+gendats.extend([gendat_nested0, gendat_nested1])
+
+lhs = np.array([[0., 1, 1, 0, 0],])
+rhs = np.r_[0.,]
+
+# Loop over data generating models
+for gendat in gendats:
+
+    pvalues = []
+    params = []
+    std_errors = []
+    dparams = []
+
+    for j in range(nrep):
+
+        da,va = gendat()
+        ga = Gaussian()
+
+        md = GEE(da.endog, da.exog, da.group, da.time, ga, va)
+        mdf = md.fit()
+
+        scale_inv = 1 / md.estimate_scale()
+        dparams.append(np.r_[va.dparams, scale_inv])
+        params.append(np.asarray(mdf.params))
+        std_errors.append(np.asarray(mdf.standard_errors))
+
+        da,va = gendat()
+        ga = Gaussian()
+
+        md = GEE(da.endog, da.exog, da.group, da.time, ga, va,
+                 constraint=(lhs, rhs))
+        mdf = md.fit()
+        score = md.score_test_results
+        pvalue = score["p-value"]
+        pvalues.append(pvalue)
+
+    dparams_mean = np.array(sum(dparams) / len(dparams))
+    OUT.write("Checking dependence parameters:\n")
+    da.print_dparams(dparams_mean)
+
+    params = np.array(params)
+    eparams = params.mean(0)
+    sdparams = params.std(0)
+    std_errors = np.array(std_errors)
+    std_errors = std_errors.mean(0)
+
+    OUT.write("Checking parameter values:\n")
+    OUT.write("Observed:            ")
+    OUT.write(np.array_str(eparams) + "\n")
+    OUT.write("Expected:            ")
+    OUT.write(np.array_str(da.params) + "\n")
+    OUT.write("Absolute difference: ")
+    OUT.write(np.array_str(eparams - da.params) + "\n")
+    OUT.write("Relative difference: ")
+    OUT.write(np.array_str((eparams - da.params) / da.params) + "\n")
+    OUT.write("\n")
+
+    OUT.write("Checking standard errors\n")
+    OUT.write("Observed:            ")
+    OUT.write(np.array_str(sdparams) + "\n")
+    OUT.write("Expected:            ")
+    OUT.write(np.array_str(std_errors) + "\n")
+    OUT.write("Absolute difference: ")
+    OUT.write(np.array_str(sdparams - std_errors) + "\n")
+    OUT.write("Relative difference: ")
+    OUT.write(np.array_str((sdparams - std_errors) / std_errors) + "\n")
+    OUT.write("\n")
+
+    pvalues.sort()
+    OUT.write("Checking constrained estimation:\n")
+    OUT.write("Left hand side:\n")
+    OUT.write(np.array_str(lhs) + "\n")
+    OUT.write("Right hand side:\n")
+    OUT.write(np.array_str(rhs) + "\n")
+    OUT.write("Observed p-values   Expected Null p-values\n")
+    for q in np.arange(0.1, 0.91, 0.1):
+        OUT.write("%20.3f %20.3f\n" % (pvalues[int(q*len(pvalues))], q))
+
+    OUT.write("=" * 80 + "\n\n")
+
+OUT.close()
diff --git a/statsmodels/genmod/tests/results/epil.csv b/statsmodels/genmod/tests/results/epil.csv
new file mode 100644
index 00000000000..829c4ec33d5
--- /dev/null
+++ b/statsmodels/genmod/tests/results/epil.csv
@@ -0,0 +1,237 @@
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diff --git a/statsmodels/genmod/tests/results/gee_generate_tests.py b/statsmodels/genmod/tests/results/gee_generate_tests.py
new file mode 100644
index 00000000000..b08ef1359d3
--- /dev/null
+++ b/statsmodels/genmod/tests/results/gee_generate_tests.py
@@ -0,0 +1,239 @@
+from statsmodels.compat.python import range
+import numpy as np
+from scipy.stats.distributions import norm
+
+def generate_logistic():
+
+    # Number of clusters
+    nclust = 100
+
+    # Regression coefficients
+    beta = np.array([1,-2,1], dtype=np.float64)
+
+    ## Covariate correlations
+    r = 0.4
+
+    ## Cluster effects of covariates
+    rx = 0.5
+
+    ## Within-cluster outcome dependence
+    re = 0.3
+
+    p = len(beta)
+
+    OUT = open("gee_logistic_1.csv", "w")
+
+    for i in range(nclust):
+
+        n = np.random.randint(3, 6) # Cluster size
+
+        x = np.random.normal(size=(n,p))
+        x = rx*np.random.normal() + np.sqrt(1-rx**2)*x
+        x[:,2] = r*x[:,1] + np.sqrt(1-r**2)*x[:,2]
+        pr = 1/(1+np.exp(-np.dot(x, beta)))
+        z = re*np.random.normal() +\
+            np.sqrt(1-re**2)*np.random.normal(size=n)
+        u = norm.cdf(z)
+        y = 1*(u < pr)
+
+        for j in range(n):
+            OUT.write("%d,%d," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+
+
+def generate_linear():
+
+    # Number of clusters
+    nclust = 100
+
+    # Regression coefficients
+    beta = np.array([1,-2,1], dtype=np.float64)
+
+    ## Within cluster covariate correlations
+    r = 0.4
+
+    ## Between cluster covariate effects
+    rx = 0.5
+
+    ## Within-cluster outcome dependence
+    re = 0.3
+
+    p = len(beta)
+
+    OUT = open("gee_linear_1.csv", "w")
+
+    for i in range(nclust):
+
+        n = np.random.randint(3, 6) # Cluster size
+
+        x = np.random.normal(size=(n,p))
+        x = rx*np.random.normal() + np.sqrt(1-rx**2)*x
+        x[:,2] = r*x[:,1] + np.sqrt(1-r**2)*x[:,2]
+        e = np.sqrt(1-re**2)*np.random.normal(size=n) +\
+            re*np.random.normal()
+        y = np.dot(x, beta) + np.random.normal(size=n)
+
+        for j in range(n):
+            OUT.write("%d,%d," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+
+def generate_nested_linear():
+
+    # Number of clusters (clusters have 10 values, partitioned into 2
+    # subclusters of size 5).
+    nclust = 200
+
+    # Regression coefficients
+    beta = np.array([1,-2,1], dtype=np.float64)
+
+    ## Top level cluster variance component
+    v1 = 1
+
+    ## Subcluster variance component
+    v2 = 0.5
+
+    ## Error variance component
+    v3 = 1.5
+
+    p = len(beta)
+
+    OUT = open("gee_nested_linear_1.csv", "w")
+
+    for i in range(nclust):
+
+        x = np.random.normal(size=(10,p))
+        y = np.dot(x, beta)
+
+        y += np.sqrt(v1)*np.random.normal()
+        y[0:5] += np.sqrt(v2)*np.random.normal()
+        y[5:10] += np.sqrt(v2)*np.random.normal()
+        y += np.sqrt(v3)*np.random.normal(size=10)
+
+        for j in range(10):
+            OUT.write("%d,%.3f," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+
+def generate_ordinal():
+
+    ## Regression coefficients
+    beta = np.zeros(5, dtype=np.float64)
+    beta[2] = 1
+    beta[4] = -1
+
+    rz = 0.5
+
+    OUT = open("gee_ordinal_1.csv", "w")
+
+    for i in range(200):
+
+        n = np.random.randint(3, 6) # Cluster size
+
+        x = np.random.normal(size=(n,5))
+        for j in range(5):
+            x[:,j] += np.random.normal()
+        pr = np.dot(x, beta)
+        pr = np.array([1,0,-0.5]) + pr[:,None]
+        pr = 1 / (1 + np.exp(-pr))
+
+        z = rz*np.random.normal() +\
+            np.sqrt(1-rz**2)*np.random.normal(size=n)
+        u = norm.cdf(z)
+
+        y = (u[:,None] > pr).sum(1)
+
+        for j in range(n):
+            OUT.write("%d,%d," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+def generate_nominal():
+
+    ## Regression coefficients
+    beta1 = np.r_[0.5, 0.5]
+    beta2 = np.r_[-1, -0.5]
+    p = len(beta1)
+
+    rz = 0.5
+
+    OUT = open("gee_nominal_1.csv", "w")
+
+    for i in range(200):
+
+        n = np.random.randint(3, 6) # Cluster size
+
+        x = np.random.normal(size=(n,p))
+        x[:,0] = 1
+        for j in range(1,x.shape[1]):
+            x[:,j] += np.random.normal()
+        pr1 = np.exp(np.dot(x, beta1))[:,None]
+        pr2 = np.exp(np.dot(x, beta2))[:,None]
+        den = 1 + pr1 + pr2
+        pr = np.hstack((pr1/den, pr2/den, 1/den))
+        cpr = np.cumsum(pr, 1)
+
+        z = rz*np.random.normal() +\
+            np.sqrt(1-rz**2)*np.random.normal(size=n)
+        u = norm.cdf(z)
+
+        y = (u[:,None] > cpr).sum(1)
+
+        for j in range(n):
+            OUT.write("%d,%d," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+def generate_poisson():
+
+    ## Regression coefficients
+    beta = np.zeros(5, dtype=np.float64)
+    beta[2] = 0.5
+    beta[4] = -0.5
+
+    nclust = 100
+
+    rz = 0.5
+
+    OUT = open("gee_poisson_1.csv", "w")
+
+    for i in range(nclust):
+
+        n = np.random.randint(3, 6) # Cluster size
+
+        x = np.random.normal(size=(n,5))
+        for j in range(5):
+            x[:,j] += np.random.normal()
+        lp = np.dot(x, beta)
+        E = np.exp(lp)
+        y = [np.random.poisson(e) for e in E]
+        y = np.array(y)
+
+        for j in range(n):
+            OUT.write("%d,%d," % (i, y[j]))
+            OUT.write(",".join(["%.3f" % b for b in x[j,:]]) + "\n")
+
+    OUT.close()
+
+
+if __name__ == '__main__':
+    #generate_logistic()
+    #generate_linear()
+    #generate_ordinal()
+    #generate_poisson()
+    generate_nested_linear()
+    #generate_nominal()
diff --git a/statsmodels/genmod/tests/results/gee_linear_1.csv b/statsmodels/genmod/tests/results/gee_linear_1.csv
new file mode 100644
index 00000000000..58eafcf027d
--- /dev/null
+++ b/statsmodels/genmod/tests/results/gee_linear_1.csv
@@ -0,0 +1,413 @@
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diff --git a/statsmodels/genmod/tests/results/gee_logistic_1.csv b/statsmodels/genmod/tests/results/gee_logistic_1.csv
new file mode 100644
index 00000000000..6ef8e065ff4
--- /dev/null
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diff --git a/statsmodels/genmod/tests/results/gee_nested_linear_1.csv b/statsmodels/genmod/tests/results/gee_nested_linear_1.csv
new file mode 100644
index 00000000000..33e09b04ae0
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diff --git a/statsmodels/genmod/tests/results/gee_nominal_1.csv b/statsmodels/genmod/tests/results/gee_nominal_1.csv
new file mode 100644
index 00000000000..61831f25c7b
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diff --git a/statsmodels/genmod/tests/results/gee_ordinal_1.csv b/statsmodels/genmod/tests/results/gee_ordinal_1.csv
new file mode 100644
index 00000000000..e58bf64e756
--- /dev/null
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diff --git a/statsmodels/genmod/tests/results/gee_poisson_1.csv b/statsmodels/genmod/tests/results/gee_poisson_1.csv
new file mode 100644
index 00000000000..08fee180891
--- /dev/null
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diff --git a/statsmodels/genmod/tests/results/results_glm.py b/statsmodels/genmod/tests/results/results_glm.py
index 5d5e8d76f15..2e0d4bfff59 100644
--- a/statsmodels/genmod/tests/results/results_glm.py
+++ b/statsmodels/genmod/tests/results/results_glm.py
@@ -6,8 +6,8 @@
 IRLS.
 """
 import numpy as np
-from statsmodels.compatnp.py3k import asbytes
-import glm_test_resids
+from statsmodels.compat.python import asbytes
+from . import glm_test_resids
 import os
 from statsmodels.api import add_constant, categorical
 
diff --git a/statsmodels/genmod/tests/test_gee.py b/statsmodels/genmod/tests/test_gee.py
new file mode 100644
index 00000000000..36add2c3918
--- /dev/null
+++ b/statsmodels/genmod/tests/test_gee.py
@@ -0,0 +1,753 @@
+"""
+Test functions for GEE
+
+External comparisons are to R.  The statmodels GEE implementation
+should generally agree with the R GEE implementation for the
+independence and exchangeable correlation structures.  For other
+correlation structures, the details of the correlation estimation
+differ among implementations and the results will not agree exactly.
+"""
+
+from statsmodels.compat import lrange
+import numpy as np
+import os
+from numpy.testing import assert_almost_equal
+from statsmodels.genmod.generalized_estimating_equations import (GEE,
+     OrdinalGEE, NominalGEE, GEEMargins, Multinomial)
+from statsmodels.genmod.families import Gaussian, Binomial, Poisson
+from statsmodels.genmod.dependence_structures import (Exchangeable,
+    Independence, GlobalOddsRatio, Autoregressive, Nested)
+import pandas as pd
+import statsmodels.formula.api as sm
+from scipy.stats.distributions import norm
+
+def load_data(fname, icept=True):
+    """
+    Load a data set from the results directory.  The data set should
+    be a CSV file with the following format:
+
+    Column 0: Group indicator
+    Column 1: endog variable
+    Columns 2-end: exog variables
+
+    If `icept` is True, an intercept is prepended to the exog
+    variables.
+    """
+
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    Z = np.genfromtxt(os.path.join(cur_dir, 'results', fname),
+                      delimiter=",")
+
+    group = Z[:,0]
+    endog = Z[:,1]
+    exog = Z[:,2:]
+
+    if icept:
+        exog = np.concatenate((np.ones((exog.shape[0],1)), exog),
+                              axis=1)
+
+    return endog,exog,group
+
+
+class TestGEE(object):
+
+
+    def test_margins(self):
+
+        n = 300
+        exog = np.random.normal(size=(n, 4))
+        exog[:,0] = 1
+        exog[:,1] = 1*(exog[:,2] < 0)
+
+        group = np.kron(np.arange(n/4), np.ones(4))
+        time = np.zeros((n, 1))
+
+        beta = np.r_[0, 1, -1, 0.5]
+        lpr = np.dot(exog, beta)
+        prob = 1 / (1 + np.exp(-lpr))
+
+        endog = 1*(np.random.uniform(size=n) < prob)
+
+        fa = Binomial()
+        ex = Exchangeable()
+
+        md = GEE(endog, exog, group, time, fa, ex)
+        mdf = md.fit()
+
+        marg = GEEMargins(mdf, ())
+        marg.summary()
+
+
+    # This is in the release announcement for version 0.6.
+    def test_poisson_epil(self):
+
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        fname = os.path.join(cur_dir, "results", "epil.csv")
+        data = pd.read_csv(fname)
+
+        fam = Poisson()
+        ind = Independence()
+        mod1 = GEE.from_formula("y ~ age + trt + base", data["subject"],
+                                data, cov_struct=ind, family=fam)
+        rslt1 = mod1.fit()
+
+        # Coefficients should agree with GLM
+        from statsmodels.genmod.generalized_linear_model import GLM
+        from statsmodels.genmod import families
+
+        mod2 = GLM.from_formula("y ~ age + trt + base", data,
+                               family=families.Poisson())
+        rslt2 = mod2.fit(scale="X2")
+
+        assert_almost_equal(rslt1.params, rslt2.params, decimal=6)
+        assert_almost_equal(rslt1.scale, rslt2.scale, decimal=6)
+
+
+
+    # TODO: why does this test fail?
+    def t_est_missing(self):
+
+        Y = np.random.normal(size=100)
+        X1 = np.random.normal(size=100)
+        X2 = np.random.normal(size=100)
+        X3 = np.random.normal(size=100)
+        groups = np.kron(lrange(20), np.ones(5))
+
+        Y[0] = np.nan
+        Y[5:7] = np.nan
+        X2[10:12] = np.nan
+
+        D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3,
+                          "groups": groups})
+
+        md = GEE.from_formula("Y ~ X1 + X2 + X3", D["groups"],
+                              missing='drop')
+        mdf = md.fit()
+
+        assert(len(md.endog) == 95)
+        assert(md.exog.shape) == (95,4)
+
+    def test_default_time(self):
+        """
+        Check that the time defaults work correctly.
+        """
+
+        endog,exog,group = load_data("gee_logistic_1.csv")
+
+        # Time values for the autoregressive model
+        T = np.zeros(len(endog))
+        idx = set(group)
+        for ii in idx:
+            jj = np.flatnonzero(group == ii)
+            T[jj] = lrange(len(jj))
+
+        family = Binomial()
+        va = Autoregressive()
+
+
+        md1 = GEE(endog, exog, group, family=family, cov_struct=va)
+        mdf1 = md1.fit()
+
+        md2 = GEE(endog, exog, group, time=T, family=family,
+                  cov_struct=va)
+        mdf2 = md2.fit()
+
+        assert_almost_equal(mdf1.params, mdf2.params, decimal=6)
+        assert_almost_equal(mdf1.standard_errors(),
+                            mdf2.standard_errors(), decimal=6)
+
+
+
+    def test_logistic(self):
+        """
+        R code for comparing results:
+
+        library(gee)
+        Z = read.csv("results/gee_logistic_1.csv", header=FALSE)
+        Y = Z[,2]
+        Id = Z[,1]
+        X1 = Z[,3]
+        X2 = Z[,4]
+        X3 = Z[,5]
+
+        mi = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
+                 corstr="independence")
+        smi = summary(mi)
+        u = coefficients(smi)
+        cfi = paste(u[,1], collapse=",")
+        sei = paste(u[,4], collapse=",")
+
+        me = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
+                 corstr="exchangeable")
+        sme = summary(me)
+        u = coefficients(sme)
+        cfe = paste(u[,1], collapse=",")
+        see = paste(u[,4], collapse=",")
+
+        ma = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
+                 corstr="AR-M")
+        sma = summary(ma)
+        u = coefficients(sma)
+        cfa = paste(u[,1], collapse=",")
+        sea = paste(u[,4], collapse=",")
+
+        sprintf("cf = [[%s],[%s],[%s]]", cfi, cfe, cfa)
+        sprintf("se = [[%s],[%s],[%s]]", sei, see, sea)
+        """
+
+        endog,exog,group = load_data("gee_logistic_1.csv")
+
+        # Time values for the autoregressive model
+        T = np.zeros(len(endog))
+        idx = set(group)
+        for ii in idx:
+            jj = np.flatnonzero(group == ii)
+            T[jj] = lrange(len(jj))
+
+        family = Binomial()
+        ve = Exchangeable()
+        vi = Independence()
+        va = Autoregressive()
+
+        # From R gee
+        cf = [[0.0167272965285882,1.13038654425893,
+               -1.86896345082962,1.09397608331333],
+              [0.0178982283915449,1.13118798191788,
+               -1.86133518416017,1.08944256230299],
+              [0.0109621937947958,1.13226505028438,
+               -1.88278757333046,1.09954623769449]]
+        se = [[0.127291720283049,0.166725808326067,
+               0.192430061340865,0.173141068839597],
+              [0.127045031730155,0.165470678232842,
+               0.192052750030501,0.173174779369249],
+              [0.127240302296444,0.170554083928117,
+               0.191045527104503,0.169776150974586]]
+
+        for j,v in enumerate((vi,ve,va)):
+            md = GEE(endog, exog, group, T, family, v)
+            mdf = md.fit()
+            if id(v) != id(va):
+                assert_almost_equal(mdf.params, cf[j], decimal=6)
+                assert_almost_equal(mdf.standard_errors(), se[j],
+                                    decimal=6)
+
+        # Test with formulas
+        D = np.concatenate((endog[:,None], group[:,None], exog[:,1:]),
+                           axis=1)
+        D = pd.DataFrame(D)
+        D.columns = ["Y","Id",] + ["X%d" % (k+1)
+                                   for k in range(exog.shape[1]-1)]
+        for j,v in enumerate((vi,ve)):
+             md = GEE.from_formula("Y ~ X1 + X2 + X3", "Id", D,
+                                   family=family, cov_struct=v)
+             mdf = md.fit()
+             assert_almost_equal(mdf.params, cf[j], decimal=6)
+             assert_almost_equal(mdf.standard_errors(), se[j],
+                                 decimal=6)
+
+        # Check for run-time exceptions in summary
+        # print(mdf.summary())
+
+    def test_autoregressive(self):
+
+        dep_params_true = [0, 0.589208623896, 0.559823804948]
+
+        params_true = [[1.08043787, 1.12709319, 0.90133927],
+                       [0.9613677, 1.05826987, 0.90832055],
+                       [1.05370439, 0.96084864, 0.93923374]]
+
+        np.random.seed(342837482)
+
+        num_group = 100
+        ar_param = 0.5
+        k = 3
+
+        ga = Gaussian()
+
+        for gsize in 1,2,3:
+
+            ix = np.arange(gsize)[:,None] - np.arange(gsize)[None,:]
+            ix = np.abs(ix)
+            cmat = ar_param ** ix
+            cmat_r = np.linalg.cholesky(cmat)
+
+            endog = []
+            exog = []
+            groups = []
+            for i in range(num_group):
+                x = np.random.normal(size=(gsize,k))
+                exog.append(x)
+                expval = x.sum(1)
+                errors = np.dot(cmat_r, np.random.normal(size=gsize))
+                endog.append(expval + errors)
+                groups.append(i*np.ones(gsize))
+
+            endog = np.concatenate(endog)
+            groups = np.concatenate(groups)
+            exog = np.concatenate(exog, axis=0)
+
+            ar = Autoregressive()
+            md = GEE(endog, exog, groups, family=ga, cov_struct = ar)
+            mdf = md.fit()
+            assert_almost_equal(ar.dep_params, dep_params_true[gsize-1])
+            assert_almost_equal(mdf.params, params_true[gsize-1])
+
+    def test_post_estimation(self):
+
+        family = Gaussian()
+        endog,exog,group = load_data("gee_linear_1.csv")
+
+        ve = Exchangeable()
+
+        md = GEE(endog, exog, group, None, family, ve)
+        mdf = md.fit()
+
+        assert_almost_equal(np.dot(exog, mdf.params),
+                            mdf.fittedvalues)
+        assert_almost_equal(endog - np.dot(exog, mdf.params),
+                            mdf.resid)
+
+    def test_scoretest(self):
+        # Regression tests
+
+        np.random.seed(6432)
+        n = 200 # Must be divisible by 4
+        exog = np.random.normal(size=(n, 4))
+        endog = exog[:, 0] + exog[:, 1] + exog[:, 2]
+        endog += 3*np.random.normal(size=n)
+        group = np.kron(np.arange(n/4), np.ones(4))
+
+        # Test under the null.
+        L = np.array([[1., -1, 0, 0]])
+        R = np.array([0.,])
+        family = Gaussian()
+        va = Independence()
+        mod1 = GEE(endog, exog, group, family=family,
+                  cov_struct=va, constraint=(L, R))
+        rslt1 = mod1.fit()
+        assert_almost_equal(mod1.score_test_results["statistic"],
+                            1.08126334)
+        assert_almost_equal(mod1.score_test_results["p-value"],
+                            0.2984151086)
+
+        # Test under the alternative.
+        L = np.array([[1., -1, 0, 0]])
+        R = np.array([1.0,])
+        family = Gaussian()
+        va = Independence()
+        mod2 = GEE(endog, exog, group, family=family,
+                   cov_struct=va, constraint=(L, R))
+        rslt2 = mod2.fit()
+        assert_almost_equal(mod2.score_test_results["statistic"],
+                            3.491110965)
+        assert_almost_equal(mod2.score_test_results["p-value"],
+                            0.0616991659)
+
+        # Compare to Wald tests
+        exog = np.random.normal(size=(n, 2))
+        L = np.array([[1, -1]])
+        R = np.array([0.])
+        f = np.r_[1, -1]
+        for i in range(10):
+            endog = exog[:, 0] + (0.5 + i/10.)*exog[:, 1] +\
+                    np.random.normal(size=n)
+            family = Gaussian()
+            va = Independence()
+            mod0 = GEE(endog, exog, group, family=family,
+                       cov_struct=va)
+            rslt0 = mod0.fit()
+            family = Gaussian()
+            va = Independence()
+            mod1 = GEE(endog, exog, group, family=family,
+                       cov_struct=va, constraint=(L, R))
+            rslt1 = mod1.fit()
+            se = np.sqrt(np.dot(f, np.dot(rslt0.cov_params(), f)))
+            wald_z = np.dot(f, rslt0.params) / se
+            wald_p = 2*norm.cdf(-np.abs(wald_z))
+            score_p = mod1.score_test_results["p-value"]
+            assert(np.abs(wald_p - score_p) < 0.02)
+
+
+    def test_linear(self):
+        """
+        library(gee)
+
+        Z = read.csv("results/gee_linear_1.csv", header=FALSE)
+        Y = Z[,2]
+        Id = Z[,1]
+        X1 = Z[,3]
+        X2 = Z[,4]
+        X3 = Z[,5]
+        mi = gee(Y ~ X1 + X2 + X3, id=Id, family=gaussian,
+                 corstr="independence", tol=1e-8, maxit=100)
+        smi = summary(mi)
+        u = coefficients(smi)
+
+        cfi = paste(u[,1], collapse=",")
+        sei = paste(u[,4], collapse=",")
+
+        me = gee(Y ~ X1 + X2 + X3, id=Id, family=gaussian,
+                 corstr="exchangeable", tol=1e-8, maxit=100)
+        sme = summary(me)
+        u = coefficients(sme)
+
+        cfe = paste(u[,1], collapse=",")
+        see = paste(u[,4], collapse=",")
+
+        sprintf("cf = [[%s],[%s]]", cfi, cfe)
+        sprintf("se = [[%s],[%s]]", sei, see)
+        """
+
+        family = Gaussian()
+
+        endog,exog,group = load_data("gee_linear_1.csv")
+
+        vi = Independence()
+        ve = Exchangeable()
+
+        # From R gee
+        cf = [[-0.01850226507491,0.81436304278962,
+                -1.56167635393184,0.794239361055003],
+              [-0.0182920577154767,0.814898414022467,
+                -1.56194040106201,0.793499517527478]]
+        se = [[0.0440733554189401,0.0479993639119261,
+               0.0496045952071308,0.0479467597161284],
+              [0.0440369906460754,0.0480069787567662,
+               0.049519758758187,0.0479760443027526]]
+
+        for j,v in enumerate((vi, ve)):
+            md = GEE(endog, exog, group, None, family, v)
+            mdf = md.fit()
+            assert_almost_equal(mdf.params, cf[j], decimal=10)
+            assert_almost_equal(mdf.standard_errors(), se[j],
+                                decimal=10)
+
+        # Test with formulas
+        D = np.concatenate((endog[:,None], group[:,None], exog[:,1:]),
+                           axis=1)
+        D = pd.DataFrame(D)
+        D.columns = ["Y","Id",] + ["X%d" % (k+1)
+                                   for k in range(exog.shape[1]-1)]
+        for j,v in enumerate((vi,ve)):
+            md = GEE.from_formula("Y ~ X1 + X2 + X3", "Id", D,
+                                  family=family, cov_struct=v)
+            mdf = md.fit()
+            assert_almost_equal(mdf.params, cf[j], decimal=10)
+            assert_almost_equal(mdf.standard_errors(), se[j],
+                                decimal=10)
+
+    def test_linear_constrained(self):
+
+        family = Gaussian()
+
+        exog = np.random.normal(size=(300,4))
+        exog[:,0] = 1
+        endog = np.dot(exog, np.r_[1, 1, 0, 0.2]) +\
+            np.random.normal(size=300)
+        group = np.kron(np.arange(100), np.r_[1,1,1])
+
+        vi = Independence()
+        ve = Exchangeable()
+
+        L = np.r_[[[0, 0, 0, 1]]]
+        R = np.r_[0,]
+
+        for j,v in enumerate((vi,ve)):
+            md = GEE(endog, exog, group, None, family, v,
+                     constraint=(L,R))
+            mdf = md.fit()
+            assert_almost_equal(mdf.params[3], 0, decimal=10)
+
+
+    def test_nested_linear(self):
+
+        family = Gaussian()
+
+        endog,exog,group = load_data("gee_nested_linear_1.csv")
+
+        group_n = []
+        for i in range(endog.shape[0]//10):
+            group_n.extend([0,]*5)
+            group_n.extend([1,]*5)
+        group_n = np.array(group_n)[:,None]
+
+        dp = Independence()
+        md = GEE(endog, exog, group, None, family, dp)
+        mdf1 = md.fit()
+
+        # From statsmodels.GEE (not an independent test)
+        cf = np.r_[-0.1671073 ,  1.00467426, -2.01723004,  0.97297106]
+        se = np.r_[0.08629606,  0.04058653,  0.04067038,  0.03777989]
+        assert_almost_equal(mdf1.params, cf, decimal=6)
+        assert_almost_equal(mdf1.standard_errors(), se,
+                            decimal=6)
+
+        ne = Nested()
+        md = GEE(endog, exog, group, None, family, ne,
+                 dep_data=group_n)
+        mdf2 = md.fit(start_params=mdf1.params)
+
+        # From statsmodels.GEE (not an independent test)
+        cf = np.r_[-0.16655319,  1.02183688, -2.00858719,  1.00101969]
+        se = np.r_[0.08632616,  0.02913582,  0.03114428,  0.02893991]
+        assert_almost_equal(mdf2.params, cf, decimal=6)
+        assert_almost_equal(mdf2.standard_errors(), se,
+                            decimal=6)
+
+
+    def test_ordinal(self):
+
+        family = Binomial()
+
+        endog, exog, groups = load_data("gee_ordinal_1.csv",
+                                        icept=False)
+
+        v = GlobalOddsRatio("ordinal")
+
+        md = OrdinalGEE(endog, exog, groups, None, family, v)
+        mdf = md.fit()
+
+        cf = np.r_[1.09250002, 0.0217443 , -0.39851092, -0.01812116,
+                   0.03023969, 1.18258516, 0.01803453, -1.10203381]
+
+        se = np.r_[0.10883461, 0.10330197, 0.11177088, 0.05486569,
+                   0.05997153, 0.09168148, 0.05953324, 0.0853862]
+
+        assert_almost_equal(mdf.params, cf, decimal=5)
+        assert_almost_equal(mdf.bse, se, decimal=5)
+
+
+    def test_nominal(self):
+
+        family = Multinomial(3)
+
+        endog, exog, groups = load_data("gee_nominal_1.csv",
+                                        icept=False)
+
+        # Test with independence correlation
+        v = Independence()
+        md = NominalGEE(endog, exog, groups, None, family, v)
+        mdf1 = md.fit()
+
+        # From statsmodels.GEE (not an independent test)
+        cf1 = np.r_[0.44944752,  0.45569985, -0.92007064, -0.46766728]
+        se1 = np.r_[0.09801821,  0.07718842,  0.13229421,  0.08544553]
+        assert_almost_equal(mdf1.params, cf1, decimal=5)
+        assert_almost_equal(mdf1.standard_errors(), se1, decimal=5)
+
+        # Test with global odds ratio dependence
+        v = GlobalOddsRatio("nominal")
+        md = NominalGEE(endog, exog, groups, None, family, v)
+        mdf2 = md.fit(start_params=mdf1.params)
+
+        # From statsmodels.GEE (not an independent test)
+        cf2 = np.r_[0.45448248,  0.41945568, -0.92008924, -0.50485758]
+        se2 = np.r_[0.09632274,  0.07433944,  0.13264646,  0.0911768]
+        assert_almost_equal(mdf2.params, cf2, decimal=5)
+        assert_almost_equal(mdf2.standard_errors(), se2, decimal=5)
+
+
+
+    def test_poisson(self):
+        """
+        library(gee)
+        Z = read.csv("results/gee_poisson_1.csv", header=FALSE)
+        Y = Z[,2]
+        Id = Z[,1]
+        X1 = Z[,3]
+        X2 = Z[,4]
+        X3 = Z[,5]
+        X4 = Z[,6]
+        X5 = Z[,7]
+
+        mi = gee(Y ~ X1 + X2 + X3 + X4 + X5, id=Id, family=poisson,
+                corstr="independence", scale.fix=TRUE)
+        smi = summary(mi)
+        u = coefficients(smi)
+        cfi = paste(u[,1], collapse=",")
+        sei = paste(u[,4], collapse=",")
+
+        me = gee(Y ~ X1 + X2 + X3 + X4 + X5, id=Id, family=poisson,
+                corstr="exchangeable", scale.fix=TRUE)
+        sme = summary(me)
+
+        u = coefficients(sme)
+        cfe = paste(u[,1], collapse=",")
+        see = paste(u[,4], collapse=",")
+
+        sprintf("cf = [[%s],[%s]]", cfi, cfe)
+        sprintf("se = [[%s],[%s]]", sei, see)
+        """
+
+        family = Poisson()
+
+        endog,exog,group_n = load_data("gee_poisson_1.csv")
+
+        vi = Independence()
+        ve = Exchangeable()
+
+        # From R gee
+        cf = [[-0.0364450410793481,-0.0543209391301178,
+                0.0156642711741052,0.57628591338724,
+                -0.00465659951186211,-0.477093153099256],
+              [-0.0315615554826533,-0.0562589480840004,
+                0.0178419412298561,0.571512795340481,
+                -0.00363255566297332,-0.475971696727736]]
+        se = [[0.0611309237214186,0.0390680524493108,
+               0.0334234174505518,0.0366860768962715,
+               0.0304758505008105,0.0316348058881079],
+              [0.0610840153582275,0.0376887268649102,
+               0.0325168379415177,0.0369786751362213,
+               0.0296141014225009,0.0306115470200955]]
+
+        for j,v in enumerate((vi,ve)):
+            md = GEE(endog, exog, group_n, None, family, v)
+            mdf = md.fit()
+            assert_almost_equal(mdf.params, cf[j], decimal=5)
+            assert_almost_equal(mdf.standard_errors(), se[j],
+                                decimal=6)
+
+        # Test with formulas
+        D = np.concatenate((endog[:,None], group_n[:,None],
+                            exog[:,1:]), axis=1)
+        D = pd.DataFrame(D)
+        D.columns = ["Y","Id",] + ["X%d" % (k+1)
+                                   for k in range(exog.shape[1]-1)]
+        for j,v in enumerate((vi,ve)):
+             md = GEE.from_formula("Y ~ X1 + X2 + X3 + X4 + X5", "Id",
+                                   D, family=family, cov_struct=v)
+             mdf = md.fit()
+             assert_almost_equal(mdf.params, cf[j], decimal=5)
+             assert_almost_equal(mdf.standard_errors(), se[j],
+                                 decimal=6)
+             # print(mdf.params)
+
+
+    def test_compare_OLS(self):
+        """
+        Gaussian GEE with independence correlation should agree
+        exactly with OLS for parameter estimates and standard errors
+        derived from the naive covariance estimate.
+        """
+
+        vs = Independence()
+        family = Gaussian()
+
+        Y = np.random.normal(size=100)
+        X1 = np.random.normal(size=100)
+        X2 = np.random.normal(size=100)
+        X3 = np.random.normal(size=100)
+        groups = np.kron(lrange(20), np.ones(5))
+
+        D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
+
+        md = GEE.from_formula("Y ~ X1 + X2 + X3", groups, D,
+                              family=family, cov_struct=vs)
+        mdf = md.fit()
+
+        ols = sm.ols("Y ~ X1 + X2 + X3", data=D).fit()
+
+        assert_almost_equal(ols.params.values, mdf.params, decimal=10)
+
+        se = mdf.standard_errors(covariance_type="naive")
+        assert_almost_equal(ols.bse, se, decimal=10)
+
+        naive_tvalues = mdf.params / \
+            np.sqrt(np.diag(mdf.naive_covariance))
+        assert_almost_equal(naive_tvalues, ols.tvalues, decimal=10)
+
+    def test_formulas(self):
+        """
+        Check formulas, especially passing groups and time as either
+        variable names or arrays.
+        """
+
+        n = 100
+        Y = np.random.normal(size=n)
+        X1 = np.random.normal(size=n)
+        mat = np.concatenate((np.ones((n,1)), X1[:, None]), axis=1)
+        Time = np.random.uniform(size=n)
+        groups = np.kron(lrange(20), np.ones(5))
+
+        data = pd.DataFrame({"Y": Y, "X1": X1, "Time": Time, "groups": groups})
+
+        va = Autoregressive()
+        family = Gaussian()
+
+        mod1 = GEE(Y, mat, groups, time=Time, family=family,
+                   cov_struct=va)
+        rslt1 = mod1.fit()
+
+        mod2 = GEE.from_formula("Y ~ X1", groups, data, time=Time,
+                                family=family, cov_struct=va)
+        rslt2 = mod2.fit()
+
+        mod3 = GEE.from_formula("Y ~ X1", groups, data, time="Time",
+                                family=family, cov_struct=va)
+        rslt3 = mod3.fit()
+
+        mod4 = GEE.from_formula("Y ~ X1", "groups", data, time=Time,
+                                family=family, cov_struct=va)
+        rslt4 = mod4.fit()
+
+        mod5 = GEE.from_formula("Y ~ X1", "groups", data, time="Time",
+                                family=family, cov_struct=va)
+        rslt5 = mod5.fit()
+
+        assert_almost_equal(rslt1.params, rslt2.params, decimal=8)
+        assert_almost_equal(rslt1.params, rslt3.params, decimal=8)
+        assert_almost_equal(rslt1.params, rslt4.params, decimal=8)
+        assert_almost_equal(rslt1.params, rslt5.params, decimal=8)
+
+    def test_compare_logit(self):
+
+        vs = Independence()
+        family = Binomial()
+
+        Y = 1*(np.random.normal(size=100) < 0)
+        X1 = np.random.normal(size=100)
+        X2 = np.random.normal(size=100)
+        X3 = np.random.normal(size=100)
+        groups = np.random.randint(0, 4, size=100)
+
+        D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
+
+        mod1 = GEE.from_formula("Y ~ X1 + X2 + X3", groups, D,
+                                family=family, cov_struct=vs)
+        rslt1 = mod1.fit()
+
+        mod2 = sm.logit("Y ~ X1 + X2 + X3", data=D)
+        rslt2 = mod2.fit()
+
+        assert_almost_equal(rslt1.params, rslt2.params, decimal=10)
+
+
+    def test_compare_poisson(self):
+
+        vs = Independence()
+        family = Poisson()
+
+        Y = np.ceil(-np.log(np.random.uniform(size=100)))
+        X1 = np.random.normal(size=100)
+        X2 = np.random.normal(size=100)
+        X3 = np.random.normal(size=100)
+        groups = np.random.randint(0, 4, size=100)
+
+        D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
+
+        mod1 = GEE.from_formula("Y ~ X1 + X2 + X3", groups, D,
+                                family=family, cov_struct=vs)
+        rslt1 = mod1.fit()
+
+        mod2 = sm.poisson("Y ~ X1 + X2 + X3", data=D)
+        rslt2 = mod2.fit(disp=False)
+
+        assert_almost_equal(rslt1.params, rslt2.params, decimal=10)
+
+
+if  __name__=="__main__":
+
+    import nose
+
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
+                   exit=False)
diff --git a/statsmodels/genmod/tests/test_glm.py b/statsmodels/genmod/tests/test_glm.py
index afb7c8082cb..752ec79e4ee 100644
--- a/statsmodels/genmod/tests/test_glm.py
+++ b/statsmodels/genmod/tests/test_glm.py
@@ -1,15 +1,19 @@
 """
-
 Test functions for models.GLM
 """
+
+from statsmodels.compat import range
+
 import os
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_equal, assert_raises
+from numpy.testing import (assert_almost_equal, assert_equal, assert_raises,
+                           assert_allclose, assert_, assert_array_less)
 from scipy import stats
 import statsmodels.api as sm
 from statsmodels.genmod.generalized_linear_model import GLM
 from statsmodels.tools.tools import add_constant
 from statsmodels.tools.sm_exceptions import PerfectSeparationError
+from statsmodels.discrete import discrete_model as discrete
 from nose import SkipTest
 
 # Test Precisions
@@ -117,6 +121,52 @@ def test_tpvalues(self):
         assert_almost_equal(self.res1.pvalues, pvalues)
         assert_almost_equal(self.res1.conf_int(), conf_int)
 
+
+class CheckComparisonMixin(object):
+
+    def test_compare_discrete(self):
+        res1 = self.res1
+        resd = self.resd
+
+        assert_allclose(res1.llf, resd.llf, rtol=1e-10)
+        score_obs1 = res1.model.score_obs(res1.params)
+        score_obsd = resd.model.jac(resd.params)
+        assert_allclose(score_obs1, score_obsd, rtol=1e-10)
+
+        # score
+        score1 = res1.model.score(res1.params)
+        assert_allclose(score1, score_obs1.sum(0), atol=1e-20)
+        assert_allclose(score1, np.zeros(score_obs1.shape[1]), atol=1e-7)
+
+        hessian1 = res1.model.hessian(res1.params, observed=False)
+        hessiand = resd.model.hessian(resd.params)
+        assert_allclose(hessian1, hessiand, rtol=1e-10)
+
+        hessian1 = res1.model.hessian(res1.params, observed=True)
+        hessiand = resd.model.hessian(resd.params)
+        assert_allclose(hessian1, hessiand, rtol=1e-9)
+
+    def test_score_test(self):
+        res1 = self.res1
+        # fake example, should be zero, k_constraint should be 0
+        st, pv, df = res1.model.score_test(res1.params, k_constraints=1)
+        assert_allclose(st, 0, atol=1e-20)
+        assert_allclose(pv, 1, atol=1e-10)
+        assert_equal(df, 1)
+
+        st, pv, df = res1.model.score_test(res1.params, k_constraints=0)
+        assert_allclose(st, 0, atol=1e-20)
+        assert_(np.isnan(pv), msg=repr(pv))
+        assert_equal(df, 0)
+
+        # TODO: no verified numbers largely SMOKE test
+        exog_extra = res1.model.exog[:,1]**2
+        st, pv, df = res1.model.score_test(res1.params, exog_extra=exog_extra)
+        assert_array_less(0.1, st)
+        assert_array_less(0.1, pv)
+        assert_equal(df, 1)
+
+
 class TestGlmGaussian(CheckModelResultsMixin):
     def __init__(self):
         '''
@@ -133,9 +183,27 @@ def __init__(self):
         self.data.exog = add_constant(self.data.exog, prepend=False)
         self.res1 = GLM(self.data.endog, self.data.exog,
                         family=sm.families.Gaussian()).fit()
-        from results.results_glm import Longley
+        from .results.results_glm import Longley
         self.res2 = Longley()
 
+
+    def test_compare_OLS(self):
+        res1 = self.res1
+        # OLS doesn't define score_obs
+        from statsmodels.regression.linear_model import OLS
+        resd = OLS(self.data.endog, self.data.exog).fit()
+        self.resd = resd  # attach to access from the outside
+
+        assert_allclose(res1.llf, resd.llf, rtol=1e-10)
+        score_obs1 = res1.model.score_obs(res1.params)
+        score_obsd = resd.resid[:, None] / resd.scale * resd.model.exog
+        # low precision because of badly scaled exog
+        assert_allclose(score_obs1, score_obsd, rtol=1e-8)
+
+        score_obs1 = res1.model.score_obs(res1.params, scale=1)
+        score_obsd = resd.resid[:, None] * resd.model.exog
+        assert_allclose(score_obs1, score_obsd, rtol=1e-8)
+
 #    def setup(self):
 #        if skipR:
 #            raise SkipTest, "Rpy not installed."
@@ -163,7 +231,7 @@ def __init__(self):
         GaussLog_Model = GLM(self.lny, self.X, \
                 family=sm.families.Gaussian(sm.families.links.log))
         self.res1 = GaussLog_Model.fit()
-        from results.results_glm import GaussianLog
+        from .results.results_glm import GaussianLog
         self.res2 = GaussianLog()
 
 #    def setup(self):
@@ -192,7 +260,7 @@ def __init__(self):
                 family=sm.families.Gaussian(sm.families.links.inverse_power))
         InverseLink_Res = InverseLink_Model.fit()
         self.res1 = InverseLink_Res
-        from results.results_glm import GaussianInverse
+        from .results.results_glm import GaussianInverse
         self.res2 = GaussianInverse()
 
 #    def setup(self):
@@ -211,7 +279,7 @@ def __init__(self):
         self.decimal_bic = DECIMAL_2
 
         from statsmodels.datasets.star98 import load
-        from results.results_glm import Star98
+        from .results.results_glm import Star98
         data = load()
         data.exog = add_constant(data.exog, prepend=False)
         self.res1 = GLM(data.endog, data.exog, \
@@ -247,13 +315,57 @@ def __init__(self):
 #TODO: need include logc link
 #    pass
 
-class TestGlmBernoulli(CheckModelResultsMixin):
+class TestGlmBernoulli(CheckModelResultsMixin, CheckComparisonMixin):
     def __init__(self):
-        from results.results_glm import Lbw
+        from .results.results_glm import Lbw
         self.res2 = Lbw()
         self.res1 = GLM(self.res2.endog, self.res2.exog,
                 family=sm.families.Binomial()).fit()
 
+        modd = discrete.Logit(self.res2.endog, self.res2.exog)
+        self.resd = modd.fit(start_params=self.res1.params * 0.9)
+
+
+    def score_test_r(self):
+        res1 = self.res1
+        res2 = self.res2
+        st, pv, df = res1.model.score_test(res1.params,
+                                           exog_extra=res1.model.exog[:, 1]**2)
+        st_res = 0.2837680293459376  # (-0.5326988167303712)**2
+        assert_allclose(st, st_res, rtol=1e-4)
+
+        st, pv, df = res1.model.score_test(res1.params,
+                                          exog_extra=res1.model.exog[:, 0]**2)
+        st_res = 0.6713492821514992  # (-0.8193590679009413)**2
+        assert_allclose(st, st_res, rtol=1e-4)
+
+        select = list(range(9))
+        select.pop(7)
+
+        res1b = GLM(res2.endog, res2.exog[:, select],
+                    family=sm.families.Binomial()).fit()
+        tres = res1b.model.score_test(res1b.params,
+                                      exog_extra=res1.model.exog[:, -2])
+        tres = np.asarray(tres[:2]).ravel()
+        tres_r = (2.7864148487452, 0.0950667)
+        assert_allclose(tres, tres_r, rtol=1e-4)
+
+        cmd_r = """\
+        data = read.csv("...statsmodels\\statsmodels\\genmod\\tests\\results\\stata_lbw_glm.csv")
+
+        data["race_black"] = data["race"] == "black"
+        data["race_other"] = data["race"] == "other"
+        mod = glm(low ~ age + lwt + race_black + race_other + smoke + ptl + ht + ui, family=binomial, data=data)
+        options(digits=16)
+        anova(mod, test="Rao")
+
+        library(statmod)
+        s = glm.scoretest(mod, data["age"]**2)
+        s**2
+        s = glm.scoretest(mod, data["lwt"]**2)
+        s**2
+        """
+
 #class TestGlmBernoulliIdentity(CheckModelResultsMixin):
 #    pass
 
@@ -286,7 +398,7 @@ def __init__(self):
         self.decimal_resids = DECIMAL_2
 
         from statsmodels.datasets.scotland import load
-        from results.results_glm import Scotvote
+        from .results.results_glm import Scotvote
         data = load()
         data.exog = add_constant(data.exog, prepend=False)
         res1 = GLM(data.endog, data.exog, \
@@ -304,7 +416,7 @@ def __init__(self):
         self.decimal_aic_R = DECIMAL_0
         self.decimal_fittedvalues = DECIMAL_3
 
-        from results.results_glm import CancerLog
+        from .results.results_glm import CancerLog
         res2 = CancerLog()
         self.res1 = GLM(res2.endog, res2.exog,
             family=sm.families.Gamma(link=sm.families.links.log)).fit()
@@ -326,7 +438,7 @@ def __init__(self):
         self.decimal_aic_R = DECIMAL_0
         self.decimal_loglike = DECIMAL_1
 
-        from results.results_glm import CancerIdentity
+        from .results.results_glm import CancerIdentity
         res2 = CancerIdentity()
         self.res1 = GLM(res2.endog, res2.exog,
             family=sm.families.Gamma(link=sm.families.links.identity)).fit()
@@ -339,14 +451,14 @@ def __init__(self):
 #            family=r.Gamma(link="identity"))
 #        self.res2.null_deviance = 27.92207137420696 # from R, Rpy bug
 
-class TestGlmPoisson(CheckModelResultsMixin):
+class TestGlmPoisson(CheckModelResultsMixin, CheckComparisonMixin):
     def __init__(self):
         '''
         Tests Poisson family with canonical log link.
 
         Test results were obtained by R.
         '''
-        from results.results_glm import Cpunish
+        from .results.results_glm import Cpunish
         from statsmodels.datasets.cpunish import load
         self.data = load()
         self.data.exog[:,3] = np.log(self.data.exog[:,3])
@@ -354,6 +466,9 @@ def __init__(self):
         self.res1 = GLM(self.data.endog, self.data.exog,
                     family=sm.families.Poisson()).fit()
         self.res2 = Cpunish()
+        # compare with discrete, start close to save time
+        modd = discrete.Poisson(self.data.endog, self.data.exog)
+        self.resd = modd.fit(start_params=self.res1.params * 0.9)
 
 #class TestGlmPoissonIdentity(CheckModelResultsMixin):
 #    pass
@@ -376,7 +491,7 @@ def __init__(self):
         self.decimal_aic_R = DECIMAL_0
         self.decimal_loglike = DECIMAL_0
 
-        from results.results_glm import InvGauss
+        from .results.results_glm import InvGauss
         res2 = InvGauss()
         res1 = GLM(res2.endog, res2.exog, \
                 family=sm.families.InverseGaussian()).fit()
@@ -389,7 +504,7 @@ def __init__(self):
         self.decimal_aic_R = -10 # Big difference vs R.
         self.decimal_resids = DECIMAL_3
 
-        from results.results_glm import InvGaussLog
+        from .results.results_glm import InvGaussLog
         res2 = InvGaussLog()
         self.res1 = GLM(res2.endog, res2.exog,
             family=sm.families.InverseGaussian(link=\
@@ -411,12 +526,12 @@ def __init__(self):
         self.decimal_fittedvalues = DECIMAL_3
         self.decimal_params = DECIMAL_3
 
-        from results.results_glm import Medpar1
+        from .results.results_glm import Medpar1
         data = Medpar1()
         self.res1 = GLM(data.endog, data.exog,
             family=sm.families.InverseGaussian(link=\
             sm.families.links.identity)).fit()
-        from results.results_glm import InvGaussIdentity
+        from .results.results_glm import InvGaussIdentity
         self.res2 = InvGaussIdentity()
 
 #    def setup(self):
@@ -446,7 +561,7 @@ def __init__(self):
         self.data.exog = add_constant(self.data.exog, prepend=False)
         self.res1 = GLM(self.data.endog, self.data.exog,
                 family=sm.families.NegativeBinomial()).fit()
-        from results.results_glm import Committee
+        from .results.results_glm import Committee
         res2 = Committee()
         res2.aic_R += 2 # They don't count a degree of freedom for the scale
         self.res2 = res2
@@ -472,7 +587,7 @@ def __init__(self):
 class TestGlmPoissonOffset(CheckModelResultsMixin):
     @classmethod
     def setupClass(cls):
-        from results.results_glm import Cpunish
+        from .results.results_glm import Cpunish
         from statsmodels.datasets.cpunish import load
         data = load()
         data.exog[:,3] = np.log(data.exog[:,3])
@@ -494,6 +609,58 @@ def test_missing(self):
                     exposure=self.exposure, missing='drop')
         assert_equal(mod.exposure.shape[0], 13)
 
+    def test_offset_exposure(self):
+        # exposure=x and offset=log(x) should have the same effect
+        np.random.seed(382304)
+        endog = np.random.randint(0, 10, 100)
+        exog = np.random.normal(size=(100,3))
+        exposure = np.random.uniform(1, 2, 100)
+        offset = np.random.uniform(1, 2, 100)
+        mod1 = GLM(endog, exog, family=sm.families.Poisson(),
+                   offset=offset, exposure=exposure).fit()
+        offset2 = offset + np.log(exposure)
+        mod2 = GLM(endog, exog, family=sm.families.Poisson(),
+                   offset=offset2).fit()
+        assert_almost_equal(mod1.params, mod2.params)
+
+    def test_predict(self):
+        np.random.seed(382304)
+        endog = np.random.randint(0, 10, 100)
+        exog = np.random.normal(size=(100,3))
+        exposure = np.random.uniform(1, 2, 100)
+        mod1 = GLM(endog, exog, family=sm.families.Poisson(),
+                   exposure=exposure).fit()
+        exog1 = np.random.normal(size=(10,3))
+        exposure1 = np.random.uniform(1, 2, 10)
+
+        # Doubling exposure time should double expected response
+        pred1 = mod1.predict(exog=exog1, exposure=exposure1)
+        pred2 = mod1.predict(exog=exog1, exposure=2*exposure1)
+        assert_almost_equal(pred2, 2*pred1)
+
+        # Check exposure defaults
+        pred3 = mod1.predict()
+        pred4 = mod1.predict(exposure=exposure)
+        pred5 = mod1.predict(exog=exog, exposure=exposure)
+        assert_almost_equal(pred3, pred4)
+        assert_almost_equal(pred4, pred5)
+
+        # Check offset defaults
+        offset = np.random.uniform(1, 2, 100)
+        mod2 = GLM(endog, exog, offset=offset, family=sm.families.Poisson()).fit()
+        pred1 = mod2.predict()
+        pred2 = mod2.predict(offset=offset)
+        pred3 = mod2.predict(exog=exog, offset=offset)
+        assert_almost_equal(pred1, pred2)
+        assert_almost_equal(pred2, pred3)
+
+        # Check that offset shifts the linear predictor
+        mod3 = GLM(endog, exog, family=sm.families.Poisson()).fit()
+        offset = np.random.uniform(1, 2, 10)
+        pred1 = mod3.predict(exog=exog1, offset=offset, linear=True)
+        pred2 = mod3.predict(exog=exog1, offset=2*offset, linear=True)
+        assert_almost_equal(pred2, pred1+offset)
+
 
 def test_prefect_pred():
     cur_dir = os.path.dirname(os.path.abspath(__file__))
@@ -508,10 +675,29 @@ def test_prefect_pred():
     assert_raises(PerfectSeparationError, glm.fit)
 
 
+def test_score_test_OLS():
+    # nicer example than Longley
+    from statsmodels.regression.linear_model import OLS
+    np.random.seed(5)
+    nobs = 100
+    sige = 0.5
+    x = np.random.uniform(0, 1, size=(nobs, 5))
+    x[:, 0] = 1
+    beta = 1. / np.arange(1., x.shape[1] + 1)
+    y = x.dot(beta) + sige * np.random.randn(nobs)
+
+    res_ols = OLS(y, x).fit()
+    res_olsc = OLS(y, x[:, :-2]).fit()
+    co = res_ols.compare_lm_test(res_olsc, demean=False)
+
+    res_glm = GLM(y, x[:, :-2], family=sm.families.Gaussian()).fit()
+    co2 = res_glm.model.score_test(res_glm.params, exog_extra=x[:, -2:])
+    # difference in df_resid versus nobs in scale see #1786
+    assert_allclose(co[0] * 97 / 100., co2[0], rtol=1e-13)
+
+
 def test_attribute_writable_resettable():
-    """
-    Regression test for mutables and class constructors.
-    """
+    # Regression test for mutables and class constructors.
     data = sm.datasets.longley.load()
     endog, exog = data.endog, data.exog
     glm_model = sm.GLM(endog, exog)
@@ -521,6 +707,56 @@ def test_attribute_writable_resettable():
     glm_model2 = sm.GLM(endog, exog)
     assert_equal(glm_model2.family.link.power, 1.0)
 
+
+class Test_start_params(CheckModelResultsMixin):
+    def __init__(self):
+        '''
+        Test Gaussian family with canonical identity link
+        '''
+        # Test Precisions
+        self.decimal_resids = DECIMAL_3
+        self.decimal_params = DECIMAL_2
+        self.decimal_bic = DECIMAL_0
+        self.decimal_bse = DECIMAL_3
+
+        from statsmodels.datasets.longley import load
+        self.data = load()
+        self.data.exog = add_constant(self.data.exog, prepend=False)
+        params = sm.OLS(self.data.endog, self.data.exog).fit().params
+        self.res1 = GLM(self.data.endog, self.data.exog,
+                        family=sm.families.Gaussian()).fit(start_params=params)
+        from .results.results_glm import Longley
+        self.res2 = Longley()
+
+
+def test_glm_start_params():
+    # see 1604
+    y2 = np.array('0 1 0 0 0 1'.split(), int)
+    wt = np.array([50,1,50,1,5,10])
+    y2 = np.repeat(y2, wt)
+    x2 = np.repeat([0,0,0.001,100,-1,-1], wt)
+    mod = sm.GLM(y2, sm.add_constant(x2), family=sm.families.Binomial())
+    res = mod.fit(start_params=[-4, -5])
+    np.testing.assert_almost_equal(res.params, [-4.60305022, -5.29634545], 6)
+
+
+def test_loglike_no_opt():
+    # see 1728
+
+    y = np.asarray([0, 1, 0, 0, 1, 1, 0, 1, 1, 1])
+    x = np.arange(10, dtype=np.float64)
+
+    def llf(params):
+        lin_pred = params[0] + params[1]*x
+        pr = 1 / (1 + np.exp(-lin_pred))
+        return np.sum(y*np.log(pr) + (1-y)*np.log(1-pr))
+
+    for params in [0,0], [0,1], [0.5,0.5]:
+        mod = sm.GLM(y, sm.add_constant(x), family=sm.families.Binomial())
+        res = mod.fit(start_params=params, maxiter=0)
+        like = llf(params)
+        assert_almost_equal(like, res.llf)
+
 if __name__=="__main__":
     #run_module_suite()
     #taken from Fernando Perez:
diff --git a/statsmodels/graphics/api.py b/statsmodels/graphics/api.py
index 47bd67fc6bb..d97885ba79f 100644
--- a/statsmodels/graphics/api.py
+++ b/statsmodels/graphics/api.py
@@ -8,4 +8,4 @@
                               plot_leverage_resid2)
 from .factorplots import interaction_plot
 from .plottools import rainbow
-import tsaplots as tsa
+from . import tsaplots as tsa
diff --git a/statsmodels/graphics/boxplots.py b/statsmodels/graphics/boxplots.py
index 246f81781a5..08861aae255 100644
--- a/statsmodels/graphics/boxplots.py
+++ b/statsmodels/graphics/boxplots.py
@@ -3,7 +3,7 @@
 # Author: Ralf Gommers
 # Based on code by Flavio Coelho and Teemu Ikonen.
 
-
+from statsmodels.compat.python import zip
 import numpy as np
 from scipy.stats import gaussian_kde
 
@@ -211,7 +211,7 @@ def _set_ticks_labels(ax, data, labels, positions, plot_opts):
     if labels is not None:
         if not len(labels) == len(data):
             msg = "Length of `labels` should equal length of `data`."
-            raise(ValueError, msg)
+            raise ValueError(msg)
 
         xticknames = ax.set_xticklabels(labels)
         if label_fontsize:
diff --git a/statsmodels/graphics/dotplots.py b/statsmodels/graphics/dotplots.py
new file mode 100644
index 00000000000..5ff367d50c7
--- /dev/null
+++ b/statsmodels/graphics/dotplots.py
@@ -0,0 +1,453 @@
+import numpy as np
+from statsmodels.compat import range
+from . import utils
+
+
+def dot_plot(points, intervals=None, lines=None, sections=None,
+             styles=None, marker_props=None, line_props=None,
+             split_names=None, section_order=None, line_order=None,
+             stacked=False, styles_order=None, striped=False,
+             horizontal=True, ax=None):
+    """
+    Produce a dotplot similar in style to those in Cleveland's
+    "Visualizing Data" book.  These are also known as "forest plots".
+
+    Parameters
+    ----------
+    points : array_like
+        The quantitative values to be plotted as markers.
+    intervals : array_like
+        The intervals to be plotted around the points.  The elements
+        of `intervals` are either scalars or sequences of length 2.  A
+        scalar indicates the half width of a symmetric interval.  A
+        sequence of length 2 contains the left and right half-widths
+        (respectively) of a nonsymmetric interval.  If None, no
+        intervals are drawn.
+    lines : array_like
+        A grouping variable indicating which points/intervals are
+        drawn on a common line.  If None, each point/interval appears
+        on its own line.
+    sections : array_like
+        A grouping variable indicating which lines are grouped into
+        sections.  If None, everything is drawn in a single section.
+    styles : array_like
+        A grouping label defining the plotting style of the markers
+        and intervals.
+    marker_props : dict
+        A dictionary mapping style codes (the values in `styles`) to
+        dictionaries defining key/value pairs to be passed as keyword
+        arguments to `plot` when plotting markers.  Useful keyword
+        arguments are "color", "marker", and "ms" (marker size).
+    line_props : dict
+        A dictionary mapping style codes (the values in `styles`) to
+        dictionaries defining key/value pairs to be passed as keyword
+        arguments to `plot` when plotting interval lines.  Useful
+        keyword arguments are "color", "linestyle", "solid_capstyle",
+        and "linewidth".
+    split_names : string
+        If not None, this is used to split the values of `lines` into
+        substrings that are drawn in the left and right margins,
+        respectively.  If None, the values of `lines` are drawn in the
+        left margin.
+    section_order : array_like
+        The section labels in the order in which they appear in the
+        dotplot.
+    line_order : array_like
+        The line labels in the order in which they appear in the
+        dotplot.
+    stacked : boolean
+        If True, when multiple points or intervals are drawn on the
+        same line, they are offset from each other.
+    styles_order : array_like
+        If stacked=True, this is the order in which the point styles
+        on a given line are drawn from top to bottom (if horizontal
+        is True) or from left to right (if horiontal is False).  If
+        None (default), the order is lexical.
+    striped : boolean
+        If True, every other line is enclosed in a shaded box.
+    horizontal : boolean
+        If True (default), the lines are drawn horizontally, otherwise
+        they are drawn vertically.
+    ax : matplotlib.axes
+        The axes on which the dotplot is drawn.  If None, a new axes
+        is created.
+
+    Returns
+    -------
+    fig : Figure
+        The figure given by `ax.figure` or a new instance.
+
+    Notes
+    -----
+    `points`, `intervals`, `lines`, `sections`, `styles` must all have
+    the same length whenever present.
+
+    Examples
+    --------
+    This is a simple dotplot with one point per line:
+    >>> dot_plot(points=point_values)
+
+    This dotplot has labels on the lines (if elements in
+    `label_values` are repeated, the correponding points appear on the
+    same line):
+    >>> dot_plot(points=point_values, lines=label_values)
+
+    References
+    ----------
+      * Cleveland, William S. (1993). "Visualizing Data". Hobart
+        Press.
+      * Jacoby, William G. (2006) "The Dot Plot: A Graphical Display
+        for Labeled Quantitative Values." The Political Methodologist
+        14(1): 6-14.
+    """
+
+    import matplotlib.transforms as transforms
+
+    fig, ax = utils.create_mpl_ax(ax)
+
+    # Convert to numpy arrays if that is not what we are given.
+    points = np.asarray(points)
+    if intervals is not None:
+        intervals = np.asarray(intervals)
+    if lines is not None:
+        lines = np.asarray(lines)
+    if sections is not None:
+        sections = np.asarray(sections)
+    if styles is not None:
+        styles = np.asarray(styles)
+
+    # Total number of points
+    npoint = len(points)
+
+    # Set default line values if needed
+    if lines is None:
+        lines = np.arange(npoint)
+
+    # Set default section values if needed
+    if sections is None:
+        sections = np.zeros(npoint)
+
+    # Set default style values if needed
+    if styles is None:
+        styles = np.zeros(npoint)
+
+    # The vertical space (in inches) for a section title
+    section_title_space = 0.5
+
+    # The number of sections
+    nsect = len(set(sections))
+
+    # The number of section titles
+    nsect_title = nsect if nsect > 1 else 0
+
+    # The total vertical space devoted to section titles.
+    section_space_total = section_title_space * nsect_title
+
+    # Add a bit of room so that points that fall at the axis limits
+    # are not cut in half.
+    ax.set_xmargin(0.02)
+    ax.set_ymargin(0.02)
+
+    if section_order is None:
+        lines0 = list(set(sections))
+        lines0.sort()
+    else:
+        lines0 = section_order
+
+    if line_order is None:
+        lines1 = list(set(lines))
+        lines1.sort()
+    else:
+        lines1 = line_order
+
+    # A map from (section,line) codes to index positions.
+    lines_map = {}
+    for i in range(npoint):
+        ky = (sections[i], lines[i])
+        if ky not in lines_map:
+            lines_map[ky] = []
+        lines_map[ky].append(i)
+
+    # Get the size of the axes on the parent figure in inches
+    bbox = ax.get_window_extent().transformed(
+        fig.dpi_scale_trans.inverted())
+    awidth, aheight = bbox.width, bbox.height
+
+    # The number of lines in the plot.
+    nrows = len(lines_map)
+
+    # The positions of the lowest and highest guideline in axes
+    # coordinates (for horizontal dotplots), or the leftmost and
+    # rightmost guidelines (for vertical dotplots).
+    bottom, top = 0, 1
+
+    if horizontal:
+        # x coordinate is data, y coordinate is axes
+        trans = transforms.blended_transform_factory(ax.transData,
+                                                     ax.transAxes)
+    else:
+        # x coordinate is axes, y coordinate is data
+        trans = transforms.blended_transform_factory(ax.transAxes,
+                                                     ax.transData)
+
+    # Space used for a section title, in axes coordinates
+    title_space_axes = section_title_space / aheight
+
+    # Space between lines
+    if horizontal:
+        dpos = (top - bottom - nsect_title*title_space_axes) /\
+            float(nrows)
+    else:
+        dpos = (top - bottom) / float(nrows)
+
+    # Determine the spacing for stacked points
+    if styles_order is not None:
+        style_codes = styles_order
+    else:
+        style_codes = list(set(styles))
+        style_codes.sort()
+    # Order is top to bottom for horizontal plots, so need to
+    # flip.
+    if horizontal:
+        style_codes = style_codes[::-1]
+    # nval is the maximum number of points on one line.
+    nval = len(style_codes)
+    if nval > 1:
+        stackd = dpos / (2.5*(float(nval)-1))
+    else:
+        stackd = 0.
+
+    # Map from style code to its integer position
+    #style_codes_map = {x: style_codes.index(x) for x in style_codes}
+    # python 2.6 compat version:
+    style_codes_map = dict((x, style_codes.index(x)) for x in style_codes)
+
+    # Setup default marker styles
+    colors = ["r", "g", "b", "y", "k", "purple", "orange"]
+    if marker_props is None:
+        #marker_props = {x: {} for x in style_codes}
+        # python 2.6 compat version:
+        marker_props = dict((x, {}) for x in style_codes)
+    for j in range(nval):
+        sc = style_codes[j]
+        if "color" not in marker_props[sc]:
+            marker_props[sc]["color"] = colors[j % len(colors)]
+        if "marker" not in marker_props[sc]:
+            marker_props[sc]["marker"] = "o"
+        if "ms" not in marker_props[sc]:
+            marker_props[sc]["ms"] = 10 if stackd == 0 else 6
+
+    # Setup default line styles
+    if line_props is None:
+        #line_props = {x: {} for x in style_codes}
+        # python 2.6 compat version:
+        line_props = dict((x, {}) for x in style_codes)
+    for j in range(nval):
+        sc = style_codes[j]
+        if "color" not in line_props[sc]:
+            line_props[sc]["color"] = "grey"
+        if "linewidth" not in line_props[sc]:
+            line_props[sc]["linewidth"] = 2 if stackd > 0 else 8
+
+    if horizontal:
+        # The vertical position of the first line.
+        pos = top - dpos/2 if nsect == 1 else top
+    else:
+        # The horizontal position of the first line.
+        pos = bottom + dpos/2
+
+    # Points that have already been labeled
+    labeled = set()
+
+    # Positions of the y axis grid lines
+    ticks = []
+
+    # Loop through the sections
+    for k0 in lines0:
+
+        # Draw a section title
+        if nsect_title > 0:
+
+            if horizontal:
+
+                y0 = pos + dpos/2 if k0 == lines0[0] else pos
+
+                ax.fill_between((0, 1), (y0,y0),
+                                (pos-0.7*title_space_axes,
+                                 pos-0.7*title_space_axes),
+                                color='darkgrey',
+                                transform=ax.transAxes,
+                                zorder=1)
+
+                txt = ax.text(0.5, pos - 0.35*title_space_axes, k0,
+                              horizontalalignment='center',
+                              verticalalignment='center',
+                              transform=ax.transAxes)
+                txt.set_fontweight("bold")
+                pos -= title_space_axes
+
+            else:
+
+                m = len([k for k in lines_map if k[0] == k0])
+
+                ax.fill_between((pos-dpos/2+0.01,
+                                 pos+(m-1)*dpos+dpos/2-0.01),
+                                (1.01,1.01), (1.06,1.06),
+                                color='darkgrey',
+                                transform=ax.transAxes,
+                                zorder=1, clip_on=False)
+
+                txt = ax.text(pos + (m-1)*dpos/2, 1.02, k0,
+                              horizontalalignment='center',
+                              verticalalignment='bottom',
+                              transform=ax.transAxes)
+                txt.set_fontweight("bold")
+
+        jrow = 0
+        for k1 in lines1:
+
+            # No data to plot
+            if (k0, k1) not in lines_map:
+                continue
+
+            # Draw the guideline
+            if horizontal:
+                ax.axhline(pos, color='grey')
+            else:
+                ax.axvline(pos, color='grey')
+
+            # Set up the labels
+            if split_names is not None:
+                us = k1.split(split_names)
+                if len(us) >= 2:
+                    left_label, right_label = us[0], us[1]
+                else:
+                    left_label, right_label = k1, None
+            else:
+                left_label, right_label = k1, None
+
+            # Draw the stripe
+            if striped and jrow % 2 == 0:
+                if horizontal:
+                    ax.fill_between((0, 1), (pos-dpos/2, pos-dpos/2),
+                                    (pos+dpos/2, pos+dpos/2),
+                                    color='lightgrey',
+                                    transform=ax.transAxes,
+                                    zorder=0)
+                else:
+                    ax.fill_between((pos-dpos/2, pos+dpos/2),
+                                    (0, 0), (1, 1),
+                                    color='lightgrey',
+                                    transform=ax.transAxes,
+                                    zorder=0)
+
+            jrow += 1
+
+            # Draw the left margin label
+            if horizontal:
+                ax.text(-0.1/awidth, pos, left_label,
+                           horizontalalignment="right",
+                           verticalalignment='center',
+                           transform=ax.transAxes, family='monospace')
+            else:
+                ax.text(pos, -0.1/aheight, left_label,
+                           horizontalalignment="center",
+                           verticalalignment='top',
+                           transform=ax.transAxes, family='monospace')
+
+            # Draw the right margin label
+            if right_label is not None:
+                if horizontal:
+                    ax.text(1 + 0.1/awidth, pos, right_label,
+                            horizontalalignment="left",
+                            verticalalignment='center',
+                            transform=ax.transAxes,
+                            family='monospace')
+                else:
+                    ax.text(pos, 1 + 0.1/aheight, right_label,
+                            horizontalalignment="center",
+                            verticalalignment='bottom',
+                            transform=ax.transAxes,
+                            family='monospace')
+
+            # Save the vertical position so that we can place the
+            # tick marks
+            ticks.append(pos)
+
+            # Loop over the points in one line
+            for ji,jp in enumerate(lines_map[(k0,k1)]):
+
+                # Calculate the vertical offset
+                yo = 0
+                if stacked:
+                    yo = -dpos/5 + style_codes_map[styles[jp]]*stackd
+
+                pt = points[jp]
+
+                # Plot the interval
+                if intervals is not None:
+
+                    # Symmetric interval
+                    if np.isscalar(intervals[jp]):
+                        lcb, ucb = pt - intervals[jp],\
+                            pt + intervals[jp]
+
+                    # Nonsymmetric interval
+                    else:
+                        lcb, ucb = pt - intervals[jp][0],\
+                            pt + intervals[jp][1]
+
+                    # Draw the interval
+                    if horizontal:
+                        ax.plot([lcb, ucb], [pos+yo, pos+yo], '-',
+                                transform=trans,
+                                **line_props[styles[jp]])
+                    else:
+                        ax.plot([pos+yo, pos+yo], [lcb, ucb], '-',
+                                transform=trans,
+                                **line_props[styles[jp]])
+
+
+                # Plot the point
+                sl = styles[jp]
+                sll = sl if sl not in labeled else None
+                labeled.add(sl)
+                if horizontal:
+                    ax.plot([pt,], [pos+yo,], ls='None',
+                            transform=trans, label=sll,
+                            **marker_props[sl])
+                else:
+                    ax.plot([pos+yo,], [pt,], ls='None',
+                            transform=trans, label=sll,
+                            **marker_props[sl])
+
+            if horizontal:
+                pos -= dpos
+            else:
+                pos += dpos
+
+    # Set up the axis
+    if horizontal:
+        ax.xaxis.set_ticks_position("bottom")
+        ax.yaxis.set_ticks_position("none")
+        ax.set_yticklabels([])
+        ax.spines['left'].set_color('none')
+        ax.spines['right'].set_color('none')
+        ax.spines['top'].set_color('none')
+        ax.spines['bottom'].set_position(('axes', -0.1/aheight))
+        ax.set_ylim(0, 1)
+        ax.yaxis.set_ticks(ticks)
+        ax.autoscale_view(scaley=False, tight=True)
+    else:
+        ax.yaxis.set_ticks_position("left")
+        ax.xaxis.set_ticks_position("none")
+        ax.set_xticklabels([])
+        ax.spines['bottom'].set_color('none')
+        ax.spines['right'].set_color('none')
+        ax.spines['top'].set_color('none')
+        ax.spines['left'].set_position(('axes', -0.1/awidth))
+        ax.set_xlim(0, 1)
+        ax.xaxis.set_ticks(ticks)
+        ax.autoscale_view(scalex=False, tight=True)
+
+    return fig
diff --git a/statsmodels/graphics/factorplots.py b/statsmodels/graphics/factorplots.py
index 14fd937e994..1d022cf6c8f 100644
--- a/statsmodels/graphics/factorplots.py
+++ b/statsmodels/graphics/factorplots.py
@@ -2,10 +2,11 @@
 """
 Authors:    Josef Perktold, Skipper Seabold, Denis A. Engemann
 """
+from statsmodels.compat.python import get_function_name, iterkeys, lrange, zip, iteritems
 import numpy as np
 
 from statsmodels.graphics.plottools import rainbow
-import utils
+import statsmodels.graphics.utils as utils
 
 
 def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
@@ -91,7 +92,7 @@ def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
     fig, ax = utils.create_mpl_ax(ax)
 
     response_name = ylabel or getattr(response, 'name', 'response')
-    ylabel = '%s of %s' % (func.func_name, response_name)
+    ylabel = '%s of %s' % (get_function_name(func), response_name)
     xlabel = xlabel or getattr(x, 'name', 'X')
     legendtitle = legendtitle or getattr(trace, 'name', 'Trace')
 
@@ -101,7 +102,7 @@ def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
     x_values = x_levels = None
     if isinstance(x[0], str):
         x_levels = [l for l in np.unique(x)]
-        x_values = xrange(len(x_levels))
+        x_values = lrange(len(x_levels))
         x = _recode(x, dict(zip(x_levels, x_values)))
 
     data = DataFrame(dict(x=x, trace=trace, response=response))
@@ -114,21 +115,21 @@ def interaction_plot(x, trace, response, func=np.mean, ax=None, plottype='b',
     if linestyles:
         try:
             assert len(linestyles) == n_trace
-        except AssertionError, err:
+        except AssertionError as err:
             raise ValueError("Must be a linestyle for each trace level")
     else:  # set a default
         linestyles = ['-'] * n_trace
     if markers:
         try:
             assert len(markers) == n_trace
-        except AssertionError, err:
+        except AssertionError as err:
             raise ValueError("Must be a linestyle for each trace level")
     else:  # set a default
         markers = ['.'] * n_trace
     if colors:
         try:
             assert len(colors) == n_trace
-        except AssertionError, err:
+        except AssertionError as err:
             raise ValueError("Must be a linestyle for each trace level")
     else:  # set a default
         #TODO: how to get n_trace different colors?
@@ -196,12 +197,12 @@ def _recode(x, levels):
         raise ValueError('This is not a valid value for levels.'
                          ' Dict required.')
 
-    elif not (np.unique(x) == np.unique(levels.keys())).all():
+    elif not (np.unique(x) == np.unique(list(iterkeys(levels)))).all():
         raise ValueError('The levels do not match the array values.')
 
     else:
         out = np.empty(x.shape[0], dtype=np.int)
-        for level, coding in levels.items():
+        for level, coding in iteritems(levels):
             out[x == level] = coding
 
         if name:
diff --git a/statsmodels/graphics/functional.py b/statsmodels/graphics/functional.py
index 3c7e6873b1c..1fe01e24538 100644
--- a/statsmodels/graphics/functional.py
+++ b/statsmodels/graphics/functional.py
@@ -1,7 +1,6 @@
 """Module for functional boxplots."""
 
-from statsmodels.compatnp.iter_compat import combinations
-
+from statsmodels.compat.python import combinations, range
 import numpy as np
 from scipy import stats
 from scipy.misc import factorial
diff --git a/statsmodels/graphics/gofplots.py b/statsmodels/graphics/gofplots.py
index b904c183b76..d81a7897af5 100644
--- a/statsmodels/graphics/gofplots.py
+++ b/statsmodels/graphics/gofplots.py
@@ -1,7 +1,11 @@
+from statsmodels.compat.python import lzip, string_types
 import numpy as np
 from scipy import stats
 from statsmodels.regression.linear_model import OLS
 from statsmodels.tools.tools import add_constant
+from statsmodels.tools.decorators import (resettable_cache,
+                                          cache_readonly,
+                                          cache_writable)
 
 from . import utils
 
@@ -127,7 +131,7 @@ def __init__(self, data, dist=stats.norm, fit=False,
         self.distargs = distargs
         self.fit = fit
 
-        if isinstance(dist, basestring):
+        if isinstance(dist, string_types):
             dist = getattr(stats, dist)
 
         self.fit_params = dist.fit(data)
@@ -148,46 +152,56 @@ def __init__(self, data, dist=stats.norm, fit=False,
             self.loc = loc
             self.scale = scale
 
+        # propertes
+        self._cache = resettable_cache()
 
+    @cache_readonly
     def theoretical_percentiles(self):
         return plotting_pos(self.nobs, self.a)
 
+    @cache_readonly
     def theoretical_quantiles(self):
         try:
-            return self.dist.ppf(self.theoretical_percentiles())
+            return self.dist.ppf(self.theoretical_percentiles)
         except TypeError:
-            print('%s requires more parameters to compute ppf' % \
-                    (self.dist.name,))
+            msg = '%s requires more parameters to ' \
+                  'compute ppf'.format(self.dist.name,)
+            raise TypeError(msg)
         except:
-            print('failed to compute the ppf of %s' % \
-                    (self.dist.name,))
+            msg = 'failed to compute the ppf of {0}'.format(self.dist.name,)
+            raise
 
+    @cache_readonly
     def sorted_data(self):
         sorted_data = np.array(self.data, copy=True)
         sorted_data.sort()
         return sorted_data
 
+    @cache_readonly
     def sample_quantiles(self):
         if self.fit and self.loc != 0 and self.scale != 1:
-            return (self.sorted_data() - self.loc)/self.scale
+            return (self.sorted_data-self.loc)/self.scale
         else:
-            return self.sorted_data()
+            return self.sorted_data
 
+    @cache_readonly
     def sample_percentiles(self):
-        qntls = (self.sorted_data() - self.fit_params[-2])/self.fit_params[-1]
-        return self.dist.cdf(qntls)
+        quantiles = \
+            (self.sorted_data - self.fit_params[-2])/self.fit_params[-1]
+        return self.dist.cdf(quantiles)
 
-    def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
+    def ppplot(self, xlabel=None, ylabel=None, line=None, other=None,
+               ax=None, **plotkwargs):
         """
         P-P plot of the percentiles (probabilities) of x versus the
         probabilities (percetiles) of a distribution.
 
         Parameters
         ----------
-        xlabel, ylabel : str or None
+        xlabel, ylabel : str or None, optional
             User-provided lables for the x-axis and y-axis. If None (default),
             other values are used depending on the status of the kwarg `other`.
-        line : str {'45', 's', 'r', q'} or None
+        line : str {'45', 's', 'r', q'} or None, optional
             Options for the reference line to which the data is compared:
 
             - '45' - 45-degree line
@@ -197,10 +211,8 @@ def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
             - 'r' - A regression line is fit
             - 'q' - A line is fit through the quartiles.
             - None - by default no reference line is added to the plot.
-            - If True a reference line is drawn on the graph. The default is to
-              fit a line via OLS regression.
 
-        other : `ProbPlot` instance, array-like, or None
+        other : `ProbPlot` instance, array-like, or None, optional
             If provided, the sample quantiles of this `ProbPlot` instance are
             plotted against the sample quantiles of the `other` `ProbPlot`
             instance. If an array-like object is provided, it will be turned
@@ -209,6 +221,8 @@ def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
         ax : Matplotlib AxesSubplot instance, optional
             If given, this subplot is used to plot in instead of a new figure
             being created.
+        **plotkwargs : additional matplotlib arguments to be passed to the
+            `plot` command.
 
         Returns
         -------
@@ -221,9 +235,10 @@ def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
             if not check_other:
                 other = ProbPlot(other)
 
-            fig, ax = _do_plot(other.sample_percentiles(),
-                               self.sample_percentiles(),
-                               self.dist, ax=ax, line=line)
+            fig, ax = _do_plot(other.sample_percentiles,
+                               self.sample_percentiles,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
 
             if xlabel is None:
                 xlabel = 'Probabilities of 2nd Sample'
@@ -231,9 +246,10 @@ def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
                 ylabel = 'Probabilities of 1st Sample'
 
         else:
-            fig, ax = _do_plot(self.theoretical_percentiles(),
-                               self.sample_percentiles(),
-                               self.dist, ax=ax, line=line)
+            fig, ax = _do_plot(self.theoretical_percentiles,
+                               self.sample_percentiles,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
             if xlabel is None:
                 xlabel = "Theoretical Probabilities"
             if ylabel is None:
@@ -247,19 +263,18 @@ def ppplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
 
         return fig
 
-        return fig
-
-    def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
+    def qqplot(self, xlabel=None, ylabel=None, line=None, other=None,
+               ax=None, **plotkwargs):
         """
         Q-Q plot of the quantiles of x versus the quantiles/ppf of a
         distribution or the quantiles of another `ProbPlot` instance.
 
         Parameters
         ----------
-        xlabel, ylabel : str or None
+        xlabel, ylabel : str or None, optional
             User-provided lables for the x-axis and y-axis. If None (default),
             other values are used depending on the status of the kwarg `other`.
-        line : str {'45', 's', 'r', q'} or None
+        line : str {'45', 's', 'r', q'} or None, optional
             Options for the reference line to which the data is compared:
 
             - '45' - 45-degree line
@@ -269,18 +284,18 @@ def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
             - 'r' - A regression line is fit
             - 'q' - A line is fit through the quartiles.
             - None - by default no reference line is added to the plot.
-            - If True a reference line is drawn on the graph. The default is to
-              fit a line via OLS regression.
 
-        other : `ProbPlot` instance, array-like, or None
+        other : `ProbPlot` instance, array-like, or None, optional
             If provided, the sample quantiles of this `ProbPlot` instance are
             plotted against the sample quantiles of the `other` `ProbPlot`
             instance. If an array-like object is provided, it will be turned
-            into a `ProbPlot` instance using default parameters. If not provided
-            (defualt), the theoretical quantiles are used.
+            into a `ProbPlot` instance using default parameters. If not
+            provided (default), the theoretical quantiles are used.
         ax : Matplotlib AxesSubplot instance, optional
             If given, this subplot is used to plot in instead of a new figure
             being created.
+        **plotkwargs : additional matplotlib arguments to be passed to the
+            `plot` command.
 
         Returns
         -------
@@ -293,9 +308,10 @@ def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
             if not check_other:
                 other = ProbPlot(other)
 
-            fig, ax = _do_plot(other.sample_quantiles(),
-                               self.sample_quantiles(),
-                               self.dist, ax=ax, line=line)
+            fig, ax = _do_plot(other.sample_quantiles,
+                               self.sample_quantiles,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
 
             if xlabel is None:
                 xlabel = 'Quantiles of 2nd Sample'
@@ -303,9 +319,10 @@ def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
                 ylabel = 'Quantiles of 1st Sample'
 
         else:
-            fig, ax = _do_plot(self.theoretical_quantiles(),
-                               self.sample_quantiles(),
-                               self.dist, ax=ax, line=line)
+            fig, ax = _do_plot(self.theoretical_quantiles,
+                               self.sample_quantiles,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
             if xlabel is None:
                 xlabel = "Theoretical Quantiles"
             if ylabel is None:
@@ -316,7 +333,8 @@ def qqplot(self, xlabel=None, ylabel=None, line=None, other=None, ax=None):
 
         return fig
 
-    def probplot(self, line=None, ax=None, exceed=False):
+    def probplot(self, xlabel=None, ylabel=None, line=None,
+                 exceed=False, ax=None, **plotkwargs):
         """
         Probability plot of the unscaled quantiles of x versus the
         probabilities of a distibution (not to be confused with a P-P plot).
@@ -326,7 +344,10 @@ def probplot(self, line=None, ax=None, exceed=False):
 
         Parameters
         ----------
-        line : str {'45', 's', 'r', q'} or None
+        xlabel, ylabel : str or None, optional
+            User-provided lables for the x-axis and y-axis. If None (default),
+            other values are used depending on the status of the kwarg `other`.
+        line : str {'45', 's', 'r', q'} or None, optional
             Options for the reference line to which the data is compared:
 
             - '45' - 45-degree line
@@ -336,19 +357,20 @@ def probplot(self, line=None, ax=None, exceed=False):
             - 'r' - A regression line is fit
             - 'q' - A line is fit through the quartiles.
             - None - by default no reference line is added to the plot.
-            - If True a reference line is drawn on the graph. The default is to
-              fit a line via OLS regression.
 
-        ax : Matplotlib AxesSubplot instance, optional
-            If given, this subplot is used to plot in instead of a new figure
-            being created.
-        excced : boolean
+        exceed : boolean, optional
+
              - If False (default) the raw sample quantiles are plotted against
                the theoretical quantiles, show the probability that a sample
                will not exceed a given value
              - If True, the theoretical quantiles are flipped such that the
                figure displays the probability that a sample will exceed a
                given value.
+        ax : Matplotlib AxesSubplot instance, optional
+            If given, this subplot is used to plot in instead of a new figure
+            being created.
+        **plotkwargs : additional matplotlib arguments to be passed to the
+            `plot` command.
 
         Returns
         -------
@@ -357,25 +379,32 @@ def probplot(self, line=None, ax=None, exceed=False):
             `ax` is connected.
         """
         if exceed:
-            fig, ax = _do_plot(self.theoretical_quantiles()[::-1],
-                               self.sorted_data(),
-                               self.dist, ax=ax, line=line)
-            xlabel = 'Probability of Exceedance (%)'
+            fig, ax = _do_plot(self.theoretical_quantiles[::-1],
+                               self.sorted_data,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
+            if xlabel is None:
+                xlabel = 'Probability of Exceedance (%)'
 
         else:
-            fig, ax = _do_plot(self.theoretical_quantiles(),
-                               self.sorted_data(),
-                               self.dist, ax=ax, line=line)
-            xlabel = 'Non-exceedance Probability (%)'
+            fig, ax = _do_plot(self.theoretical_quantiles,
+                               self.sorted_data,
+                               self.dist, ax=ax, line=line,
+                               **plotkwargs)
+            if xlabel is None:
+                xlabel = 'Non-exceedance Probability (%)'
+
+        if ylabel is None:
+            ylabel = "Sample Quantiles"
 
         ax.set_xlabel(xlabel)
-        ax.set_ylabel("Sample Quantiles")
+        ax.set_ylabel(ylabel)
         _fmt_probplot_axis(ax, self.dist, self.nobs)
 
         return fig
 
 def qqplot(data, dist=stats.norm, distargs=(), a=0, loc=0, scale=1, fit=False,
-           line=False, ax=None):
+           line=None, ax=None):
     """
     Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution.
 
@@ -416,8 +445,6 @@ def qqplot(data, dist=stats.norm, distargs=(), a=0, loc=0, scale=1, fit=False,
         - 'r' - A regression line is fit
         - 'q' - A line is fit through the quartiles.
         - None - by default no reference line is added to the plot.
-        - If True a reference line is drawn on the graph. The default is to
-          fit a line via OLS regression.
 
     ax : Matplotlib AxesSubplot instance, optional
         If given, this subplot is used to plot in instead of a new figure being
@@ -502,8 +529,6 @@ def qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None):
         - 'r' - A regression line is fit
         - 'q' - A line is fit through the quartiles.
         - None - by default no reference line is added to the plot.
-        - If True a reference line is drawn on the graph. The default is to
-          fit a line via OLS regression.
 
     ax : Matplotlib AxesSubplot instance, optional
         If given, this subplot is used to plot in instead of a new figure being
@@ -579,7 +604,7 @@ def qqline(ax, line, x=None, y=None, dist=None, fmt='r-'):
     There is no return value. The line is plotted on the given `ax`.
     """
     if line == '45':
-        end_pts = zip(ax.get_xlim(), ax.get_ylim())
+        end_pts = lzip(ax.get_xlim(), ax.get_ylim())
         end_pts[0] = min(end_pts[0])
         end_pts[1] = max(end_pts[1])
         ax.plot(end_pts, end_pts, fmt)
@@ -674,7 +699,7 @@ def _fmt_probplot_axis(ax, dist, nobs):
                        verticalalignment='center')
     ax.set_xlim([axis_qntls.min(), axis_qntls.max()])
 
-def _do_plot(x, y, dist=None, line=False, ax=None, fmt='bo'):
+def _do_plot(x, y, dist=None, line=False, ax=None, fmt='bo', **kwargs):
     """
     Boiler plate plotting function for the `ppplot`, `qqplot`, and
     `probplot` methods of the `ProbPlot` class
@@ -692,6 +717,8 @@ def _do_plot(x, y, dist=None, line=False, ax=None, fmt='bo'):
         created.
     fmt : str, optional
         matplotlib-compatible formatting string for the data markers
+    kwargs : keywords
+        These are passed to matplotlib.plot
 
     Returns
     -------
@@ -701,7 +728,7 @@ def _do_plot(x, y, dist=None, line=False, ax=None, fmt='bo'):
     """
     fig, ax = utils.create_mpl_ax(ax)
     ax.set_xmargin(0.02)
-    ax.plot(x, y, fmt)
+    ax.plot(x, y, fmt, **kwargs)
     if line:
         if line not in ['r','q','45','s']:
             msg = "%s option for line not understood" % line
diff --git a/statsmodels/graphics/mosaicplot.py b/statsmodels/graphics/mosaicplot.py
index 41b15190946..8d6c456ceba 100644
--- a/statsmodels/graphics/mosaicplot.py
+++ b/statsmodels/graphics/mosaicplot.py
@@ -8,9 +8,10 @@
 # Author: Enrico Giampieri - 21 Jan 2013
 
 from __future__ import division
-
+from statsmodels.compat.python import (iteritems, iterkeys, lrange, string_types, lzip,
+                                itervalues, zip, range)
 import numpy as np
-from statsmodels.compatnp.collections import OrderedDict
+from statsmodels.compat.collections import OrderedDict
 from itertools import product
 
 from numpy import iterable, r_, cumsum, array
@@ -94,7 +95,7 @@ def _reduce_dict(count_dict, partial_key):
     Given a match for the beginning of the category, it will sum each value.
     """
     L = len(partial_key)
-    count = sum(v for k, v in count_dict.items() if k[:L] == partial_key)
+    count = sum(v for k, v in iteritems(count_dict) if k[:L] == partial_key)
     return count
 
 
@@ -107,7 +108,7 @@ def _key_splitting(rect_dict, keys, values, key_subset, horizontal, gap):
     """
     result = OrderedDict()
     L = len(key_subset)
-    for name, (x, y, w, h) in rect_dict.items():
+    for name, (x, y, w, h) in iteritems(rect_dict):
         if key_subset == name[:L]:
             # split base on the values given
             divisions = _split_rect(x, y, w, h, values, horizontal, gap)
@@ -122,7 +123,7 @@ def _tuplify(obj):
     """convert an object in a tuple of strings (even if it is not iterable,
     like a single integer number, but keep the string healthy)
     """
-    if np.iterable(obj) and not isinstance(obj, basestring):
+    if np.iterable(obj) and not isinstance(obj, string_types):
         res = tuple(str(o) for o in obj)
     else:
         res = (str(obj),)
@@ -184,7 +185,7 @@ def _hierarchical_split(count_dict, horizontal=True, gap=0.05):
     # this is the unit square that we are going to divide
     base_rect = OrderedDict([(tuple(), (0, 0, 1, 1))])
     # get the list of each possible value for each level
-    categories_levels = _categories_level(list(count_dict.keys()))
+    categories_levels = _categories_level(list(iterkeys(count_dict)))
     L = len(categories_levels)
 
     # recreate the gaps vector starting from an int
@@ -231,7 +232,7 @@ def _create_default_properties(data):
     decoration on the rectangle.  Doesn't manage more than four
     level of categories
     """
-    categories_levels = _categories_level(list(data.keys()))
+    categories_levels = _categories_level(list(iterkeys(data)))
     Nlevels = len(categories_levels)
     # first level, the hue
     L = len(categories_levels[0])
@@ -247,13 +248,13 @@ def _create_default_properties(data):
     L = len(categories_levels[3]) if Nlevels > 3 else 1
     hatch = ['', '/', '-', '|', '+'][:L + 1]
     # convert in list and merge with the levels
-    hue = list(zip(list(hue), categories_levels[0]))
-    saturation = list(zip(list(saturation),
-                     categories_levels[1] if Nlevels > 1 else ['']))
-    value = list(zip(list(value),
-                     categories_levels[2] if Nlevels > 2 else ['']))
-    hatch = list(zip(list(hatch),
-                     categories_levels[3] if Nlevels > 3 else ['']))
+    hue = lzip(list(hue), categories_levels[0])
+    saturation = lzip(list(saturation),
+                     categories_levels[1] if Nlevels > 1 else [''])
+    value = lzip(list(value),
+                     categories_levels[2] if Nlevels > 2 else [''])
+    hatch = lzip(list(hatch),
+                     categories_levels[3] if Nlevels > 3 else [''])
     # create the properties dictionary
     properties = {}
     for h, s, v, t in product(hue, saturation, value, hatch):
@@ -288,7 +289,7 @@ def _normalize_data(data, index):
         index = None
     # can it be used as a dictionary?
     try:
-        items = list(data.iteritems())
+        items = list(iteritems(data))
     except AttributeError:
         # ok, I cannot use the data as a dictionary
         # Try to convert it to a numpy array, or die trying
@@ -298,10 +299,10 @@ def _normalize_data(data, index):
             name = tuple(i for i in idx)
             temp[name] = data[idx]
         data = temp
-        items = data.items()
+        items = list(iteritems(data))
     # make all the keys a tuple, even if simple numbers
     data = OrderedDict([_tuplify(k), v] for k, v in items)
-    categories_levels = _categories_level(list(data.keys()))
+    categories_levels = _categories_level(list(iterkeys(data)))
     # fill the void in the counting dictionary
     indexes = product(*categories_levels)
     contingency = OrderedDict([(k, data.get(k, 0)) for k in indexes])
@@ -309,9 +310,9 @@ def _normalize_data(data, index):
     # reorder the keys order according to the one specified by the user
     # or if the index is None convert it into a simple list
     # right now it doesn't do any check, but can be modified in the future
-    index = list(range(len(categories_levels))) if index is None else index
+    index = lrange(len(categories_levels)) if index is None else index
     contingency = OrderedDict()
-    for key, value in data.items():
+    for key, value in iteritems(data):
         new_key = tuple(key[i] for i in index)
         contingency[new_key] = value
     data = contingency
@@ -336,9 +337,9 @@ def _statistical_coloring(data):
     It will encounter problem if one category has all zeros
     """
     data = _normalize_data(data, None)
-    categories_levels = _categories_level(list(data.keys()))
+    categories_levels = _categories_level(list(iterkeys(data)))
     Nlevels = len(categories_levels)
-    total = 1.0 * sum(v for v in data.values())
+    total = 1.0 * sum(v for v in itervalues(data))
     # count the proportion of observation
     # for each level that has the given name
     # at each level
@@ -347,7 +348,7 @@ def _statistical_coloring(data):
         proportion = {}
         for level in categories_levels[level_idx]:
             proportion[level] = 0.0
-            for key, value in data.items():
+            for key, value in iteritems(data):
                 if level == key[level_idx]:
                     proportion[level] += value
             proportion[level] /= total
@@ -356,16 +357,16 @@ def _statistical_coloring(data):
     # and it's standard deviation from a binomial distribution
     # under the hipothesys of independence
     expected = {}
-    for key, value in data.items():
+    for key, value in iteritems(data):
         base = 1.0
         for i, k in enumerate(key):
             base *= levels_count[i][k]
         expected[key] = base * total, np.sqrt(total * base * (1.0 - base))
     # now we have the standard deviation of distance from the
     # expected value for each tile. We create the colors from this
-    sigmas = dict((k, (data[k] - m) / s) for k, (m, s) in expected.items())
+    sigmas = dict((k, (data[k] - m) / s) for k, (m, s) in iteritems(expected))
     props = {}
-    for key, dev in sigmas.items():
+    for key, dev in iteritems(sigmas):
         red = 0.0 if dev < 0 else (dev / (1 + dev))
         blue = 0.0 if dev > 0 else (dev / (-1 + dev))
         green = (1.0 - red - blue) / 2.0
@@ -382,14 +383,14 @@ def _create_labels(rects, horizontal, ax, rotation):
     ax: the axis on which the label should be applied
     rotation: the rotation list for each side
     """
-    categories = _categories_level(list(rects.keys()))
+    categories = _categories_level(list(iterkeys(rects)))
     if len(categories) > 4:
         msg = ("maximum of 4 level supported for axes labeling..and 4"
                "is alreay a lot of level, are you sure you need them all?")
         raise NotImplementedError(msg)
     labels = {}
     #keep it fixed as will be used a lot of times
-    items = list(rects.items())
+    items = list(iteritems(rects))
     vertical = not horizontal
 
     #get the axis ticks and labels locator to put the correct values!
@@ -439,7 +440,8 @@ def _create_labels(rects, horizontal, ax, rotation):
             #mean of all these center on the area of the tile
             #this should give me the (more or less) correct position
             #of the center of the category
-            vals = list(subset.values())
+
+            vals = list(itervalues(subset))
             W = sum(w * h for (x, y, w, h) in vals)
             x_lab = sum((x + w / 2.0) * w * h / W for (x, y, w, h) in vals)
             y_lab = sum((y + h / 2.0) * w * h / W for (x, y, w, h) in vals)
@@ -449,8 +451,9 @@ def _create_labels(rects, horizontal, ax, rotation):
             side = (level_idx + vertical) % 4
             level_ticks[value] = y_lab if side % 2 else x_lab
         #now we add the labels of this level to the correct axis
-        ticks_pos[level_idx](list(level_ticks.values()))
-        ticks_lab[level_idx](list(level_ticks.keys()),
+
+        ticks_pos[level_idx](list(itervalues(level_ticks)))
+        ticks_lab[level_idx](list(iterkeys(level_ticks)),
                              rotation=rotation[level_idx])
     return labels
 
@@ -616,7 +619,7 @@ def mosaic(data, index=None, ax=None, horizontal=True, gap=0.005,
     if isinstance(properties, dict):
         color_dict = properties
         properties = lambda key: color_dict.get(key, None)
-    for k, v in rects.items():
+    for k, v in iteritems(rects):
         # create each rectangle and put a label on it
         x, y, w, h = v
         conf = properties(k)
diff --git a/statsmodels/graphics/plot_grids.py b/statsmodels/graphics/plot_grids.py
index 626923ebbff..c8e3310eb3a 100644
--- a/statsmodels/graphics/plot_grids.py
+++ b/statsmodels/graphics/plot_grids.py
@@ -11,6 +11,7 @@
 '''
 
 
+from statsmodels.compat.python import range
 import numpy as np
 from scipy import stats
 
diff --git a/statsmodels/graphics/regressionplots.py b/statsmodels/graphics/regressionplots.py
index 6cecaf1821b..d44bb528dc8 100644
--- a/statsmodels/graphics/regressionplots.py
+++ b/statsmodels/graphics/regressionplots.py
@@ -10,8 +10,9 @@
 2011-10-27 : docstrings
 
 '''
-
+from statsmodels.compat.python import lrange, string_types, lzip, range
 import numpy as np
+from patsy import dmatrix
 
 from statsmodels.regression.linear_model import OLS
 from statsmodels.sandbox.regression.predstd import wls_prediction_std
@@ -31,6 +32,7 @@ def _high_leverage(results):
     #TODO: replace 1 with k_constant
     return 2. * (results.df_model + 1)/results.nobs
 
+
 def add_lowess(ax, lines_idx=0, frac=.2, **lowess_kwargs):
     """
     Add Lowess line to a plot.
@@ -55,9 +57,10 @@ def add_lowess(ax, lines_idx=0, frac=.2, **lowess_kwargs):
     y0 = ax.get_lines()[lines_idx]._y
     x0 = ax.get_lines()[lines_idx]._x
     lres = lowess(y0, x0, frac=frac, **lowess_kwargs)
-    ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5)
+    ax.plot(lres[:, 0], lres[:, 1], 'r', lw=1.5)
     return ax.figure
 
+
 def plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs):
     """Plot fit against one regressor.
 
@@ -92,7 +95,6 @@ def plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs):
 
     >>> import statsmodels.api as sm
     >>> import matplotlib.pyplot as plt
-    >>> import numpy as np
 
     >>> data = sm.datasets.statecrime.load_pandas().data
     >>> murder = data['murder']
@@ -138,7 +140,7 @@ def plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs):
     ax.plot(x1, results.fittedvalues[x1_argsort], 'D', color='r',
             label='fitted', **kwargs)
     ax.vlines(x1, iv_l[x1_argsort], iv_u[x1_argsort], linewidth=1, color='k',
-            alpha=.7)
+              alpha=.7)
     #ax.fill_between(x1, iv_l[x1_argsort], iv_u[x1_argsort], alpha=0.1,
     #                    color='k')
     ax.set_title(title)
@@ -178,39 +180,40 @@ def plot_regress_exog(results, exog_idx, fig=None):
 
     #maybe add option for wendog, wexog
     y_name = results.model.endog_names
-    x1 = results.model.exog[:,exog_idx]
+    x1 = results.model.exog[:, exog_idx]
     prstd, iv_l, iv_u = wls_prediction_std(results)
 
-    ax = fig.add_subplot(2,2,1)
+    ax = fig.add_subplot(2, 2, 1)
     ax.plot(x1, results.model.endog, 'o', color='b', alpha=0.9, label=y_name)
     ax.plot(x1, results.fittedvalues, 'D', color='r', label='fitted',
-                alpha=.5)
+            alpha=.5)
     ax.vlines(x1, iv_l, iv_u, linewidth=1, color='k', alpha=.7)
     ax.set_title('Y and Fitted vs. X', fontsize='large')
     ax.set_xlabel(exog_name)
     ax.set_ylabel(y_name)
     ax.legend(loc='best')
 
-    ax = fig.add_subplot(2,2,2)
+    ax = fig.add_subplot(2, 2, 2)
     ax.plot(x1, results.resid, 'o')
     ax.axhline(y=0, color='black')
     ax.set_title('Residuals versus %s' % exog_name, fontsize='large')
     ax.set_xlabel(exog_name)
     ax.set_ylabel("resid")
 
-    ax = fig.add_subplot(2,2,3)
+    ax = fig.add_subplot(2, 2, 3)
     exog_noti = np.ones(results.model.exog.shape[1], bool)
     exog_noti[exog_idx] = False
     exog_others = results.model.exog[:, exog_noti]
     from pandas import Series
     fig = plot_partregress(results.model.data.orig_endog,
-            Series(x1, name=exog_name, index=results.model.data.row_labels),
-            exog_others, obs_labels=False, ax=ax)
+                           Series(x1, name=exog_name,
+                                  index=results.model.data.row_labels),
+                           exog_others, obs_labels=False, ax=ax)
     ax.set_title('Partial regression plot', fontsize='large')
     #ax.set_ylabel("Fitted values")
     #ax.set_xlabel(exog_name)
 
-    ax = fig.add_subplot(2,2,4)
+    ax = fig.add_subplot(2, 2, 4)
     fig = plot_ccpr(results, exog_idx, ax=ax)
     ax.set_title('CCPR Plot', fontsize='large')
     #ax.set_xlabel(exog_name)
@@ -317,25 +320,19 @@ def plot_partregress(endog, exog_i, exog_others, data=None,
     #obs_labels yet, so this will need to be tweaked a bit for this case
     fig, ax = utils.create_mpl_ax(ax)
 
-    if (isinstance(endog, basestring) or isinstance(exog_others,
-                                                   (basestring, list)) or
-        isinstance(exog_i, basestring)):
-        from patsy import dmatrix
-
     # strings, use patsy to transform to data
-    if isinstance(endog, basestring):
+    if isinstance(endog, string_types):
         endog = dmatrix(endog + "-1", data)
 
-    if isinstance(exog_others, basestring):
-        RHS = dmatrix(RHS, data)
+    if isinstance(exog_others, string_types):
+        RHS = dmatrix(exog_others, data)
     elif isinstance(exog_others, list):
         RHS = "+".join(exog_others)
         RHS = dmatrix(RHS, data)
     else:
         RHS = exog_others
 
-    if isinstance(exog_i, basestring):
-        varname = exog_i
+    if isinstance(exog_i, string_types):
         exog_i = dmatrix(exog_i + "-1", data)
 
     # all arrays or pandas-like
@@ -346,7 +343,7 @@ def plot_partregress(endog, exog_i, exog_others, data=None,
     fitted_line = OLS(res_yaxis.resid, res_xaxis.resid).fit()
     fig = abline_plot(0, fitted_line.params[0], color='k', ax=ax)
     x_axis_endog_name = res_xaxis.model.endog_names
-    if x_axis_endog_name == 'y': # for no names regression will just get a y
+    if x_axis_endog_name == 'y':  # for no names regression will just get a y
         x_axis_endog_name = 'x'  # this is misleading, so use x
     ax.set_xlabel("e(%s | X)" % x_axis_endog_name)
     ax.set_ylabel("e(%s | X)" % res_yaxis.model.endog_names)
@@ -367,19 +364,19 @@ def plot_partregress(endog, exog_i, exog_others, data=None,
         #NOTE: row_labels can be None.
         #Maybe we should fix this to never be the case.
         if obs_labels is None:
-            obs_labels = range(len(exog_i))
+            obs_labels = lrange(len(exog_i))
 
-    if obs_labels is not False: # could be array-like
+    if obs_labels is not False:  # could be array-like
         if len(obs_labels) != len(exog_i):
             raise ValueError("obs_labels does not match length of exog_i")
         label_kwargs.update(dict(ha="center", va="bottom"))
-        ax = utils.annotate_axes(range(len(obs_labels)), obs_labels,
-                            zip(res_xaxis.resid, res_yaxis.resid),
-                            [(0, 5)] * len(obs_labels), "x-large", ax=ax,
-                            **label_kwargs)
+        ax = utils.annotate_axes(lrange(len(obs_labels)), obs_labels,
+                                 lzip(res_xaxis.resid, res_yaxis.resid),
+                                 [(0, 5)] * len(obs_labels), "x-large", ax=ax,
+                                 **label_kwargs)
 
     if ret_coords:
-        return fig, (res_axis.resid, res_yaxis.resid)
+        return fig, (res_xaxis.resid, res_yaxis.resid)
     else:
         return fig
 
@@ -449,8 +446,8 @@ def plot_partregress_grid(results, exog_idx=None, grid=None, fig=None):
 
     # for indexing purposes
     other_names = np.array(results.model.exog_names)
-    for i,idx in enumerate(exog_idx):
-        others = range(k_vars)
+    for i, idx in enumerate(exog_idx):
+        others = lrange(k_vars)
         others.pop(idx)
         exog_others = pandas.DataFrame(exog[:, others],
                                        columns=other_names[others])
@@ -591,7 +588,7 @@ def plot_ccpr_grid(results, exog_idx=None, grid=None, fig=None):
 
     seen_constant = 0
     for i, idx in enumerate(exog_idx):
-        if results.model.exog[:,idx].var() == 0:
+        if results.model.exog[:, idx].var() == 0:
             seen_constant = 1
             continue
 
@@ -648,19 +645,18 @@ def abline_plot(intercept=None, slope=None, horiz=None, vert=None,
     >>> import matplotlib.pyplot as plt
     >>> plt.show()
     """
-    if ax is not None: # get axis limits first thing, don't change these
+    if ax is not None:  # get axis limits first thing, don't change these
         x = ax.get_xlim()
-        y = ax.get_ylim()
     else:
         x = None
 
-    fig,ax = utils.create_mpl_ax(ax)
+    fig, ax = utils.create_mpl_ax(ax)
 
     if model_results:
         intercept, slope = model_results.params
         if x is None:
-            x = [model_results.model.exog[:,1].min(),
-                 model_results.model.exog[:,1].max()]
+            x = [model_results.model.exog[:, 1].min(),
+                 model_results.model.exog[:, 1].max()]
     else:
         if not (intercept is not None and slope is not None):
             raise ValueError("specify slope and intercepty or model_results")
@@ -680,10 +676,10 @@ def update_datalim(self, ax):
 
             children = ax.get_children()
             abline = [children[i] for i in range(len(children))
-                       if isinstance(children[i], ABLine2D)][0]
+                      if isinstance(children[i], ABLine2D)][0]
             x = ax.get_xlim()
             y = [x[0]*slope+intercept, x[1]*slope+intercept]
-            abline.set_data(x,y)
+            abline.set_data(x, y)
             ax.figure.canvas.draw()
     #TODO: how to intercept something like a margins call and adjust?
 
@@ -692,7 +688,6 @@ def update_datalim(self, ax):
     ax.callbacks.connect('xlim_changed', line.update_datalim)
     ax.callbacks.connect('ylim_changed', line.update_datalim)
 
-
     if horiz:
         ax.hline(horiz)
     if vert:
@@ -701,7 +696,7 @@ def update_datalim(self, ax):
 
 
 def influence_plot(results, external=True, alpha=.05, criterion="cooks",
-                    size=48, plot_alpha=.75, ax=None, **kwargs):
+                   size=48, plot_alpha=.75, ax=None, **kwargs):
     """
     Plot of influence in regression. Plots studentized resids vs. leverage.
 
@@ -777,10 +772,10 @@ def influence_plot(results, external=True, alpha=.05, criterion="cooks",
     # add point labels
     labels = results.model.data.row_labels
     if labels is None:
-        labels = range(len(resids))
+        labels = lrange(len(resids))
     ax = utils.annotate_axes(np.where(large_points)[0], labels,
-                             zip(leverage, resids),
-                             zip(-(psize/2)**.5, (psize/2)**.5), "x-large",
+                             lzip(leverage, resids),
+                             lzip(-(psize/2)**.5, (psize/2)**.5), "x-large",
                              ax)
 
     #TODO: make configurable or let people do it ex-post?
@@ -813,7 +808,7 @@ def plot_leverage_resid2(results, alpha=.05, label_kwargs={}, ax=None,
     fig : matplotlib Figure
         A matplotlib figure instance.
     """
-    from scipy.stats import zscore, norm, t
+    from scipy.stats import zscore, norm
     fig, ax = utils.create_mpl_ax(ax)
 
     infl = results.get_influence()
@@ -830,9 +825,9 @@ def plot_leverage_resid2(results, alpha=.05, label_kwargs={}, ax=None,
     large_resid = np.abs(resid) > cutoff
     labels = results.model.data.row_labels
     if labels is None:
-        labels = range(results.nobs)
+        labels = lrange(results.nobs)
     index = np.where(np.logical_or(large_leverage, large_resid))[0]
-    ax = utils.annotate_axes(index, labels, zip(resid**2, leverage),
+    ax = utils.annotate_axes(index, labels, lzip(resid**2, leverage),
                              [(0, 5)]*int(results.nobs), "large",
                              ax=ax, ha="center", va="bottom")
     ax.margins(.075, .075)
diff --git a/statsmodels/graphics/tests/test_boxplots.py b/statsmodels/graphics/tests/test_boxplots.py
index 0a305138b2a..6b86bffe85b 100644
--- a/statsmodels/graphics/tests/test_boxplots.py
+++ b/statsmodels/graphics/tests/test_boxplots.py
@@ -14,7 +14,7 @@
 
 @dec.skipif(not have_matplotlib)
 def test_violinplot_beanplot():
-    """Test violinplot and beanplot with the same dataset."""
+    # Test violinplot and beanplot with the same dataset.
     data = anes96.load_pandas()
     party_ID = np.arange(7)
     labels = ["Strong Democrat", "Weak Democrat", "Independent-Democrat",
diff --git a/statsmodels/graphics/tests/test_correlation.py b/statsmodels/graphics/tests/test_correlation.py
index da4fd26fafb..7a354dfbf2b 100644
--- a/statsmodels/graphics/tests/test_correlation.py
+++ b/statsmodels/graphics/tests/test_correlation.py
@@ -15,7 +15,7 @@
 @dec.skipif(not have_matplotlib)
 def test_plot_corr():
     hie_data = randhie.load_pandas()
-    corr_matrix = np.corrcoef(hie_data.data.T)
+    corr_matrix = np.corrcoef(hie_data.data.values.T)
 
     fig = plot_corr(corr_matrix, xnames=hie_data.names)
     plt.close(fig)
@@ -30,7 +30,7 @@ def test_plot_corr():
 @dec.skipif(not have_matplotlib)
 def test_plot_corr_grid():
     hie_data = randhie.load_pandas()
-    corr_matrix = np.corrcoef(hie_data.data.T)
+    corr_matrix = np.corrcoef(hie_data.data.values.T)
 
     fig = plot_corr_grid([corr_matrix] * 2, xnames=hie_data.names)
     plt.close(fig)
diff --git a/statsmodels/graphics/tests/test_dotplot.py b/statsmodels/graphics/tests/test_dotplot.py
new file mode 100644
index 00000000000..98dbba846de
--- /dev/null
+++ b/statsmodels/graphics/tests/test_dotplot.py
@@ -0,0 +1,400 @@
+import numpy as np
+from statsmodels.graphics.dotplots import dot_plot
+import pandas as pd
+from numpy.testing import dec
+
+# If true, the output is written to a multi-page pdf file.
+pdf_output = False
+
+try:
+    import matplotlib.pyplot as plt
+    import matplotlib
+    if matplotlib.__version__ < '1':
+        raise
+    have_matplotlib = True
+except:
+    have_matplotlib = False
+
+
+def close_or_save(pdf, fig):
+    if pdf_output:
+        pdf.savefig(fig)
+    else:
+        plt.close(fig)
+
+@dec.skipif(not have_matplotlib)
+def test_all():
+
+    if pdf_output:
+        from matplotlib.backends.backend_pdf import PdfPages
+        pdf = PdfPages("test_dotplot.pdf")
+    else:
+        pdf = None
+
+    # Basic dotplot with points only
+    plt.clf()
+    points = range(20)
+    ax = plt.axes()
+    fig = dot_plot(points, ax=ax)
+    ax.set_title("Basic horizontal dotplot")
+    close_or_save(pdf, fig)
+
+    # Basic vertical dotplot
+    plt.clf()
+    points = range(20)
+    ax = plt.axes()
+    fig = dot_plot(points, ax=ax, horizontal=False)
+    ax.set_title("Basic vertical dotplot")
+    close_or_save(pdf, fig)
+
+    # Tall and skinny
+    plt.figure(figsize=(4,12))
+    ax = plt.axes()
+    vals = np.arange(40)
+    fig = dot_plot(points, ax=ax)
+    ax.set_title("Tall and skinny dotplot")
+    ax.set_xlabel("x axis label")
+    close_or_save(pdf, fig)
+
+    # Short and wide
+    plt.figure(figsize=(12,4))
+    ax = plt.axes()
+    vals = np.arange(40)
+    fig = dot_plot(points, ax=ax, horizontal=False)
+    ax.set_title("Short and wide dotplot")
+    ax.set_ylabel("y axis label")
+    close_or_save(pdf, fig)
+
+    # Tall and skinny striped dotplot
+    plt.figure(figsize=(4,12))
+    ax = plt.axes()
+    points = np.arange(40)
+    fig = dot_plot(points, ax=ax, striped=True)
+    ax.set_title("Tall and skinny striped dotplot")
+    ax.set_xlim(-10, 50)
+    close_or_save(pdf, fig)
+
+    # Short and wide striped
+    plt.figure(figsize=(12,4))
+    ax = plt.axes()
+    points = np.arange(40)
+    fig = dot_plot(points, ax=ax, striped=True, horizontal=False)
+    ax.set_title("Short and wide striped dotplot")
+    ax.set_ylim(-10, 50)
+    close_or_save(pdf, fig)
+
+    # Basic dotplot with few points
+    plt.figure()
+    ax = plt.axes()
+    points = np.arange(4)
+    fig = dot_plot(points, ax=ax)
+    ax.set_title("Basic horizontal dotplot with few lines")
+    close_or_save(pdf, fig)
+
+    # Basic dotplot with few points
+    plt.figure()
+    ax = plt.axes()
+    points = np.arange(4)
+    fig = dot_plot(points, ax=ax, horizontal=False)
+    ax.set_title("Basic vertical dotplot with few lines")
+    close_or_save(pdf, fig)
+
+    # Manually set the x axis limits
+    plt.figure()
+    ax = plt.axes()
+    points = np.arange(20)
+    fig = dot_plot(points, ax=ax)
+    ax.set_xlim(-10, 30)
+    ax.set_title("Dotplot with adjusted horizontal range")
+    close_or_save(pdf, fig)
+
+    # Left row labels
+    plt.clf()
+    ax = plt.axes()
+    lines = ["ABCDEFGH"[np.random.randint(0, 8)] for k in range(20)]
+    points = np.random.normal(size=20)
+    fig = dot_plot(points, lines=lines, ax=ax)
+    ax.set_title("Dotplot with user-supplied labels in the left margin")
+    close_or_save(pdf, fig)
+
+    # Left and right row labels
+    plt.clf()
+    ax = plt.axes()
+    points = np.random.normal(size=20)
+    lines = ["ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k+1)
+             for k in range(20)]
+    fig = dot_plot(points, lines=lines, ax=ax, split_names="::")
+    ax.set_title("Dotplot with user-supplied labels in both margins")
+    close_or_save(pdf, fig)
+
+    # Both sides row labels
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.88, 0.8])
+    points = np.random.normal(size=20)
+    lines = ["ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k+1)
+             for k in range(20)]
+    fig = dot_plot(points, lines=lines, ax=ax, split_names="::",
+            horizontal=False)
+    txt = ax.set_title("Vertical dotplot with user-supplied labels in both margins")
+    txt.set_position((0.5, 1.06))
+    close_or_save(pdf, fig)
+
+    # Custom colors and symbols
+    plt.clf()
+    ax = plt.axes([0.1, 0.07, 0.78, 0.85])
+    points = np.random.normal(size=20)
+    lines = np.kron(range(5), np.ones(4)).astype(np.int32)
+    styles = np.kron(np.ones(5), range(4)).astype(np.int32)
+    #marker_props = {k: {"color": "rgbc"[k], "marker": "osvp"[k],
+    #                    "ms": 7, "alpha": 0.6} for k in range(4)}
+    # python 2.6 compat, can be removed later
+    marker_props = dict((k, {"color": "rgbc"[k], "marker": "osvp"[k],
+                        "ms": 7, "alpha": 0.6}) for k in range(4))
+    fig = dot_plot(points, lines=lines, styles=styles, ax=ax,
+            marker_props=marker_props)
+    ax.set_title("Dotplot with custom colors and symbols")
+    close_or_save(pdf, fig)
+
+    # Basic dotplot with symmetric intervals
+    plt.clf()
+    ax = plt.axes()
+    points = range(20)
+    fig = dot_plot(points, intervals=np.ones(20), ax=ax)
+    ax.set_title("Dotplot with symmetric intervals")
+    close_or_save(pdf, fig)
+
+    # Basic dotplot with symmetric intervals, pandas inputs.
+    plt.clf()
+    ax = plt.axes()
+    points = pd.Series(range(20))
+    intervals = pd.Series(np.ones(20))
+    fig = dot_plot(points, intervals=intervals, ax=ax)
+    ax.set_title("Dotplot with symmetric intervals (Pandas inputs)")
+    close_or_save(pdf, fig)
+
+    # Basic dotplot with nonsymmetric intervals
+    plt.clf()
+    ax = plt.axes()
+    points = np.arange(20)
+    intervals = [(1, 3) for i in range(20)]
+    fig = dot_plot(points, intervals=intervals, ax=ax)
+    ax.set_title("Dotplot with nonsymmetric intervals")
+    close_or_save(pdf, fig)
+
+    # Vertical dotplot with nonsymmetric intervals
+    plt.clf()
+    ax = plt.axes()
+    points = np.arange(20)
+    intervals = [(1, 3) for i in range(20)]
+    fig = dot_plot(points, intervals=intervals, ax=ax, horizontal=False)
+    ax.set_title("Vertical dotplot with nonsymmetric intervals")
+    close_or_save(pdf, fig)
+
+    # Dotplot with nonsymmetric intervals, adjust line properties
+    plt.clf()
+    ax = plt.axes()
+    points = np.arange(20)
+    intervals = [(1, 3) for x in range(20)]
+    line_props = {0: {"color": "lightgrey",
+                      "solid_capstyle": "round"}}
+    fig = dot_plot(points, intervals=intervals, line_props=line_props, ax=ax)
+    ax.set_title("Dotplot with custom line properties")
+    close_or_save(pdf, fig)
+
+    # Dotplot with two points per line and a legend
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = np.kron(range(20), (1,1))
+    intervals = [(1,3) for k in range(40)]
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
+            ax=ax, stacked=True)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Dotplot with two points per line")
+    close_or_save(pdf, fig)
+
+    # Dotplot with two points per line and a legend
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    fig = dot_plot(points, intervals=intervals, lines=lines,
+                  styles=styles, ax=ax, stacked=True,
+                  styles_order=["Dog", "Cat"])
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Dotplot with two points per line (reverse order)")
+    close_or_save(pdf, fig)
+
+    # Vertical dotplot with two points per line and a legend
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = np.kron(range(20), (1,1))
+    intervals = [(1,3) for k in range(40)]
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
+            ax=ax, stacked=True, horizontal=False)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Vertical dotplot with two points per line")
+    close_or_save(pdf, fig)
+
+    # Vertical dotplot with two points per line and a legend
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    styles_order = ["Dog", "Cat"]
+    fig = dot_plot(points, intervals=intervals, lines=lines,
+                  styles=styles, ax=ax, stacked=True,
+                  horizontal=False, styles_order=styles_order)
+    handles, labels = ax.get_legend_handles_labels()
+    lh = dict(zip(labels, handles))
+    handles = [lh[l] for l in styles_order]
+    leg = plt.figlegend(handles, styles_order, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Vertical dotplot with two points per line (reverse order)")
+    close_or_save(pdf, fig)
+
+    # Vertical dotplot with two points per line and a legend
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = np.kron(range(20), (1,1))
+    intervals = [(1,3) for k in range(40)]
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
+            ax=ax, stacked=True, striped=True, horizontal=False)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    plt.ylim(-20, 20)
+    ax.set_title("Vertical dotplot with two points per line")
+    close_or_save(pdf, fig)
+
+    # Dotplot with color-matched points and intervals
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = np.kron(range(20), (1,1))
+    intervals = [(1,3) for k in range(40)]
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    marker_props = {"Cat": {"color": "orange"},
+                    "Dog": {"color": "purple"}}
+    line_props = {"Cat": {"color": "orange"},
+                  "Dog": {"color": "purple"}}
+    fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
+            ax=ax, stacked=True, marker_props=marker_props,
+            line_props=line_props)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Dotplot with color-matched points and intervals")
+    close_or_save(pdf, fig)
+
+    # Dotplot with color-matched points and intervals
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = np.kron(range(20), (1,1))
+    intervals = [(1,3) for k in range(40)]
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    marker_props = {"Cat": {"color": "orange"},
+                    "Dog": {"color": "purple"}}
+    line_props = {"Cat": {"color": "orange"},
+                  "Dog": {"color": "purple"}}
+    fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
+            ax=ax, stacked=True, marker_props=marker_props,
+            line_props=line_props, horizontal=False)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Dotplot with color-matched points and intervals")
+    close_or_save(pdf, fig)
+
+    # Dotplot with sections
+    plt.clf()
+    ax = plt.axes()
+    points = range(30)
+    lines = np.kron(range(15), (1,1)).astype(np.int32)
+    styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
+    sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
+    sections = [["Axx", "Byy", "Czz"][k] for k in sections]
+    fig = dot_plot(points, lines=lines, styles=styles, sections=sections, ax=ax)
+    ax.set_title("Dotplot with sections")
+    close_or_save(pdf, fig)
+
+    # Vertical dotplot with sections
+    plt.clf()
+    ax = plt.axes([0.1,0.1,0.9,0.75])
+    points = range(30)
+    lines = np.kron(range(15), (1,1)).astype(np.int32)
+    styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
+    sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
+    sections = [["Axx", "Byy", "Czz"][k] for k in sections]
+    fig = dot_plot(points, lines=lines, styles=styles,
+                  sections=sections, ax=ax, horizontal=False)
+    txt = ax.set_title("Vertical dotplot with sections")
+    txt.set_position((0.5, 1.08))
+    close_or_save(pdf, fig)
+
+    # Reorder sections
+    plt.clf()
+    ax = plt.axes()
+    points = range(30)
+    lines = np.kron(range(15), (1,1)).astype(np.int32)
+    styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
+    sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
+    sections = [["Axx", "Byy", "Czz"][k] for k in sections]
+    fig = dot_plot(points, lines=lines, styles=styles, sections=sections, ax=ax,
+            section_order=["Byy", "Axx", "Czz"])
+    ax.set_title("Dotplot with sections in specified order")
+    close_or_save(pdf, fig)
+
+    # Reorder the lines.
+    plt.figure()
+    ax = plt.axes()
+    points = np.arange(4)
+    lines = ["A", "B", "C", "D"]
+    line_order = ["B", "C", "A", "D"]
+    fig = dot_plot(points, lines=lines, line_order=line_order, ax=ax)
+    ax.set_title("Dotplot with reordered lines")
+    close_or_save(pdf, fig)
+
+    # Dotplot with different numbers of points per line
+    plt.clf()
+    ax = plt.axes([0.1, 0.1, 0.75, 0.8])
+    points = 5*np.random.normal(size=40)
+    lines = []
+    ii = 0
+    while len(lines) < 40:
+        for k in range(np.random.randint(1, 4)):
+            lines.append(ii)
+        ii += 1
+    styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
+    styles = [["Cat", "Dog"][i] for i in styles]
+    fig = dot_plot(points, lines=lines, styles=styles,
+            ax=ax, stacked=True)
+    handles, labels = ax.get_legend_handles_labels()
+    leg = plt.figlegend(handles, labels, "center right", numpoints=1,
+                        handletextpad=0.0001)
+    leg.draw_frame(False)
+    ax.set_title("Dotplot with different numbers of points per line")
+    close_or_save(pdf, fig)
+
+    if pdf_output:
+        pdf.close()
diff --git a/statsmodels/graphics/tests/test_factorplots.py b/statsmodels/graphics/tests/test_factorplots.py
index 36df239ebe6..a902c59e2ac 100644
--- a/statsmodels/graphics/tests/test_factorplots.py
+++ b/statsmodels/graphics/tests/test_factorplots.py
@@ -7,7 +7,7 @@
     import matplotlib.pyplot as plt
     import matplotlib
     if matplotlib.__version__ < '1':
-        raise
+        raise ImportError("matplotlib is too old. Please upgrade.")
     have_matplotlib = True
 except:
     have_matplotlib = False
diff --git a/statsmodels/graphics/tests/test_functional.py b/statsmodels/graphics/tests/test_functional.py
index 66729bc9f0b..8582b3dbeda 100644
--- a/statsmodels/graphics/tests/test_functional.py
+++ b/statsmodels/graphics/tests/test_functional.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import range
 import numpy as np
 from numpy.testing import dec, assert_equal, assert_almost_equal
 
@@ -9,7 +10,7 @@
     import matplotlib.pyplot as plt
     import matplotlib
     if matplotlib.__version__ < '1':
-        raise
+        raise ImportError("matplotlib is too old. Please upgrade")
     have_matplotlib = True
 except:
     have_matplotlib = False
@@ -52,7 +53,7 @@ def test_banddepth_MBD():
 
 @dec.skipif(not have_matplotlib)
 def test_fboxplot_rainbowplot():
-    """Test fboxplot and rainbowplot together, is much faster."""
+    # Test fboxplot and rainbowplot together, is much faster.
     def harmfunc(t):
         """Test function, combination of a few harmonic terms."""
         # Constant, 0 with p=0.9, 1 with p=1 - for creating outliers
diff --git a/statsmodels/graphics/tests/test_gofplots.py b/statsmodels/graphics/tests/test_gofplots.py
index 054f292aaf9..4957688c798 100644
--- a/statsmodels/graphics/tests/test_gofplots.py
+++ b/statsmodels/graphics/tests/test_gofplots.py
@@ -10,93 +10,170 @@
     import matplotlib.pyplot as plt
     import matplotlib
     if matplotlib.__version__ < '1':
-        raise
+        raise ImportError("matplotlib is too old. Please upgrade.")
     have_matplotlib = True
 except:
     have_matplotlib = False
 
 
-@dec.skipif(not have_matplotlib)
-def test_qqplot():
-    #just test that it runs
-    data = sm.datasets.longley.load()
-    data.exog = sm.add_constant(data.exog, prepend=False)
-    mod_fit = sm.OLS(data.endog, data.exog).fit()
-    res = mod_fit.resid
-    fig = sm.qqplot(res, line='r')
-
-    plt.close('all')
-
-@dec.skipif(not have_matplotlib)
-def test_ProbPlot():
-    #just test that it runs
-    data = sm.datasets.longley.load()
-    data.exog = sm.add_constant(data.exog, prepend=False)
-    mod_fit = sm.OLS(data.endog, data.exog).fit()
-    res = sm.ProbPlot(mod_fit.resid, stats.t, distargs=(4,))
-
-    # basic tests modeled after example in docstring
-    fig1 = res.qqplot(line='r')
-    fig2 = res.ppplot(line='r')
-    fig3 = res.probplot(line='r')
-
-    plt.close('all')
-
-@dec.skipif(not have_matplotlib)
-def test_ProbPlot_comparison():
-    # two fake samples for comparison
-    x = np.random.normal(loc=8.25, scale=3.25, size=37)
-    y = np.random.normal(loc=8.25, scale=3.25, size=37)
-    pp_x = sm.ProbPlot(x)
-    pp_y = sm.ProbPlot(y)
-
-    # test `other` kwarg with `ProbPlot` instance
-    fig4 = pp_x.qqplot(other=pp_y)
-    fig5 = pp_x.ppplot(other=pp_y)
-
-    plt.close('all')
-
-@dec.skipif(not have_matplotlib)
-def test_ProbPlot_comparison_arrays():
-    # two fake samples for comparison
-    x = np.random.normal(loc=8.25, scale=3.25, size=37)
-    y = np.random.normal(loc=8.25, scale=3.25, size=37)
-    pp_x = sm.ProbPlot(x)
-    pp_y = sm.ProbPlot(y)
-
-    # test `other` kwarg with array
-    fig6 = pp_x.qqplot(other=y)
-    fig7 = pp_x.ppplot(other=y)
-    plt.close('all')
-
-@dec.skipif(not have_matplotlib)
-def test_qqplot_2samples():
-    #just test that it runs
-    x = np.random.normal(loc=8.25, scale=3.25, size=37)
-    y = np.random.normal(loc=8.25, scale=3.25, size=37)
-
-    pp_x = sm.ProbPlot(x)
-    pp_y = sm.ProbPlot(y)
-
-    # also tests all values for line
-    for line in ['r', 'q', '45', 's']:
-        # test with `ProbPlot` instances
-        fig2 = sm.qqplot_2samples(pp_x, pp_y, line=line)
-
-    plt.close('all')
-
-@dec.skipif(not have_matplotlib)
-def test_qqplot_2samples_arrays():
-    #just test that it runs
-    x = np.random.normal(loc=8.25, scale=3.25, size=37)
-    y = np.random.normal(loc=8.25, scale=3.25, size=37)
-
-    pp_x = sm.ProbPlot(x)
-    pp_y = sm.ProbPlot(y)
-
-    # also tests all values for line
-    for line in ['r', 'q', '45', 's']:
-        # test with arrays
-        fig1 = sm.qqplot_2samples(x, y, line=line)
-
-    plt.close('all')
+class BaseProbplotMixin(object):
+    def base_setup(self):
+        if have_matplotlib:
+            self.fig, self.ax = plt.subplots()
+        self.other_array = np.random.normal(size=self.prbplt.data.shape)
+        self.other_prbplot = sm.ProbPlot(self.other_array)
+
+    def teardown(self):
+        if have_matplotlib:
+            plt.close('all')
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot(self):
+        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line)
+
+    @dec.skipif(not have_matplotlib)
+    def test_ppplot(self):
+        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line)
+
+    @dec.skipif(not have_matplotlib)
+    def test_probplot(self):
+        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line)
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_other_array(self):
+        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line,
+                                        other=self.other_array)
+    @dec.skipif(not have_matplotlib)
+    def test_ppplot_other_array(self):
+        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line,
+                                        other=self.other_array)
+    @dec.skipif(not have_matplotlib)
+    def t_est_probplot_other_array(self):
+        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line,
+                                        other=self.other_array)
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_other_prbplt(self):
+        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line,
+                                        other=self.other_prbplot)
+    @dec.skipif(not have_matplotlib)
+    def test_ppplot_other_prbplt(self):
+        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line,
+                                        other=self.other_prbplot)
+    @dec.skipif(not have_matplotlib)
+    def t_est_probplot_other_prbplt(self):
+        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line,
+                                        other=self.other_prbplot)
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_custom_labels(self):
+        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line,
+                                      xlabel='Custom X-Label',
+                                      ylabel='Custom Y-Label')
+
+    @dec.skipif(not have_matplotlib)
+    def test_ppplot_custom_labels(self):
+        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line,
+                                      xlabel='Custom X-Label',
+                                      ylabel='Custom Y-Label')
+
+    @dec.skipif(not have_matplotlib)
+    def test_probplot_custom_labels(self):
+        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line,
+                                        xlabel='Custom X-Label',
+                                        ylabel='Custom Y-Label')
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_pltkwargs(self):
+        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line,
+                                      marker='d',
+                                      markerfacecolor='cornflowerblue',
+                                      markeredgecolor='white',
+                                      alpha=0.5)
+
+    @dec.skipif(not have_matplotlib)
+    def test_ppplot_pltkwargs(self):
+        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line,
+                                      marker='d',
+                                      markerfacecolor='cornflowerblue',
+                                      markeredgecolor='white',
+                                      alpha=0.5)
+
+    @dec.skipif(not have_matplotlib)
+    def test_probplot_pltkwargs(self):
+        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line,
+                                        marker='d',
+                                        markerfacecolor='cornflowerblue',
+                                        markeredgecolor='white',
+                                        alpha=0.5)
+
+
+class TestProbPlotLongely(BaseProbplotMixin):
+    def setup(self):
+        np.random.seed(5)
+        self.data = sm.datasets.longley.load()
+        self.data.exog = sm.add_constant(self.data.exog, prepend=False)
+        self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit()
+        self.prbplt = sm.ProbPlot(self.mod_fit.resid, stats.t, distargs=(4,))
+        self.line = 'r'
+        self.base_setup()
+
+
+class TestProbPlotRandomNormalMinimal(BaseProbplotMixin):
+    def setup(self):
+        np.random.seed(5)
+        self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
+        self.prbplt = sm.ProbPlot(self.data)
+        self.line = None
+        self.base_setup()
+
+
+class TestProbPlotRandomNormalWithFit(BaseProbplotMixin):
+    def setup(self):
+        np.random.seed(5)
+        self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
+        self.prbplt = sm.ProbPlot(self.data, fit=True)
+        self.line = 'q'
+        self.base_setup()
+
+
+class TestProbPlotRandomNormalLocScale(BaseProbplotMixin):
+    def setup(self):
+        np.random.seed(5)
+        self.data = np.random.normal(loc=8.25, scale=3.25, size=37)
+        self.prbplt = sm.ProbPlot(self.data, loc=8.25, scale=3.25)
+        self.line = '45'
+        self.base_setup()
+
+
+class TestTopLevel(object):
+    def setup(self):
+        self.data = sm.datasets.longley.load()
+        self.data.exog = sm.add_constant(self.data.exog, prepend=False)
+        self.mod_fit = sm.OLS(self.data.endog, self.data.exog).fit()
+        self.res = self.mod_fit.resid
+        self.prbplt = sm.ProbPlot(self.mod_fit.resid, stats.t, distargs=(4,))
+        self.other_array = np.random.normal(size=self.prbplt.data.shape)
+        self.other_prbplot = sm.ProbPlot(self.other_array)
+
+    def teardown(self):
+        if have_matplotlib:
+            plt.close('all')
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot(self):
+        fig = sm.qqplot(self.res, line='r')
+
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_2samples_ProbPlotObjects(self):
+        # also tests all values for line
+        for line in ['r', 'q', '45', 's']:
+            # test with `ProbPlot` instances
+            fig = sm.qqplot_2samples(self.prbplt, self.other_prbplot,
+                                     line=line)
+    @dec.skipif(not have_matplotlib)
+    def test_qqplot_2samples_arrays(self):
+        # also tests all values for line
+        for line in ['r', 'q', '45', 's']:
+            # test with arrays
+            fig = sm.qqplot_2samples(self.res, self.other_array, line=line)
diff --git a/statsmodels/graphics/tests/test_mosaicplot.py b/statsmodels/graphics/tests/test_mosaicplot.py
index 9694269d005..4864df9d372 100644
--- a/statsmodels/graphics/tests/test_mosaicplot.py
+++ b/statsmodels/graphics/tests/test_mosaicplot.py
@@ -1,10 +1,12 @@
 from __future__ import division
+from statsmodels.compat.python import iterkeys, zip, lrange, iteritems, range
 
 from numpy.testing import assert_, assert_raises, dec
 from numpy.testing import run_module_suite
 
 # utilities for the tests
-from statsmodels.compatnp.collections import OrderedDict
+
+from statsmodels.compat.collections import OrderedDict
 from statsmodels.api import datasets
 
 import numpy as np
@@ -164,8 +166,9 @@ def test_mosaic_very_complex():
                 ji = max(i, j)
                 ij = min(i, j)
                 temp_data = OrderedDict([((k[ij], k[ji]) + tuple(k[r] for r in m), v)
-                                            for k, v in data.items()])
-                keys = temp_data.keys()
+                                            for k, v in iteritems(data)])
+
+                keys = list(iterkeys(temp_data))
                 for k in keys:
                     value = _reduce_dict(temp_data, k[:2])
                     temp_data[k[:2]] = value
@@ -201,13 +204,13 @@ def test_recursive_split():
     keys = list(product('mf'))
     data = OrderedDict(zip(keys, [1] * len(keys)))
     res = _hierarchical_split(data, gap=0)
-    assert_(res.keys() == keys)
+    assert_(list(iterkeys(res)) == keys)
     res[('m',)] = (0.0, 0.0, 0.5, 1.0)
     res[('f',)] = (0.5, 0.0, 0.5, 1.0)
     keys = list(product('mf', 'yao'))
     data = OrderedDict(zip(keys, [1] * len(keys)))
     res = _hierarchical_split(data, gap=0)
-    assert_(res.keys() == keys)
+    assert_(list(iterkeys(res)) == keys)
     res[('m', 'y')] = (0.0, 0.0, 0.5, 1 / 3)
     res[('m', 'a')] = (0.0, 1 / 3, 0.5, 1 / 3)
     res[('m', 'o')] = (0.0, 2 / 3, 0.5, 1 / 3)
@@ -221,7 +224,7 @@ def test__reduce_dict():
     eq(_reduce_dict(data, ('m',)), 4)
     eq(_reduce_dict(data, ('m', 'o')), 2)
     eq(_reduce_dict(data, ('m', 'o', 'w')), 1)
-    data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), range(8)))
+    data = OrderedDict(zip(list(product('mf', 'oy', 'wn')), lrange(8)))
     eq(_reduce_dict(data, ('m',)), 6)
     eq(_reduce_dict(data, ('m', 'o')), 1)
     eq(_reduce_dict(data, ('m', 'o', 'w')), 0)
@@ -231,19 +234,19 @@ def test__key_splitting():
     # subdivide starting with an empty tuple
     base_rect = {tuple(): (0, 0, 1, 1)}
     res = _key_splitting(base_rect, ['a', 'b'], [1, 1], tuple(), True, 0)
-    assert_(res.keys() == [('a',), ('b',)])
+    assert_(list(iterkeys(res)) == [('a',), ('b',)])
     eq(res[('a',)], (0, 0, 0.5, 1))
     eq(res[('b',)], (0.5, 0, 0.5, 1))
     # subdivide a in two sublevel
     res_bis = _key_splitting(res, ['c', 'd'], [1, 1], ('a',), False, 0)
-    assert_(res_bis.keys() == [('a', 'c'), ('a', 'd'), ('b',)])
+    assert_(list(iterkeys(res_bis)) == [('a', 'c'), ('a', 'd'), ('b',)])
     eq(res_bis[('a', 'c')], (0.0, 0.0, 0.5, 0.5))
     eq(res_bis[('a', 'd')], (0.0, 0.5, 0.5, 0.5))
     eq(res_bis[('b',)], (0.5, 0, 0.5, 1))
     # starting with a non empty tuple and uneven distribution
     base_rect = {('total',): (0, 0, 1, 1)}
     res = _key_splitting(base_rect, ['a', 'b'], [1, 2], ('total',), True, 0)
-    assert_(res.keys() == [('total',) + (e,) for e in ['a', 'b']])
+    assert_(list(iterkeys(res)) == [('total',) + (e,) for e in ['a', 'b']])
     eq(res[('total', 'a')], (0, 0, 1 / 3, 1))
     eq(res[('total', 'b')], (1 / 3, 0, 2 / 3, 1))
 
diff --git a/statsmodels/graphics/tests/test_tsaplots.py b/statsmodels/graphics/tests/test_tsaplots.py
index 52ae7525d5d..c6ad0540a2c 100644
--- a/statsmodels/graphics/tests/test_tsaplots.py
+++ b/statsmodels/graphics/tests/test_tsaplots.py
@@ -1,8 +1,10 @@
+from statsmodels.compat.python import lmap, lzip, map
 import numpy as np
+import pandas as pd
 from numpy.testing import dec
 
 import statsmodels.api as sm
-from statsmodels.graphics.tsaplots import plot_acf
+from statsmodels.graphics.tsaplots import plot_acf, month_plot, quarter_plot
 import statsmodels.tsa.arima_process as tsp
 
 
@@ -27,3 +29,55 @@ def test_plot_acf():
 
     plt.close(fig)
 
+
+@dec.skipif(not have_matplotlib)
+def test_plot_month():
+    dta = sm.datasets.elnino.load_pandas().data
+    dta['YEAR'] = dta.YEAR.astype(int).apply(str)
+    dta = dta.set_index('YEAR').T.unstack()
+    dates = lmap(lambda x : pd.datetools.parse('1 '+' '.join(x)),
+                                            dta.index.values)
+
+    # test dates argument
+    fig = month_plot(dta.values, dates=dates, ylabel='el nino')
+    plt.close(fig)
+
+    # test with a TimeSeries DatetimeIndex with no freq
+    dta.index = pd.DatetimeIndex(dates)
+    fig = month_plot(dta)
+    plt.close(fig)
+
+    # w freq
+    dta.index = pd.DatetimeIndex(dates, freq='M')
+    fig = month_plot(dta)
+    plt.close(fig)
+
+    # test with a TimeSeries PeriodIndex
+    dta.index = pd.PeriodIndex(dates, freq='M')
+    fig = month_plot(dta)
+    plt.close(fig)
+
+@dec.skipif(not have_matplotlib)
+def test_plot_quarter():
+    dta = sm.datasets.macrodata.load_pandas().data
+    dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
+                              dta.quarter.astype(int).apply(str)))
+    # test dates argument
+    quarter_plot(dta.unemp.values, dates)
+
+    # test with a DatetimeIndex with no freq
+    parser = pd.datetools.parse_time_string
+    dta.set_index(pd.DatetimeIndex((x[0] for x in map(parser, dates))),
+                  inplace=True)
+    quarter_plot(dta.unemp)
+
+    # w freq
+    # see pandas #6631
+    dta.index = pd.DatetimeIndex((x[0] for x in map(parser, dates)),
+                                   freq='QS-Oct')
+    quarter_plot(dta.unemp)
+
+    # w PeriodIndex
+    dta.index = pd.PeriodIndex((x[0] for x in map(parser, dates)),
+                                   freq='Q')
+    quarter_plot(dta.unemp)
diff --git a/statsmodels/graphics/tsaplots.py b/statsmodels/graphics/tsaplots.py
index d4ba73d7bb0..2a51cea7247 100644
--- a/statsmodels/graphics/tsaplots.py
+++ b/statsmodels/graphics/tsaplots.py
@@ -6,7 +6,6 @@
 from statsmodels.graphics import utils
 from statsmodels.tsa.stattools import acf, pacf
 
-
 def plot_acf(x, ax=None, lags=None, alpha=.05, use_vlines=True, unbiased=False,
             fft=False, **kwargs):
     """Plot the autocorrelation function
@@ -171,3 +170,137 @@ def plot_pacf(x, ax=None, lags=None, alpha=.05, method='ywm',
     ax.set_title("Partial Autocorrelation")
 
     return fig
+
+def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
+    """
+    Consider using one of month_plot or quarter_plot unless you need
+    irregular plotting.
+
+    Parameters
+    ----------
+    grouped_x : iterable of DataFrames
+        Should be a GroupBy object (or similar pair of group_names and groups
+        as DataFrames) with a DatetimeIndex or PeriodIndex
+    """
+    fig, ax = utils.create_mpl_ax(ax)
+    start = 0
+    ticks = []
+    for season, df in grouped_x:
+        df = df.copy() # or sort balks for series. may be better way
+        df.sort()
+        nobs = len(df)
+        x_plot = np.arange(start, start + nobs)
+        ticks.append(x_plot.mean())
+        ax.plot(x_plot, df.values, 'k')
+        ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='k')
+        start += nobs
+
+    ax.set_xticks(ticks)
+    ax.set_xticklabels(xticklabels)
+    ax.set_ylabel(ylabel)
+    ax.margins(.1, .05)
+    return fig
+
+
+def month_plot(x, dates=None, ylabel=None, ax=None):
+    """
+    Seasonal plot of monthly data
+
+    Parameters
+    ----------
+    x : array-like
+        Seasonal data to plot. If dates is None, x must be a pandas object
+        with a PeriodIndex or DatetimeIndex with a monthly frequency.
+    dates : array-like, optional
+        If `x` is not a pandas object, then dates must be supplied.
+    ylabel : str, optional
+        The label for the y-axis. Will attempt to use the `name` attribute
+        of the Series.
+    ax : matplotlib.axes, optional
+        Existing axes instance.
+
+    Returns
+    -------
+    matplotlib.Figure
+
+    Examples
+    --------
+    >>> import statsmodels.api as sm
+    >>> import pandas as pd
+
+    >>> dta = sm.datasets.elnino.load_pandas().data
+    >>> dta['YEAR'] = dta.YEAR.astype(int).astype(str)
+    >>> dta = dta.set_index('YEAR').T.unstack()
+    >>> dates = map(lambda x : pd.datetools.parse('1 '+' '.join(x)),
+    ...                                        dta.index.values)
+
+    >>> dta.index = pd.DatetimeIndex(dates, freq='M')
+    >>> fig = sm.graphics.tsa.month_plot(dta)
+
+    .. plot:: plots/graphics_month_plot.py
+    """
+    from pandas import DataFrame
+
+    if dates is None:
+        from statsmodels.tools.data import _check_period_index
+        _check_period_index(x, freq="M")
+    else:
+        from pandas import Series, PeriodIndex
+        x = Series(x, index=PeriodIndex(dates, freq="M"))
+
+    xticklabels = ['j','f','m','a','m','j','j','a','s','o','n','d']
+    return seasonal_plot(x.groupby(lambda y : y.month), xticklabels,
+                         ylabel=ylabel, ax=ax)
+
+def quarter_plot(x, dates=None, ylabel=None, ax=None):
+    """
+    Seasonal plot of quarterly data
+
+    Parameters
+    ----------
+    x : array-like
+        Seasonal data to plot. If dates is None, x must be a pandas object
+        with a PeriodIndex or DatetimeIndex with a monthly frequency.
+    dates : array-like, optional
+        If `x` is not a pandas object, then dates must be supplied.
+    ylabel : str, optional
+        The label for the y-axis. Will attempt to use the `name` attribute
+        of the Series.
+    ax : matplotlib.axes, optional
+        Existing axes instance.
+
+    Returns
+    -------
+    matplotlib.Figure
+    """
+    from pandas import DataFrame
+
+    if dates is None:
+        from statsmodels.tools.data import _check_period_index
+        _check_period_index(x, freq="Q")
+    else:
+        from pandas import Series, PeriodIndex
+        x = Series(x, index=PeriodIndex(dates, freq="Q"))
+
+    xticklabels = ['q1', 'q2', 'q3', 'q4']
+    return seasonal_plot(x.groupby(lambda y : y.quarter), xticklabels,
+                         ylabel=ylabel, ax=ax)
+
+
+if __name__ == "__main__":
+    import pandas as pd
+
+    #R code to run to load that dataset in this directory
+    #data(co2)
+    #library(zoo)
+    #write.csv(as.data.frame(list(date=as.Date(co2), co2=coredata(co2))), "co2.csv", row.names=FALSE)
+    co2 = pd.read_csv("co2.csv", index_col=0, parse_dates=True)
+    month_plot(co2.co2)
+
+    #will work when dates are sorted
+    #co2 = sm.datasets.get_rdataset("co2", cache=True)
+
+    x = pd.Series(np.arange(20),
+                  index=pd.PeriodIndex(start='1/1/1990', periods=20, freq='Q'))
+    quarter_plot(x)
+
diff --git a/statsmodels/graphics/tukeyplot.py b/statsmodels/graphics/tukeyplot.py
index 7e17264caf3..93ecebd7311 100644
--- a/statsmodels/graphics/tukeyplot.py
+++ b/statsmodels/graphics/tukeyplot.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import range
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
diff --git a/statsmodels/graphics/utils.py b/statsmodels/graphics/utils.py
index 7c310c04786..b7f81ed88b9 100644
--- a/statsmodels/graphics/utils.py
+++ b/statsmodels/graphics/utils.py
@@ -1,5 +1,5 @@
 """Helper functions for graphics with Matplotlib."""
-
+from statsmodels.compat.python import lrange, range
 
 __all__ = ['create_mpl_ax', 'create_mpl_fig']
 
@@ -97,7 +97,7 @@ def maybe_name_or_idx(idx, model):
     column in a design matrix.
     """
     if idx is None:
-        idx = range(model.exog.shape[1])
+        idx = lrange(model.exog.shape[1])
     if isinstance(idx, int):
         exog_name = model.exog_names[idx]
         exog_idx = idx
diff --git a/statsmodels/iolib/__init__.py b/statsmodels/iolib/__init__.py
index df2fab0ac56..723105daa3a 100644
--- a/statsmodels/iolib/__init__.py
+++ b/statsmodels/iolib/__init__.py
@@ -1,6 +1,6 @@
-from foreign import StataReader, genfromdta, savetxt
-from table import SimpleTable, csv2st
-from smpickle import save_pickle, load_pickle
+from .foreign import StataReader, genfromdta, savetxt
+from .table import SimpleTable, csv2st
+from .smpickle import save_pickle, load_pickle
 
 from statsmodels import NoseWrapper as Tester
 test = Tester().test
diff --git a/statsmodels/iolib/api.py b/statsmodels/iolib/api.py
index 74b440f724a..9a8d3812d13 100644
--- a/statsmodels/iolib/api.py
+++ b/statsmodels/iolib/api.py
@@ -1,4 +1,4 @@
-from foreign import StataReader, genfromdta, savetxt, StataWriter
-from table import SimpleTable, csv2st
-from smpickle import save_pickle, load_pickle
+from .foreign import StataReader, genfromdta, savetxt, StataWriter
+from .table import SimpleTable, csv2st
+from .smpickle import save_pickle, load_pickle
 
diff --git a/statsmodels/iolib/foreign.py b/statsmodels/iolib/foreign.py
index bcd5ece689d..3b4f59c3f2e 100644
--- a/statsmodels/iolib/foreign.py
+++ b/statsmodels/iolib/foreign.py
@@ -9,14 +9,14 @@
 ---------
 numpy.lib.io
 """
-
+from statsmodels.compat.python import (zip, lzip, lmap, lrange, string_types, long, lfilter,
+                                asbytes, asstr, range)
 from struct import unpack, calcsize, pack
 from struct import error as struct_error
 import datetime
 import sys
 import numpy as np
 from numpy.lib._iotools import _is_string_like, easy_dtype
-from statsmodels.compatnp.py3k import asbytes, asstr
 import statsmodels.tools.data as data_util
 from pandas import isnull
 
@@ -50,7 +50,7 @@ def _datetime_to_stata_elapsed(date, fmt):
                 delta.microseconds/1000)
     elif fmt in ["%tC", "tC"]:
         from warnings import warn
-        warn("Stata Internal Format tC not supported.")
+        warn("Stata Internal Format tC not supported.", UserWarning)
         return date
     elif fmt in ["%td", "td"]:
         return (date- stata_epoch).days
@@ -120,7 +120,8 @@ def _stata_elapsed_date_to_datetime(date, fmt):
         return stata_epoch + relativedelta(microseconds=date*1000)
     elif fmt in ["%tC", "tC"]:
         from warnings import warn
-        warn("Encountered %tC format. Leaving in Stata Internal Format.")
+        warn("Encountered %tC format. Leaving in Stata Internal Format.",
+             UserWarning)
         return date
     elif fmt in ["%td", "td"]:
         return stata_epoch + datetime.timedelta(int(date))
@@ -171,7 +172,7 @@ class _StataMissingValue(object):
 
     def __init__(self, offset, value):
         self._value = value
-        if type(value) is int or type(value) is long:
+        if isinstance(value, (int, long)):
             self._str = value-offset is 1 and \
                     '.' or ('.' + chr(value-offset+96))
         else:
@@ -286,10 +287,10 @@ class StataReader(object):
     #NOTE: the byte type seems to be reserved for categorical variables
     # with a label, but the underlying variable is -127 to 100
     # we're going to drop the label and cast to int
-    DTYPE_MAP = dict(zip(range(1,245), ['a' + str(i) for i in range(1,245)]) + \
+    DTYPE_MAP = dict(lzip(lrange(1,245), ['a' + str(i) for i in range(1,245)]) + \
                     [(251, np.int16),(252, np.int32),(253, int),
                         (254, np.float32), (255, np.float64)])
-    TYPE_MAP = range(251)+list('bhlfd')
+    TYPE_MAP = lrange(251)+list('bhlfd')
     #NOTE: technically, some of these are wrong. there are more numbers
     # that can be represented. it's the 27 ABOVE and BELOW the max listed
     # numeric data type in [U] 12.2.2 of the 11.2 manual
@@ -356,7 +357,7 @@ def variables(self):
         """
         Returns a list of the dataset's StataVariables objects.
         """
-        return map(_StataVariable, zip(range(self._header['nvar']),
+        return lmap(_StataVariable, zip(lrange(self._header['nvar']),
             self._header['typlist'], self._header['varlist'],
             self._header['srtlist'],
             self._header['fmtlist'], self._header['lbllist'],
@@ -391,7 +392,7 @@ def dataset(self, as_dict=False):
             pass
 
         if as_dict:
-            vars = map(str, self.variables())
+            vars = lmap(str, self.variables())
             for i in range(len(self)):
                 yield dict(zip(vars, self._next()))
         else:
@@ -416,7 +417,7 @@ def __getitem__(self, k):
 
         k is zero-indexed.  Prefer using R.data() for performance.
         """
-        if not (type(k) is int or type(k) is long) or k < 0 or k > len(self)-1:
+        if not (isinstance(k, (int, long))) or k < 0 or k > len(self)-1:
             raise IndexError(k)
         loc = self._data_location + sum(self._col_size()) * k
         if self._file.tell() != loc:
@@ -499,18 +500,18 @@ def _parse_header(self, file_object):
 
         # other state vars
         self._data_location = self._file.tell()
-        self._has_string_data = len(filter(lambda x: type(x) is int,
+        self._has_string_data = len(lfilter(lambda x: isinstance(x, int),
             self._header['typlist'])) > 0
         self._col_size()
 
     def _calcsize(self, fmt):
-        return type(fmt) is int and fmt or \
+        return isinstance(fmt, int) and fmt or \
                 calcsize(self._header['byteorder']+fmt)
 
     def _col_size(self, k = None):
         """Calculate size of a data record."""
         if len(self._col_sizes) == 0:
-            self._col_sizes = map(lambda x: self._calcsize(x),
+            self._col_sizes = lmap(lambda x: self._calcsize(x),
                     self._header['typlist'])
         if k == None:
             return self._col_sizes
@@ -533,7 +534,7 @@ def _next(self):
         if self._has_string_data:
             data = [None]*self._header['nvar']
             for i in range(len(data)):
-                if type(typlist[i]) is int:
+                if isinstance(typlist[i], int):
                     data[i] = self._null_terminate(self._file.read(typlist[i]),
                                 self._encoding)
                 else:
@@ -541,9 +542,9 @@ def _next(self):
                             self._file.read(self._col_size(i)))
             return data
         else:
-            return map(lambda i: self._unpack(typlist[i],
+            return lmap(lambda i: self._unpack(typlist[i],
                 self._file.read(self._col_size(i))),
-                range(self._header['nvar']))
+                lrange(self._header['nvar']))
 
 def _open_file_binary_write(fname, encoding):
     if hasattr(fname, 'write'):
@@ -719,10 +720,10 @@ class StataWriter(object):
     #NOTE: the byte type seems to be reserved for categorical variables
     # with a label, but the underlying variable is -127 to 100
     # we're going to drop the label and cast to int
-    DTYPE_MAP = dict(zip(range(1,245), ['a' + str(i) for i in range(1,245)]) + \
+    DTYPE_MAP = dict(lzip(lrange(1,245), ['a' + str(i) for i in range(1,245)]) + \
                     [(251, np.int16),(252, np.int32),(253, int),
                         (254, np.float32), (255, np.float64)])
-    TYPE_MAP = range(251)+list('bhlfd')
+    TYPE_MAP = lrange(251)+list('bhlfd')
     MISSING_VALUES = { 'b': 101,
                        'h': 32741,
                        'l' : 2147483621,
@@ -998,7 +999,7 @@ def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
         If convert_dates is True, then Stata formatted dates will be converted
         to datetime types according to the variable's format.
     """
-    if isinstance(fname, basestring):
+    if isinstance(fname, string_types):
         fhd = StataReader(open(fname, 'rb'), missing_values=False,
                 encoding=encoding)
     elif not hasattr(fname, 'read'):
@@ -1023,7 +1024,7 @@ def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
     data = np.zeros((nobs,numvars))
     stata_dta = fhd.dataset()
 
-    dt = np.dtype(zip(varnames, types))
+    dt = np.dtype(lzip(varnames, types))
     data = np.zeros((nobs), dtype=dt) # init final array
 
     for rownum,line in enumerate(stata_dta):
@@ -1041,7 +1042,7 @@ def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
         from pandas import DataFrame
         data = DataFrame.from_records(data)
         if convert_dates:
-            cols = np.where(map(lambda x : x in _date_formats, fmtlist))[0]
+            cols = np.where(lmap(lambda x : x in _date_formats, fmtlist))[0]
             for col in cols:
                 i = col
                 col = data.columns[col]
@@ -1051,7 +1052,7 @@ def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
         #date_cols = np.where(map(lambda x : x in _date_formats,
         #                                                    fmtlist))[0]
         # make the dtype for the datetime types
-        cols = np.where(map(lambda x : x in _date_formats, fmtlist))[0]
+        cols = np.where(lmap(lambda x : x in _date_formats, fmtlist))[0]
         dtype = data.dtype.descr
         dtype = [(dt[0], object) if i in cols else dt for i,dt in
                  enumerate(dtype)]
@@ -1059,7 +1060,7 @@ def genfromdta(fname, missing_flt=-999., encoding=None, pandas=False,
         for col in cols:
             def convert(x):
                 return _stata_elapsed_date_to_datetime(x, fmtlist[col])
-            data[data.dtype.names[col]] = map(convert,
+            data[data.dtype.names[col]] = lmap(convert,
                                               data[data.dtype.names[col]])
     return data
 
@@ -1182,11 +1183,11 @@ def savetxt(fname, X, names=None, fmt='%.18e', delimiter=' '):
 
     # `fmt` can be a string with multiple insertion points or a list of formats.
     # E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
-    if type(fmt) in (list, tuple):
+    if isinstance(fmt, (list, tuple)):
         if len(fmt) != ncol:
             raise AttributeError('fmt has wrong shape.  %s' % str(fmt))
         format = delimiter.join(fmt)
-    elif type(fmt) is str:
+    elif isinstance(fmt, string_types):
         if fmt.count('%') == 1:
             fmt = [fmt, ]*ncol
             format = delimiter.join(fmt)
diff --git a/statsmodels/iolib/smpickle.py b/statsmodels/iolib/smpickle.py
index 4f2e2e22835..e06c910dacc 100644
--- a/statsmodels/iolib/smpickle.py
+++ b/statsmodels/iolib/smpickle.py
@@ -1,4 +1,5 @@
 '''Helper files for pickling'''
+from statsmodels.compat.python import cPickle
 
 def _get_file_obj(fname, mode):
     """
@@ -19,9 +20,8 @@ def save_pickle(obj, fname):
     fname : str
         Filename to pickle to
     """
-    import cPickle as pickle
     fout = _get_file_obj(fname, 'wb')
-    pickle.dump(obj, fout, protocol=-1)
+    cPickle.dump(obj, fout, protocol=-1)
 
 
 def load_pickle(fname):
@@ -37,7 +37,6 @@ def load_pickle(fname):
     -----
     This method can be used to load *both* models and results.
     """
-    import cPickle as pickle
     fin = _get_file_obj(fname, 'rb')
-    return pickle.load(fin)
+    return cPickle.load(fin)
 
diff --git a/statsmodels/iolib/summary.py b/statsmodels/iolib/summary.py
index d4982fe886f..2d3856c41aa 100644
--- a/statsmodels/iolib/summary.py
+++ b/statsmodels/iolib/summary.py
@@ -1,7 +1,5 @@
-
-
+from statsmodels.compat.python import range, lrange, lmap, lzip, zip_longest
 import numpy as np
-from statsmodels.compatnp.iter_compat import zip_longest
 from statsmodels.iolib.table import SimpleTable
 from statsmodels.iolib.tableformatting import (gen_fmt, fmt_2,
                                                 fmt_params, fmt_base, fmt_2cols)
@@ -201,12 +199,12 @@ def summary(self, yname=None, xname=None, title=0, alpha=.05,
     params = self.params
     conf_int = self.conf_int(alpha)
     std_err = self.bse
-    exog_len = xrange(len(xname))
+    exog_len = lrange(len(xname))
     tstat = tstats[modeltype]
     prob_stat = prob_stats[modeltype]
 
     # Simpletable should be able to handle the formating
-    params_data = zip(["%#6.4g" % (params[i]) for i in exog_len],
+    params_data = lzip(["%#6.4g" % (params[i]) for i in exog_len],
                        ["%#6.4f" % (std_err[i]) for i in exog_len],
                        ["%#6.4f" % (tstat[i]) for i in exog_len],
                        ["%#6.4f" % (prob_stat[i]) for i in exog_len],
@@ -407,7 +405,7 @@ def summary_top(results, title=None, gleft=None, gright=None, yname=None, xname=
 
 
 def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
-                   skip_header=False):
+                   skip_header=False, title=None):
     '''create a summary table for the parameters
 
     Parameters
@@ -468,14 +466,14 @@ def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
 
     params_stubs = xname
 
-    exog_idx = xrange(len(xname))
+    exog_idx = lrange(len(xname))
 
     #center confidence intervals if they are unequal lengths
 #    confint = ["(%#6.3g, %#6.3g)" % tuple(conf_int[i]) for i in \
 #                                                             exog_idx]
-    confint = ["%s %s" % tuple(map(forg, conf_int[i])) for i in \
+    confint = ["%s %s" % tuple(lmap(forg, conf_int[i])) for i in \
                                                              exog_idx]
-    len_ci = map(len, confint)
+    len_ci = lmap(len, confint)
     max_ci = max(len_ci)
     min_ci = min(len_ci)
 
@@ -483,7 +481,7 @@ def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
         confint = [ci.center(max_ci) for ci in confint]
 
     #explicit f/g formatting, now uses forg, f or g depending on values
-#    params_data = zip(["%#6.4g" % (params[i]) for i in exog_idx],
+#    params_data = lzip(["%#6.4g" % (params[i]) for i in exog_idx],
 #                       ["%#6.4f" % (std_err[i]) for i in exog_idx],
 #                       ["%#6.3f" % (tvalues[i]) for i in exog_idx],
 #                       ["%#6.3f" % (pvalues[i]) for i in exog_idx],
@@ -492,7 +490,7 @@ def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
 ##                                                             exog_idx]
 #                      )
 
-    params_data = zip([forg(params[i], prec=4) for i in exog_idx],
+    params_data = lzip([forg(params[i], prec=4) for i in exog_idx],
                        [forg(std_err[i]) for i in exog_idx],
                        [forg(tvalues[i]) for i in exog_idx],
                        ["%#6.3f" % (pvalues[i]) for i in exog_idx],
@@ -503,12 +501,77 @@ def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
     parameter_table = SimpleTable(params_data,
                                   param_header,
                                   params_stubs,
-                                  title = None,
+                                  title = title,
                                   txt_fmt = fmt_params #gen_fmt #fmt_2, #gen_fmt,
                                   )
 
     return parameter_table
 
+
+def summary_params_frame(results, yname=None, xname=None, alpha=.05,
+                         use_t=True):
+    '''create a summary table for the parameters
+
+    Parameters
+    ----------
+    res : results instance
+        some required information is directly taken from the result
+        instance
+    yname : string or None
+        optional name for the endogenous variable, default is "y"
+    xname : list of strings or None
+        optional names for the exogenous variables, default is "var_xx"
+    alpha : float
+        significance level for the confidence intervals
+    use_t : bool
+        indicator whether the p-values are based on the Student-t
+        distribution (if True) or on the normal distribution (if False)
+    skip_headers : bool
+        If false (default), then the header row is added. If true, then no
+        header row is added.
+
+    Returns
+    -------
+    params_table : SimpleTable instance
+    '''
+
+    #Parameters part of the summary table
+    #------------------------------------
+    #Note: this is not necessary since we standardized names, only t versus normal
+
+    if isinstance(results, tuple):
+        #for multivariate endog
+        #TODO: check whether I don't want to refactor this
+        #we need to give parameter alpha to conf_int
+        results, params, std_err, tvalues, pvalues, conf_int = results
+    else:
+        params = results.params
+        std_err = results.bse
+        tvalues = results.tvalues  #is this sometimes called zvalues
+        pvalues = results.pvalues
+        conf_int = results.conf_int(alpha)
+
+
+    #Dictionary to store the header names for the parameter part of the
+    #summary table. look up by modeltype
+    alp = str((1-alpha)*100)+'%'
+    if use_t:
+        param_header = ['coef', 'std err', 't', 'P>|t|',
+                        'Conf. Int. Low', 'Conf. Int. Upp.']
+    else:
+        param_header = ['coef', 'std err', 'z', 'P>|z|',
+                        'Conf. Int. Low', 'Conf. Int. Upp.']
+
+    _, xname = _getnames(results, yname=yname, xname=xname)
+
+
+    #------------------
+
+    from pandas import DataFrame
+    table = np.column_stack((params, std_err, tvalues, pvalues, conf_int))
+    return DataFrame(table, columns=param_header, index=xname)
+
+
 def summary_params_2d(result, extras=None, endog_names=None, exog_names=None,
                       title=None):
     '''create summary table of regression parameters with several equations
@@ -556,9 +619,9 @@ def summary_params_2d(result, extras=None, endog_names=None, exog_names=None,
                                 for col in getattr(result, what)]
                                 for what in extras
                                 ]
-        data = zip(res_params, *extras_list)
+        data = lzip(res_params, *extras_list)
         data = [i for j in data for i in j]  #flatten
-        stubs = zip(endog_names, *[['']*len(endog_names)]*len(extras))
+        stubs = lzip(endog_names, *[['']*len(endog_names)]*len(extras))
         stubs = [i for j in stubs for i in j] #flatten
         #return SimpleTable(data, headers=exog_names, stubs=stubs)
     else:
@@ -715,7 +778,7 @@ def summary_return(tables, return_fmt='text'):
     if return_fmt == 'text':
         strdrop = lambda x: str(x).rsplit('\n',1)[0]
         #convert to string drop last line
-        return '\n'.join(map(strdrop, tables[:-1]) + [str(tables[-1])])
+        return '\n'.join(lmap(strdrop, tables[:-1]) + [str(tables[-1])])
     elif return_fmt == 'tables':
         return tables
     elif return_fmt == 'csv':
diff --git a/statsmodels/iolib/summary2.py b/statsmodels/iolib/summary2.py
index 8bf8fc9aef5..c2692647f67 100644
--- a/statsmodels/iolib/summary2.py
+++ b/statsmodels/iolib/summary2.py
@@ -1,13 +1,14 @@
+from statsmodels.compat.python import (lrange, iterkeys, iteritems, lzip,
+                                       reduce, itervalues, zip, string_types,
+                                       range)
+from statsmodels.compat.collections import OrderedDict
 import numpy as np
 import pandas as pd
 import datetime
-import copy
-#import collections  # OrderedDict requires python >= 2.7
-from statsmodels.compatnp.collections import OrderedDict
-import StringIO
 import textwrap
-from table import SimpleTable
-from tableformatting import fmt_latex, fmt_txt
+from .table import SimpleTable
+from .tableformatting import fmt_latex, fmt_txt
+
 
 class Summary(object):
     def __init__(self):
@@ -43,8 +44,8 @@ def add_df(self, df, index=True, header=True, float_format='%.4f',
             Data alignment (l/c/r)
         '''
 
-        settings = {'index':index, 'header':header,
-                    'float_format':float_format, 'align':align}
+        settings = {'index': index, 'header': header,
+                    'float_format': float_format, 'align': align}
         self.tables.append(df)
         self.settings.append(settings)
 
@@ -62,7 +63,7 @@ def add_array(self, array, align='r', float_format="%.4f"):
 
         table = pd.DataFrame(array)
         self.add_df(table, index=False, header=False,
-                float_format=float_format, align=align)
+                    float_format=float_format, align=align)
 
     def add_dict(self, d, ncols=2, align='l', float_format="%.4f"):
         '''Add the contents of a Dict to summary table
@@ -78,16 +79,16 @@ def add_dict(self, d, ncols=2, align='l', float_format="%.4f"):
             Data alignment (l/c/r)
         '''
 
-        keys = [_formatter(x, float_format) for x in d.keys()]
-        vals = [_formatter(x, float_format) for x in d.values()]
-        data = np.array(zip(keys, vals))
+        keys = [_formatter(x, float_format) for x in iterkeys(d)]
+        vals = [_formatter(x, float_format) for x in itervalues(d)]
+        data = np.array(lzip(keys, vals))
 
         if data.shape[0] % ncols != 0:
             pad = ncols - (data.shape[0] % ncols)
-            data = np.vstack([data, np.array(pad * [['','']])])
+            data = np.vstack([data, np.array(pad * [['', '']])])
 
         data = np.split(data, ncols)
-        data = reduce(lambda x,y: np.hstack([x,y]), data)
+        data = reduce(lambda x, y: np.hstack([x, y]), data)
         self.add_array(data, align=align)
 
     def add_text(self, string):
@@ -102,7 +103,7 @@ def add_title(self, title=None, results=None):
         provided but a results instance is provided, statsmodels attempts
         to construct a useful title automatically.
         '''
-        if type(title) == str:
+        if isinstance(title, string_types):
             self.title = title
         else:
             try:
@@ -114,7 +115,7 @@ def add_title(self, title=None, results=None):
                 self.title = ''
 
     def add_base(self, results, alpha=0.05, float_format="%.4f", title=None,
-            xname=None, yname=None):
+                 xname=None, yname=None):
         '''Try to construct a basic summary instance.
 
         Parameters
@@ -132,11 +133,11 @@ def add_base(self, results, alpha=0.05, float_format="%.4f", title=None,
             Name of the dependent variable (optional)
         '''
 
-        param = summary_params(results, alpha=alpha)
+        param = summary_params(results, alpha=alpha, use_t=results.use_t)
         info = summary_model(results)
-        if xname != None:
+        if xname is not None:
             param.index = xname
-        if yname != None:
+        if yname is not None:
             info['Dependent Variable:'] = yname
         self.add_dict(info, align='l')
         self.add_df(param, float_format=float_format)
@@ -154,6 +155,7 @@ def as_text(self):
         pad_col, pad_index, widest = _measure_tables(tables, settings)
 
         rule_equal = widest * '='
+        #TODO: this isn't used anywhere?
         rule_dash = widest * '-'
 
         simple_tables = _simple_tables(tables, settings, pad_col, pad_index)
@@ -165,7 +167,7 @@ def as_text(self):
         tab.append(rule_equal)
         tab = '\n'.join(tab)
 
-        if title != None:
+        if title is not None:
             title = title
             if len(title) < widest:
                 title = ' ' * int(widest/2 - len(title)/2) + title
@@ -186,6 +188,7 @@ def as_html(self):
 
         tables = self.tables
         settings = self.settings
+        #TODO: this isn't used anywhere
         title = self.title
 
         simple_tables = _simple_tables(tables, settings)
@@ -200,7 +203,7 @@ def as_latex(self):
         tables = self.tables
         settings = self.settings
         title = self.title
-        if title != None:
+        if title is not None:
             title = '\\caption{' + title + '} \\\\'
         else:
             title = '\\caption{}'
@@ -213,6 +216,7 @@ def as_latex(self):
         out = '\n'.join(out)
         return out
 
+
 def _measure_tables(tables, settings):
     '''Compare width of ascii tables in a list and calculate padding values.
     We add space to each col_sep to get us as close as possible to the
@@ -240,18 +244,19 @@ def _measure_tables(tables, settings):
 
 # Useful stuff
 _model_types = {'OLS' : 'Ordinary least squares',
-               'GLS' : 'Generalized least squares',
-               'GLSAR' : 'Generalized least squares with AR(p)',
-               'WLS' : 'Weigthed least squares',
-               'RLM' : 'Robust linear model',
-               'NBin': 'Negative binomial model',
-               'GLM' : 'Generalized linear model'
-               }
+                'GLS' : 'Generalized least squares',
+                'GLSAR' : 'Generalized least squares with AR(p)',
+                'WLS' : 'Weigthed least squares',
+                'RLM' : 'Robust linear model',
+                'NBin': 'Negative binomial model',
+                'GLM' : 'Generalized linear model'
+                }
+
 
 def summary_model(results):
     '''Create a dict with information about the model
     '''
-    def time_now(**kwrds):
+    def time_now(*args, **kwds):
         now = datetime.datetime.now()
         return now.strftime('%Y-%m-%d %H:%M')
     info = OrderedDict()
@@ -259,7 +264,7 @@ def time_now(**kwrds):
     info['Model Family:'] = lambda x: x.family.__class.__name__
     info['Link Function:'] = lambda x: x.family.link.__class__.__name__
     info['Dependent Variable:'] = lambda x: x.model.endog_names
-    info['Date:'] = time_now()
+    info['Date:'] = time_now
     info['No. Observations:'] = lambda x: "%#6d" % x.nobs
     info['Df Model:'] = lambda x: "%#6d" % x.df_model
     info['Df Residuals:'] = lambda x: "%#6d" % x.df_resid
@@ -283,13 +288,15 @@ def time_now(**kwrds):
     info['Prob (F-statistic):'] = lambda x: "%#6.3g" % x.f_pvalue
     info['Scale:'] = lambda x: "%#8.5g" % x.scale
     out = OrderedDict()
-    for key in info.keys():
+    for key, func in iteritems(info):
         try:
-            out[key] = info[key](results)
-        except:
+            out[key] = func(results)
+        # NOTE: some models don't have loglike defined (RLM), so that's NIE
+        except (AttributeError, KeyError, NotImplementedError):
             pass
     return out
 
+
 def summary_params(results, yname=None, xname=None, alpha=.05, use_t=True,
                    skip_header=False, float_format="%.4f"):
     '''create a summary table of parameters from results instance
@@ -358,27 +365,28 @@ def _col_params(result, float_format='%.4f', stars=True):
     for col in res.columns[:2]:
         res[col] = res[col].apply(lambda x: float_format % x)
     # Std.Errors in parentheses
-    res.ix[:,1] = '(' + res.ix[:,1] + ')'
+    res.ix[:, 1] = '(' + res.ix[:, 1] + ')'
     # Significance stars
     if stars:
-        idx = res.ix[:,3] < .1
-        res.ix[:,0][idx] = res.ix[:,0][idx] + '*'
-        idx = res.ix[:,3] < .05
-        res.ix[:,0][idx] = res.ix[:,0][idx] + '*'
-        idx = res.ix[:,3] < .01
-        res.ix[:,0][idx] = res.ix[:,0][idx] + '*'
+        idx = res.ix[:, 3] < .1
+        res.ix[:, 0][idx] = res.ix[:, 0][idx] + '*'
+        idx = res.ix[:, 3] < .05
+        res.ix[:, 0][idx] = res.ix[:, 0][idx] + '*'
+        idx = res.ix[:, 3] < .01
+        res.ix[:, 0][idx] = res.ix[:, 0][idx] + '*'
     # Stack Coefs and Std.Errors
-    res = res.ix[:,:2]
+    res = res.ix[:, :2]
     res = res.stack()
     res = pd.DataFrame(res)
     res.columns = [str(result.model.endog_names)]
     return res
 
+
 def _col_info(result, info_dict=None):
     '''Stack model info in a column
     '''
 
-    if info_dict == None:
+    if info_dict is None:
         info_dict = {}
     out = []
     index = []
@@ -391,7 +399,7 @@ def _col_info(result, info_dict=None):
         except:
             out.append('')
         index.append(i)
-    out = pd.DataFrame({str(result.model.endog_names):out}, index=index)
+    out = pd.DataFrame({str(result.model.endog_names): out}, index=index)
     return out
 
 
@@ -404,13 +412,14 @@ def _make_unique(list_of_names):
     header = []
     for _name in list_of_names:
         name_counter[_name] += "I"
-        header.append(_name+" " +name_counter[_name])
+        header.append(_name+" " + name_counter[_name])
     return header
 
 
 def summary_col(results, float_format='%.4f', model_names=[], stars=False,
-        info_dict=None, regressor_order=[]):
-    '''Summarize multiple results instances side-by-side (coefs and SEs)
+                info_dict=None, regressor_order=[]):
+    """
+    Summarize multiple results instances side-by-side (coefs and SEs)
 
     Parameters
     ----------
@@ -424,21 +433,23 @@ def summary_col(results, float_format='%.4f', model_names=[], stars=False,
         print significance stars
     info_dict : dict
         dict of lambda functions to be applied to results instances to retrieve
-        model info. To use specific information for different models, add a (nested)
-        info_dict with model name as the key.
-        Example: `info_dict = {"N":..., "R2": ..., "OLS":{"R2":...}}` would only show
-        `R2` for OLS regression models, but additionally `N` for all other results.
-        Default : None (use the info_dict specified in result.default_model_infos, if
-        this property exists)
+        model info. To use specific information for different models, add a
+        (nested) info_dict with model name as the key.
+        Example: `info_dict = {"N":..., "R2": ..., "OLS":{"R2":...}}` would
+        only show `R2` for OLS regression models, but additionally `N` for
+        all other results.
+        Default : None (use the info_dict specified in
+        result.default_model_infos, if this property exists)
     regressor_order : list of strings
         list of names of the regressors in the desired order. All regressors
         not specified will be appended to the end of the list.
-    '''
+    """
 
-    if type(results) != list:
+    if not isinstance(results, list):
         results = [results]
 
-    cols = [_col_params(x, stars=stars, float_format=float_format) for x in results]
+    cols = [_col_params(x, stars=stars, float_format=float_format) for x in
+            results]
 
     # Unique column names (pandas has problems merging otherwise)
     if model_names:
@@ -448,7 +459,8 @@ def summary_col(results, float_format='%.4f', model_names=[], stars=False,
     for i in range(len(cols)):
         cols[i].columns = [colnames[i]]
 
-    merg = lambda x,y: x.merge(y, how='outer', right_index=True, left_index=True)
+    merg = lambda x, y: x.merge(y, how='outer', right_index=True,
+                                left_index=True)
     summ = reduce(merg, cols)
 
     if regressor_order:
@@ -462,20 +474,23 @@ def summary_col(results, float_format='%.4f', model_names=[], stars=False,
         summ = summ.reindex(f(order))
         summ.index = [x[:-4] for x in summ.index]
 
-    idx = pd.Series(range(summ.shape[0])) %2 == 1
+    idx = pd.Series(lrange(summ.shape[0])) % 2 == 1
     summ.index = np.where(idx, '', summ.index.get_level_values(0))
 
     # add infos about the models.
     if info_dict:
-        cols = [_col_info(x, info_dict.get(x.model.__class__.__name__, info_dict)) for x in results]
+        cols = [_col_info(x, info_dict.get(x.model.__class__.__name__,
+                                           info_dict)) for x in results]
     else:
-        cols = [_col_info(x, getattr(x, "default_model_infos", None)) for x in results]
+        cols = [_col_info(x, getattr(x, "default_model_infos", None)) for x in
+                results]
     # use unique column names, otherwise the merge will not succeed
     for df , name in zip(cols, _make_unique([df.columns[0] for df in cols])):
         df.columns = [name]
-    merg = lambda x,y: x.merge(y, how='outer', right_index=True, left_index=True)
+    merg = lambda x, y: x.merge(y, how='outer', right_index=True,
+                                left_index=True)
     info = reduce(merg, cols)
-    dat = pd.DataFrame(np.vstack([summ,info])) # pd.concat better, but error
+    dat = pd.DataFrame(np.vstack([summ, info]))  # pd.concat better, but error
     dat.columns = summ.columns
     dat.index = pd.Index(summ.index.tolist() + info.index.tolist())
     summ = dat
@@ -499,9 +514,9 @@ def _formatter(element, float_format='%.4f'):
     return out.strip()
 
 
-def _df_to_simpletable(df, align='r', float_format="%.4f", header=True, index=True,
-        table_dec_above='-', table_dec_below=None, header_dec_below='-',
-        pad_col=0, pad_index=0):
+def _df_to_simpletable(df, align='r', float_format="%.4f", header=True,
+                       index=True, table_dec_above='-', table_dec_below=None,
+                       header_dec_below='-', pad_col=0, pad_index=0):
     dat = df.copy()
     dat = dat.applymap(lambda x: _formatter(x, float_format))
     if header:
@@ -511,10 +526,10 @@ def _df_to_simpletable(df, align='r', float_format="%.4f", header=True, index=Tr
     if index:
         stubs = [str(x) + int(pad_index) * ' ' for x in dat.index.tolist()]
     else:
-        dat.ix[:,0] = [str(x) + int(pad_index) * ' ' for x in dat.ix[:,0]]
+        dat.ix[:, 0] = [str(x) + int(pad_index) * ' ' for x in dat.ix[:, 0]]
         stubs = None
     st = SimpleTable(np.array(dat), headers=headers, stubs=stubs,
-            ltx_fmt=fmt_latex, txt_fmt=fmt_txt)
+                     ltx_fmt=fmt_latex, txt_fmt=fmt_txt)
     st.output_formats['latex']['data_aligns'] = align
     st.output_formats['txt']['data_aligns'] = align
     st.output_formats['txt']['table_dec_above'] = table_dec_above
@@ -527,15 +542,17 @@ def _df_to_simpletable(df, align='r', float_format="%.4f", header=True, index=Tr
 def _simple_tables(tables, settings, pad_col=None, pad_index=None):
     simple_tables = []
     float_format = '%.4f'
-    if pad_col == None:
+    if pad_col is None:
         pad_col = [0] * len(tables)
-    if pad_index == None:
+    if pad_index is None:
         pad_index = [0] * len(tables)
-    for i,v in enumerate(tables):
+    for i, v in enumerate(tables):
         index = settings[i]['index']
         header = settings[i]['header']
         align = settings[i]['align']
         simple_tables.append(_df_to_simpletable(v, align=align,
-            float_format=float_format, header=header, index=index,
-            pad_col=pad_col[i], pad_index=pad_index[i]))
+                                                float_format=float_format,
+                                                header=header, index=index,
+                                                pad_col=pad_col[i],
+                                                pad_index=pad_index[i]))
     return simple_tables
diff --git a/statsmodels/iolib/table.py b/statsmodels/iolib/table.py
index cf8151316ff..1868143acce 100644
--- a/statsmodels/iolib/table.py
+++ b/statsmodels/iolib/table.py
@@ -67,8 +67,7 @@ def mylabeller(cell):
 - Calls ``next`` instead of ``__next__``.
   The 2to3 tool should handle that no problem.
   (We will switch to the `next` function if 2.5 support is ever dropped.)
-- from __future__ import division, with_statement
-- from itertools import izip as zip
+- from __future__ import division
 - Let me know if you find other problems.
 
 :contact: alan dot isaac at gmail dot com
@@ -82,17 +81,12 @@ def mylabeller(cell):
 :change: 2010-05-02 eliminate newlines that came before and after table
 :change: 2010-05-06 add `label_cells` to `SimpleTable`
 """
-from __future__ import division, with_statement
-import logging
 
-from statsmodels.compatnp.iter_compat import zip_longest
+from __future__ import division
+from statsmodels.compat.python import (lmap, lrange, zip, next, iteritems, zip_longest,
+                                range)
+import logging
 
-try: #plan for Python 3
-    #from itertools import izip_longest, izip as zip
-    from itertools import izip as zip
-    pass   # accommodate 2to3 tool
-except ImportError:
-    pass
 from itertools import cycle
 from collections import defaultdict
 import csv
@@ -112,7 +106,7 @@ def csv2st(csvfile, headers=False, stubs=False, title=None):
             try:
                 headers = next(reader)
             except NameError: #must be Python 2.5 or earlier
-                headers = reader.next()
+                headers = next(reader)
         elif headers is False:
             headers=()
         if stubs is True:
@@ -195,7 +189,7 @@ def __init__(self, data, headers=None, stubs=None, title='',
         """
         #self._raw_data = data
         self.title = title
-        self._datatypes = datatypes or range(len(data[0]))
+        self._datatypes = datatypes or lrange(len(data[0]))
         #start with default formatting
         self._txt_fmt = default_txt_fmt.copy()
         self._latex_fmt = default_latex_fmt.copy()
@@ -223,6 +217,7 @@ def __init__(self, data, headers=None, stubs=None, title='',
         rows = self._data2rows(data)  # a list of Row instances
         list.__init__(self, rows)
         self._add_headers_stubs(headers, stubs)
+        self._colwidths = dict()
     def __str__(self):
         return self.as_text()
     def __repr__(self):
@@ -283,7 +278,7 @@ def insert_stubs(self, loc, stubs):
                 try:
                     row.insert_stub(loc, next(stubs))
                 except NameError: #Python 2.5 or earlier
-                    row.insert_stub(loc, stubs.next())
+                    row.insert_stub(loc, next(stubs))
                 except StopIteration:
                     raise ValueError('length of stubs must match table length')
     def _data2rows(self, raw_data):
@@ -301,7 +296,7 @@ def _data2rows(self, raw_data):
                 try:
                     cell.datatype = next(dtypes)
                 except NameError: #Python 2.5 or earlier
-                    cell.datatype = dtypes.next()
+                    cell.datatype = next(dtypes)
                 cell.row = newrow  #a cell knows its row
             rows.append(newrow)
         logging.debug('Exit SimpleTable.data2rows.')
@@ -309,7 +304,8 @@ def _data2rows(self, raw_data):
     def pad(self, s, width, align):
         """DEPRECATED: just use the pad function"""
         return pad(s, width, align)
-    def get_colwidths(self, output_format, **fmt_dict):
+    def _get_colwidths(self, output_format, **fmt_dict):
+        """Return list, the calculated widths of each column."""
         output_format = get_output_format(output_format)
         fmt = self.output_formats[output_format].copy()
         fmt.update(fmt_dict)
@@ -327,8 +323,24 @@ def get_colwidths(self, output_format, **fmt_dict):
         for col in zip(*self):
             maxwidth = max(len(c.format(0,output_format,**fmt)) for c in col)
             min_widths.append(maxwidth)
-        result = map(max, min_widths, request)
+        result = lmap(max, min_widths, request)
         return result
+    def get_colwidths(self, output_format, **fmt_dict):
+        """Return list, the widths of each column."""
+        call_args = [output_format]
+        for k, v in sorted(iteritems(fmt_dict)):
+            if isinstance(v, list):
+                call_args.append((k, tuple(v)))
+            elif isinstance(v, dict):
+                call_args.append((k, tuple(sorted(iteritems(v)))))
+            else:
+                call_args.append((k, v))
+        key = tuple(call_args)
+        try:
+            return self._colwidths[key]
+        except KeyError:
+            self._colwidths[key] = self._get_colwidths(output_format, **fmt_dict)
+            return self._colwidths[key]
     def _get_fmt(self, output_format, **fmt_dict):
         """Return dict, the formatting options.
         """
@@ -385,17 +397,18 @@ def as_html(self, **fmt_dict):
         formatted_rows.extend( row.as_string('html', **fmt) for row in self )
         formatted_rows.append('</table>')
         return '\n'.join(formatted_rows)
-    def as_latex_tabular(self, **fmt_dict):
+    def as_latex_tabular(self, center=True, **fmt_dict):
         '''Return string, the table as a LaTeX tabular environment.
         Note: will require the booktabs package.'''
         #fetch the text format, override with fmt_dict
         fmt = self._get_fmt('latex', **fmt_dict)
-        
-        formatted_rows = ["\\begin{center}"]
-        
+
+        if center:
+            formatted_rows = ["\\begin{center}"]
+
         table_dec_above = fmt['table_dec_above'] or ''
         table_dec_below = fmt['table_dec_below'] or ''
-        
+
         prev_aligns = None
         last = None
         for row in self + [last]:
@@ -403,7 +416,7 @@ def as_latex_tabular(self, **fmt_dict):
                 aligns = None
             else:
                 aligns = row.get_aligns('latex', **fmt)
-            
+
             if aligns != prev_aligns:
                 # When the number/type of columns changes...
                 if prev_aligns:
@@ -424,7 +437,8 @@ def as_latex_tabular(self, **fmt_dict):
         if self.title:
             title = r'%%\caption{%s}' % self.title
             formatted_rows.append(title)
-        formatted_rows.append( "\\end{center}" )
+        if center:
+            formatted_rows.append( "\\end{center}" )
         return '\n'.join(formatted_rows)
         """
         if fmt_dict['strip_backslash']:
@@ -685,7 +699,7 @@ def format(self, width, output_format='txt', **fmt_dict):
                 if isinstance( data, str ):
                     for repl in fmt["replacements"]:
                         data = data.replace( repl, fmt["replacements"][repl] )
-            
+
             dfmt = fmt.get(datatype)
             try:
                 content = dfmt % (data,)
diff --git a/statsmodels/iolib/tests/results/macrodata.npy_ b/statsmodels/iolib/tests/results/macrodata.npy_
deleted file mode 100644
index bb245dd2b29..00000000000
Binary files a/statsmodels/iolib/tests/results/macrodata.npy_ and /dev/null differ
diff --git a/statsmodels/iolib/tests/test_foreign.py b/statsmodels/iolib/tests/test_foreign.py
index a4a86c301ad..5fdee6cd191 100644
--- a/statsmodels/iolib/tests/test_foreign.py
+++ b/statsmodels/iolib/tests/test_foreign.py
@@ -10,7 +10,7 @@
 from pandas import DataFrame, isnull
 import pandas.util.testing as ptesting
 
-from statsmodels.compatnp.py3k import BytesIO, asbytes
+from statsmodels.compat.python import BytesIO, asbytes
 import statsmodels.api as sm
 from statsmodels.iolib.foreign import (StataWriter, genfromdta,
             _datetime_to_stata_elapsed, _stata_elapsed_date_to_datetime)
@@ -33,11 +33,8 @@ def test_genfromdta():
     #NOTE: Stata handles data very oddly.  Round tripping from csv to dta
     #    to ndarray 2710.349 (csv) -> 2510.2491 (stata) -> 2710.34912109375
     #    (dta/ndarray)
-    #res2 = np.load(curdir+'/results/macrodata.npy')
-    #res2 = res2.view((float,len(res2[0])))
-    from results.macrodata import macrodata_result as res2
+    from .results.macrodata import macrodata_result as res2
     res1 = genfromdta(curdir+'/../../datasets/macrodata/macrodata.dta')
-    #res1 = res1.view((float,len(res1[0])))
     assert_array_equal(res1 == res2, True)
 
 def test_genfromdta_pandas():
diff --git a/statsmodels/iolib/tests/test_pickle.py b/statsmodels/iolib/tests/test_pickle.py
index 3c3aaac4fde..4e1732cb7cc 100644
--- a/statsmodels/iolib/tests/test_pickle.py
+++ b/statsmodels/iolib/tests/test_pickle.py
@@ -1,11 +1,11 @@
-
+from statsmodels.compat.python import lrange, BytesIO
 from statsmodels.iolib.smpickle import save_pickle, load_pickle
 
 def test_pickle():
     import tempfile
     from numpy.testing import assert_equal
     tmpdir = tempfile.mkdtemp(prefix='pickle')
-    a = range(10)
+    a = lrange(10)
     save_pickle(a, tmpdir+'/res.pkl')
     b = load_pickle(tmpdir+'/res.pkl')
     assert_equal(a, b)
@@ -20,7 +20,6 @@ def test_pickle():
     assert not os.path.exists(tmpdir)
 
     #test with file handle
-    from statsmodels.compatnp.py3k import BytesIO
     fh = BytesIO()
     save_pickle(a, fh)
     fh.seek(0,0)
diff --git a/statsmodels/iolib/tests/test_summary.py b/statsmodels/iolib/tests/test_summary.py
index ca8e331e64b..5e24277599f 100644
--- a/statsmodels/iolib/tests/test_summary.py
+++ b/statsmodels/iolib/tests/test_summary.py
@@ -2,7 +2,7 @@
 
 
 '''
-
+from __future__ import print_function
 if __name__ == '__main__':
 
     from statsmodels.regression.tests.test_regression  import TestOLS
@@ -12,14 +12,14 @@
     TestOLS.setupClass()
     results = aregression.res1
     r_summary = str(results.summary_old())
-    print r_summary
+    print(r_summary)
     olsres = results
 
-    print '\n\n'
+    print('\n\n')
 
     r_summary = str(results.summary())
-    print r_summary
-    print '\n\n'
+    print(r_summary)
+    print('\n\n')
 
 
     from statsmodels.discrete.tests.test_discrete  import TestProbitNewton
@@ -28,8 +28,8 @@
     TestProbitNewton.setupClass()
     results = aregression.res1
     r_summary = str(results.summary())
-    print r_summary
-    print '\n\n'
+    print(r_summary)
+    print('\n\n')
 
     probres = results
 
@@ -39,10 +39,10 @@
     #TestHampel.setupClass()
     results = aregression.res1
     r_summary = str(results.summary())
-    print r_summary
+    print(r_summary)
     rlmres = results
 
-    print '\n\n'
+    print('\n\n')
 
     from statsmodels.genmod.tests.test_glm  import TestGlmBinomial
 
@@ -50,13 +50,13 @@
     #TestGlmBinomial.setupClass()
     results = aregression.res1
     r_summary = str(results.summary())
-    print r_summary
+    print(r_summary)
 
-    #print results.summary2(return_fmt='latex')
-    #print results.summary2(return_fmt='csv')
+    #print(results.summary2(return_fmt='latex'))
+    #print(results.summary2(return_fmt='csv'))
 
     smry = olsres.summary()
-    print smry.as_csv()
+    print(smry.as_csv())
 
 #    import matplotlib.pyplot as plt
 #    plt.plot(rlmres.model.endog,'o')
diff --git a/statsmodels/iolib/tests/test_summary_old.py b/statsmodels/iolib/tests/test_summary_old.py
index f1d68796442..984dc1f57b2 100644
--- a/statsmodels/iolib/tests/test_summary_old.py
+++ b/statsmodels/iolib/tests/test_summary_old.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import zip
 import warnings
 
 def _est_regression_summary():
diff --git a/statsmodels/iolib/tests/test_table.py b/statsmodels/iolib/tests/test_table.py
index 181c8862004..be114ca1286 100644
--- a/statsmodels/iolib/tests/test_table.py
+++ b/statsmodels/iolib/tests/test_table.py
@@ -11,7 +11,7 @@
 
 class TestSimpleTable(unittest.TestCase):
     def test_SimpleTable_1(self):
-        """Basic test, test_SimpleTable_1"""
+        # Basic test, test_SimpleTable_1
         desired = '''
 =====================
       header1 header2
@@ -29,7 +29,7 @@ def test_SimpleTable_1(self):
         self.assertEqual(desired, str(actual))
 
     def test_SimpleTable_2(self):
-        """ Test SimpleTable.extend_right()"""
+        #  Test SimpleTable.extend_right()
         desired = '''
 =============================================================
            header s1 header d1            header s2 header d2
@@ -51,7 +51,7 @@ def test_SimpleTable_2(self):
         self.assertEqual(desired, str(actual))
 
     def test_SimpleTable_3(self):
-        """ Test SimpleTable.extend() as in extend down"""
+        # Test SimpleTable.extend() as in extend down
         desired = '''
 ==============================
            header s1 header d1
@@ -77,8 +77,7 @@ def test_SimpleTable_3(self):
         self.assertEqual(desired, str(actual))
 
     def test_SimpleTable_4(self):
-        """Basic test, test_SimpleTable_4
-        test uses custom txt_fmt"""
+        # Basic test, test_SimpleTable_4 test uses custom txt_fmt
         txt_fmt1 = dict(data_fmts = ['%3.2f', '%d'],
                         empty_cell = ' ',
                         colwidths = 1,
@@ -108,7 +107,7 @@ def test_SimpleTable_4(self):
         tbl = SimpleTable(table1data, test1header, test1stubs,txt_fmt=txt_fmt1,
                           ltx_fmt=ltx_fmt1, html_fmt=html_fmt1)
         def test_txt_fmt1(self):
-            """Limited test of custom txt_fmt"""
+            # Limited test of custom txt_fmt
             desired = """
 *****************************
 *       * header1 * header2 *
@@ -122,7 +121,7 @@ def test_txt_fmt1(self):
             #print(desired)
             self.assertEqual(actual, desired)
             def test_ltx_fmt1(self):
-                """Limited test of custom ltx_fmt"""
+                # Limited test of custom ltx_fmt
                 desired = r"""
 \begin{tabular}{lcc}
 \toprule
@@ -138,7 +137,7 @@ def test_ltx_fmt1(self):
             #print(desired)
             self.assertEqual(actual, desired)
         def test_html_fmt1(self):
-            """Limited test of custom html_fmt"""
+            # Limited test of custom html_fmt
             desired = """
 <table class="simpletable">
 <tr>
@@ -170,14 +169,18 @@ def test_regression_with_tuples(self):
         endo_Df = endo_groups.agg( [np.sum, np.max] )
         reg = OLS(exo_Df[[("x", "sum")]],endo_Df).fit()
         interesting_lines = []
-        for line in str( reg.summary() ).splitlines():
-            if "('" in line:
-                interesting_lines.append( line[:38] )
-        
+        import warnings
+        with warnings.catch_warnings():
+            # Catch ominormal warning, not interesting here
+            warnings.simplefilter("ignore")
+            for line in str( reg.summary() ).splitlines():
+                if "('" in line:
+                    interesting_lines.append( line[:38] )
+
         desired = ["Dep. Variable:           ('x', 'sum') ",
                    "('y', 'sum')      1.4595      0.209   ",
                    "('y', 'amax')     0.2432      0.035   "]
-        
+
         self.assertEqual(sorted(desired), sorted(interesting_lines)  )
 
 
diff --git a/statsmodels/iolib/tests/test_table_econpy.py b/statsmodels/iolib/tests/test_table_econpy.py
index 184eeb8b901..9ca364e0838 100644
--- a/statsmodels/iolib/tests/test_table_econpy.py
+++ b/statsmodels/iolib/tests/test_table_econpy.py
@@ -6,13 +6,10 @@
 :see: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/305292
 '''
 from __future__ import absolute_import
+from statsmodels.compat.python import zip
 import unittest
 
-try:
-    import numpy as np
-    has_numpy = True
-except ImportError:
-    has_numpy = False
+import numpy as np
 
 __docformat__ = "restructuredtext en"
 
@@ -68,7 +65,7 @@ def test_celldata(self):
 
 class test_SimpleTable(unittest.TestCase):
     def test_txt_fmt1(self):
-        """Limited test of custom txt_fmt"""
+        # Limited test of custom txt_fmt
         desired = """
 *****************************
 *       * header1 * header2 *
@@ -102,7 +99,7 @@ def test_ltx_fmt1(self):
         #print(desired)
         self.assertEqual(actual, desired)
     def test_html_fmt1(self):
-        """Limited test of custom html_fmt"""
+        # Limited test of custom html_fmt
         desired = """
 <table class="simpletable">
 <tr>
@@ -125,13 +122,12 @@ def test_html_fmt1(self):
         #print len(actual), len(desired)
         self.assertEqual(actual, desired)
     def test_customlabel(self):
-        """Limited test of custom custom labeling"""
-        if has_numpy:
-            tbl = SimpleTable(table1data, test1header, test1stubs, txt_fmt=txt_fmt1)
-            tbl[1][1].data = np.nan
-            tbl.label_cells(custom_labeller)
-            #print([[c.datatype for c in row] for row in tbl])
-            desired = """
+        # Limited test of custom custom labeling
+        tbl = SimpleTable(table1data, test1header, test1stubs, txt_fmt=txt_fmt1)
+        tbl[1][1].data = np.nan
+        tbl.label_cells(custom_labeller)
+        #print([[c.datatype for c in row] for row in tbl])
+        desired = """
 *****************************
 *       * header1 * header2 *
 *****************************
@@ -139,10 +135,8 @@ def test_customlabel(self):
 * stub2 *    2.00 *       3 *
 *****************************
 """
-            actual = '\n%s\n' % tbl.as_text(missing='--')
-            #print(actual)
-            #print(desired)
-            self.assertEqual(actual, desired)
+        actual = '\n%s\n' % tbl.as_text(missing='--')
+        self.assertEqual(actual, desired)
 
 if __name__=="__main__":
     unittest.main()
diff --git a/statsmodels/miscmodels/count.py b/statsmodels/miscmodels/count.py
index c3b1b7b500a..a1c04e64e6c 100644
--- a/statsmodels/miscmodels/count.py
+++ b/statsmodels/miscmodels/count.py
@@ -28,7 +28,7 @@
 
 
 """
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 from scipy.misc import factorial
@@ -125,7 +125,7 @@ def predict_distribution(self, exog):
         '''return frozen scipy.stats distribution with mu at estimated prediction
         '''
         if not hasattr(self, result):
-            raise
+            raise ValueError
         else:
             mu = np.exp(np.dot(exog, params))
             return stats.poisson(mu, loc=0)
@@ -243,7 +243,7 @@ def nloglikeobs(self, params):
         beta = params[:-1]
         gamm = 1 / (1 + np.exp(params[-1]))  #check this
         # replace with np.dot(self.exogZ, gamma)
-        #print np.shape(self.offset), self.exog.shape, beta.shape
+        #print(np.shape(self.offset), self.exog.shape, beta.shape
         XB = self.offset + np.dot(self.exog, beta)
         endog = self.endog
         nloglik = -np.log(1-gamm) + np.exp(XB) -  endog*XB + np.log(factorial(endog))
@@ -263,20 +263,20 @@ def nloglikeobs(self, params):
     data_exog = sm.add_constant(data_exog, prepend=False)
     xbeta = 1 + 0.1*rvs.sum(1)
     data_endog = np.random.poisson(np.exp(xbeta))
-    #print data_endog
+    #print(data_endog
 
     modp = MyPoisson(data_endog, data_exog)
     resp = modp.fit()
-    print resp.params
-    print resp.bse
+    print(resp.params)
+    print(resp.bse)
 
     from statsmodels.discretemod import Poisson
     resdp = Poisson(data_endog, data_exog).fit()
-    print '\ncompare with discretemod'
-    print 'compare params'
-    print resdp.params - resp.params
-    print 'compare bse'
-    print resdp.bse - resp.bse
+    print('\ncompare with discretemod')
+    print('compare params')
+    print(resdp.params - resp.params)
+    print('compare bse')
+    print(resdp.bse - resp.bse)
 
     gmlp = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
     resgp = gmlp.fit()
@@ -284,27 +284,27 @@ def nloglikeobs(self, params):
     c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\decorators.py:105: CacheWriteWarning: The attribute 'bse' cannot be overwritten
       warnings.warn(errmsg, CacheWriteWarning)
     '''
-    print '\ncompare with GLM'
-    print 'compare params'
-    print resgp.params - resp.params
-    print 'compare bse'
-    print resgp.bse - resp.bse
+    print('\ncompare with GLM')
+    print('compare params')
+    print(resgp.params - resp.params)
+    print('compare bse')
+    print(resgp.bse - resp.bse)
 
     lam = np.exp(np.dot(data_exog, resp.params))
     '''mean of Poisson distribution'''
     predmean = stats.poisson.stats(lam,moments='m')
-    print np.max(np.abs(predmean - lam))
+    print(np.max(np.abs(predmean - lam)))
 
     fun = lambda params: np.exp(np.dot(data_exog.mean(0), params))
 
     lamcov = NonlinearDeltaCov(fun, resp.params, resdp.cov_params())
-    print lamcov.cov().shape
-    print lamcov.cov()
+    print(lamcov.cov().shape)
+    print(lamcov.cov())
 
-    print 'analytical'
+    print('analytical')
     xm = data_exog.mean(0)
-    print np.dot(np.dot(xm, resdp.cov_params()), xm.T) * \
-            np.exp(2*np.dot(data_exog.mean(0), resp.params))
+    print(np.dot(np.dot(xm, resdp.cov_params()), xm.T) * \
+            np.exp(2*np.dot(data_exog.mean(0), resp.params)))
 
     ''' cov of linear transformation of params
     >>> np.dot(np.dot(xm, resdp.cov_params()), xm.T)
@@ -315,24 +315,24 @@ def nloglikeobs(self, params):
     0.00038902428119179394
     '''
 
-    print lamcov.wald(1.)
-    print lamcov.wald(2.)
-    print lamcov.wald(2.6)
+    print(lamcov.wald(1.))
+    print(lamcov.wald(2.))
+    print(lamcov.wald(2.6))
 
     do_bootstrap = False
     if do_bootstrap:
         m,s,r = resp.bootstrap(method='newton')
-        print m
-        print s
-        print resp.bse
-
-
-    print '\ncomparison maxabs, masabsrel'
-    print 'discr params', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params)
-    print 'discr bse   ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse)
-    print 'discr bsejac', maxabs(resdp.bse, resp.bsejac), maxabsrel(resdp.bse, resp.bsejac)
-    print 'discr bsejhj', maxabs(resdp.bse, resp.bsejhj), maxabsrel(resdp.bse, resp.bsejhj)
-    print
-    print 'glm params  ', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params)
-    print 'glm bse     ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse)
+        print(m)
+        print(s)
+        print(resp.bse)
+
+
+    print('\ncomparison maxabs, masabsrel')
+    print('discr params', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params))
+    print('discr bse   ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse))
+    print('discr bsejac', maxabs(resdp.bse, resp.bsejac), maxabsrel(resdp.bse, resp.bsejac))
+    print('discr bsejhj', maxabs(resdp.bse, resp.bsejhj), maxabsrel(resdp.bse, resp.bsejhj))
+    print('')
+    print('glm params  ', maxabs(resdp.params, resp.params), maxabsrel(resdp.params, resp.params))
+    print('glm bse     ', maxabs(resdp.bse, resp.bse), maxabsrel(resdp.bse, resp.bse))
 
diff --git a/statsmodels/miscmodels/nonlinls.py b/statsmodels/miscmodels/nonlinls.py
index 69de8c9fcad..8719c91e736 100644
--- a/statsmodels/miscmodels/nonlinls.py
+++ b/statsmodels/miscmodels/nonlinls.py
@@ -302,8 +302,8 @@ def error2(params, x, y):
 
     cf_params, cf_pcov = optimize.curve_fit(func0, x, y)
     cf_bse = np.sqrt(np.diag(cf_pcov))
-    print res[0]
-    print cf_params
-    print resmy.params
-    print cf_bse
-    print resmy.bse
+    print(res[0])
+    print(cf_params)
+    print(resmy.params)
+    print(cf_bse)
+    print(resmy.bse)
diff --git a/statsmodels/miscmodels/tests/test_poisson.py b/statsmodels/miscmodels/tests/test_poisson.py
index e2f0231fe1d..74a967aebb9 100644
--- a/statsmodels/miscmodels/tests/test_poisson.py
+++ b/statsmodels/miscmodels/tests/test_poisson.py
@@ -175,5 +175,5 @@ def test_exog_names_warning(self):
         mod1 = PoissonOffsetGMLE(mod.endog, mod.exog, offset=mod.offset)
         from numpy.testing import assert_warns
         mod1.data.xnames = mod1.data.xnames * 2
-        assert_warns(UserWarning, mod1.fit)
+        assert_warns(UserWarning, mod1.fit, disp=0)
 
diff --git a/statsmodels/miscmodels/tests/test_tmodel.py b/statsmodels/miscmodels/tests/test_tmodel.py
index 434f38d01a6..9772a61f2c4 100644
--- a/statsmodels/miscmodels/tests/test_tmodel.py
+++ b/statsmodels/miscmodels/tests/test_tmodel.py
@@ -145,7 +145,7 @@ def setup_class(cls):
         modf = TLinearModel.from_formula("price ~ CRSP",
                                 data={"price":mm.m_marietta, "CRSP":mm.CRSP})
         resf = modf.fit(method='bfgs', disp=False)
-        from results_tmodel import res_t_dfest as res2
+        from .results_tmodel import res_t_dfest as res2
         cls.res2 = res2
         cls.res1 = res  # take from module scope temporarily
         cls.resf = resf
diff --git a/statsmodels/miscmodels/try_mlecov.py b/statsmodels/miscmodels/try_mlecov.py
index b106e186b3e..26039b2c51d 100644
--- a/statsmodels/miscmodels/try_mlecov.py
+++ b/statsmodels/miscmodels/try_mlecov.py
@@ -5,7 +5,7 @@
 Author: josef-pktd
 '''
 
-
+from __future__ import print_function
 import numpy as np
 #from scipy import special #, stats
 from scipy import linalg
@@ -72,8 +72,8 @@ def mvn_loglike_chol(x, sigma):
     logdetsigma = np.log(np.linalg.det(sigma))
     nobs = len(x)
     from scipy import stats
-    print 'scipy.stats'
-    print np.log(stats.norm.pdf(x_whitened)).sum()
+    print('scipy.stats')
+    print(np.log(stats.norm.pdf(x_whitened)).sum())
 
     llf = - np.dot(x_whitened.T, x_whitened)
     llf -= nobs * np.log(2 * np.pi)
@@ -158,12 +158,12 @@ def _params2cov(self, params, nobs):
         '''
         ar = np.r_[[1], -params[:self.nar]]
         ma = np.r_[[1], params[-self.nma:]]
-        #print 'ar', ar
-        #print 'ma', ma
-        #print 'nobs', nobs
+        #print('ar', ar
+        #print('ma', ma
+        #print('nobs', nobs
         autocov = arma_acovf(ar, ma, nobs=nobs)
-        #print 'arma_acovf(%r, %r, nobs=%d)' % (ar, ma, nobs)
-        #print autocov.shape
+        #print('arma_acovf(%r, %r, nobs=%d)' % (ar, ma, nobs)
+        #print(autocov.shape
         #something is strange  fixed in aram_acovf
         autocov = autocov[:nobs]
         sigma = toeplitz(autocov)
@@ -200,12 +200,12 @@ def fit_invertible(self, *args, **kwds):
     mod.nar, mod.nma = 2, 2   #needs to be added, no init method
     mod.nobs = len(y)
     res = mod.fit(start_params=[0.1, -0.8, 0.2, 0.1, 1.])
-    print 'DGP', ar, ma
-    print res.params
+    print('DGP', ar, ma)
+    print(res.params)
     from statsmodels.regression import yule_walker
-    print yule_walker(y, 2)
+    print(yule_walker(y, 2))
     #resi = mod.fit_invertible(start_params=[0.1,0,0.2,0, 0.5])
-    #print resi.params
+    #print(resi.params
 
     arpoly, mapoly = getpoly(mod, res.params[:-1])
 
@@ -217,7 +217,7 @@ def fit_invertible(self, *args, **kwds):
 ##    mods.nar, mods.nma = 13, 1   #needs to be added, no init method
 ##    mods.nobs = len(ys)
 ##    ress = mods.fit(start_params=np.r_[0.4, np.zeros(12), [0.2, 5.]],maxiter=200)
-##    print ress.params
+##    print(ress.params
 ##    #from statsmodels.sandbox.tsa import arima as tsaa
 ##    #tsaa
 ##    import matplotlib.pyplot as plt
@@ -225,11 +225,11 @@ def fit_invertible(self, *args, **kwds):
 ##    #plt.show()
 
     sigma = mod._params2cov(res.params[:-1], nobs) * res.params[-1]**2
-    print mvn_loglike(y, sigma)
+    print(mvn_loglike(y, sigma))
     llo = mvn_nloglike_obs(y, sigma)
-    print llo.sum(), llo.shape
-    print mvn_loglike_chol(y, sigma)
-    print mvn_loglike_sum(y, sigma)
+    print(llo.sum(), llo.shape)
+    print(mvn_loglike_chol(y, sigma))
+    print(mvn_loglike_sum(y, sigma))
 
 
 
diff --git a/statsmodels/nonparametric/_kernel_base.py b/statsmodels/nonparametric/_kernel_base.py
index 3c061eaa536..459033065d7 100644
--- a/statsmodels/nonparametric/_kernel_base.py
+++ b/statsmodels/nonparametric/_kernel_base.py
@@ -2,7 +2,7 @@
 Module containing the base object for multivariate kernel density and
 regression, plus some utilities.
 """
-
+from statsmodels.compat.python import range, string_types
 import copy
 
 import numpy as np
@@ -15,7 +15,7 @@
 except ImportError:
     has_joblib = False
 
-import kernels
+from . import kernels
 
 
 kernel_func = dict(wangryzin=kernels.wang_ryzin,
@@ -60,19 +60,19 @@ def _compute_subset(class_type, data, bw, co, do, n_cvars, ix_ord,
         sub_data = data[bound[0]:bound[1], :]
 
     if class_type == 'KDEMultivariate':
-        from kernel_density import KDEMultivariate
+        from .kernel_density import KDEMultivariate
         var_type = class_vars[0]
         sub_model = KDEMultivariate(sub_data, var_type, bw=bw,
                         defaults=EstimatorSettings(efficient=False))
     elif class_type == 'KDEMultivariateConditional':
-        from kernel_density import KDEMultivariateConditional
+        from .kernel_density import KDEMultivariateConditional
         k_dep, dep_type, indep_type = class_vars
         endog = sub_data[:, :k_dep]
         exog = sub_data[:, k_dep:]
         sub_model = KDEMultivariateConditional(endog, exog, dep_type,
             indep_type, bw=bw, defaults=EstimatorSettings(efficient=False))
     elif class_type == 'KernelReg':
-        from kernel_regression import KernelReg
+        from .kernel_regression import KernelReg
         var_type, k_vars, reg_type = class_vars
         endog = _adjust_shape(sub_data[:, 0], 1)
         exog = _adjust_shape(sub_data[:, 1:], k_vars)
@@ -126,7 +126,7 @@ def _compute_bw(self, bw):
             bwfunc = self.bw_func['normal_reference']
             return bwfunc()
 
-        if not isinstance(bw, basestring):
+        if not isinstance(bw, string_types):
             self._bw_method = "user-specified"
             res = np.asarray(bw)
         else:
@@ -173,6 +173,15 @@ def _compute_efficient(self, bw):
         ----------
         See p.9 in socserv.mcmaster.ca/racine/np_faq.pdf
         """
+
+        if bw is None:
+            self._bw_method = 'normal_reference'
+        if isinstance(bw, string_types):
+            self._bw_method = bw
+        else: 
+            self._bw_method = "user-specified"
+            return bw
+
         nobs = self.nobs
         n_sub = self.n_sub
         data = copy.deepcopy(self.data)
@@ -204,13 +213,13 @@ def _compute_efficient(self, bw):
                 for i in range(n_blocks))
         else:
             res = []
-            for i in xrange(n_blocks):
+            for i in range(n_blocks):
                 res.append(_compute_subset(class_type, data, bw, co, do,
                                            n_cvars, ix_ord, ix_unord, n_sub,
                                            class_vars, self.randomize,
                                            bounds[i]))
 
-        for i in xrange(n_blocks):
+        for i in range(n_blocks):
             sample_scale[i, :] = res[i][0]
             only_bw[i, :] = res[i][1]
 
@@ -365,7 +374,7 @@ class EstimatorSettings(object):
         ``joblib.Parallel``.  Default is -1, meaning ``n_cores - 1``, with
         ``n_cores`` the number of available CPU cores.
         See the `joblib documentation
-        <http://packages.python.org/joblib/parallel.html>`_ for more details.
+        <https://pythonhosted.org/joblib/parallel.html>`_ for more details.
 
     Examples
     --------
@@ -412,7 +421,7 @@ def __iter__(self):
         X = self.X
         nobs, k_vars = np.shape(X)
 
-        for i in xrange(nobs):
+        for i in range(nobs):
             index = np.ones(nobs, dtype=np.bool)
             index[i] = False
             yield X[index, :]
diff --git a/statsmodels/nonparametric/_smoothers_lowess.pyx b/statsmodels/nonparametric/_smoothers_lowess.pyx
index 7cb1d3754ef..54c95807c6a 100644
--- a/statsmodels/nonparametric/_smoothers_lowess.pyx
+++ b/statsmodels/nonparametric/_smoothers_lowess.pyx
@@ -18,7 +18,6 @@ cimport numpy as np
 import numpy as np
 from cpython cimport bool
 cimport cython
-from libc.math cimport fabs
 
 # there's no fmax in math.h with windows SDK apparently
 cdef inline double fmax(double x, double y): return x if x >= y else y
diff --git a/statsmodels/nonparametric/api.py b/statsmodels/nonparametric/api.py
index b7c9ccc0066..449eb0d9271 100644
--- a/statsmodels/nonparametric/api.py
+++ b/statsmodels/nonparametric/api.py
@@ -1,6 +1,6 @@
 from .kde import KDE, KDEUnivariate
 from .smoothers_lowess import lowess
-import bandwidths
+from . import bandwidths
 
 from .kernel_density import \
     KDEMultivariate, KDEMultivariateConditional, EstimatorSettings
diff --git a/statsmodels/nonparametric/bandwidths.py b/statsmodels/nonparametric/bandwidths.py
index 03bedb5ec9f..f89cd42ea5e 100644
--- a/statsmodels/nonparametric/bandwidths.py
+++ b/statsmodels/nonparametric/bandwidths.py
@@ -1,5 +1,6 @@
 import numpy as np
 from scipy.stats import scoreatpercentile as sap
+from statsmodels.sandbox.nonparametric import kernels
 
 #from scipy.stats import norm
 
@@ -20,7 +21,7 @@ def _select_sigma(X):
 
 
 ## Univariate Rule of Thumb Bandwidths ##
-def bw_scott(x):
+def bw_scott(x, kernel=None):
     """
     Scott's Rule of Thumb
 
@@ -28,6 +29,8 @@ def bw_scott(x):
     ----------
     x : array-like
         Array for which to get the bandwidth
+    kernel : CustomKernel object
+        Unused
 
     Returns
     -------
@@ -49,9 +52,9 @@ def bw_scott(x):
     """
     A = _select_sigma(x)
     n = len(x)
-    return 1.059 * A * n ** -.2
+    return 1.059 * A * n ** (-0.2)
 
-def bw_silverman(x):
+def bw_silverman(x, kernel=None):
     """
     Silverman's Rule of Thumb
 
@@ -59,6 +62,8 @@ def bw_silverman(x):
     ----------
     x : array-like
         Array for which to get the bandwidth
+    kernel : CustomKernel object
+        Unused
 
     Returns
     -------
@@ -79,7 +84,51 @@ def bw_silverman(x):
     """
     A = _select_sigma(x)
     n = len(x)
-    return .9 * A * n ** -.2
+    return .9 * A * n ** (-0.2)
+
+
+def bw_normal_reference(x, kernel=kernels.Gaussian):
+    """
+    Plug-in bandwidth with kernel specific constant based on normal reference.
+
+    This bandwidth minimizes the mean integrated square error if the true
+    distribution is the normal. This choice is an appropriate bandwidth for
+    single peaked distributions that are similar to the normal distribution.
+
+    Parameters
+    ----------
+    x : array-like
+        Array for which to get the bandwidth
+    kernel : CustomKernel object
+        Used to calculate the constant for the plug-in bandwidth.
+
+    Returns
+    -------
+    bw : float
+        The estimate of the bandwidth
+
+    Notes
+    -----
+    Returns C * A * n ** (-1/5.) where ::
+
+       A = min(std(x, ddof=1), IQR/1.349)
+       IQR = np.subtract.reduce(np.percentile(x, [75,25]))
+       C = constant from Hansen (2009)
+
+    When using a Gaussian kernel this is equivalent to the 'scott' bandwidth up
+    to two decimal places. This is the accuracy to which the 'scott' constant is
+    specified.
+
+    References
+    ----------
+
+    Silverman, B.W. (1986) `Density Estimation.`
+    Hansen, B.E. (2009) `Lecture Notes on Nonparametrics.`
+    """
+    C = kernel.normal_reference_constant
+    A = _select_sigma(x)
+    n = len(x)
+    return C * A * n ** (-0.2)
 
 ## Plug-In Methods ##
 
@@ -87,7 +136,12 @@ def bw_silverman(x):
 
 ## Helper Functions ##
 
-bandwidth_funcs = dict(scott=bw_scott,silverman=bw_silverman)
+bandwidth_funcs = {
+    "scott": bw_scott,
+    "silverman": bw_silverman,
+    "normal_reference": bw_normal_reference,
+}
+
 
 def select_bandwidth(x, bw, kernel):
     """
@@ -100,8 +154,8 @@ def select_bandwidth(x, bw, kernel):
     x : array-like
         Array for which to get the bandwidth
     bw : string
-        name of bandwidth selection rule, currently "scott" and "silverman"
-        are supported
+        name of bandwidth selection rule, currently supported are:
+        %s
     kernel : not used yet
 
     Returns
@@ -111,11 +165,13 @@ def select_bandwidth(x, bw, kernel):
 
     """
     bw = bw.lower()
-    if bw not in ["scott","silverman"]:
+    if bw not in bandwidth_funcs:
         raise ValueError("Bandwidth %s not understood" % bw)
 #TODO: uncomment checks when we have non-rule of thumb bandwidths for diff. kernels
 #    if kernel == "gauss":
-    return bandwidth_funcs[bw](x)
+    return bandwidth_funcs[bw](x, kernel)
 #    else:
 #        raise ValueError("Only Gaussian Kernels are currently supported")
 
+# Interpolate docstring to plugin supported bandwidths
+select_bandwidth.__doc__ %=  (", ".join(sorted(bandwidth_funcs.keys())),)
diff --git a/statsmodels/nonparametric/kde.py b/statsmodels/nonparametric/kde.py
index beea483f434..270ab41f035 100644
--- a/statsmodels/nonparametric/kde.py
+++ b/statsmodels/nonparametric/kde.py
@@ -11,7 +11,8 @@
 
 Silverman, B.W.  Density Estimation for Statistics and Data Analysis.
 """
-from __future__ import absolute_import
+from __future__ import absolute_import, print_function
+from statsmodels.compat.python import range
 # for 2to3 with extensions
 import warnings
 
@@ -29,7 +30,7 @@
 kernel_switch = dict(gau=kernels.Gaussian, epa=kernels.Epanechnikov,
                     uni=kernels.Uniform, tri=kernels.Triangular,
                     biw=kernels.Biweight, triw=kernels.Triweight,
-                    cos=kernels.Cosine)
+                    cos=kernels.Cosine, cos2=kernels.Cosine2)
 
 def _checkisfit(self):
     try:
@@ -82,7 +83,7 @@ class KDEUnivariate(object):
     def __init__(self, endog):
         self.endog = np.asarray(endog)
 
-    def fit(self, kernel="gau", bw="scott", fft=True, weights=None,
+    def fit(self, kernel="gau", bw="normal_reference", fft=True, weights=None,
             gridsize=None, adjust=1, cut=3, clip=(-np.inf, np.inf)):
         """
         Attach the density estimate to the KDEUnivariate class.
@@ -107,6 +108,9 @@ def fit(self, kernel="gau", bw="scott", fft=True, weights=None,
               `min(std(X),IQR/1.34)`
             - "silverman" - .9 * A * nobs ** (-1/5.), where A is
               `min(std(X),IQR/1.34)`
+            - "normal_reference" - C * A * nobs ** (-1/5.), where C is
+              calculated from the kernel. Equivalent (up to 2 dp) to the
+              "scott" bandwidth for gaussian kernels. See bandwidths.py
             - If a float is given, it is the bandwidth.
 
         fft : bool
@@ -150,6 +154,9 @@ def fit(self, kernel="gau", bw="scott", fft=True, weights=None,
         self.kernel = kernel_switch[kernel](h=bw) # we instantiate twice,
                                                 # should this passed to funcs?
         # put here to ensure empty cache after re-fit with new options
+        self.kernel.weights = weights
+        if weights is not None:
+            self.kernel.weights /= weights.sum()
         self._cache = resettable_cache()
 
     @cache_readonly
@@ -175,7 +182,7 @@ def cdf(self):
         gridsize = len(support)
         endog = self.endog
         probs = [integrate.quad(func, support[i-1], support[i],
-                    args=endog)[0] for i in xrange(1,gridsize)]
+                    args=endog)[0] for i in range(1,gridsize)]
         return np.cumsum(probs)
 
     @cache_readonly
@@ -267,7 +274,7 @@ def __init__(self, endog):
 
 #### Kernel Density Estimator Functions ####
 
-def kdensity(X, kernel="gau", bw="scott", weights=None, gridsize=None,
+def kdensity(X, kernel="gau", bw="normal_reference", weights=None, gridsize=None,
              adjust=1, clip=(-np.inf,np.inf), cut=3, retgrid=True):
     """
     Rosenblatt-Parzen univariate kernel density estimator.
@@ -334,17 +341,22 @@ def kdensity(X, kernel="gau", bw="scott", weights=None, gridsize=None,
         weights = np.ones(nobs)
         q = nobs
     else:
+        # ensure weights is a numpy array
+        weights = np.asarray(weights)
         if len(weights) != len(clip_x):
             msg = "The length of the weights must be the same as the given X."
             raise ValueError(msg)
         weights = weights[clip_x.squeeze()]
         q = weights.sum()
 
+    # Get kernel object corresponding to selection
+    kern = kernel_switch[kernel]()
+
     # if bw is None, select optimal bandwidth for kernel
     try:
         bw = float(bw)
     except:
-        bw = bandwidths.select_bandwidth(X, bw, kernel)
+        bw = bandwidths.select_bandwidth(X, bw, kern)
     bw *= adjust
 
     a = np.min(X,axis=0) - cut*bw
@@ -353,8 +365,9 @@ def kdensity(X, kernel="gau", bw="scott", weights=None, gridsize=None,
 
     k = (X.T - grid[:,None])/bw  # uses broadcasting to make a gridsize x nobs
 
-    # instantiate kernel class
-    kern = kernel_switch[kernel](h=bw)
+    # set kernel bandwidth
+    kern.seth(bw)
+
     # truncate to domain
     if kern.domain is not None: # won't work for piecewise kernels like parzen
         z_lo, z_high = kern.domain
@@ -373,7 +386,7 @@ def kdensity(X, kernel="gau", bw="scott", weights=None, gridsize=None,
     else:
         return dens, bw
 
-def kdensityfft(X, kernel="gau", bw="scott", weights=None, gridsize=None,
+def kdensityfft(X, kernel="gau", bw="normal_reference", weights=None, gridsize=None,
                 adjust=1, clip=(-np.inf,np.inf), cut=3, retgrid=True):
     """
     Rosenblatt-Parzen univariate kernel density estimator
@@ -447,10 +460,14 @@ def kdensityfft(X, kernel="gau", bw="scott", weights=None, gridsize=None,
     X = np.asarray(X)
     X = X[np.logical_and(X>clip[0], X<clip[1])] # won't work for two columns.
                                                 # will affect underlying data?
+    
+    # Get kernel object corresponding to selection
+    kern = kernel_switch[kernel]()
+
     try:
         bw = float(bw)
     except:
-        bw = bandwidths.select_bandwidth(X, bw, kernel) # will cross-val fit this pattern?
+        bw = bandwidths.select_bandwidth(X, bw, kern) # will cross-val fit this pattern?
     bw *= adjust
 
     nobs = float(len(X)) # after trim
@@ -543,4 +560,4 @@ def kdensityfft(X, kernel="gau", bw="scott", weights=None, gridsize=None,
     except:
 #        ft = np.loadtxt('./ft_silver.csv')
 #        smooth = np.loadtxt('./smooth_silver.csv')
-        print "Didn't get the estimates from the Silverman algorithm"
+        print("Didn't get the estimates from the Silverman algorithm")
diff --git a/statsmodels/nonparametric/kdetools.py b/statsmodels/nonparametric/kdetools.py
index ad48b5a9111..b2e2ae17105 100644
--- a/statsmodels/nonparametric/kdetools.py
+++ b/statsmodels/nonparametric/kdetools.py
@@ -1,4 +1,5 @@
 #### Convenience Functions to be moved to kerneltools ####
+from statsmodels.compat.python import range
 import numpy as np
 
 def forrt(X,m=None):
diff --git a/statsmodels/nonparametric/kernel_density.py b/statsmodels/nonparametric/kernel_density.py
index f47f75a5e30..e0998ed1043 100644
--- a/statsmodels/nonparametric/kernel_density.py
+++ b/statsmodels/nonparametric/kernel_density.py
@@ -30,11 +30,11 @@
 
 # TODO: make default behavior efficient=True above a certain n_obs
 
-
+from statsmodels.compat.python import range, next
 import numpy as np
 
-import kernels
-from _kernel_base import GenericKDE, EstimatorSettings, gpke, \
+from . import kernels
+from ._kernel_base import GenericKDE, EstimatorSettings, gpke, \
     LeaveOneOut, _adjust_shape
 
 
@@ -190,7 +190,7 @@ def pdf(self, data_predict=None):
             data_predict = _adjust_shape(data_predict, self.k_vars)
 
         pdf_est = []
-        for i in xrange(np.shape(data_predict)[0]):
+        for i in range(np.shape(data_predict)[0]):
             pdf_est.append(gpke(self.bw, data=self.data,
                                 data_predict=data_predict[i, :],
                                 var_type=self.var_type) / self.nobs)
@@ -235,7 +235,7 @@ def cdf(self, data_predict=None):
             data_predict = _adjust_shape(data_predict, self.k_vars)
 
         cdf_est = []
-        for i in xrange(np.shape(data_predict)[0]):
+        for i in range(np.shape(data_predict)[0]):
             cdf_est.append(gpke(self.bw, data=self.data,
                                 data_predict=data_predict[i, :],
                                 var_type=self.var_type,
@@ -461,7 +461,7 @@ def loo_likelihood(self, bw, func=lambda x: x):
         xLOO = LeaveOneOut(self.exog).__iter__()
         L = 0
         for i, Y_j in enumerate(yLOO):
-            X_not_i = xLOO.next()
+            X_not_i = next(xLOO)
             f_yx = gpke(bw, data=-Y_j, data_predict=-self.data[i, :],
                         var_type=(self.dep_type + self.indep_type))
             f_x = gpke(bw[self.k_dep:], data=-X_not_i,
@@ -513,7 +513,7 @@ def pdf(self, endog_predict=None, exog_predict=None):
 
         pdf_est = []
         data_predict = np.column_stack((endog_predict, exog_predict))
-        for i in xrange(np.shape(data_predict)[0]):
+        for i in range(np.shape(data_predict)[0]):
             f_yx = gpke(self.bw, data=self.data,
                         data_predict=data_predict[i, :],
                         var_type=(self.dep_type + self.indep_type))
@@ -567,7 +567,7 @@ def cdf(self, endog_predict=None, exog_predict=None):
 
         N_data_predict = np.shape(exog_predict)[0]
         cdf_est = np.empty(N_data_predict)
-        for i in xrange(N_data_predict):
+        for i in range(N_data_predict):
             mu_x = gpke(self.bw[self.k_dep:], data=self.exog,
                         data_predict=exog_predict[i, :],
                         var_type=self.indep_type) / self.nobs
diff --git a/statsmodels/nonparametric/kernel_regression.py b/statsmodels/nonparametric/kernel_regression.py
index b073f9b598b..93aced00e00 100644
--- a/statsmodels/nonparametric/kernel_regression.py
+++ b/statsmodels/nonparametric/kernel_regression.py
@@ -30,14 +30,14 @@
 
 # TODO: make default behavior efficient=True above a certain n_obs
 
-
+from statsmodels.compat.python import range, string_types, next
 import copy
 
 import numpy as np
 from scipy import optimize
 from scipy.stats.mstats import mquantiles
 
-from _kernel_base import GenericKDE, EstimatorSettings, gpke, \
+from ._kernel_base import GenericKDE, EstimatorSettings, gpke, \
     LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR
 
 
@@ -109,7 +109,7 @@ def __init__(self, endog, exog, var_type, reg_type='ll', bw='cv_ls',
             self.bw = self._compute_efficient(bw)
 
     def _compute_reg_bw(self, bw):
-        if not isinstance(bw, basestring):
+        if not isinstance(bw, string_types):
             self._bw_method = "user-specified"
             return np.asarray(bw)
         else:
@@ -310,7 +310,7 @@ def cv_loo(self, bw, func):
         LOO_Y = LeaveOneOut(self.endog).__iter__()
         L = 0
         for ii, X_not_i in enumerate(LOO_X):
-            Y = LOO_Y.next()
+            Y = next(LOO_Y)
             G = func(bw, endog=Y, exog=-X_not_i,
                      data_predict=-self.exog[ii, :])[0]
             L += (self.endog[ii] - G) ** 2
@@ -370,7 +370,7 @@ def fit(self, data_predict=None):
         N_data_predict = np.shape(data_predict)[0]
         mean = np.empty((N_data_predict,))
         mfx = np.empty((N_data_predict, self.k_vars))
-        for i in xrange(N_data_predict):
+        for i in range(N_data_predict):
             mean_mfx = func(self.bw, self.endog, self.exog,
                             data_predict=data_predict[i, :])
             mean[i] = mean_mfx[0]
@@ -403,7 +403,7 @@ def sig_test(self, var_pos, nboot=50, nested_res=25, pivot=False):
         ix_cont, ix_ord, ix_unord = _get_type_pos(self.var_type)
         if np.any(ix_cont[var_pos]):
             if np.any(ix_ord[var_pos]) or np.any(ix_unord[var_pos]):
-                raise "Discrete variable in hypothesis. Must be continuous"
+                raise ValueError("Discrete variable in hypothesis. Must be continuous")
 
             Sig = TestRegCoefC(self, var_pos, nboot, nested_res, pivot)
         else:
@@ -525,9 +525,9 @@ def censored(self, censor_val):
         self.exog = np.squeeze(self.exog[ix])
         self.d = np.squeeze(self.d[ix])
         self.W_in = np.empty((self.nobs, 1))
-        for i in xrange(1, self.nobs + 1):
+        for i in range(1, self.nobs + 1):
             P=1
-            for j in xrange(1, i):
+            for j in range(1, i):
                 P *= ((self.nobs - j)/(float(self.nobs)-j+1))**self.d[j-1]
             self.W_in[i-1,0] = P * self.d[i-1] / (float(self.nobs) - i + 1 )
 
@@ -637,8 +637,8 @@ def cv_loo(self, bw, func):
         LOO_W = LeaveOneOut(self.W_in).__iter__()
         L = 0
         for ii, X_not_i in enumerate(LOO_X):
-            Y = LOO_Y.next()
-            w = LOO_W.next()
+            Y = next(LOO_Y)
+            w = next(LOO_W)
             G = func(bw, endog=Y, exog=-X_not_i,
                      data_predict=-self.exog[ii, :], W=w)[0]
             L += (self.endog[ii] - G) ** 2
@@ -659,7 +659,7 @@ def fit(self, data_predict=None):
         N_data_predict = np.shape(data_predict)[0]
         mean = np.empty((N_data_predict,))
         mfx = np.empty((N_data_predict, self.k_vars))
-        for i in xrange(N_data_predict):
+        for i in range(N_data_predict):
             mean_mfx = func(self.bw, self.endog, self.exog,
                             data_predict=data_predict[i, :],
                             W=self.W_in)
@@ -779,7 +779,7 @@ def _compute_se_lambda(self, Y, X):
         """
         n = np.shape(Y)[0]
         lam = np.empty(shape=(self.nres, ))
-        for i in xrange(self.nres):
+        for i in range(self.nres):
             ind = np.random.random_integers(0, n-1, size=(n,1))
             Y1 = Y[ind, 0]
             X1 = X[ind, :]
@@ -808,7 +808,7 @@ def _compute_sig(self):
         M = np.reshape(M, (n, 1))
         e = Y - M
         e = e - np.mean(e)  # recenter residuals
-        for i in xrange(self.nboot):
+        for i in range(self.nboot):
             ind = np.random.random_integers(0, n-1, size=(n,1))
             e_boot = e[ind, 0]
             Y_boot = M + e_boot
@@ -902,7 +902,7 @@ def _compute_sig(self):
         u2 = fct2 * u
         r = fct2 / (5 ** 0.5)
         I_dist = np.empty((self.nboot,1))
-        for j in xrange(self.nboot):
+        for j in range(self.nboot):
             u_boot = copy.deepcopy(u2)
 
             prob = np.random.uniform(0,1, size = (n,1))
diff --git a/statsmodels/nonparametric/smoothers_lowess_old.py b/statsmodels/nonparametric/smoothers_lowess_old.py
index 2fc0bdd0966..31c0ca6a0e8 100644
--- a/statsmodels/nonparametric/smoothers_lowess_old.py
+++ b/statsmodels/nonparametric/smoothers_lowess_old.py
@@ -7,7 +7,7 @@
 
 Cleveland, W.S. (1979) "Robust Locally Weighted Regression and Smoothing Scatterplots". Journal of the American Statistical Association 74 (368): 829-836.
 """
-
+from statsmodels.compat.python import range
 import numpy as np
 from scipy.linalg import lstsq
 
@@ -113,7 +113,7 @@ def lowess(endog, exog, frac=2./3, it=3):
 
     fitted, weights = _lowess_initial_fit(x_copy, y_copy, k, n)
 
-    for i in xrange(it):
+    for i in range(it):
         _lowess_robustify_fit(x_copy, y_copy, fitted,
                               weights, k, n)
 
@@ -155,7 +155,7 @@ def _lowess_initial_fit(x_copy, y_copy, k, n):
     X = np.ones((k,2))
     fitted = np.zeros(n)
 
-    for i in xrange(n):
+    for i in range(n):
         #note: all _lowess functions are inplace, no return
         left_width = x_copy[i] - x_copy[nn_indices[0]]
         right_width = x_copy[nn_indices[1]-1] - x_copy[i]
@@ -247,7 +247,7 @@ def _lowess_robustify_fit(x_copy, y_copy, fitted, weights, k, n):
     residual_weights[too_big] = 0
 
 
-    for i in xrange(n):
+    for i in range(n):
         total_weights = weights[i,:] * np.sqrt(residual_weights[nn_indices[0]:
                                                         nn_indices[1]])
 
diff --git a/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv b/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv
new file mode 100644
index 00000000000..84e1c0d1764
--- /dev/null
+++ b/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv
@@ -0,0 +1,61 @@
+x,weights,x_gau_wd,x_epa_wd,x_epan2_wd,x_bi_wd,x_par_wd,x_tri_wd,x_cos_wd,x_rec_wd
+.50370319,.60467487,.27195577,.27358917,.28859682,.30192719,.31283399,.29793749,.31904846,.25210428
+.41308592,.60446553,.28380106,.28108376,.3181792,.32885763,.34548845,.32667957,.3553171,.3010559
+-1.13905,.84897398,.26095184,.24941442,.32307763,.34872245,.38725314,.34866867,.42649716,.30003257
+.66034203,.89616498,.2498343,.25886883,.24541009,.23760925,.23485521,.24529473,.23002624,.2627293
+.46479403,.58916777,.27716742,.27709199,.30071519,.31461812,.3288969,.31115181,.33274659,.27792568
+-.30933686,.92004553,.314696,.31804133,.34663542,.35808323,.38279667,.36339898,.44094594,.31994675
+-.64959307,.73271562,.29736955,.31088207,.25708713,.24806417,.2539103,.26299651,.28856418,.30887871
+.68375801,.31006212,.24646059,.25568508,.23632094,.22824775,.22264335,.23446073,.21686179,.2512418
+-.64383424,.93079,.2977045,.3114788,.25702669,.2509086,.25709325,.26391628,.29057321,.30887871
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diff --git a/statsmodels/nonparametric/tests/results/results_kernel_regression.csv b/statsmodels/nonparametric/tests/results/results_kernel_regression.csv
new file mode 100644
index 00000000000..7ffe34c6267
--- /dev/null
+++ b/statsmodels/nonparametric/tests/results/results_kernel_regression.csv
@@ -0,0 +1,41 @@
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diff --git a/statsmodels/nonparametric/tests/test_bandwidths.py b/statsmodels/nonparametric/tests/test_bandwidths.py
new file mode 100644
index 00000000000..b01e0817f73
--- /dev/null
+++ b/statsmodels/nonparametric/tests/test_bandwidths.py
@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+"""
+
+Tests for bandwidth selection and calculation.
+
+Author: Padarn Wilson
+"""
+
+import numpy as np
+from scipy import stats
+
+from statsmodels.sandbox.nonparametric import kernels
+from statsmodels.distributions.mixture_rvs import mixture_rvs
+from statsmodels.nonparametric.kde import KDEUnivariate as KDE
+from statsmodels.nonparametric.bandwidths import select_bandwidth
+
+
+
+from numpy.testing import assert_allclose
+
+# setup test data
+
+np.random.seed(12345)
+Xi = mixture_rvs([.25,.75], size=200, dist=[stats.norm, stats.norm],
+                kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+
+class TestBandwidthCalculation(object):
+
+    def test_calculate_bandwidth_gaussian(self):
+
+        bw_expected = [0.29774853596742024,
+                       0.25304408155871411,
+                       0.29781147113698891]
+
+        kern = kernels.Gaussian()
+        
+        bw_calc = [0, 0, 0]
+        for ii, bw in enumerate(['scott','silverman','normal_reference']):
+            bw_calc[ii] = select_bandwidth(Xi, bw, kern)
+
+        assert_allclose(bw_expected, bw_calc)
+
+
+class CheckNormalReferenceConstant(object):
+
+    def test_calculate_normal_reference_constant(self):
+        const = self.constant
+        kern = self.kern
+        assert_allclose(const, kern.normal_reference_constant, 1e-2)
+
+
+class TestEpanechnikov(CheckNormalReferenceConstant):
+
+    kern = kernels.Epanechnikov()
+    constant = 2.34
+
+
+class TestGaussian(CheckNormalReferenceConstant):
+
+    kern = kernels.Gaussian()
+    constant = 1.06
+
+
+class TestBiweight(CheckNormalReferenceConstant):
+
+    kern = kernels.Biweight()
+    constant = 2.78
+
+
+class TestTriweight(CheckNormalReferenceConstant):
+
+    kern = kernels.Triweight()
+    constant = 3.15
+
+
+if __name__ == "__main__":
+    import nose
+    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False)
diff --git a/statsmodels/nonparametric/tests/test_kde.py b/statsmodels/nonparametric/tests/test_kde.py
index 85ca575e283..7cf2d8b34a2 100644
--- a/statsmodels/nonparametric/tests/test_kde.py
+++ b/statsmodels/nonparametric/tests/test_kde.py
@@ -1,5 +1,6 @@
 import os
 import numpy.testing as npt
+from nose import SkipTest
 import numpy as np
 from statsmodels.distributions.mixture_rvs import mixture_rvs
 from statsmodels.nonparametric.kde import KDEUnivariate as KDE
@@ -12,6 +13,9 @@
 #print rfname
 KDEResults = np.genfromtxt(open(rfname, 'rb'), delimiter=",", names=True)
 
+rfname = os.path.join(curdir,'results','results_kde_univ_weights.csv')
+KDEWResults = np.genfromtxt(open(rfname, 'rb'), delimiter=",", names=True)
+
 # setup test data
 
 np.random.seed(12345)
@@ -19,11 +23,28 @@
                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
 
 class CheckKDE(object):
+
     decimal_density = 7
+
     def test_density(self):
         npt.assert_almost_equal(self.res1.density, self.res_density,
                 self.decimal_density)
 
+    def test_evaluate(self):
+        # disable test
+        # fails for Epan, Triangular and Biweight, only Gaussian is correct
+        # added it as test method to TestKDEGauss below
+        # inDomain is not vectorized
+        #kde_vals = self.res1.evaluate(self.res1.support)
+        kde_vals = [self.res1.evaluate(xi) for xi in self.res1.support]
+        kde_vals = np.squeeze(kde_vals)  #kde_vals is a "column_list"
+        mask_valid = np.isfinite(kde_vals)
+        # TODO: nans at the boundaries
+        kde_vals[~mask_valid] = 0
+        npt.assert_almost_equal(kde_vals, self.res_density,
+                                self.decimal_density)
+
+
 class TestKDEGauss(CheckKDE):
     @classmethod
     def setupClass(cls):
@@ -32,6 +53,17 @@ def setupClass(cls):
         cls.res1 = res1
         cls.res_density = KDEResults["gau_d"]
 
+    def test_evaluate(self):
+        #kde_vals = self.res1.evaluate(self.res1.support)
+        kde_vals = [self.res1.evaluate(xi) for xi in self.res1.support]
+        kde_vals = np.squeeze(kde_vals)  #kde_vals is a "column_list"
+        mask_valid = np.isfinite(kde_vals)
+        # TODO: nans at the boundaries
+        kde_vals[~mask_valid] = 0
+        npt.assert_almost_equal(kde_vals, self.res_density,
+                                self.decimal_density)
+
+
 class TestKDEEpanechnikov(CheckKDE):
     @classmethod
     def setupClass(cls):
@@ -67,6 +99,7 @@ def setupClass(cls):
 
 #weighted estimates taken from matlab so we can allow len(weights) != gridsize
 class TestKdeWeights(CheckKDE):
+
     @classmethod
     def setupClass(cls):
         res1 = KDE(Xi)
@@ -77,6 +110,16 @@ def setupClass(cls):
         rfname = os.path.join(curdir,'results','results_kde_weights.csv')
         cls.res_density = np.genfromtxt(open(rfname, 'rb'), skip_header=1)
 
+    def test_evaluate(self):
+        #kde_vals = self.res1.evaluate(self.res1.support)
+        kde_vals = [self.res1.evaluate(xi) for xi in self.res1.support]
+        kde_vals = np.squeeze(kde_vals)  #kde_vals is a "column_list"
+        mask_valid = np.isfinite(kde_vals)
+        # TODO: nans at the boundaries
+        kde_vals[~mask_valid] = 0
+        npt.assert_almost_equal(kde_vals, self.res_density,
+                                self.decimal_density)
+
 
 class TestKDEGaussFFT(CheckKDE):
     @classmethod
@@ -88,6 +131,126 @@ def setupClass(cls):
         rfname2 = os.path.join(curdir,'results','results_kde_fft.csv')
         cls.res_density = np.genfromtxt(open(rfname2, 'rb'))
 
+class CheckKDEWeights(object):
+
+    @classmethod
+    def setupClass(cls):
+        cls.x = x = KDEWResults['x']
+        weights = KDEWResults['weights']
+        res1 = KDE(x)
+        # default kernel was scott when reference values computed
+        res1.fit(kernel=cls.kernel_name, weights=weights, fft=False, bw="scott")
+        cls.res1 = res1
+        cls.res_density = KDEWResults[cls.res_kernel_name]
+
+    decimal_density = 7
+
+    def t_est_density(self):
+        npt.assert_almost_equal(self.res1.density, self.res_density,
+                self.decimal_density)
+
+    def test_evaluate(self):
+        if self.kernel_name == 'cos':
+            raise SkipTest("Cosine kernel fails against Stata")
+        kde_vals = [self.res1.evaluate(xi) for xi in self.x]
+        kde_vals = np.squeeze(kde_vals)  #kde_vals is a "column_list"
+        npt.assert_almost_equal(kde_vals, self.res_density,
+                                self.decimal_density)
+
+    def test_compare(self):
+        xx = self.res1.support
+        kde_vals = [self.res1.evaluate(xi) for xi in xx]
+        kde_vals = np.squeeze(kde_vals)  #kde_vals is a "column_list"
+        mask_valid = np.isfinite(kde_vals)
+        # TODO: nans at the boundaries
+        kde_vals[~mask_valid] = 0
+        npt.assert_almost_equal(self.res1.density, kde_vals,
+                                self.decimal_density)
+
+        # regression test, not compared to another package
+        nobs = len(self.res1.endog)
+        kern = self.res1.kernel
+        v = kern.density_var(kde_vals, nobs)
+        v_direct = kde_vals * kern.L2Norm / kern.h / nobs
+        npt.assert_allclose(v, v_direct, rtol=1e-10)
+
+        ci = kern.density_confint(kde_vals, nobs)
+        crit = 1.9599639845400545 #stats.norm.isf(0.05 / 2)
+        hw = kde_vals - ci[:, 0]
+        npt.assert_allclose(hw, crit * np.sqrt(v), rtol=1e-10)
+        hw = ci[:, 1] - kde_vals
+        npt.assert_allclose(hw, crit * np.sqrt(v), rtol=1e-10)
+
+    def test_kernel_constants(self):
+        kern = self.res1.kernel
+
+        nc = kern.norm_const
+        # trigger numerical integration
+        kern._norm_const = None
+        nc2 = kern.norm_const
+        npt.assert_allclose(nc, nc2, rtol=1e-10)
+
+        l2n = kern.L2Norm
+        # trigger numerical integration
+        kern._L2Norm = None
+        l2n2 = kern.L2Norm
+        npt.assert_allclose(l2n, l2n2, rtol=1e-10)
+
+        v = kern.kernel_var
+        # trigger numerical integration
+        kern._kernel_var = None
+        v2 = kern.kernel_var
+        npt.assert_allclose(v, v2, rtol=1e-10)
+
+
+class TestKDEWGauss(CheckKDEWeights):
+
+    kernel_name = "gau"
+    res_kernel_name = "x_gau_wd"
+
+
+class TestKDEWEpa(CheckKDEWeights):
+
+    kernel_name = "epa"
+    res_kernel_name = "x_epan2_wd"
+
+
+class TestKDEWTri(CheckKDEWeights):
+
+    kernel_name = "tri"
+    res_kernel_name = "x_" + kernel_name + "_wd"
+
+
+class TestKDEWBiw(CheckKDEWeights):
+
+    kernel_name = "biw"
+    res_kernel_name = "x_bi_wd"
+
+
+class TestKDEWCos(CheckKDEWeights):
+
+    kernel_name = "cos"
+    res_kernel_name = "x_cos_wd"
+
+
+class TestKDEWCos2(CheckKDEWeights):
+
+    kernel_name = "cos2"
+    res_kernel_name = "x_cos_wd"
+
+
+class T_estKDEWRect(CheckKDEWeights):
+    #TODO in docstring but not in kernel_switch
+    kernel_name = "rect"
+    res_kernel_name = "x_rec_wd"
+
+
+class T_estKDEWPar(CheckKDEWeights):
+    # TODO in docstring but not implemented in kernels
+    kernel_name = "par"
+    res_kernel_name = "x_par_wd"
+
+
 class test_kde_refit():
     np.random.seed(12345)
     data1 = np.random.randn(100) * 100
diff --git a/statsmodels/nonparametric/tests/test_kernel_density.py b/statsmodels/nonparametric/tests/test_kernel_density.py
index 8c1e840387a..bf2f8cf803e 100644
--- a/statsmodels/nonparametric/tests/test_kernel_density.py
+++ b/statsmodels/nonparametric/tests/test_kernel_density.py
@@ -58,6 +58,62 @@ def setUp(self):
        0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
        0, 0, 0, 0]
 
+        self.weights = np.random.random(nobs)
+
+
+class TestKDEUnivariate(MyTest):
+
+    def test_pdf_non_fft(self):
+
+        kde = nparam.KDEUnivariate(self.noise)
+        kde.fit(fft=False, bw='scott')
+
+
+        grid = kde.support
+        testx = [grid[10*i] for i in range(6)]
+
+        # Test against values from R 'ks' package
+        kde_expected = [0.00016808277984236013,
+                        0.030759614592368954,
+                        0.14123404934759243,
+                        0.28807147408162409,
+                        0.25594519303876273,
+                        0.056593973915651047]
+
+        kde_vals0 = kde.density[10 * np.arange(6)]
+        kde_vals = kde.evaluate(testx)
+
+        npt.assert_allclose(kde_vals, kde_expected,
+                            atol=1e-6)
+        npt.assert_allclose(kde_vals0, kde_expected,
+                            atol=1e-6)
+
+
+    def test_weighted_pdf_non_fft(self):
+
+        kde = nparam.KDEUnivariate(self.noise)
+        kde.fit(weights=self.weights, fft=False, bw='scott')
+
+        grid = kde.support
+        testx = [grid[10*i] for i in range(6)]
+
+        # Test against values from R 'ks' package
+        kde_expected = [9.1998858033950757e-05,
+                        0.018761981151370496,
+                        0.14425925509365087,
+                        0.30307631742267443,
+                        0.2405445849994125,
+                        0.06433170684797665]
+
+        kde_vals0 = kde.density[10 * np.arange(6)]
+        kde_vals = kde.evaluate(testx)
+
+        npt.assert_allclose(kde_vals, kde_expected,
+                            atol=1e-6)
+        npt.assert_allclose(kde_vals0, kde_expected,
+                            atol=1e-6)
+
+
 
 class TestKDEMultivariate(MyTest):
     @dec.slow
@@ -191,6 +247,19 @@ def test_efficient_notrandom(self):
         dens = nparam.KDEMultivariate(data=[Y, C1], var_type='cc', bw='cv_ml')
         npt.assert_allclose(dens.bw, dens_efficient.bw, atol=0.1, rtol = 0.2)
 
+    def test_efficient_user_specified_bw(self):
+        nobs = 400
+        np.random.seed(12345)
+        C1 = np.random.normal(size=(nobs, ))
+        C2 = np.random.normal(2, 1, size=(nobs, ))
+        bw_user=[0.23, 434697.22]
+        
+        dens = nparam.KDEMultivariate(data=[C1, C2], var_type='cc',
+            bw=bw_user, defaults=nparam.EstimatorSettings(efficient=True,
+                                                          randomize=False,
+                                                          n_sub=100))
+        npt.assert_equal(dens.bw, bw_user)
+
 
 class TestKDEMultivariateConditional(MyTest):
     @dec.slow
@@ -305,3 +374,20 @@ def test_continuous_cvml_efficient(self):
         bw_expected = np.array([0.73387, 0.43715])
         npt.assert_allclose(dens_efficient.bw, bw_expected, atol=0, rtol=1e-3)
 
+    def test_efficient_user_specified_bw(self):
+        nobs = 400
+        np.random.seed(12345)
+        C1 = np.random.normal(size=(nobs, ))
+        C2 = np.random.normal(2, 1, size=(nobs, ))
+        bw_user=[0.23, 434697.22]
+        
+        dens = nparam.KDEMultivariate(data=[C1, C2], var_type='cc',
+            bw=bw_user, defaults=nparam.EstimatorSettings(efficient=True,
+                                                          randomize=False,
+                                                          n_sub=100))
+        npt.assert_equal(dens.bw, bw_user)
+
+if __name__ == "__main__":
+    import nose
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'],
+                       exit=False)
\ No newline at end of file
diff --git a/statsmodels/nonparametric/tests/test_kernel_regression.py b/statsmodels/nonparametric/tests/test_kernel_regression.py
index 80d3dba79e3..8a228d9efe5 100644
--- a/statsmodels/nonparametric/tests/test_kernel_regression.py
+++ b/statsmodels/nonparametric/tests/test_kernel_regression.py
@@ -300,3 +300,35 @@ def test_significance_discrete(self):
         npt.assert_equal(sig_var1 == 'Not Significant', False)
         sig_var2 = model.sig_test([1], nboot=nboot)  # H0: b2 = 0
         npt.assert_equal(sig_var2 == 'Not Significant', True)
+
+    def test_efficient_user_specificed_bw(self):
+
+        bw_user=[0.23, 434697.22]
+        model = nparam.KernelReg(endog=[self.y], exog=[self.c1, self.c2],
+                                 reg_type='lc', var_type='cc', bw=bw_user, 
+                                 defaults=nparam.EstimatorSettings(efficient=True))
+        # Bandwidth
+        npt.assert_equal(model.bw, bw_user)
+
+    def test_censored_efficient_user_specificed_bw(self):
+        nobs = 200
+        np.random.seed(1234)
+        C1 = np.random.normal(size=(nobs, ))
+        C2 = np.random.normal(2, 1, size=(nobs, ))
+        noise = np.random.normal(size=(nobs, ))
+        Y = 0.3 +1.2 * C1 - 0.9 * C2 + noise
+        Y[Y>0] = 0  # censor the data
+
+        bw_user=[0.23, 434697.22]
+        model = nparam.KernelCensoredReg(endog=[Y], exog=[C1, C2],
+                                         reg_type='ll', var_type='cc',
+                                         bw=bw_user, censor_val=0, 
+                                 defaults=nparam.EstimatorSettings(efficient=True))
+        # Bandwidth
+        npt.assert_equal(model.bw, bw_user)
+
+
+if __name__ == "__main__":
+    import nose
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb'],
+                       exit=False)
diff --git a/statsmodels/nonparametric/tests/test_kernels.py b/statsmodels/nonparametric/tests/test_kernels.py
new file mode 100644
index 00000000000..f8566865914
--- /dev/null
+++ b/statsmodels/nonparametric/tests/test_kernels.py
@@ -0,0 +1,154 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Sat Dec 14 17:23:25 2013
+
+Author: Josef Perktold
+"""
+from __future__ import print_function
+import os
+import numpy as np
+from statsmodels.sandbox.nonparametric import kernels
+
+from numpy.testing import assert_allclose, assert_array_less
+
+DEBUG = 0
+
+curdir = os.path.dirname(os.path.abspath(__file__))
+fname = 'results/results_kernel_regression.csv'
+results = np.recfromcsv(os.path.join(curdir, fname))
+
+y = results['accident']
+x = np.log(results['service'])
+use_mask = ~np.isnan(x)
+x = x[use_mask]
+y = y[use_mask]
+xg = np.linspace(x.min(), x.max(), 40) # grid points default in Stata
+
+#kern_name = 'gau'
+#kern = kernels.Gaussian()
+#kern_name = 'epan2'
+#kern = kernels.Epanechnikov()
+#kern_name = 'rec'
+#kern = kernels.Uniform()  # ours looks awful
+#kern_name = 'tri'
+#kern = kernels.Triangular()
+#kern_name = 'cos'
+#kern = kernels.Cosine()  #doesn't match up, nan in Stata results ?
+#kern_name = 'bi'
+#kern = kernels.Biweight()
+
+class CheckKernelMixin(object):
+
+    se_rtol = 0.7
+    upp_rtol = 0.1
+    low_rtol = 0.2
+    low_atol = 0.3
+
+    def test_smoothconf(self):
+        kern_name = self.kern_name
+        kern = self.kern
+        #fittedg = np.array([kernels.Epanechnikov().smoothconf(x, y, xi) for xi in xg])
+        fittedg = np.array([kern.smoothconf(x, y, xi) for xi in xg])
+        # attach for inspection from outside of test run
+        self.fittedg = fittedg
+
+        res_fitted = results['s_' + kern_name]
+        res_se = results['se_' + kern_name]
+        crit = 1.9599639845400545  # norm.isf(0.05 / 2)
+        # implied standard deviation from conf_int
+        se = (fittedg[:, 2] - fittedg[:, 1]) / crit
+        fitted = fittedg[:, 1]
+
+        # check both rtol & atol
+        assert_allclose(fitted, res_fitted, rtol=5e-7, atol=1e-20)
+        assert_allclose(fitted, res_fitted, rtol=0, atol=1e-6)
+
+        # TODO: check we are using a different algorithm for se
+        # The following are very rough checks
+
+        self.se = se
+        self.res_se = res_se
+        se_valid = np.isfinite(res_se)
+        # if np.any(~se_valid):
+        #    print('nan in stata result', self.__class__.__name__)
+        assert_allclose(se[se_valid], res_se[se_valid], rtol=self.se_rtol, atol=0.2)
+        # check that most values are closer
+        mask = np.abs(se - res_se) > (0.2 + 0.2 * res_se)
+        if not hasattr(self, 'se_n_diff'):
+            se_n_diff = 40 * 0.125
+        else:
+            se_n_diff = self.se_n_diff
+        assert_array_less(mask.sum(), se_n_diff + 1)  # at most 5 large diffs
+
+        if DEBUG:
+            # raises: RuntimeWarning: invalid value encountered in divide
+            print(fitted / res_fitted - 1)
+            print(se / res_se - 1)
+        # Stata only displays ci, doesn't save it
+        res_upp = res_fitted + crit * res_se
+        res_low = res_fitted - crit * res_se
+        self.res_fittedg = np.column_stack((res_low, res_fitted, res_upp))
+        if DEBUG:
+            print(fittedg[:, 2] / res_upp - 1)
+            print(fittedg[:, 2] - res_upp)
+            print(fittedg[:, 0] - res_low)
+            print(np.max(np.abs(fittedg[:, 2] / res_upp - 1)))
+        assert_allclose(fittedg[se_valid, 2], res_upp[se_valid],
+                        rtol=self.upp_rtol, atol=0.2)
+        assert_allclose(fittedg[se_valid, 0], res_low[se_valid],
+                        rtol=self.low_rtol, atol=self.low_atol)
+
+        #assert_allclose(fitted, res_fitted, rtol=0, atol=1e-6)
+
+    def t_est_smoothconf_data(self):
+        kern = self.kern
+        crit = 1.9599639845400545  # norm.isf(0.05 / 2)
+        # no reference results saved to csv yet
+        fitted_x = np.array([kern.smoothconf(x, y, xi) for xi in x])
+        if DEBUG:
+            print(fitted_x[:, 2] - fitted_x[:, 1]) / crit
+
+class TestEpan(CheckKernelMixin):
+    kern_name = 'epan2'
+    kern = kernels.Epanechnikov()
+
+class TestGau(CheckKernelMixin):
+    kern_name = 'gau'
+    kern = kernels.Gaussian()
+
+class TestUniform(CheckKernelMixin):
+    kern_name = 'rec'
+    kern = kernels.Uniform()
+    se_rtol = 0.8
+    se_n_diff = 8
+    upp_rtol = 0.4
+    low_rtol = 0.2
+    low_atol = 0.8
+
+class TestTriangular(CheckKernelMixin):
+    kern_name = 'tri'
+    kern = kernels.Triangular()
+    se_n_diff = 10
+    upp_rtol = 0.15
+    low_rtol = 0.3
+
+class T_estCosine(CheckKernelMixin):
+    # Stata results for Cosine look strange, has nans
+    kern_name = 'cos'
+    kern = kernels.Cosine2()
+
+class TestBiweight(CheckKernelMixin):
+    kern_name = 'bi'
+    kern = kernels.Biweight()
+    se_n_diff = 9
+    low_rtol = 0.3
+
+if __name__ == '__main__':
+    tt = TestEpan()
+    tt = TestGau()
+    tt = TestBiweight()
+    tt.test_smoothconf()
+    diff_rel = tt.fittedg / tt.res_fittedg - 1
+    diff_abs = tt.fittedg - tt.res_fittedg
+    mask = np.abs(tt.fittedg - tt.res_fittedg) > (0.3 + 0.1 * tt.res_fittedg)
diff --git a/statsmodels/regression/__init__.py b/statsmodels/regression/__init__.py
index ebc8a37bfe3..cf70fbf023e 100644
--- a/statsmodels/regression/__init__.py
+++ b/statsmodels/regression/__init__.py
@@ -1,4 +1,4 @@
-from linear_model import yule_walker
+from .linear_model import yule_walker
 
 from statsmodels import NoseWrapper as Tester
 test = Tester().test
diff --git a/statsmodels/regression/feasible_gls.py b/statsmodels/regression/feasible_gls.py
index 5974767f424..075aedb2d59 100644
--- a/statsmodels/regression/feasible_gls.py
+++ b/statsmodels/regression/feasible_gls.py
@@ -8,7 +8,7 @@
 
 """
 
-
+from statsmodels.compat.python import range
 import numpy as np
 import statsmodels.base.model as base
 from statsmodels.regression.linear_model import OLS, GLS, WLS, RegressionResults
diff --git a/statsmodels/regression/linear_model.py b/statsmodels/regression/linear_model.py
index 27b0dd9e8e3..7a46a77022c 100644
--- a/statsmodels/regression/linear_model.py
+++ b/statsmodels/regression/linear_model.py
@@ -1,10 +1,17 @@
+# TODO: Check tests with constant and without.  This might be an issue since df_model does nto include constant
+# TODO: Determine which tests are valid for GLSAR, and under what conditions
+# TODO: Fix HCCM to work with GLS/WLS
+# TODO: Fix issue with constant and GLS
+# TODO: GLS: add options Iterative GLS, for iterative fgls if sigma is None
+# TODO: GLS: default if sigma is none should be two-step GLS
+# TODO: Check nesting when performing model based tests, lr, wald, lm
 """
-This module implements some standard regression models:
+This module implements standard regression models:
 
-Generalized Least Squares (GLS),
-Ordinary Least Squares (OLS),
-and Weighted Least Squares (WLS),
-as well as an GLS model with autoregressive error terms GLSAR(p)
+Generalized Least Squares (GLS)
+Ordinary Least Squares (OLS)
+Weighted Least Squares (WLS)
+Generalized Least Squares with autoregressive error terms GLSAR(p)
 
 Models are specified with an endogenous response variable and an
 exogenous design matrix and are fit using their `fit` method.
@@ -26,6 +33,8 @@
 W. Green.  "Econometric Analysis," 5th ed., Pearson, 2003.
 """
 
+from __future__ import print_function
+from statsmodels.compat.python import lrange, lzip, range
 __docformat__ = 'restructuredtext en'
 
 __all__ = ['GLS', 'WLS', 'OLS', 'GLSAR']
@@ -34,21 +43,26 @@
 from scipy.linalg import toeplitz
 from scipy import stats
 from scipy.stats.stats import ss
-from statsmodels.tools.tools import (add_constant, rank,
-                                             recipr, chain_dot)
+from scipy import optimize
+from scipy.stats import chi2
+
+from statsmodels.compat.numpy import np_matrix_rank
+from statsmodels.tools.tools import add_constant, chain_dot, pinv_extended
 from statsmodels.tools.decorators import (resettable_cache,
-        cache_readonly, cache_writable)
+                                          cache_readonly,
+                                          cache_writable)
 import statsmodels.base.model as base
 import statsmodels.base.wrapper as wrap
 from statsmodels.emplike.elregress import _ELRegOpts
-from scipy import optimize
-from scipy.stats import chi2
+import warnings
+from statsmodels.tools.sm_exceptions import InvalidTestWarning
 
 def _get_sigma(sigma, nobs):
     """
-    Returns sigma for GLS and the inverse of its Cholesky decomposition.
-    Handles dimensions and checks integrity. If sigma is None, returns
-    None, None. Otherwise returns sigma, cholsigmainv.
+    Returns sigma (matrix, nobs by nobs) for GLS and the inverse of its
+    Cholesky decomposition.  Handles dimensions and checks integrity.
+    If sigma is None, returns None, None. Otherwise returns sigma,
+    cholsigmainv.
     """
     if sigma is None:
         return None, None
@@ -58,20 +72,20 @@ def _get_sigma(sigma, nobs):
     if sigma.ndim == 1:
         if sigma.shape != (nobs,):
             raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d "
-                             "array of shape %s x %s" % (nobs, nobs))
-        cholsigmainv = np.diag(1/sigma**.5)
-        sigma = np.diag(sigma)
+                             "array of shape %s x %s" % (nobs, nobs, nobs))
+        cholsigmainv = 1/np.sqrt(sigma)
     else:
         if sigma.shape != (nobs, nobs):
             raise ValueError("Sigma must be a scalar, 1d of length %s or a 2d "
-                             "array of shape %s x %s" % (nobs, nobs))
+                             "array of shape %s x %s" % (nobs, nobs, nobs))
         cholsigmainv = np.linalg.cholesky(np.linalg.pinv(sigma)).T
 
     return sigma, cholsigmainv
 
+
 class RegressionModel(base.LikelihoodModel):
     """
-    Base class for linear regression models not used by users.
+    Base class for linear regression models. Should not be directly called.
 
     Intended for subclassing.
     """
@@ -80,15 +94,51 @@ def __init__(self, endog, exog, **kwargs):
         self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights'])
 
     def initialize(self):
-        #print "calling initialize, now whitening"  #for debugging
         self.wexog = self.whiten(self.exog)
         self.wendog = self.whiten(self.endog)
         # overwrite nobs from class Model:
         self.nobs = float(self.wexog.shape[0])
-        self.rank = rank(self.exog)
-        self.df_model = float(self.rank - self.k_constant)
-        self.df_resid = self.nobs - self.rank
-        self.df_model = float(rank(self.exog) - self.k_constant)
+
+        self._df_model = None
+        self._df_resid = None
+        self.rank = None
+
+    @property
+    def df_model(self):
+        """
+        The model degree of freedom, defined as the rank of the regressor
+        matrix minus 1 if a constant is included.
+        """
+        if self._df_model is None:
+            if self.rank is None:
+                self.rank = np_matrix_rank(self.exog)
+            self._df_model = float(self.rank - self.k_constant)
+        return self._df_model
+
+    @df_model.setter
+    def df_model(self, value):
+        self._df_model = value
+
+    @property
+    def df_resid(self):
+        """
+        The residual degree of freedom, defined as the number of observations
+        minus the rank of the regressor matrix.
+        """
+
+        if self._df_resid is None:
+            if self.rank is None:
+                self.rank = np_matrix_rank(self.exog)
+            self._df_resid = self.nobs - self.rank
+        return self._df_resid
+
+    @df_resid.setter
+    def df_resid(self, value):
+        self._df_resid = value
+
+
+    def whiten(self, X):
+        raise NotImplementedError("Subclasses should implement.")
 
     def fit(self, method="pinv", **kwargs):
         """
@@ -117,32 +167,45 @@ def fit(self, method="pinv", **kwargs):
         The fit method uses the pseudoinverse of the design/exogenous variables
         to solve the least squares minimization.
         """
-        exog = self.wexog
-        endog = self.wendog
-
         if method == "pinv":
             if ((not hasattr(self, 'pinv_wexog')) or
-                (not hasattr(self, 'normalized_cov_params'))):
-                #print "recalculating pinv"   #for debugging
-                self.pinv_wexog = pinv_wexog = np.linalg.pinv(self.wexog)
-                self.normalized_cov_params = np.dot(pinv_wexog,
-                                                 np.transpose(pinv_wexog))
-            beta = np.dot(self.pinv_wexog, endog)
+                (not hasattr(self, 'normalized_cov_params')) or
+                (not hasattr(self, 'rank'))):
+
+                self.pinv_wexog, singular_values = pinv_extended(self.wexog)
+                self.normalized_cov_params = np.dot(self.pinv_wexog,
+                                        np.transpose(self.pinv_wexog))
+
+                # Cache these singular values for use later.
+                self.wexog_singular_values = singular_values
+                self.rank = np_matrix_rank(np.diag(singular_values))
+
+            beta = np.dot(self.pinv_wexog, self.wendog)
 
         elif method == "qr":
             if ((not hasattr(self, 'exog_Q')) or
-                (not hasattr(self, 'normalized_cov_params'))):
-                Q, R = np.linalg.qr(exog)
+                (not hasattr(self, 'exog_R')) or
+                (not hasattr(self, 'normalized_cov_params')) or
+                (getattr(self, 'rank', None) is None)):
+                Q, R = np.linalg.qr(self.wexog)
                 self.exog_Q, self.exog_R = Q, R
                 self.normalized_cov_params = np.linalg.inv(np.dot(R.T, R))
+
+                # Cache singular values from R.
+                self.wexog_singular_values = np.linalg.svd(R, 0, 0)
+                self.rank = np_matrix_rank(R)
             else:
                 Q, R = self.exog_Q, self.exog_R
 
             # used in ANOVA
-            self.effects = effects = np.dot(Q.T, endog)
+            self.effects = effects = np.dot(Q.T, self.wendog)
             beta = np.linalg.solve(R, effects)
 
-            # no upper triangular solve routine in numpy/scipy?
+        if self._df_model is None:
+            self._df_model = float(self.rank - self.k_constant)
+        if self._df_resid is None:
+            self.df_resid = self.nobs - self.rank
+
         if isinstance(self, OLS):
             lfit = OLSResults(self, beta,
                        normalized_cov_params=self.normalized_cov_params)
@@ -151,13 +214,129 @@ def fit(self, method="pinv", **kwargs):
                        normalized_cov_params=self.normalized_cov_params)
         return RegressionResultsWrapper(lfit)
 
+    def fit_regularized(self, method="coord_descent", maxiter=1000,
+                        alpha=0., L1_wt=1., start_params=None,
+                        cnvrg_tol=1e-8, zero_tol=1e-8, **kwargs):
+        """
+        Return a regularized fit to a linear regression model.
+
+        Parameters
+        ----------
+        method : string
+            Only the coordinate descent algorithm is implemented.
+        maxiter : integer
+            The maximum number of iteration cycles (an iteration cycle
+            involves running coordinate descent on all variables).
+        alpha : scalar or array-like
+            The penalty weight.  If a scalar, the same penalty weight
+            applies to all variables in the model.  If a vector, it
+            must have the same length as `params`, and contains a
+            penalty weight for each coefficient.
+        L1_wt : scalar
+            The fraction of the penalty given to the L1 penalty term.
+            Must be between 0 and 1 (inclusive).  If 0, the fit is
+            ridge regression.  If 1, the fit is the lasso.
+        start_params : array-like
+            Starting values for `params`.
+        cnvrg_tol : scalar
+            If `params` changes by less than this amount (in sup-norm)
+            in once iteration cycle, the algorithm terminates with
+            convergence.
+        zero_tol : scalar
+            Any estimated coefficient smaller than this value is
+            replaced with zero.
+
+        Returns
+        -------
+        A RegressionResults object, of the same type returned by
+        `fit`.
+
+        Notes
+        -----
+        The approach closely follows that implemented in the glmnet
+        package in R.  The penalty is the "elastic net" penalty, which
+        is a convex combination of L1 and L2 penalties.
+
+        The function that is minimized is:
+
+        0.5*RSS/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)
+
+        where RSS is the usual regression sum of squares, n is the
+        sample size, and |*|_1 and |*|_2 are the L1 and L2 norms.
+
+        Post-estimation results are based on the same data used to
+        select variables, hence may be subject to overfitting biases.
+
+        References
+        ----------
+        Friedman, Hastie, Tibshirani (2008).  Regularization paths for
+        generalized linear models via coordinate descent.  Journal of
+        Statistical Software 33(1), 1-22 Feb 2010.
+        """
+
+        k_exog = self.wexog.shape[1]
+
+        if np.isscalar(alpha):
+            alpha = alpha * np.ones(k_exog, dtype=np.float64)
+
+        # Below we work with RSS + penalty, so we need to rescale.
+        alpha *= 2 * self.wexog.shape[0]
+
+        if start_params is None:
+            params = np.zeros(k_exog, dtype=np.float64)
+        else:
+            params = start_params.copy()
+
+        converged = False
+        xxprod = 2*(self.wexog**2).sum(0)
+
+        # Coordinate descent
+        for itr in range(maxiter):
+
+            params_save = params.copy()
+            for k in range(self.wexog.shape[1]):
+
+                params[k] = 0.
+                wendog_adj = self.wendog - np.dot(self.wexog, params)
+                xyprod = 2*np.dot(self.wexog[:,k], wendog_adj)
+                den = xxprod[k] + alpha[k] * (1 - L1_wt)
+                a = alpha[k] * L1_wt
+                if a >= np.abs(xyprod):
+                    params[k] = 0.
+                elif xyprod > 0:
+                    params[k] = (xyprod - a) / den
+                else:
+                    params[k] = (xyprod + a) / den
+
+            # Check for convergence
+            pchange = np.max(np.abs(params - params_save))
+            if pchange < cnvrg_tol:
+                converged = True
+                break
+
+        # Set approximate zero coefficients to be exactly zero
+        params *= np.abs(params) >= zero_tol
+
+        # Fit the reduced model to get standard errors and other
+        # post-estimation results.
+        ii = np.flatnonzero(params)
+        model = self.__class__(self.wendog, self.wexog[:,ii])
+        rslt = model.fit()
+        cov = np.zeros((k_exog, k_exog), dtype=np.float64)
+        cov[np.ix_(ii, ii)] = rslt.normalized_cov_params
+
+        lfit = RegressionResults(self, params,
+                                 normalized_cov_params=cov)
+        lfit.converged = converged
+        return RegressionResultsWrapper(lfit)
+
     def predict(self, params, exog=None):
         """
         Return linear predicted values from a design matrix.
 
         Parameters
         ----------
-        params : array-like, optional after fit has been called
+        params : array-like
             Parameters of a linear model
         exog : array-like, optional.
             Design / exogenous data. Model exog is used if None.
@@ -168,7 +347,7 @@ def predict(self, params, exog=None):
 
         Notes
         -----
-        If the model as not yet been fit, params is not optional.
+        If the model has not yet been fit, params is not optional.
         """
         #JP: this doesn't look correct for GLMAR
         #SS: it needs its own predict method
@@ -182,17 +361,17 @@ class GLS(RegressionModel):
 
     %(params)s
     sigma : scalar or array
-           `sigma` is the weighting matrix of the covariance.
-           The default is None for no scaling.  If `sigma` is a scalar, it is
-           assumed that `sigma` is an n x n diagonal matrix with the given
-           scalar, `sigma` as the value of each diagonal element.  If `sigma`
-           is an n-length vector, then `sigma` is assumed to be a diagonal
-           matrix with the given `sigma` on the diagonal.  This should be the
-           same as WLS.
+        `sigma` is the weighting matrix of the covariance.
+        The default is None for no scaling.  If `sigma` is a scalar, it is
+        assumed that `sigma` is an n x n diagonal matrix with the given
+        scalar, `sigma` as the value of each diagonal element.  If `sigma`
+        is an n-length vector, then `sigma` is assumed to be a diagonal
+        matrix with the given `sigma` on the diagonal.  This should be the
+        same as WLS.
     %(extra_params)s
 
-    Attributes
-    ----------
+    **Attributes**
+
     pinv_wexog : array
         `pinv_wexog` is the p x n Moore-Penrose pseudoinverse of `wexog`.
     cholsimgainv : array
@@ -246,15 +425,16 @@ class GLS(RegressionModel):
 
     >>> gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)
     >>> gls_results = gls_model.fit()
-    >>> print gls_results.summary()
+    >>> print(gls_results.summary()))
 
     """ % {'params' : base._model_params_doc,
            'extra_params' : base._missing_param_doc + base._extra_param_doc}
 
     def __init__(self, endog, exog, sigma=None, missing='none', hasconst=None):
     #TODO: add options igls, for iterative fgls if sigma is None
-    #TODO: default is sigma is none should be two-step GLS
+    #TODO: default if sigma is none should be two-step GLS
         sigma, cholsigmainv = _get_sigma(sigma, len(endog))
+
         super(GLS, self).__init__(endog, exog, missing=missing,
                                   hasconst=hasconst, sigma=sigma,
                                   cholsigmainv=cholsigmainv)
@@ -281,16 +461,23 @@ def whiten(self, X):
         regression.GLS
         """
         X = np.asarray(X)
-        if np.any(self.sigma) and not self.sigma.shape == ():
-            return np.dot(self.cholsigmainv, X)
-        else:
+        if self.sigma is None or self.sigma.shape == ():
             return X
+        elif self.sigma.ndim == 1:
+            if X.ndim == 1:
+                return X * self.cholsigmainv
+            else:
+                return X * self.cholsigmainv[:, None]
+        else:
+            return np.dot(self.cholsigmainv, X)
+
+
 
     def loglike(self, params):
         """
-        Returns the value of the gaussian loglikelihood function at params.
+        Returns the value of the Gaussian log-likelihood function at params.
 
-        Given the whitened design matrix, the loglikelihood is evaluated
+        Given the whitened design matrix, the log-likelihood is evaluated
         at the parameter vector `params` for the dependent variable `endog`.
 
         Parameters
@@ -301,14 +488,14 @@ def loglike(self, params):
         Returns
         -------
         loglike : float
-            The value of the loglikelihood function for a GLS Model.
+            The value of the log-likelihood function for a GLS Model.
 
 
         Notes
         -----
-        The loglikelihood function for the normal distribution is
+        The log-likelihood function for the normal distribution is
 
-        .. math:: -\\frac{n}{2}\\log\\left(Y-\\hat{Y}\\right)-\\frac{n}{2}\\left(1+\\log\\left(\\frac{2\\pi}{n}\\right)\\right)-\\frac{1}{2}\\log\\left(\\left|\\Sigma\\right|\\right)
+        .. math:: -\\frac{n}{2}\\log\\left(\\left(Y-\\hat{Y}\\right)^{\\prime}\\left(Y-\\hat{Y}\\right)\\right)-\\frac{n}{2}\\left(1+\\log\\left(\\frac{2\\pi}{n}\\right)\\right)-\\frac{1}{2}\\log\\left(\\left|\\Sigma\\right|\\right)
 
         Y and Y-hat are whitened.
 
@@ -318,9 +505,13 @@ def loglike(self, params):
         SSR = ss(self.wendog - np.dot(self.wexog,params))
         llf = -np.log(SSR) * nobs2      # concentrated likelihood
         llf -= (1+np.log(np.pi/nobs2))*nobs2  # with likelihood constant
-        if np.any(self.sigma) and self.sigma.ndim == 2:
+        if np.any(self.sigma):
         #FIXME: robust-enough check?  unneeded if _det_sigma gets defined
-            llf -= .5*np.log(np.linalg.det(self.sigma))
+            if self.sigma.ndim==2:
+                det = np.linalg.slogdet(self.sigma)
+                llf -= .5*det[1]
+            else:
+                llf -= 0.5*np.sum(np.log(self.sigma))
             # with error covariance matrix
         return llf
 
@@ -353,20 +544,20 @@ class WLS(RegressionModel):
     >>> Y = [1,3,4,5,2,3,4]
     >>> X = range(1,8)
     >>> X = sm.add_constant(X)
-    >>> wls_model = sm.WLS(Y,X, weights=range(1,8))
+    >>> wls_model = sm.WLS(Y,X, weights=list(range(1,8)))
     >>> results = wls_model.fit()
     >>> results.params
     array([ 2.91666667,  0.0952381 ])
     >>> results.tvalues
     array([ 2.0652652 ,  0.35684428])
-    >>> print results.t_test([1, 0])
+    >>> print(results.t_test([1, 0]))
     <T test: effect=array([ 2.91666667]), sd=array([[ 1.41224801]]), t=array([[ 2.0652652]]), p=array([[ 0.04690139]]), df_denom=5>
-    >>> print results.f_test([0, 1])
+    >>> print(results.f_test([0, 1]))
     <F test: F=array([[ 0.12733784]]), p=[[ 0.73577409]], df_denom=5, df_num=1>
 
     Notes
     -----
-    If the weights are a function of the data, then the postestimation
+    If the weights are a function of the data, then the post estimation
     statistics such as fvalue and mse_model might not be correct, as the
     package does not yet support no-constant regression.
     """ % {'params' : base._model_params_doc,
@@ -385,7 +576,9 @@ def __init__(self, endog, exog, weights=1., missing='none', hasconst=None):
                                   weights=weights, hasconst=hasconst)
         nobs = self.exog.shape[0]
         weights = self.weights
-        if len(weights) != nobs and weights.size == nobs:
+        # Experimental normalization of weights
+        weights = weights / np.sum(weights) * nobs
+        if weights.size != nobs and weights.shape[0] != nobs:
             raise ValueError('Weights must be scalar or same length as design')
 
     def whiten(self, X):
@@ -401,18 +594,18 @@ def whiten(self, X):
         -------
         sqrt(weights)*X
         """
-        #print self.weights.var()
+        #print(self.weights.var()))
         X = np.asarray(X)
         if X.ndim == 1:
             return X * np.sqrt(self.weights)
         elif X.ndim == 2:
-            return np.sqrt(self.weights)[:,None]*X
+            return np.sqrt(self.weights)[:, None]*X
 
     def loglike(self, params):
         """
-        Returns the value of the gaussian loglikelihood function at params.
+        Returns the value of the gaussian log-likelihood function at params.
 
-        Given the whitened design matrix, the loglikelihood is evaluated
+        Given the whitened design matrix, the log-likelihood is evaluated
         at the parameter vector `params` for the dependent variable `Y`.
 
         Parameters
@@ -422,7 +615,8 @@ def loglike(self, params):
 
         Returns
         -------
-        The value of the loglikelihood function for a WLS Model.
+        llf : float
+            The value of the log-likelihood function for a WLS Model.
 
         Notes
         --------
@@ -434,6 +628,7 @@ def loglike(self, params):
         SSR = ss(self.wendog - np.dot(self.wexog,params))
         llf = -np.log(SSR) * nobs2      # concentrated likelihood
         llf -= (1+np.log(np.pi/nobs2))*nobs2  # with constant
+        llf += 0.5 * np.sum(np.log(self.weights))
         return llf
 
 
@@ -449,7 +644,9 @@ class OLS(WLS):
     weights : scalar
         Has an attribute weights = array(1.0) due to inheritance from WLS.
 
-    See regression.GLS
+    See Also
+    --------
+    GLS
 
     Examples
     --------
@@ -467,9 +664,9 @@ class OLS(WLS):
     array([ 2.14285714,  0.25      ])
     >>> results.tvalues
     array([ 1.87867287,  0.98019606])
-    >>> print results.t_test([1, 0])
+    >>> print(results.t_test([1, 0])))
     <T test: effect=array([ 2.14285714]), sd=array([[ 1.14062282]]), t=array([[ 1.87867287]]), p=array([[ 0.05953974]]), df_denom=5>
-    >>> print results.f_test(np.identity(2))
+    >>> print(results.f_test(np.identity(2)))
     <F test: F=array([[ 19.46078431]]), p=[[ 0.00437251]], df_denom=5, df_num=2>
 
     Notes
@@ -483,19 +680,19 @@ def __init__(self, endog, exog=None, missing='none', hasconst=None):
                                   hasconst=hasconst)
 
     def loglike(self, params):
-        '''
+        """
         The likelihood function for the clasical OLS model.
 
         Parameters
         ----------
         params : array-like
-            The coefficients with which to estimate the loglikelihood.
+            The coefficients with which to estimate the log-likelihood.
 
         Returns
         -------
         The concentrated likelihood function evaluated at params.
-        '''
-        nobs2 = self.nobs/2.
+        """
+        nobs2 = self.nobs / 2.0
         return -nobs2*np.log(2*np.pi)-nobs2*np.log(1/(2*nobs2) *\
                 np.dot(np.transpose(self.endog -
                     np.dot(self.exog, params)),
@@ -526,7 +723,7 @@ class GLSAR(GLS):
     >>> model = sm.GLSAR(Y, X, rho=2)
     >>> for i in range(6):
     ...     results = model.fit()
-    ...     print "AR coefficients:", model.rho
+    ...     print("AR coefficients: {0}".format(model.rho))
     ...     rho, sigma = sm.regression.yule_walker(results.resid,
     ...                                            order=model.order)
     ...     model = sm.GLSAR(Y, X, rho)
@@ -541,9 +738,9 @@ class GLSAR(GLS):
     array([-0.66661205,  1.60850853])
     >>> results.tvalues
     array([ -2.10304127,  21.8047269 ])
-    >>> print results.t_test([1, 0])
+    >>> print(results.t_test([1, 0]))
     <T test: effect=array([-0.66661205]), sd=array([[ 0.31697526]]), t=array([[-2.10304127]]), p=array([[ 0.06309969]]), df_denom=3>
-    >>> print results.f_test(np.identity(2))
+    >>> print(results.f_test(np.identity(2)))
     <F test: F=array([[ 1815.23061844]]), p=[[ 0.00002372]], df_denom=3, df_num=2>
 
     Or, equivalently
@@ -708,11 +905,12 @@ def yule_walker(X, order=1, method="unbiased", df=None, inv=False, demean=True):
 
     rho = np.linalg.solve(R, r[1:])
     sigmasq = r[0] - (r[1:]*rho).sum()
-    if inv == True:
+    if inv==True:
         return rho, np.sqrt(sigmasq), np.linalg.inv(R)
     else:
         return rho, np.sqrt(sigmasq)
 
+
 class RegressionResults(base.LikelihoodModelResults):
     """
     This class summarizes the fit of a linear regression model.
@@ -738,14 +936,16 @@ class RegressionResults(base.LikelihoodModelResults):
     centered_tss
         The total (weighted) sum of squares centered about the mean.
     cov_HC0
-        See HC0_se below.  Only available after calling HC0_se.
+        Heteroscedasticity robust covariance matrix. See HC0_se below.
     cov_HC1
-        See HC1_se below.  Only available after calling HC1_se.
+        Heteroscedasticity robust covariance matrix. See HC1_se below.
     cov_HC2
-        See HC2_se below.  Only available after calling HC2_se.
+        Heteroscedasticity robust covariance matrix. See HC2_se below.
     cov_HC3
-        See HC3_se below.  Only available after calling HC3_se.
-    df_model :
+        Heteroscedasticity robust covariance matrix. See HC3_se below.
+    cov_type
+        Parameter covariance estimator used for standard errors and t-stats
+    df_model
         Model degress of freedom. The number of regressors `p`. Does not
         include the constant if one is present
     df_resid
@@ -764,45 +964,43 @@ class RegressionResults(base.LikelihoodModelResults):
     fittedvalues
         The predicted the values for the original (unwhitened) design.
     het_scale
-        Only available if HC#_se is called.  See HC#_se for more information.
+        adjusted squared residuals for heteroscedasticity robust standard
+        errors. Is only available after `HC#_se` or `cov_HC#` is called.
+        See HC#_se for more information.
     HC0_se
         White's (1980) heteroskedasticity robust standard errors.
         Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1)
         where e_i = resid[i]
-        HC0_se is a property.  It is not evaluated until it is called.
-        When it is called the RegressionResults instance will then have
-        another attribute cov_HC0, which is the full heteroskedasticity
-        consistent covariance matrix and also `het_scale`, which is in
-        this case just resid**2.  HCCM matrices are only appropriate for OLS.
+        HC0_se is a cached property.
+        When HC0_se or cov_HC0 is called the RegressionResults instance will
+        then have another attribute `het_scale`, which is in this case is just
+        resid**2.
     HC1_se
         MacKinnon and White's (1985) alternative heteroskedasticity robust
         standard errors.
         Defined as sqrt(diag(n/(n-p)*HC_0)
-        HC1_se is a property.  It is not evaluated until it is called.
-        When it is called the RegressionResults instance will then have
-        another attribute cov_HC1, which is the full HCCM and also `het_scale`,
-        which is in this case n/(n-p)*resid**2.  HCCM matrices are only
-        appropriate for OLS.
+        HC1_see is a cached property.
+        When HC1_se or cov_HC1 is called the RegressionResults instance will
+        then have another attribute `het_scale`, which is in this case is
+        n/(n-p)*resid**2.
     HC2_se
         MacKinnon and White's (1985) alternative heteroskedasticity robust
         standard errors.
         Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1)
         where h_ii = x_i(X.T X)^(-1)x_i.T
-        HC2_se is a property.  It is not evaluated until it is called.
-        When it is called the RegressionResults instance will then have
-        another attribute cov_HC2, which is the full HCCM and also `het_scale`,
-        which is in this case is resid^(2)/(1-h_ii).  HCCM matrices are only
-        appropriate for OLS.
+        HC2_see is a cached property.
+        When HC2_se or cov_HC2 is called the RegressionResults instance will
+        then have another attribute `het_scale`, which is in this case is
+        resid^(2)/(1-h_ii).  HCCM matrices are only appropriate for OLS.
     HC3_se
         MacKinnon and White's (1985) alternative heteroskedasticity robust
         standard errors.
         Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1)
         where h_ii = x_i(X.T X)^(-1)x_i.T
-        HC3_se is a property.  It is not evaluated until it is called.
-        When it is called the RegressionResults instance will then have
-        another attribute cov_HC3, which is the full HCCM and also `het_scale`,
-        which is in this case is resid^(2)/(1-h_ii)^(2).  HCCM matrices are
-        only appropriate for OLS.
+        HC3_see is a cached property.
+        When HC3_se or cov_HC3 is called the RegressionResults instance will
+        then have another attribute `het_scale`, which is in this case is
+        resid^(2)/(1-h_ii)^(2).
     model
         A pointer to the model instance that called fit() or results.
     mse_model
@@ -825,6 +1023,8 @@ class RegressionResults(base.LikelihoodModelResults):
         The two-tailed p values for the t-stats of the params.
     resid
         The residuals of the model.
+    resid_pearson
+        `wresid` normalized to have unit variance.
     rsquared
         R-squared of a model with an intercept.  This is defined here as
         1 - `ssr`/`centered_tss` if the constant is included in the model and
@@ -846,19 +1046,28 @@ class RegressionResults(base.LikelihoodModelResults):
         The residuals of the transformed/whitened regressand and regressor(s)
     """
 
-    # For robust covariance matrix properties
-    _HC0_se = None
-    _HC1_se = None
-    _HC2_se = None
-    _HC3_se = None
-
     _cache = {} # needs to be a class attribute for scale setter?
 
     def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         super(RegressionResults, self).__init__(model, params,
-                                                 normalized_cov_params,
-                                                 scale)
+                                                normalized_cov_params,
+                                                scale)
+
         self._cache = resettable_cache()
+        if hasattr(model, 'wexog_singular_values'):
+            self._wexog_singular_values = model.wexog_singular_values
+        else:
+            self._wexog_singular_values = None
+        self.cov_type = 'nonrobust'
+        self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
+                         'covariance matrix of the errors is correctly ' +
+                         'specified.'}
+
+        self.df_model = model.df_model
+        self.df_resid = model.df_resid
+
+        self.use_t = True  # default for linear models
+
 
     def __str__(self):
         self.summary()
@@ -879,27 +1088,10 @@ def conf_int(self, alpha=.05, cols=None):
         -----
         The confidence interval is based on Student's t-distribution.
         """
-        bse = self.bse
-        params = self.params
-        dist = stats.t
-        q = dist.ppf(1 - alpha / 2, self.df_resid)
-
-        if cols is None:
-            lower = self.params - q * bse
-            upper = self.params + q * bse
-        else:
-            cols = np.asarray(cols)
-            lower = params[cols] - q * bse[cols]
-            upper = params[cols] + q * bse[cols]
-        return np.asarray(zip(lower, upper))
+        # keep method for docstring for now
+        ci = super(RegressionResults, self).conf_int(alpha=alpha, cols=cols)
+        return ci
 
-    @cache_readonly
-    def df_resid(self):
-        return self.model.df_resid
-
-    @cache_readonly
-    def df_model(self):
-        return self.model.df_model
 
     @cache_readonly
     def nobs(self):
@@ -937,7 +1129,7 @@ def centered_tss(self):
         if weights is not None:
             return np.sum(weights*(model.endog - np.average(model.endog,
                                                         weights=weights))**2)
-        else: # this is probably broken for GLS
+        else:  # this is probably broken for GLS
             centered_endog = model.wendog - model.wendog.mean()
             return np.dot(centered_endog, centered_endog)
 
@@ -977,11 +1169,29 @@ def mse_total(self):
         if self.k_constant:
             return self.centered_tss / (self.df_resid + self.df_model)
         else:
-            return self.uncentered_tss/ (self.df_resid + self.df_model)
+            return self.uncentered_tss / (self.df_resid + self.df_model)
 
     @cache_readonly
     def fvalue(self):
-        return self.mse_model/self.mse_resid
+        if hasattr(self, 'cov_type') and self.cov_type != 'nonrobust':
+            # with heteroscedasticity or correlation robustness
+            k_params = self.normalized_cov_params.shape[0]
+            mat = np.eye(k_params)
+            const_idx = self.model.data.const_idx
+            # TODO: What if model includes implcit constant, e.g. all dummies but no constant regressor?
+            # TODO: Restats as LM test by projecting orthogonalizing to constant?
+            if self.model.data.k_constant == 1:
+                # assume const_idx exists
+                idx = lrange(k_params)
+                idx.pop(const_idx)
+                mat = mat[idx]  # remove constant
+            ft = self.f_test(mat)
+            # using backdoor to set another attribute that we already have
+            self._cache['f_pvalue'] = ft.pvalue
+            return ft.fvalue
+        else:
+            # for standard homoscedastic case
+            return self.mse_model/self.mse_resid
 
     @cache_readonly
     def f_pvalue(self):
@@ -991,9 +1201,6 @@ def f_pvalue(self):
     def bse(self):
         return np.sqrt(np.diag(self.cov_params()))
 
-    @cache_readonly
-    def pvalues(self):
-        return stats.t.sf(np.abs(self.tvalues), self.df_resid)*2
 
     @cache_readonly
     def aic(self):
@@ -1004,84 +1211,263 @@ def bic(self):
         return (-2 * self.llf + np.log(self.nobs) * (self.df_model +
                                                      self.k_constant))
 
+    @cache_readonly
+    def eigenvals(self):
+        """
+        Return eigenvalues sorted in decreasing order.
+        """
+        if self._wexog_singular_values is not None:
+            eigvals = self._wexog_singular_values ** 2
+        else:
+            eigvals = np.linalg.linalg.eigvalsh(np.dot(self.model.wexog.T, self.model.wexog))
+        return np.sort(eigvals)[::-1]
+
+    @cache_readonly
+    def condition_number(self):
+        """
+        Return condition number of exogenous matrix, calculated as ratio of largest to smallest eigenvalue.
+        """
+        eigvals = self.eigenvals
+        return np.sqrt(eigvals[0]/eigvals[-1])
+
     #TODO: make these properties reset bse
     def _HCCM(self, scale):
         H = np.dot(self.model.pinv_wexog,
             scale[:,None]*self.model.pinv_wexog.T)
         return H
 
-    @property
+
+    @cache_readonly
+    def cov_HC0(self):
+        """
+        See statsmodels.RegressionResults
+        """
+
+        self.het_scale = self.wresid**2
+        cov_HC0 = self._HCCM(self.het_scale)
+        return cov_HC0
+
+
+    @cache_readonly
+    def cov_HC1(self):
+        """
+        See statsmodels.RegressionResults
+        """
+
+        self.het_scale = self.nobs/(self.df_resid)*(self.wresid**2)
+        cov_HC1 = self._HCCM(self.het_scale)
+        return cov_HC1
+
+
+    @cache_readonly
+    def cov_HC2(self):
+        """
+        See statsmodels.RegressionResults
+        """
+
+        # probably could be optimized
+        h = np.diag(chain_dot(self.model.wexog,
+                              self.normalized_cov_params,
+                              self.model.wexog.T))
+        self.het_scale = self.wresid**2/(1-h)
+        cov_HC2 = self._HCCM(self.het_scale)
+        return cov_HC2
+
+
+    @cache_readonly
+    def cov_HC3(self):
+        """
+        See statsmodels.RegressionResults
+        """
+
+        # above probably could be optimized to only calc the diag
+        h = np.diag(chain_dot(self.model.wexog,
+                              self.normalized_cov_params,
+                              self.model.wexog.T))
+        self.het_scale=(self.wresid/(1-h))**2
+        cov_HC3 = self._HCCM(self.het_scale)
+        return cov_HC3
+
+
+    @cache_readonly
     def HC0_se(self):
         """
         See statsmodels.RegressionResults
         """
-        if self._HC0_se is None:
-            self.het_scale = self.resid**2 # or whitened residuals? only OLS?
-            self.cov_HC0 = self._HCCM(self.het_scale)
-            self._HC0_se = np.sqrt(np.diag(self.cov_HC0))
-        return self._HC0_se
+        return np.sqrt(np.diag(self.cov_HC0))
 
-    @property
+
+    @cache_readonly
     def HC1_se(self):
         """
         See statsmodels.RegressionResults
         """
-        if self._HC1_se is None:
-            self.het_scale = self.nobs/(self.df_resid)*(self.resid**2)
-            self.cov_HC1 = self._HCCM(self.het_scale)
-            self._HC1_se = np.sqrt(np.diag(self.cov_HC1))
-        return self._HC1_se
+        return np.sqrt(np.diag(self.cov_HC1))
 
-    @property
+
+    @cache_readonly
     def HC2_se(self):
         """
         See statsmodels.RegressionResults
         """
-        if self._HC2_se is None:
-            # probably could be optimized
-            h = np.diag(chain_dot(self.model.exog,
-                                  self.normalized_cov_params,
-                                  self.model.exog.T))
-            self.het_scale = self.resid**2/(1-h)
-            self.cov_HC2 = self._HCCM(self.het_scale)
-            self._HC2_se = np.sqrt(np.diag(self.cov_HC2))
-        return self._HC2_se
+        return np.sqrt(np.diag(self.cov_HC2))
 
-    @property
+
+    @cache_readonly
     def HC3_se(self):
         """
         See statsmodels.RegressionResults
         """
-        if self._HC3_se is None:
-            # above probably could be optimized to only calc the diag
-            h = np.diag(chain_dot(self.model.exog,
-                                  self.normalized_cov_params,
-                                  self.model.exog.T))
-            self.het_scale=(self.resid/(1-h))**2
-            self.cov_HC3 = self._HCCM(self.het_scale)
-            self._HC3_se = np.sqrt(np.diag(self.cov_HC3))
-        return self._HC3_se
+        return np.sqrt(np.diag(self.cov_HC3))
 
-    #TODO: this needs a test
-    def norm_resid(self):
+
+    @cache_readonly
+    def resid_pearson(self):
         """
-        Residuals, normalized to have unit length and unit variance.
+        Residuals, normalized to have unit variance.
 
         Returns
         -------
         An array wresid/sqrt(scale)
+        """
+
+        if not hasattr(self, 'resid'):
+            raise ValueError('Method requires residuals.')
+        eps = np.finfo(self.wresid.dtype).eps
+        if np.sqrt(self.scale) < 10 * eps * self.model.endog.mean():
+            # don't divide if scale is zero close to numerical precision
+            from warnings import warn
+            warn("All residuals are 0, cannot compute normed residuals.",
+                 RuntimeWarning)
+            return self.wresid
+        else:
+            return self.wresid / np.sqrt(self.scale)
+
+    def _is_nested(self, restricted):
+        """
+        Parameters
+        ----------
+        restricted : Result instance
+            The restricted model is assumed to be nested in the current
+            model. The result instance of the restricted model is required to
+            have two attributes, residual sum of squares, `ssr`, residual
+            degrees of freedom, `df_resid`.
+
+        Returns
+        -------
+        nested : bool
+            True if nested, otherwise false
 
         Notes
         -----
-        This method is untested
+        A most nests another model if the regressors in the smaller model are spanned
+        by the regressors in the larger model and the regressand is identical.
         """
-        if not hasattr(self, 'resid'):
-            raise ValueError('need normalized residuals to estimate standard '
-                             'deviation')
-        return self.wresid * recipr(np.sqrt(self.scale))
+
+        if self.model.nobs != restricted.model.nobs:
+            return False
+
+        full_rank = self.model.rank
+        restricted_rank = restricted.model.rank
+        if full_rank <= restricted_rank:
+            return False
+
+        restricted_exog = restricted.model.wexog
+        full_wresid  = self.wresid
+
+        scores = restricted_exog * full_wresid[:,None]
+        score_l2 = np.sqrt(np.mean(scores.mean(0) ** 2))
+        # TODO: Could be improved, and may fail depending on scale of regressors
+        return np.allclose(score_l2,0)
+
+
+    def compare_lm_test(self, restricted, demean=True, use_lr=False):
+        """Use Lagrange Multiplier test to test whether restricted model is correct
+
+        Parameters
+        ----------
+        restricted : Result instance
+            The restricted model is assumed to be nested in the current
+            model. The result instance of the restricted model is required to
+            have two attributes, residual sum of squares, `ssr`, residual
+            degrees of freedom, `df_resid`.
+
+        demean : bool
+            Flag indicating whether the demean the scores based on the residuals
+            from the restricted model.  If True, the covariance of the scores
+            are used and the LM test is identical to the large sample version
+            of the LR test.
+
+        Returns
+        -------
+        lm_value : float
+            test statistic, chi2 distributed
+        p_value : float
+            p-value of the test statistic
+        df_diff : int
+            degrees of freedom of the restriction, i.e. difference in df between
+            models
+
+        Notes
+        -----
+        TODO: explain LM text
+        """
+        import statsmodels.stats.sandwich_covariance as sw
+        from numpy.linalg import inv
+
+        if not self._is_nested(restricted):
+            raise ValueError("Restricted model is not nested by full model.")
+
+        wresid = restricted.wresid
+        wexog = self.model.wexog
+        scores = wexog * wresid[:,None]
+
+        n = self.nobs
+        df_full = self.df_resid
+        df_restr = restricted.df_resid
+        df_diff = (df_restr - df_full)
+
+        s = scores.mean(axis=0)
+        if use_lr:
+            scores = wexog * self.wresid[:,None]
+            demean = False
+
+        if demean:
+            scores = scores - scores.mean(0)[None,:]
+        # Form matters here.  If homoskedastics can be sigma^2 (X'X)^-1
+        # If Heteroskedastic then the form below is fine
+        # If HAC then need to use HAC
+        # If Cluster, shoudl use cluster
+
+        cov_type = getattr(self, 'cov_type', 'nonrobust')
+        if cov_type == 'nonrobust':
+            sigma2 = np.mean(wresid**2)
+            XpX = np.dot(wexog.T,wexog) / n
+            Sinv = inv(sigma2 * XpX)
+        elif cov_type in ('HC0', 'HC1', 'HC2', 'HC3'):
+            Sinv = inv(np.dot(scores.T,scores) / n)
+        elif cov_type == 'HAC':
+            print("HAC")
+            maxlags = self.cov_kwds['maxlags']
+            use_correction = self.cov_kwds['use_correction']
+            Sinv = inv(sw.S_hac_simple(scores, maxlags) / n)
+        elif cov_type == 'cluster':
+            #cluster robust standard errors
+            groups = self.cov_kwds['groups']
+            # TODO: Might need demean option in S_crosssection by group?
+            Sinv = inv(sw.S_crosssection(scores, groups))
+        else:
+            raise ValueError('Only nonrobust, HC, HAC and cluster are ' +
+                             'currently connected')
+
+        lm_value = n * chain_dot(s,Sinv,s.T)
+        p_value = stats.chi2.sf(lm_value, df_diff)
+        return lm_value, p_value, df_diff
+
+
 
     def compare_f_test(self, restricted):
-        '''use F test to test whether restricted model is correct
+        """use F test to test whether restricted model is correct
 
         Parameters
         ----------
@@ -1105,7 +1491,24 @@ def compare_f_test(self, restricted):
         -----
         See mailing list discussion October 17,
 
-        '''
+        This test compares the residual sum of squares of the two models.
+        This is not a valid test, if there is unspecified heteroscedasticity
+        or correlation. This method will issue a warning if this is detected
+        but still return the results under the assumption of homoscedasticity
+        and no autocorrelation (sphericity).
+
+        """
+
+        has_robust1 = getattr(self, 'cov_type', 'nonrobust') != 'nonrobust'
+        has_robust2 = (getattr(restricted, 'cov_type', 'nonrobust') !=
+                                                                   'nonrobust')
+
+        if has_robust1 or has_robust2:
+            import warnings
+            warnings.warn('F test for comparison is likely invalid with ' +
+                          'robust covariance, proceeding anyway',
+                          InvalidTestWarning)
+
         ssr_full = self.ssr
         ssr_restr = restricted.ssr
         df_full = self.df_resid
@@ -1116,8 +1519,8 @@ def compare_f_test(self, restricted):
         p_value = stats.f.sf(f_value, df_diff, df_full)
         return f_value, p_value, df_diff
 
-    def compare_lr_test(self, restricted):
-        '''
+    def compare_lr_test(self, restricted, large_sample=False):
+        """
         Likelihood ratio test to test whether restricted model is correct
 
         Parameters
@@ -1128,6 +1531,10 @@ def compare_lr_test(self, restricted):
             attributes, residual sum of squares, `ssr`, residual degrees of
             freedom, `df_resid`.
 
+        large_sample : bool
+            Flag indicating whether to use a heteroskedasticity robust version
+            of the LR test, which is a modified LM test.
+
         Returns
         -------
         lr_stat : float
@@ -1142,16 +1549,63 @@ def compare_lr_test(self, restricted):
         Notes
         -----
 
+        The exact likelihood ratio is valid for homoskedastic data, and is
+        defined as
+
         .. math:: D=-2\\log\\left(\\frac{\\mathcal{L}_{null}}
            {\\mathcal{L}_{alternative}}\\right)
 
         where :math:`\mathcal{L}` is the likelihood of the model. With :math:`D`
         distributed as chisquare with df equal to difference in number of
-        parameters or equivalently difference in residual degrees of freedom
+        parameters or equivalently difference in residual degrees of freedom.
+
+        The large sample version of the likelihood ratio is defined as
+
+        .. math:: D=n s^{\\prime}S^{-1}s
+
+        where :math:`s=n^{-1}\\sum_{i=1}^{n} s_{i}`
+
+        .. math:: s_{i} = x_{i,alternative} \\epsilon_{i,null}
+
+        is the average score of the model evaluated using the residuals from
+        null model and the regressors from the alternative model and :math:`S`
+        is the covariance of the scores, :math:`s_{i}`.  The covariance of the
+        scores is estimated using the same estimator as in the alternative model.
+
+        This test compares the loglikelihood of the two models.
+        This may not be a valid test, if there is unspecified heteroscedasticity
+        or correlation. This method will issue a warning if this is detected
+        but still return the results without taking unspecified
+        heteroscedasticity or correlation into account.
+
+        This test compares the loglikelihood of the two models.
+        This may not be a valid test, if there is unspecified heteroscedasticity
+        or correlation. This method will issue a warning if this is detected
+        but still return the results without taking unspecified
+        heteroscedasticity or correlation into account.
+
+        is the average score of the model evaluated using the residuals from
+        null model and the regressors from the alternative model and :math:`S`
+        is the covariance of the scores, :math:`s_{i}`.  The covariance of the
+        scores is estimated using the same estimator as in the alternative model.
 
         TODO: put into separate function, needs tests
-        '''
-    #        See mailing list discussion October 17,
+        """
+
+        # See mailing list discussion October 17,
+
+        if large_sample:
+            return self.compare_lm_test(restricted, use_lr=True)
+
+        has_robust1 = (getattr(self, 'cov_type', 'nonrobust') != 'nonrobust')
+        has_robust2 = (getattr(restricted, 'cov_type', 'nonrobust') !=
+                                                                  'nonrobust')
+
+        if has_robust1 or has_robust2:
+            warnings.warn('Likelihood Ratio test is likely invalid with ' +
+                          'robust covariance, proceeding anyway',
+                          InvalidTestWarning)
+
         llf_full = self.llf
         llf_restr = restricted.llf
         df_full = self.df_resid
@@ -1164,6 +1618,235 @@ def compare_lr_test(self, restricted):
         return lrstat, lr_pvalue, lrdf
 
 
+    def get_robustcov_results(self, cov_type='HC1', use_t=None, **kwds):
+        """create new results instance with robust covariance as default
+
+        Parameters
+        ----------
+        cov_type : string
+            the type of robust sandwich estimator to use. see Notes below
+        use_t : bool
+            If true, then the t distribution is used for inference.
+            If false, then the normal distribution is used.
+        kwds : depends on cov_type
+            Required or optional arguments for robust covariance calculation.
+            see Notes below
+
+        Returns
+        -------
+        results : results instance
+            This method creates a new results instance with the requested
+            robust covariance as the default covariance of the parameters.
+            Inferential statistics like p-values and hypothesis tests will be
+            based on this covariance matrix.
+
+        Notes
+        -----
+        The following covariance types and required or optional arguments are
+        currently available:
+
+        - 'HC0', 'HC1', 'HC2', 'HC3' and no keyword arguments:
+            heteroscedasticity robust covariance
+        - 'HAC' and keywords
+
+            - `maxlag` integer (required) : number of lags to use
+            - `kernel` string (optional) : kernel, default is Bartlett
+            - `use_correction` bool (optional) : If true, use small sample
+                  correction
+
+        - 'cluster' and required keyword `groups`, integer group indicator
+
+            - `groups` array_like, integer (required) :
+                  index of clusters or groups
+            - `use_correction` bool (optional) :
+                  If True the sandwich covariance is calulated with a small
+                  sample correction.
+                  If False the the sandwich covariance is calulated without
+                  small sample correction.
+            - `df_correction` bool (optional)
+                  If True (default), then the degrees of freedom for the
+                  inferential statistics and hypothesis tests, such as
+                  pvalues, f_pvalue, conf_int, and t_test and f_test, are
+                  based on the number of groups minus one instead of the
+                  total number of observations minus the number of explanatory
+                  variables. `df_resid` of the results instance is adjusted.
+                  If False, then `df_resid` of the results instance is not
+                  adjusted.
+
+        - 'hac-groupsum' Driscoll and Kraay, heteroscedasticity and
+            autocorrelation robust standard errors in panel data
+            keywords
+
+            - `time` array_like (required) : index of time periods
+            - `maxlag` integer (required) : number of lags to use
+            - `kernel` string (optional) : kernel, default is Bartlett
+            - `use_correction` False or string in ['hac', 'cluster'] (optional) :
+                  If False the the sandwich covariance is calulated without
+                  small sample correction.
+                  If `use_correction = 'cluster'` (default), then the same
+                  small sample correction as in the case of 'covtype='cluster''
+                  is used.
+            - `df_correction` bool (optional)
+                  adjustment to df_resid, see cov_type 'cluster' above
+                  #TODO: we need more options here
+
+        - 'hac-panel' heteroscedasticity and autocorrelation robust standard
+            errors in panel data.
+            The data needs to be sorted in this case, the time series for
+            each panel unit or cluster need to be stacked.
+            keywords
+
+            - `time` array_like (required) : index of time periods
+
+            - `maxlag` integer (required) : number of lags to use
+            - `kernel` string (optional) : kernel, default is Bartlett
+            - `use_correction` False or string in ['hac', 'cluster'] (optional) :
+                  If False the the sandwich covariance is calulated without
+                  small sample correction.
+            - `df_correction` bool (optional)
+                  adjustment to df_resid, see cov_type 'cluster' above
+                  #TODO: we need more options here
+
+        Reminder:
+        `use_correction` in "nw-groupsum" and "nw-panel" is not bool,
+        needs to be in [False, 'hac', 'cluster']
+
+        TODO: Currently there is no check for extra or misspelled keywords,
+        except in the case of cov_type `HCx`
+
+        """
+
+        import statsmodels.stats.sandwich_covariance as sw
+
+        res = self.__class__(self.model, self.params,
+                       normalized_cov_params=self.normalized_cov_params,
+                       scale=self.scale)
+
+        res.cov_type = cov_type = cov_type
+        res.cov_kwds = {'use_t':use_t}
+        res.use_t = use_t
+
+        adjust_df = False
+        if cov_type in ['cluster', 'nw-panel', 'nw-groupsum']:
+            df_correction = kwds.get('df_correction', None)
+            # TODO: check also use_correction, do I need all combinations?
+            if df_correction is not False: # i.e. in [None, True]:
+                # user didn't explicitely set it to False
+                adjust_df = True
+
+        res.cov_kwds['adjust_df'] = adjust_df
+
+        # verify and set kwds, and calculate cov
+        # TODO: this should be outsourced in a function so we can reuse it in
+        #       other models
+        # TODO: make it DRYer   repeated code for checking kwds
+        if cov_type in ('HC0', 'HC1', 'HC2', 'HC3'):
+            if kwds:
+                raise ValueError('heteroscedasticity robust covarians ' +
+                                 'does not use keywords')
+            res.cov_kwds['description'] = ('Standard Errors are heteroscedasticity ' +
+                                           'robust ' + '(' + cov_type + ')')
+            # TODO cannot access cov without calling se first
+            getattr(self, cov_type.upper() + '_se')
+            res.cov_params_default = getattr(self, 'cov_' + cov_type.upper())
+        elif cov_type == 'HAC':
+            maxlags = kwds['maxlags']   # required?, default in cov_hac_simple
+            res.cov_kwds['maxlags'] = maxlags
+            use_correction = kwds.get('use_correction', False)
+            res.cov_kwds['use_correction'] = use_correction
+            res.cov_kwds['description'] = ('Standard Errors are heteroscedasticity ' +
+                 'and autocorrelation robust (HAC) using %d lags and %s small ' +
+                 'sample correction') % (maxlags, ['without', 'with'][use_correction])
+
+            res.cov_params_default = sw.cov_hac_simple(self, nlags=maxlags,
+                                                 use_correction=use_correction)
+        elif cov_type == 'cluster':
+            #cluster robust standard errors, one- or two-way
+            groups = kwds['groups']
+            if not hasattr(groups, 'shape'):
+                groups = np.asarray(groups).T
+            res.cov_kwds['groups'] = groups
+            use_correction = kwds.get('use_correction', True)
+            res.cov_kwds['use_correction'] = use_correction
+            if groups.ndim == 1:
+                if adjust_df:
+                    # need to find number of groups
+                    # duplicate work
+                    self.n_groups = n_groups = len(np.unique(groups))
+                res.cov_params_default = sw.cov_cluster(self, groups,
+                                                 use_correction=use_correction)
+
+            elif groups.ndim == 2:
+                if adjust_df:
+                    # need to find number of groups
+                    # duplicate work
+                    n_groups0 = len(np.unique(groups[:,0]))
+                    n_groups1 = len(np.unique(groups[:, 1]))
+                    self.n_groups = (n_groups0, n_groups1)
+                    n_groups = min(n_groups0, n_groups1) # use for adjust_df
+
+                # Note: sw.cov_cluster_2groups has 3 returns
+                res.cov_params_default = sw.cov_cluster_2groups(self, groups,
+                                             use_correction=use_correction)[0]
+            else:
+                raise ValueError('only two groups are supported')
+            res.cov_kwds['description'] = ('Standard Errors are robust to' +
+                                'cluster correlation ' + '(' + cov_type + ')')
+
+        elif cov_type == 'nw-panel':
+            #cluster robust standard errors
+            res.cov_kwds['time'] = time = kwds['time']
+            #TODO: nlags is currently required
+            #nlags = kwds.get('nlags', True)
+            #res.cov_kwds['nlags'] = nlags
+            #TODO: `nlags` or `maxlags`
+            res.cov_kwds['maxlags'] = maxlags = kwds['maxlags']
+            use_correction = kwds.get('use_correction', 'hac')
+            res.cov_kwds['use_correction'] = use_correction
+            weights_func = kwds.get('weights_func', sw.weights_bartlett)
+            res.cov_kwds['weights_func'] = weights_func
+            # TODO: clumsy time index in cov_nw_panel
+            tt = (np.nonzero(np.diff(time) < 0)[0] + 1).tolist()
+            groupidx = lzip([0] + tt, tt + [len(time)])
+            self.n_groups = n_groups = len(groupidx)
+            res.cov_params_default = sw.cov_nw_panel(self, maxlags, groupidx,
+                                                weights_func=weights_func,
+                                                use_correction=use_correction)
+            res.cov_kwds['description'] = ('Standard Errors are robust to' +
+                                'cluster correlation ' + '(' + cov_type + ')')
+        elif cov_type == 'nw-groupsum':
+            # Driscoll-Kraay standard errors
+            res.cov_kwds['time'] = time = kwds['time']
+            #TODO: nlags is currently required
+            #nlags = kwds.get('nlags', True)
+            #res.cov_kwds['nlags'] = nlags
+            #TODO: `nlags` or `maxlags`
+            res.cov_kwds['maxlags'] = maxlags = kwds['maxlags']
+            use_correction = kwds.get('use_correction', 'cluster')
+            res.cov_kwds['use_correction'] = use_correction
+            weights_func = kwds.get('weights_func', sw.weights_bartlett)
+            res.cov_kwds['weights_func'] = weights_func
+            if adjust_df:
+                # need to find number of groups
+                tt = (np.nonzero(np.diff(time) < 0)[0] + 1)
+                self.n_groups = n_groups = len(tt) + 1
+            res.cov_params_default = sw.cov_nw_groupsum(self, maxlags, time,
+                                            weights_func=weights_func,
+                                            use_correction=use_correction)
+            res.cov_kwds['description'] = (
+                        'Driscoll and Kraay Standard Errors are robust to ' +
+                        'cluster correlation ' + '(' + cov_type + ')')
+        else:
+            raise ValueError('cov_type not recognized. See docstring for ' +
+                             'available options and spelling')
+
+        if adjust_df:
+            # Note: we leave model.df_resid unchanged at original
+            res.df_resid = n_groups - 1
+
+        return res
+
+
     def summary(self, yname=None, xname=None, title=None, alpha=.05):
         """Summarize the Regression Results
 
@@ -1198,16 +1881,12 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
         omni, omnipv = omni_normtest(self.wresid)
 
-        #TODO: reuse condno from somewhere else ?
-        #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog))
-        wexog = self.model.wexog
-        eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog))
-        eigvals = np.sort(eigvals) #in increasing order
-        condno = np.sqrt(eigvals[-1]/eigvals[0])
+        eigvals = self.eigenvals
+        condno = self.condition_number
 
         self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
                           omni=omni, omnipv=omnipv, condno=condno,
-                          mineigval=eigvals[0])
+                          mineigval=eigvals[-1])
 
         #TODO not used yet
         #diagn_left_header = ['Models stats']
@@ -1227,6 +1906,9 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
                     ('Df Model:', None), #[self.df_model])
                     ]
 
+        if hasattr(self, 'cov_type'):
+            top_left.append(('Covariance Type:', [self.cov_type]))
+
         top_right = [('R-squared:', ["%#8.3f" % self.rsquared]),
                      ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
                      ('F-statistic:', ["%#8.4g" % self.fvalue] ),
@@ -1258,7 +1940,7 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         smry.add_table_2cols(self, gleft=top_left, gright=top_right,
                           yname=yname, xname=xname, title=title)
         smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
-                             use_t=True)
+                             use_t=self.use_t)
 
         smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
                           yname=yname, xname=xname,
@@ -1266,15 +1948,17 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
 
         #add warnings/notes, added to text format only
         etext =[]
+        if hasattr(self, 'cov_type'):
+            etext.append(self.cov_kwds['description'])
         if self.model.exog.shape[0] < self.model.exog.shape[1]:
             wstr = "The input rank is higher than the number of observations."
             etext.append(wstr)
-        if eigvals[0] < 1e-10:
+        if eigvals[-1] < 1e-10:
             wstr = "The smallest eigenvalue is %6.3g. This might indicate "
             wstr += "that there are\n"
             wstr += "strong multicollinearity problems or that the design "
             wstr += "matrix is singular."
-            wstr = wstr % eigvals[0]
+            wstr = wstr % eigvals[-1]
             etext.append(wstr)
         elif condno > 1000:  #TODO: what is recommended
             wstr = "The condition number is large, %6.3g. This might "
@@ -1338,13 +2022,13 @@ def summary2(self, yname=None, xname=None, title=None, alpha=.05,
         from statsmodels.stats.stattools import (jarque_bera,
                                                  omni_normtest,
                                                  durbin_watson)
-        from numpy.linalg import (cond, eigvalsh)
-        from statsmodels.compatnp.collections import OrderedDict
+
+        from statsmodels.compat.collections import OrderedDict
         jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
         omni, omnipv = omni_normtest(self.wresid)
         dw = durbin_watson(self.wresid)
-        condno = cond(self.model.wexog)
-        eigvals = eigvalsh(np.dot(self.model.wexog.T, self.model.wexog))
+        eigvals = self.eigenvals
+        condno = self.condition_number
         eigvals = np.sort(eigvals) #in increasing order
         diagnostic = OrderedDict([
                      ('Omnibus:',  "%.3f" % omni),
@@ -1365,10 +2049,10 @@ def summary2(self, yname=None, xname=None, title=None, alpha=.05,
         smry.add_dict(diagnostic)
 
         # Warnings
-        if eigvals[0] < 1e-10:
+        if eigvals[-1] < 1e-10:
             warn = "The smallest eigenvalue is %6.3g. This might indicate that\
             there are strong multicollinearity problems or that the design\
-            matrix is singular." % eigvals[0]
+            matrix is singular." % eigvals[-1]
             smry.add_text(warn)
         if condno > 1000:
             warn = "* The condition number is large (%.g). This might indicate \
@@ -1451,13 +2135,14 @@ def el_test(self, b0_vals, param_nums, return_weights=0,
                      ret_params=0, method='nm',
                      stochastic_exog=1, return_params=0):
         """
-        Tests single or joint hypotheses of the regression parameters.
+        Tests single or joint hypotheses of the regression parameters using
+        Empirical Likelihood.
 
         Parameters
         ----------
 
         b0_vals : 1darray
-            The hypthesized value of the parameter to be tested
+            The hypothesized value of the parameter to be tested
 
         param_nums : 1darray
             The parameter number to be tested
@@ -1486,7 +2171,7 @@ def el_test(self, b0_vals, param_nums, return_weights=0,
         -------
 
         res : tuple
-            The p-value and -2 times the log likelihood ratio for the
+            The p-value and -2 times the log-likelihood ratio for the
             hypothesized values.
 
         Examples
@@ -1508,7 +2193,7 @@ def el_test(self, b0_vals, param_nums, return_weights=0,
         params = np.copy(self.params)
         opt_fun_inst = _ELRegOpts() # to store weights
         if len(param_nums) == len(params):
-            llr = opt_fun_inst._opt_nuis_regress(b0_vals,
+            llr = opt_fun_inst._opt_nuis_regress([],
                                     param_nums=param_nums,
                                     endog=self.model.endog,
                                     exog=self.model.exog,
@@ -1547,18 +2232,19 @@ def conf_int_el(self, param_num, sig=.05, upper_bound=None, lower_bound=None,
                     method='nm', stochastic_exog=1):
         """
         Computes the confidence interval for the parameter given by param_num
+        using Empirical Likelihood
 
         Parameters
         ----------
 
         param_num : float
-            The parameter thats confidence interval is desired
+            The parameter for which the confidence interval is desired
 
         sig : float
             The significance level.  Default is .05
 
         upper_bound : float
-            Tha mximum value the upper limit can be.  Default is the
+            The maximum value the upper limit can be.  Default is the
             99.9% confidence value under OLS assumptions.
 
         lower_bound : float
@@ -1596,7 +2282,7 @@ def conf_int_el(self, param_num, sig=.05, upper_bound=None, lower_bound=None,
         For alpha=.05, the value is 3.841459.
 
         To ensure optimization terminated successfully, it is suggested to
-        do test_beta([lower_limit], [param_num])
+        do el_test([lower_limit], [param_num])
 
         If the optimization does not terminate successfully, consider switching
         optimization algorithms.
@@ -1635,19 +2321,19 @@ class RegressionResultsWrapper(wrap.ResultsWrapper):
         'HC0_se' : 'columns',
         'HC1_se' : 'columns',
         'HC2_se' : 'columns',
-        'HC3_se' : 'columns'
+        'HC3_se' : 'columns',
+        'norm_resid' : 'rows',
     }
 
     _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._attrs,
                                    _attrs)
 
-    _methods = {
-        'norm_resid' : 'rows',
-    }
+    _methods = {}
 
     _wrap_methods = wrap.union_dicts(
                         base.LikelihoodResultsWrapper._wrap_methods,
                         _methods)
+
 wrap.populate_wrapper(RegressionResultsWrapper,
                       RegressionResults)
 
diff --git a/statsmodels/regression/mixed_linear_model.py b/statsmodels/regression/mixed_linear_model.py
new file mode 100644
index 00000000000..9be3f050a60
--- /dev/null
+++ b/statsmodels/regression/mixed_linear_model.py
@@ -0,0 +1,1838 @@
+"""
+Linear mixed effects models for Statsmodels
+
+The data are partitioned into disjoint groups.  The probability model
+for group i is:
+
+Y = X*beta + Z*gamma + epsilon
+
+where
+
+* n_i is the number of observations in group i
+* Y is a n_i dimensional response vector
+* X is a n_i x k_fe dimensional matrix of fixed effects
+  coefficients
+* beta is a k_fe-dimensional vector of fixed effects slopes
+* Z is a n_i x k_re dimensional matrix of random effects
+  coefficients
+* gamma is a k_re-dimensional random vector with mean 0
+  and covariance matrix Psi; note that each group
+  gets its own independent realization of gamma.
+* epsilon is a n_i dimensional vector of iid normal
+  errors with mean 0 and variance sigma^2; the epsilon
+  values are independent both within and between groups
+
+Y, X and Z must be entirely observed.  beta, Psi, and sigma^2 are
+estimated using ML or REML estimation, and gamma and epsilon are
+random so define the probability model.
+
+The mean structure is E[Y|X,Z] = X*beta.  If only the mean structure
+is of interest, GEE is a good alternative to mixed models.
+
+The primary reference for the implementation details is:
+
+MJ Lindstrom, DM Bates (1988).  "Newton Raphson and EM algorithms for
+linear mixed effects models for repeated measures data".  Journal of
+the American Statistical Association. Volume 83, Issue 404, pages
+1014-1022.
+
+See also this more recent document:
+
+http://econ.ucsb.edu/~doug/245a/Papers/Mixed%20Effects%20Implement.pdf
+
+All the likelihood, gradient, and Hessian calculations closely follow
+Lindstrom and Bates.
+
+The following two documents are written more from the perspective of
+users:
+
+http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
+
+http://lme4.r-forge.r-project.org/slides/2009-07-07-Rennes/3Longitudinal-4.pdf
+
+Notation:
+
+* `cov_re` is the random effects covariance matrix (referred to above
+  as Psi) and `scale` is the (scalar) error variance.  For a single
+  group, the marginal covariance matrix of endog given exog is scale*I
+  + Z * cov_re * Z', where Z is the design matrix for the random
+  effects in one group.
+
+Notes:
+
+1. Three different parameterizations are used here in different
+places.  The regression slopes (usually called `fe_params`) are
+identical in all three parameterizations, but the variance parameters
+differ.  The parameterizations are:
+
+* The "natural parameterization" in which cov(endog) = scale*I + Z *
+  cov_re * Z', as described above.  This is the main parameterization
+  visible to the user.
+
+* The "profile parameterization" in which cov(endog) = I +
+  Z * cov_re1 * Z'.  This is the parameterization of the profile
+  likelihood that is maximized to produce parameter estimates.
+  (see Lindstrom and Bates for details).  The "natural" cov_re is
+  equal to the "profile" cov_re1 times scale.
+
+* The "square root parameterization" in which we work with the
+  Cholesky factor of cov_re1 instead of cov_re1 directly.
+
+All three parameterizations can be "packed" by concatenating fe_params
+together with the lower triangle of the dependence structure.  Note
+that when unpacking, it is important to either square or reflect the
+dependence structure depending on which parameterization is being
+used.
+
+2. The situation where the random effects covariance matrix is
+singular is numerically challenging.  Small changes in the covariance
+parameters may lead to large changes in the likelihood and
+derivatives.
+
+3. The optimization strategy is to optionally perform a few EM steps,
+followed by optionally performing a few steepest descent steps,
+followed by conjugate gradient descent using one of the scipy gradient
+optimizers.  The EM and steepest descent steps are used to get
+adequate starting values for the conjugate gradient optimization,
+which is much faster.
+"""
+
+import numpy as np
+import statsmodels.base.model as base
+from scipy.optimize import fmin_ncg, fmin_cg, fmin_bfgs, fmin
+from scipy.stats.distributions import norm
+import pandas as pd
+import patsy
+from statsmodels.compat.collections import OrderedDict
+from statsmodels.compat import range
+import warnings
+from statsmodels.tools.sm_exceptions import ConvergenceWarning
+from statsmodels.base._penalties import Penalty
+
+# This is a global switch to use direct linear algebra calculations
+# for solving factor-structured linear systems and calculating
+# factor-structured determinants.  If False, use the
+# Sherman-Morrison-Woodbury update which is more efficient for
+# factor-structured matrices.  Should be False except when testing.
+_no_smw = False
+
+def _smw_solve(s, A, B, BI, rhs):
+    """
+    Solves the system (s*I + A*B*A') * x = rhs for x and returns x.
+
+    Parameters:
+    -----------
+    s : scalar
+        See above for usage
+    A : square symmetric ndarray
+        See above for usage
+    B : square symmetric ndarray
+        See above for usage
+    BI : square symmetric ndarray
+        The inverse of `B`.  Can be None if B is singular
+    rhs : ndarray
+        See above for usage
+
+    Returns:
+    --------
+    x : ndarray
+        See above
+
+    If the global variable `_no_smw` is True, this routine uses direct
+    linear algebra calculations.  Otherwise it uses the
+    Sherman-Morrison-Woodbury identity to speed up the calculation.
+    """
+
+    # Direct calculation
+    if _no_smw or BI is None:
+        mat = np.dot(A, np.dot(B, A.T))
+        mat += s * np.eye(A.shape[0])
+        return np.linalg.solve(mat, rhs)
+
+    # Use SMW identity
+    qmat = BI + np.dot(A.T, A) / s
+    u = np.dot(A.T, rhs)
+    qmat = np.linalg.solve(qmat, u)
+    qmat = np.dot(A, qmat)
+    rslt = rhs / s - qmat / s**2
+    return rslt
+
+
+def _smw_logdet(s, A, B, BI, B_logdet):
+    """
+    Use the matrix determinant lemma to accelerate the calculation of
+    the log determinant of s*I + A*B*A'.
+
+    Parameters:
+    -----------
+    s : scalar
+        See above for usage
+    A : square symmetric ndarray
+        See above for usage
+    B : square symmetric ndarray
+        See above for usage
+    BI : square symmetric ndarray
+        The inverse of `B`; can be None if B is singular.
+    B_logdet : real
+        The log determinant of B
+    """
+
+    if _no_smw or BI is None:
+        mat = np.dot(A, np.dot(B, A.T))
+        mat += s * np.eye(A.shape[0])
+        _, ld = np.linalg.slogdet(mat)
+        return ld
+
+    p = A.shape[0]
+    ld = p * np.log(s)
+
+    qmat = BI + np.dot(A.T, A) / s
+    _, ld1 = np.linalg.slogdet(qmat)
+
+    return B_logdet + ld + ld1
+
+
+class MixedLM(base.LikelihoodModel):
+    """
+    An object specifying a linear mixed effects model.  Use the `fit`
+    method to fit the model and obtain a results object.
+
+    Arguments:
+    ----------
+    endog : 1d array-like
+        The dependent variable
+    exog : 2d array-like
+        A matrix of covariates used to determine the
+        mean structure (the "fixed effects" covariates).
+    groups : 1d array-like
+        A vector of labels determining the groups -- data from
+        different groups are independent
+    exog_re : 2d array-like
+        A matrix of covariates used to determine the variance and
+        covariance structure (the "random effects" covariates).  If
+        None, defaults to a random intercept for each of the groups.
+        May also be set from a formula using a call to `set_random`.
+    use_sqrt : bool
+        If True, optimization is carried out using the lower
+        triangle of the square root of the random effects
+        covariance matrix, otherwise it is carried out using the
+        lower triangle of the random effects covariance matrix.
+    missing : string
+        The approach to missing data handling
+
+    Notes:
+    ------
+    The covariates in `exog` and `exog_re` may (but need not)
+    partially or wholly overlap.
+
+    `use_sqrt` should almost always be set to True.  The main use case
+    for use_sqrt=False is when complicated patterns of fixed values in
+    the covariance structure are set (using the `free` argument to
+    `fit`) that cannot be expressed in terms of the Cholesky factor L.
+    """
+
+    def __init__(self, endog, exog, groups, exog_re=None,
+                 use_sqrt=True, missing='none'):
+
+        self.use_sqrt = use_sqrt
+
+        # Some defaults
+        self.reml = True
+        self.fe_pen = None
+        self.re_pen = None
+        self.score_pat = 1.
+
+        # If there is one covariate, it may be passed in as a column
+        # vector, convert these to 2d arrays.
+        # TODO: Can this be moved up in the class hierarchy?
+        if exog is not None and exog.ndim == 1:
+            exog = exog[:,None]
+        if exog_re is not None and exog_re.ndim == 1:
+            exog_re = exog_re[:,None]
+
+        # Calling super creates self.endog, etc. as ndarrays and the
+        # original exog, endog, etc. are self.data.endog, etc.
+        super(MixedLM, self).__init__(endog, exog, groups=groups,
+                                  exog_re=exog_re, missing=missing)
+
+        if exog_re is None:
+            # Default random effects structure (random intercepts).
+            self.exog_re = np.ones((len(endog), 1), dtype=np.float64)
+            self.data.exog_re = self.exog_re
+        else:
+            # Process exog_re the same way that exog is handled
+            # upstream
+            self.data.exog_re = exog_re
+            self.exog_re = np.asarray(exog_re)
+
+        # Model dimensions
+        self.k_fe = exog.shape[1] # Number of fixed effects parameters
+        if exog_re is not None:
+
+            # Number of random effect covariates
+            self.k_re = exog_re.shape[1]
+
+            # Number of covariance parameters
+            self.k_re2 = self.k_re * (self.k_re + 1) // 2
+
+        else:
+            self.k_re = 1 # Default (random intercepts model)
+            self.k_re2 = 1
+
+        # Override the default value
+        self.nparams = self.k_fe + self.k_re2
+
+        # Convert the data to the internal representation, which is a
+        # list of arrays, corresponding to the groups.
+        group_labels = list(set(groups))
+        group_labels.sort()
+        row_indices = dict((s, []) for s in group_labels)
+        for i,g in enumerate(groups):
+            row_indices[g].append(i)
+        self.row_indices = row_indices
+        self.group_labels = group_labels
+        self.n_groups = len(self.group_labels)
+
+        # Split the data by groups
+        self.endog_li = self.group_list(self.endog)
+        self.exog_li = self.group_list(self.exog)
+        self.exog_re_li = self.group_list(self.exog_re)
+
+        # The total number of observations, summed over all groups
+        self.n_totobs = sum([len(y) for y in self.endog_li])
+
+        # Set the fixed effects parameter names
+        if self.exog_names is None:
+            self.exog_names = ["FE%d" % (k + 1) for k in
+                               range(self.exog.shape[1])]
+
+        # Set the random effect parameter names
+        if isinstance(self.exog_re, pd.DataFrame):
+            self.exog_re_names = list(self.exog_re.columns)
+        else:
+            self.exog_re_names = ["Z%d" % (k+1) for k in
+                                  range(self.exog_re.shape[1])]
+
+    @classmethod
+    def from_formula(cls, formula, data, re_formula=None, subset=None,
+                     *args, **kwargs):
+        """
+        Create a Model from a formula and dataframe.
+
+        Parameters
+        ----------
+        formula : str or generic Formula object
+            The formula specifying the model
+        data : array-like
+            The data for the model. See Notes.
+        re_formula : string
+            A one-sided formula defining the variance structure of the
+            model.  The default gives a random intercept for each
+            group.
+        subset : array-like
+            An array-like object of booleans, integers, or index
+            values that indicate the subset of df to use in the
+            model. Assumes df is a `pandas.DataFrame`
+        args : extra arguments
+            These are passed to the model
+        kwargs : extra keyword arguments
+            These are passed to the model.
+
+        Returns
+        -------
+        model : Model instance
+
+        Notes
+        ------
+        `data` must define __getitem__ with the keys in the formula
+        terms args and kwargs are passed on to the model
+        instantiation. E.g., a numpy structured or rec array, a
+        dictionary, or a pandas DataFrame.
+
+        If `re_formula` is not provided, the default is a random
+        intercept for each group.
+
+        This method currently does not correctly handle missing
+        values, so missing values should be explicitly dropped from
+        the DataFrame before calling this method.
+        """
+
+        if "groups" not in kwargs.keys():
+            raise AttributeError("'groups' is a required keyword argument in MixedLM.from_formula")
+
+        # If `groups` is a variable name, retrieve the data for the
+        # groups variable.
+        if type(kwargs["groups"]) == str:
+            kwargs["groups"] = np.asarray(data[kwargs["groups"]])
+
+        if re_formula is not None:
+            exog_re = patsy.dmatrix(re_formula, data)
+            exog_re_names = exog_re.design_info.column_names
+            exog_re = np.asarray(exog_re)
+        else:
+            exog_re = np.ones((data.shape[0], 1),
+                              dtype=np.float64)
+            exog_re_names = ["Intercept",]
+
+        mod = super(MixedLM, cls).from_formula(formula, data,
+                                               subset=None,
+                                               exog_re=exog_re,
+                                               *args, **kwargs)
+
+        return mod
+
+    def group_list(self, array):
+        """
+        Returns `array` split into subarrays corresponding to the
+        grouping structure.
+        """
+
+        if array.ndim == 1:
+            return [np.array(array[self.row_indices[k]])
+                    for k in self.group_labels]
+        else:
+            return [np.array(array[self.row_indices[k], :])
+                    for k in self.group_labels]
+
+
+    def fit_regularized(self, start_params=None, method='l1', alpha=0,
+                        ceps=1e-4, ptol=1e-6, maxit=200, **fit_args):
+        """
+        Fit a model in which the fixed effects parameters are
+        penalized.  The dependence parameters are held fixed at their
+        estimated values in the unpenalized model.
+
+        Parameters:
+        -----------
+        method : string of Penalty object
+            Method for regularization.  If a string, must be 'l1'.
+        alpha : array-like
+            Scalar or vector of penalty weights.  If a scalar, the
+            same weight is applied to all coefficients; if a vector,
+            it contains a weight for each coefficient.  If method is a
+            Penalty object, the weights are scaled by alpha.  For L1
+            regularization, the weights are used directly.
+        ceps : positive real scalar
+            Fixed effects parameters smaller than this value
+            in magnitude are treaded as being zero.
+        ptol : positive real scalar
+            Convergence occurs when the sup norm difference
+            between successive values of `fe_params` is less than
+            `ptol`.
+        maxit : integer
+            The maximum number of iterations.
+        fit_args :
+            Additional arguments passed to fit.
+
+        Returns:
+        --------
+        A MixedLMResults instance containing the results.
+
+        Notes:
+        ------
+        The covariance structure is not updated as the fixed effects
+        parameters are varied.
+
+        The algorithm used here for L1 regularization is a"shooting"
+        or cyclic coordinate descent algorithm.
+
+        If method is 'l1', then `fe_pen` and `cov_pen` are used to
+        obtain the covariance structure, but are ignored during the
+        L1-penalized fitting.
+
+        References:
+        -----------
+        Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized
+        Paths for Generalized Linear Models via Coordinate
+        Descent. Journal of Statistical Software, 33(1) (2008)
+        http://www.jstatsoft.org/v33/i01/paper
+
+        http://statweb.stanford.edu/~tibs/stat315a/Supplements/fuse.pdf
+        """
+
+        if type(method) == str and (method.lower() != 'l1'):
+            raise ValueError("Invalid regularization method")
+
+        # If method is a smooth penalty just optimize directly.
+        if isinstance(method, Penalty):
+            fit_args = dict(fit_args)
+            # Scale the penalty weights by alpha
+            method.alpha = alpha
+            fit_args.update({"fe_pen": method})
+            return self.fit(**fit_args)
+
+        if np.isscalar(alpha):
+            alpha = alpha * np.ones(self.k_fe, dtype=np.float64)
+
+        # Fit the unpenalized model to get the dependence structure.
+        mdf = self.fit(**fit_args)
+        fe_params = mdf.fe_params
+        cov_re = mdf.cov_re
+        scale = mdf.scale
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        for itr in range(maxit):
+
+            fe_params_s = fe_params.copy()
+            for j in range(self.k_fe):
+
+                if abs(fe_params[j]) < ceps:
+                    continue
+
+                # The residuals
+                fe_params[j] = 0.
+                expval = np.dot(self.exog, fe_params)
+                resid_all = self.endog - expval
+
+                # The loss function has the form
+                # a*x^2 + b*x + pwt*|x|
+                a, b = 0., 0.
+                for k, lab in enumerate(self.group_labels):
+
+                    exog = self.exog_li[k]
+                    ex_r = self.exog_re_li[k]
+                    resid = resid_all[self.row_indices[lab]]
+
+                    x = exog[:,j]
+                    u = _smw_solve(scale, ex_r, cov_re, cov_re_inv, x)
+                    a += np.dot(u, x)
+                    b -= 2 * np.dot(u, resid)
+
+                pwt1 = alpha[j]
+                if b > pwt1:
+                    fe_params[j] = -(b - pwt1) / (2 * a)
+                elif b < -pwt1:
+                    fe_params[j] = -(b + pwt1) / (2 * a)
+
+            if np.abs(fe_params_s - fe_params).max() < ptol:
+                break
+
+        # Replace the fixed effects estimates with their penalized
+        # values, leave the dependence parameters in their unpenalized
+        # state.
+        params_prof = mdf.params.copy()
+        params_prof[0:self.k_fe] = fe_params
+
+        scale = self.get_scale(fe_params, mdf.cov_re_unscaled)
+
+        # Get the Hessian including only the nonzero fixed effects,
+        # then blow back up to the full size after inverting.
+        hess = self.hessian(params_prof)
+        pcov = np.nan * np.ones_like(hess)
+        ii = np.abs(params_prof) > ceps
+        ii[self.k_fe:] = True
+        ii = np.flatnonzero(ii)
+        hess1 = hess[ii, :][:, ii]
+        pcov[np.ix_(ii,ii)] = np.linalg.inv(-hess1)
+
+        results = MixedLMResults(self, params_prof, pcov / scale)
+        results.fe_params = fe_params
+        results.cov_re = cov_re
+        results.scale = scale
+        results.cov_re_unscaled = mdf.cov_re_unscaled
+        results.method = mdf.method
+        results.converged = True
+        results.cov_pen = self.cov_pen
+        results.likeval = self.loglike(params_prof)
+        results.k_fe = self.k_fe
+        results.k_re = self.k_re
+        results.k_re2 = self.k_re2
+
+        return results
+
+
+    def hessian(self, params):
+        """
+        Hessian of log-likelihood evaluated at `params`, calculated
+        numerically.
+
+        Parameters
+        ----------
+        params : array-like
+            The model parameters, packed into a 1d array.
+
+        Returns
+        -------
+        The Hessian matrix of the log likelihood function, evaluated
+        at params.
+
+        Notes
+        -----
+        The Hessian is calculated with respect to either the lower
+        triangle of `cov_re` or its lower triangular square root (L)
+        depending on the value of `use_sqrt`.
+
+        `hessian_full` is an analytic Hessian calculation, always
+        calculated with respect to cov_re
+        """
+
+        from statsmodels.tools.numdiff import approx_hess_cs
+        hmat = approx_hess_cs(params, self.loglike)
+        return hmat.real
+
+    def _unpack(self, params, sym=True):
+        """
+        Takes as input the packed parameter vector and returns a
+        vector containing the regression slopes and a matrix defining
+        the dependence structure.
+
+        Arguments:
+        ----------
+        params : array-like
+            The packed parameters
+        sym : bool
+          If true, the variance parameters are returned as a symmetric
+          matrix; if False, the variance parameters are returned as a
+          lower triangular matrix.
+
+        Returns:
+        --------
+        params : 1d ndarray
+            The fixed effects coefficients
+        cov_re : 2d ndarray
+            The random effects covariance matrix
+        """
+
+        fe_params = params[0:self.k_fe]
+        re_params = params[self.k_fe:]
+
+        # Unpack the covariance matrix of the random effects.
+        # approx_hess_cs uses complex params values, so cov_re needs
+        # to hold complex values if params is complex
+        cov_re = np.zeros((self.k_re, self.k_re), dtype=params.dtype)
+        ix = np.tril_indices(self.k_re)
+        cov_re[ix] = re_params
+
+        if sym:
+            cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))
+
+        return fe_params, cov_re
+
+    def _pack(self, vec, mat):
+        """
+        Packs the model parameters into a single vector.
+
+        Arguments
+        ---------
+        vec : 1d ndarray
+            A vector
+        mat : 2d ndarray
+            An (assumed) symmetric matrix
+
+        Returns
+        -------
+        params : 1d ndarray
+            The vector and the lower triangle of the matrix,
+            concatenated.
+        """
+
+        ix = np.tril_indices(mat.shape[0])
+        return np.concatenate((vec, mat[ix]))
+
+    def loglike_full(self, params):
+        """
+        Evaluate the (profile) log-likelihood of the linear mixed
+        effects model, using a parameterization in which the random
+        effects covariance matrix is represented by its lower
+        triangle.  Note that this is the profile likelihood in which
+        the scale parameter scale has been profiled out.
+
+        Arguments
+        ---------
+        params : 1d ndarray
+            The parameter values, packed into a single vector.  See
+            below for details.
+
+        Returns
+        -------
+        likeval : scalar
+            The log-likelihood value at `params`.
+
+        Notes
+        -----
+        The first p elements of the packed vector are the regression
+        slopes, and the remaining q*(q+1)/2 elements are the lower
+        triangle of the random effects covariance matrix Psi, packed
+        row-wise.  The matrix Psi is used to form the covariance
+        matrix V = I + Z * Psi * Z', where Z is the design matrix for
+        the random effects structure.  To convert this to the full
+        likelihood (not profiled) parameterization, calculate the
+        error variance scale, and divide Psi by scale.
+        """
+
+        fe_params, cov_re = self._unpack(params)
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+        _, cov_re_logdet = np.linalg.slogdet(cov_re)
+
+        # The residuals
+        expval = np.dot(self.exog, fe_params)
+        resid_all = self.endog - expval
+
+        likeval = 0.
+
+        # Handle the covariance penalty
+        if self.cov_pen is not None:
+            likeval -= self.cov_pen.func(cov_re, cov_re_inv)
+
+        # Handle the fixed effects penalty
+        if self.fe_pen is not None:
+            likeval -= self.fe_pen.func(fe_params)
+
+        xvx, qf = 0., 0.
+        for k, lab in enumerate(self.group_labels):
+
+            exog = self.exog_li[k]
+            ex_r = self.exog_re_li[k]
+            resid = resid_all[self.row_indices[lab]]
+
+            # Part 1 of the log likelihood (for both ML and REML)
+            ld = _smw_logdet(1., ex_r, cov_re, cov_re_inv,
+                             cov_re_logdet)
+            likeval -= ld / 2.
+
+            # Part 2 of the log likelihood (for both ML and REML)
+            u = _smw_solve(1., ex_r, cov_re, cov_re_inv, resid)
+            qf += np.dot(resid, u)
+
+            # Adjustment for REML
+            if self.reml:
+                mat = _smw_solve(1., ex_r, cov_re, cov_re_inv, exog)
+                xvx += np.dot(exog.T, mat)
+
+        if self.reml:
+            likeval -= (self.n_totobs - self.k_fe) * np.log(qf) / 2.
+            _,ld = np.linalg.slogdet(xvx)
+            likeval -= ld / 2.
+            likeval -= (self.n_totobs - self.k_fe) * np.log(2 * np.pi) / 2.
+            likeval += ((self.n_totobs - self.k_fe) *
+                        np.log(self.n_totobs - self.k_fe) / 2.)
+            likeval -= (self.n_totobs - self.k_fe) / 2.
+        else:
+            likeval -= self.n_totobs * np.log(qf) / 2.
+            likeval -= self.n_totobs * np.log(2 * np.pi) / 2.
+            likeval += self.n_totobs * np.log(self.n_totobs) / 2.
+            likeval -= self.n_totobs / 2.
+
+        return likeval
+
+    def loglike(self, params):
+        """
+        Evaluate the (profile) log-likelihood of the linear mixed
+        effects model.  Note that this is the profile likelihood in
+        which the scale parameter scale has been profiled out.
+
+        Arguments
+        ---------
+        params : 1d ndarray
+            The parameter values, packed into a single vector.  See
+            below for details.
+
+        Returns
+        -------
+        likeval : scalar
+            The log-likelihood value at `params`.
+        """
+
+        if self.use_sqrt:
+            return self.loglike_sqrt(params)
+        else:
+            return self.loglike_full(params)
+
+
+    def _gen_dV_dPsi(self, ex_r, max_ix=None):
+        """
+        A generator that yields the derivative of the covariance
+        matrix V (=I + Z*Psi*Z') with respect to the free elements of
+        Psi.  Each call to the generator yields the index of Psi with
+        respect to which the derivative is taken, and the derivative
+        matrix with respect to that element of Psi.  Psi is a
+        symmetric matrix, so the free elements are the lower triangle.
+        If max_ix is not None, the iterations terminate after max_ix
+        values are yielded.
+        """
+
+        jj = 0
+        for j1 in range(self.k_re):
+            for j2 in range(j1 + 1):
+                if max_ix is not None and jj > max_ix:
+                    return
+                mat = np.outer(ex_r[:,j1], ex_r[:,j2])
+                if j1 != j2:
+                    mat += mat.T
+                yield jj,mat
+                jj += 1
+
+    def score_full(self, params):
+        """
+        Calculates the score vector for the mixed effects model, using
+        a parameterization in which the random effects covariance
+        matrix is represented by its lower triangle.  Note that this
+        is the score for the profile likelihood in which the scale
+        parameter scale has been profiled out.
+
+        Parameters
+        ----------
+        params : 1d ndarray
+            The model parameters in packed form
+
+        Returns
+        -------
+        scorevec : 1d ndarray
+            The score vector, calculated at `params`.
+        """
+
+        fe_params, cov_re = self._unpack(params)
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        score_fe = np.zeros(self.k_fe, dtype=np.float64)
+        score_re = np.zeros(self.k_re2, dtype=np.float64)
+
+        # Handle the covariance penalty.
+        if self.cov_pen is not None:
+            score_re -= self.cov_pen.grad(cov_re, cov_re_inv)
+
+        # Handle the fixed effects penalty.
+        if self.fe_pen is not None:
+            score_fe -= self.fe_pen.grad(fe_params)
+
+        # resid' V^{-1} resid, summed over the groups (a scalar)
+        rvir = 0.
+
+        # exog' V^{-1} resid, summed over the groups (a k_fe
+        # dimensional vector)
+        xtvir = 0.
+
+        # exog' V^{_1} exog, summed over the groups (a k_fe x k_fe
+        # matrix)
+        xtvix = 0.
+
+        # V^{-1} exog' dV/dQ_jj exog V^{-1}, where Q_jj is the jj^th
+        # covariance parameter.
+        xtax = [0.,] * self.k_re2
+
+        # Temporary related to the gradient of log |V|
+        dlv = np.zeros(self.k_re2, dtype=np.float64)
+
+        # resid' V^{-1} dV/dQ_jj V^{-1} resid (a scalar)
+        rvavr = np.zeros(self.k_re2, dtype=np.float64)
+
+        for k in range(self.n_groups):
+
+            exog = self.exog_li[k]
+            ex_r = self.exog_re_li[k]
+
+            # The residuals
+            expval = np.dot(exog, fe_params)
+            resid = self.endog_li[k] - expval
+
+            if self.reml:
+                viexog = _smw_solve(1., ex_r, cov_re, cov_re_inv, exog)
+                xtvix += np.dot(exog.T, viexog)
+
+            # Contributions to the covariance parameter gradient
+            jj = 0
+            vex = _smw_solve(1., ex_r, cov_re, cov_re_inv, ex_r)
+            vir = _smw_solve(1., ex_r, cov_re, cov_re_inv, resid)
+            for jj,mat in self._gen_dV_dPsi(ex_r):
+                dlv[jj] = np.trace(_smw_solve(1., ex_r, cov_re,
+                                     cov_re_inv, mat))
+                rvavr[jj] += np.dot(vir, np.dot(mat, vir))
+                if self.reml:
+                    xtax[jj] += np.dot(viexog.T, np.dot(mat, viexog))
+
+            # Contribution of log|V| to the covariance parameter
+            # gradient.
+            score_re -= 0.5 * dlv
+
+            # Nededed for the fixed effects params gradient
+            rvir += np.dot(resid, vir)
+            xtvir += np.dot(exog.T, vir)
+
+        fac = self.n_totobs
+        if self.reml:
+            fac -= self.exog.shape[1]
+
+        score_fe += fac * xtvir / rvir
+        score_re += 0.5 * fac * rvavr / rvir
+
+        if self.reml:
+            for j in range(self.k_re2):
+                score_re[j] += 0.5 * np.trace(np.linalg.solve(
+                    xtvix, xtax[j]))
+
+        return np.concatenate((score_fe, score_re))
+
+    def score(self, params):
+        """
+        Calculates the score vector for the mixed effects model.  Note
+        that this is the score vector for the profile likelihood in
+        which the scale parameter scale has been profiled out.
+
+        Parameters
+        ----------
+        params : 1d ndarray
+            All model parameters in packed form
+
+        Returns
+        -------
+        scorevec : 1d ndarray
+            The score vector, calculated at `params`.
+        """
+
+        if self.use_sqrt:
+            scr = self.score_pat * self.score_sqrt(params)
+        else:
+            scr = self.score_pat * self.score_full(params)
+
+        return scr
+
+    def loglike_sqrt(self, params):
+        """
+        Returns the log likelihood evaluated at a given point, for the
+        parameterization in which the random effects covariance matrix
+        is represented by the lower triangle of its Cholesky factor.
+
+        Arguments:
+        ----------
+        params : array-like
+            The model parameters (for the profile likelihood) in
+            packed form.  The first p elements are the regression
+            slopes, and the remaining elements are the lower triangle
+            of a lower triangular matrix L such that Psi = LL'
+
+        Returns:
+        --------
+        The value of the log-likelihood or REML criterion.
+        """
+
+        fe_params, L = self._unpack(params, sym=False)
+        cov_re = np.dot(L, L.T)
+
+        params_r = self._pack(fe_params, cov_re)
+
+        likeval = self.loglike_full(params_r)
+
+        return likeval
+
+
+
+    def score_sqrt(self, params):
+        """
+        Returns the score vector evaluated at a given point, using a
+        parameterization in which the random effects covariance matrix
+        is represented by the lower triangle of its Cholesky factor.
+
+        Arguments:
+        ----------
+        params : array-like
+            The model parameters (for the profile likelihood) in
+            packed form.  The first p elements are the regression
+            slopes, and the remaining elements are the lower triangle
+            of a lower triangular matrix L such that Psi = LL'
+
+        Returns:
+        --------
+        The score vector for the log-likelihood or REML criterion.
+        """
+
+        fe_params, L = self._unpack(params, sym=False)
+        cov_re = np.dot(L, L.T)
+
+        params_f = self._pack(fe_params, cov_re)
+        svec = self.score_full(params_f)
+        s_fe, s_re = self._unpack(svec, sym=False)
+
+        # Use the chain rule to get d/dL from d/dPsi
+        s_l = np.zeros(self.k_re2, dtype=np.float64)
+        jj = 0
+        for i in range(self.k_re):
+            for j in range(i+1):
+                s_l[jj] += np.dot(s_re[:,i], L[:,j])
+                s_l[jj] += np.dot(s_re[i,:], L[:,j])
+                jj += 1
+
+        gr = np.concatenate((s_fe, s_l))
+
+        return gr
+
+    def hessian_full(self, params):
+        """
+        Calculates the Hessian matrix for the mixed effects model.
+        Specifically, this is the Hessian matrix for the profile
+        likelihood in which the scale parameter scale has been profiled
+        out.  The parameters are passed in packed form, with only the
+        lower triangle of the covariance passed.
+
+        Parameters
+        ----------
+        params : 1d ndarray
+            All model parameters in packed form
+
+        Returns
+        -------
+        hess : 2d ndarray
+            The Hessian matrix, evaluated at `params`.
+        """
+
+        fe_params, cov_re = self._unpack(params)
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        # Blocks for the fixed and random effects parameters.
+        hess_fe = 0.
+        hess_re = np.zeros((self.k_re2, self.k_re2), dtype=np.float64)
+        hess_fere = np.zeros((self.k_re2, self.k_fe),
+                             dtype=np.float64)
+
+        fac = self.n_totobs
+        if self.reml:
+            fac -= self.exog.shape[1]
+
+        rvir = 0.
+        xtvix = 0.
+        xtax = [0.,] * self.k_re2
+        B = np.zeros(self.k_re2, dtype=np.float64)
+        D = np.zeros((self.k_re2, self.k_re2), dtype=np.float64)
+        F = [[0.,]*self.k_re2 for k in range(self.k_re2)]
+        for k in range(self.n_groups):
+
+            exog = self.exog_li[k]
+            ex_r = self.exog_re_li[k]
+
+            # The residuals
+            expval = np.dot(exog, fe_params)
+            resid = self.endog_li[k] - expval
+
+            viexog = _smw_solve(1., ex_r, cov_re, cov_re_inv, exog)
+            xtvix += np.dot(exog.T, viexog)
+            vir = _smw_solve(1., ex_r, cov_re, cov_re_inv, resid)
+            rvir += np.dot(resid, vir)
+
+            for jj1,mat1 in self._gen_dV_dPsi(ex_r):
+
+                hess_fere[jj1,:] += np.dot(viexog.T,
+                                           np.dot(mat1, vir))
+                if self.reml:
+                    xtax[jj1] += np.dot(viexog.T, np.dot(mat1, viexog))
+
+                B[jj1] += np.dot(vir, np.dot(mat1, vir))
+                E = _smw_solve(1., ex_r, cov_re, cov_re_inv, mat1)
+
+                for jj2,mat2 in self._gen_dV_dPsi(ex_r, jj1):
+                    Q = np.dot(mat2, E)
+                    Q1 = Q + Q.T
+                    vt = np.dot(vir, np.dot(Q1, vir))
+                    D[jj1, jj2] += vt
+                    if jj1 != jj2:
+                        D[jj2, jj1] += vt
+                    R = _smw_solve(1., ex_r, cov_re, cov_re_inv, Q)
+                    rt = np.trace(R) / 2
+                    hess_re[jj1, jj2] += rt
+                    if jj1 != jj2:
+                        hess_re[jj2, jj1] += rt
+                    if self.reml:
+                        F[jj1][jj2] += np.dot(viexog.T,
+                                              np.dot(Q, viexog))
+
+        hess_fe -= fac * xtvix / rvir
+
+        hess_re -= 0.5 * fac * (D / rvir - np.outer(B, B) / rvir**2)
+
+        hess_fere = -fac * hess_fere / rvir
+
+        if self.reml:
+            for j1 in range(self.k_re2):
+                Q1 = np.linalg.solve(xtvix, xtax[j1])
+                for j2 in range(j1 + 1):
+                    Q2 = np.linalg.solve(xtvix, xtax[j2])
+                    a = np.trace(np.dot(Q1, Q2))
+                    a -= np.trace(np.linalg.solve(xtvix, F[j1][j2]))
+                    a *= 0.5
+                    hess_re[j1, j2] += a
+                    if j1 > j2:
+                        hess_re[j2, j1] += a
+
+        # Put the blocks together to get the Hessian.
+        m = self.k_fe + self.k_re2
+        hess = np.zeros((m, m), dtype=np.float64)
+        hess[0:self.k_fe, 0:self.k_fe] = hess_fe
+        hess[0:self.k_fe, self.k_fe:] = hess_fere.T
+        hess[self.k_fe:, 0:self.k_fe] = hess_fere
+        hess[self.k_fe:, self.k_fe:] = hess_re
+
+        return hess
+
+
+    def Estep(self, fe_params, cov_re, scale):
+        """
+        The E-step of the EM algorithm.  This is for ML (not REML),
+        but it seems to be good enough to use for REML starting
+        values.
+
+        Parameters
+        ----------
+        fe_params : 1d ndarray
+            The current value of the fixed effect coefficients
+        cov_re : 2d ndarray
+            The current value of the covariance matrix of random
+            effects
+        scale : positive scalar
+            The current value of the error variance
+
+        Returns
+        -------
+        m1x : 1d ndarray
+            sum_groups X'*Z*E[gamma | Y], where X and Z are the fixed
+            and random effects covariates, gamma is the random
+            effects, and Y is the observed data
+        m1y : scalar
+            sum_groups Y'*E[gamma | Y]
+        m2 : 2d ndarray
+            sum_groups E[gamma * gamma' | Y]
+        m2xx : 2d ndarray
+            sum_groups Z'*Z * E[gamma * gamma' | Y]
+        """
+
+        m1x, m1y, m2, m2xx = 0., 0., 0., 0.
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        for k in range(self.n_groups):
+
+            # Get the residuals
+            expval = np.dot(self.exog_li[k], fe_params)
+            resid = self.endog_li[k] - expval
+
+            # Contruct the marginal covariance matrix for this group
+            ex_r = self.exog_re_li[k]
+
+            vr1 = _smw_solve(scale, ex_r, cov_re, cov_re_inv, resid)
+            vr1 = np.dot(ex_r.T, vr1)
+            vr1 = np.dot(cov_re, vr1)
+
+            vr2 = _smw_solve(scale, ex_r, cov_re, cov_re_inv,
+                            self.exog_re_li[k])
+            vr2 = np.dot(vr2, cov_re)
+            vr2 = np.dot(ex_r.T, vr2)
+            vr2 = np.dot(cov_re, vr2)
+
+            rg = np.dot(ex_r, vr1)
+            m1x += np.dot(self.exog_li[k].T, rg)
+            m1y += np.dot(self.endog_li[k].T, rg)
+            egg = cov_re - vr2 + np.outer(vr1, vr1)
+            m2 += egg
+            m2xx += np.dot(np.dot(ex_r.T, ex_r), egg)
+
+        return m1x, m1y, m2, m2xx
+
+
+    def EM(self, fe_params, cov_re, scale, niter_em=10,
+           hist=None):
+        """
+        Run the EM algorithm from a given starting point.  This is for
+        ML (not REML), but it seems to be good enough to use for REML
+        starting values.
+
+        Returns
+        -------
+        fe_params : 1d ndarray
+            The final value of the fixed effects coefficients
+        cov_re : 2d ndarray
+            The final value of the random effects covariance
+            matrix
+        scale : float
+            The final value of the error variance
+
+        Notes
+        -----
+        This uses the parameterization of the likelihood scale*I +
+        Z'*V*Z, note that this differs from the profile likelihood
+        used in the gradient calculations.
+        """
+
+        xxtot = 0.
+        for x in self.exog_li:
+            xxtot += np.dot(x.T, x)
+
+        xytot = 0.
+        for x,y in zip(self.exog_li, self.endog_li):
+            xytot += np.dot(x.T, y)
+
+        pp = []
+        for itr in range(niter_em):
+
+            m1x, m1y, m2, m2xx = self.Estep(fe_params, cov_re, scale)
+
+            fe_params = np.linalg.solve(xxtot, xytot - m1x)
+            cov_re = m2 / self.n_groups
+
+            scale = 0.
+            for x,y in zip(self.exog_li, self.endog_li):
+                scale += np.sum((y - np.dot(x, fe_params))**2)
+            scale -= 2 * m1y
+            scale += 2 * np.dot(fe_params, m1x)
+            scale += np.trace(m2xx)
+            scale /= self.n_totobs
+
+            if hist is not None:
+                hist.append(["EM", fe_params, cov_re, scale])
+
+        return fe_params, cov_re, scale
+
+
+    def get_scale(self, fe_params, cov_re):
+        """
+        Returns the estimated error variance based on given estimates
+        of the slopes and random effects covariance matrix.
+
+        Arguments:
+        ----------
+        fe_params : array-like
+            The regression slope estimates
+        cov_re : 2d array
+            Estimate of the random effects covariance matrix (Psi).
+
+        Returns:
+        --------
+        scale : float
+            The estimated error variance.
+        """
+
+        try:
+            cov_re_inv = np.linalg.inv(cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        qf = 0.
+        for k in range(self.n_groups):
+
+            exog = self.exog_li[k]
+            ex_r = self.exog_re_li[k]
+
+            # The residuals
+            expval = np.dot(exog, fe_params)
+            resid = self.endog_li[k] - expval
+
+            mat = _smw_solve(1., ex_r, cov_re, cov_re_inv, resid)
+            qf += np.dot(resid, mat)
+
+        if self.reml:
+            qf /= (self.n_totobs - self.k_fe)
+        else:
+            qf /= self.n_totobs
+
+        return qf
+
+
+    def _steepest_descent(self, func, params, score, gtol=1e-4,
+                          max_iter=50):
+        """
+        Uses the steepest descent algorithm to minimize a function.
+
+        Arguments:
+        ----------
+        func : function
+            The real-valued function to minimize.
+        params : array-like
+            A point in the domain of `func`, used as the starting
+            point for the iterative minimization.
+        score : function
+            A function implementing the score vector (gradient) of
+            `func`.
+        gtol : non-negative float
+            Return if the sup norm of the score vector is less than
+            this value.
+        max_iter: non-negative integer
+            Return once this number of iterations have occured.
+
+        Returns:
+        --------
+        params_out : array-like
+            The final value of the iterations
+        success : bool
+            True if the final score vector has sup-norm no larger
+            than `gtol`.
+        """
+
+        fval = func(params)
+
+        if max_iter == False:
+            return params, False
+
+        for itr in range(max_iter):
+
+            gro = score(params)
+            gr = gro / np.max(np.abs(gro))
+
+            sl = 1.
+            success = False
+            while sl > 1e-20:
+                params1 = params - sl * gr
+                fval1 = func(params1)
+
+                if fval1 < fval:
+                    params = params1
+                    fval = fval1
+                    success = True
+                    break
+
+                sl /= 2
+
+            if not success:
+                break
+
+        return params, np.max(np.abs(gro)) < gtol
+
+    def _starting_values(self, start_params):
+
+        if type(start_params) in [np.ndarray, pd.Series]:
+            return np.asarray(start_params)
+
+        ix = np.tril_indices(self.k_re)
+        if start_params is None:
+            start_params = {}
+        if "fe" in start_params:
+            fe_params = start_params["fe"]
+        else:
+            fe_params = np.zeros(self.exog.shape[1], dtype=np.float64)
+        if "cov_re_sqrt_unscaled" in start_params:
+            re_params = start_params["cov_re_sqrt_unscaled"]
+            if not self.use_sqrt:
+                mat = np.zeros((self.k_re, self.k_re), dtype=np.float64)
+                mat[ix] = re_params
+                mat = np.dot(mat, mat.T)
+                re_params = mat[ix]
+        elif "cov_re" in start_params:
+            cov_re_unscaled = start_params["cov_re"] / start_params["scale"]
+            if self.use_sqrt:
+                cov_re_sqrt_unscaled = np.linalg.cholesky(cov_re_unscaled)
+                re_params = cov_re_sqrt_unscaled[ix]
+            else:
+                re_params = cov_re_unscaled[ix]
+        else:
+            re_params = np.eye(self.k_re)[ix]
+
+        return np.concatenate((fe_params, re_params))
+
+
+    def fit(self, start_params=None, reml=True, niter_sd=1,
+            niter_em=0, do_cg=True, fe_pen=None, cov_pen=None,
+            free=None, full_output=False, **kwargs):
+        """
+        Fit a linear mixed model to the data.
+
+        Parameters
+        ----------
+        start_params: array-like or dict
+            If array-like, it is a 1d vector containing the starting
+            values in the internal parameterization.  If it is a
+            dictionary, it contains starting values for each component
+            separately.  `start["fe"]` contains starting values for
+            the fixed effects regression slopes.  `start["cov_re"]`
+            contains the covariance matrix of random effects as found
+            in the `cov_re` component of MixedLMResults.  If
+            `start["cov_re"]` is provided, then `start["scale"]` must
+            also be provided (this is the error variance).
+            Alternatively, the random effects may be specified as
+            `start["cov_re_sqrt_unscaled"]`, which is the packed lower
+            triangle of the covariance matrix in the
+            profile parameterization (in this case scale is not used).
+        reml : bool
+            If true, fit according to the REML likelihood, else
+            fit the standard likelihood using ML.
+        niter_sd : integer
+            The number of steepest descent iterations
+        niter_em : non-negative integer
+            The number of EM steps.  The EM steps always
+            preceed steepest descent and conjugate gradient
+            optimization.  The EM algorithm implemented here
+            is for ML estimation.
+        do_cg : bool
+            If True, a conjugate gradient algorithm is
+            used for optimization (following any steepest
+            descent or EM steps).
+        cov_pen : CovariancePenalty object
+            A penalty for the random effects covariance matrix
+        fe_pen : Penalty object
+            A penalty on the fixed effects
+        free : tuple of ndarrays
+            If not `None`, this is a tuple of length 2 containing 2
+            0/1 indicator arrays.  The first element of `free`
+            corresponds to the regression slopes and the second
+            element of `free` corresponds to the random effects
+            covariance matrix (if `use_sqrt` is False) or it square root
+            (if `use_sqrt` is True).  A 1 in either array indicates that
+            the corresponding parameter is estimated, a 0 indicates
+            that it is fixed at its starting value.  One use case if
+            to set free[1] to the identity matrix to estimate a model
+            with independent random effects.
+        full_output : bool
+            If true, attach iteration history to results
+
+        Returns
+        -------
+        A MixedLMResults instance.
+        """
+
+        self.reml = reml
+        self.cov_pen = cov_pen
+        self.fe_pen = fe_pen
+
+        self._set_score_pattern(free)
+
+        # Needed for steepest descent
+        neg_like = lambda x: -self.loglike(x)
+        neg_score = lambda x: -self.score(x)
+
+        if full_output:
+            hist = []
+        else:
+            hist = None
+
+        params_prof = self._starting_values(start_params)
+
+        success = False
+
+        # EM iterations
+        if niter_em > 0:
+            if self.use_sqrt:
+                fe_params, cov_re = self._unpack(params_prof)
+                cov_re = np.dot(cov_re, cov_re.T)
+            else:
+                fe_params, cov_re = self._unpack(params_prof, sym=True)
+            scale = 1.
+            fe_params, cov_re, scale = self.EM(fe_params, cov_re, scale,
+                                             niter_em, hist)
+
+            # Gradient algorithms use a different parameterization
+            # that profiles out sigma^2.
+            if self.use_sqrt:
+                params_prof = self._pack(fe_params, cov_re / scale)
+            else:
+                cov_re_rt = np.linalg.cholesky(cov_re / scale)
+                params_prof = self._pack(fe_params, cov_re_rt)
+
+        # Try up to 10 times to make the optimization work, using
+        # additional steepest descent steps to improve the starting
+        # values.  Usually only one cycle is used.
+        for cycle in range(10):
+
+            # Steepest descent iterations
+            params_prof, success = self._steepest_descent(neg_like,
+                                  params_prof, neg_score,
+                                  max_iter=niter_sd)
+            if success:
+                break
+
+            # Gradient iterations
+            if do_cg:
+                try:
+                    fit_args = dict(kwargs)
+                    fit_args["retall"] = hist is not None
+                    if "disp" not in fit_args:
+                        fit_args["disp"] = False
+                    # Only bfgs seems to work for some reason.
+                    fit_args["method"] = "bfgs"
+                    rslt = super(MixedLM, self).fit(start_params=params_prof, **fit_args)
+                except np.linalg.LinAlgError:
+                    continue
+
+                # The optimization succeeded
+                params_prof = rslt.params
+                success = True
+                if hist is not None:
+                    hist.append(rslt.allvecs)
+                break
+
+        if not success:
+            msg = "Gradient optimization failed."
+            warnings.warn(msg, ConvergenceWarning)
+
+        # Convert to the final parameterization (i.e. undo the square
+        # root transform of the covariance matrix, and the profiling
+        # over the error variance).
+        fe_params, cov_re_ltri = self._unpack(params_prof, sym=False)
+        if self.use_sqrt:
+            cov_re_unscaled = np.dot(cov_re_ltri, cov_re_ltri.T)
+        else:
+            cov_re_unscaled = cov_re_ltri
+        scale = self.get_scale(fe_params, cov_re_unscaled)
+        cov_re = scale * cov_re_unscaled
+
+        if np.min(np.abs(np.diag(cov_re))) < 0.01:
+            msg = "The MLE may be on the boundary of the parameter space."
+            warnings.warn(msg, ConvergenceWarning)
+
+        # Compute the Hessian at the MLE.  Note that this is the
+        # hessian with respet to the random effects covariance matrix
+        # (not its square root).  It is used for obtaining standard
+        # errors, not for optimization.
+        params_hess = self._pack(fe_params, cov_re_unscaled)
+        hess = self.hessian_full(params_hess)
+        if free is not None:
+            ii = np.flatnonzero(self.score_pat)
+            hess1 = hess[ii,:][:,ii]
+            pcov = np.zeros_like(hess)
+            pcov[np.ix_(ii,ii)] = np.linalg.inv(-hess1)
+        else:
+            pcov = np.linalg.inv(-hess)
+
+        # Prepare a results class instance
+        results = MixedLMResults(self, params_prof, pcov / scale)
+        results.fe_params = fe_params
+        results.cov_re = cov_re
+        results.scale = scale
+        results.cov_re_unscaled = cov_re_unscaled
+        results.method = "REML" if self.reml else "ML"
+        results.converged = success
+        results.hist = hist
+        results.reml = self.reml
+        results.cov_pen = self.cov_pen
+        results.likeval = -neg_like(params_prof)
+        results.k_fe = self.k_fe
+        results.k_re = self.k_re
+        results.k_re2 = self.k_re2
+
+        return results
+
+    def _set_score_pattern(self, free):
+        # TODO: could the pattern be set by a formula?
+        if free is not None:
+            pat_slopes = free[0]
+            ix = np.tril_indices(self.k_re)
+            pat_cov_re = free[1][ix]
+            self.score_pat = np.concatenate((pat_slopes, pat_cov_re))
+        else:
+            self.score_pat = np.ones(self.nparams)
+
+
+
+class MixedLMResults(base.LikelihoodModelResults):
+    '''
+    Class to contain results of fitting a linear mixed effects model.
+
+    MixedLMResults inherits from statsmodels.LikelihoodModelResults
+
+    Parameters
+    ----------
+    See statsmodels.LikelihoodModelResults
+
+    Returns
+    -------
+    **Attributes**
+
+    model : class instance
+        Pointer to PHreg model instance that called fit.
+    normalized_cov_params : array
+        The sampling covariance matrix of the estimates
+    fe_params : array
+        The fitted fixed-effects coefficients
+    re_params : array
+        The fitted random-effects covariance matrix
+    bse_fe : array
+        The standard errors of the fitted fixed effects coefficients
+    bse_re : array
+        The standard errors of the fitted random effects covariance
+        matrix
+
+    See Also
+    --------
+    statsmodels.LikelihoodModelResults
+    '''
+
+    def __init__(self, model, params, cov_params):
+
+        super(MixedLMResults, self).__init__(model, params,
+           normalized_cov_params=cov_params)
+
+    def bse_fe(self):
+        """
+        Returns the standard errors of the fixed effect regression
+        coefficients.
+        """
+        p = self.model.exog.shape[1]
+        return np.sqrt(np.diag(self.cov_params())[0:p])
+
+    def bse_re(self):
+        """
+        Returns the standard errors of the variance parameters.  Note
+        that the sampling distribution of variance parameters is
+        strongly skewed unless the sample size is large, so these
+        standard errors may not give meaningful confidence intervals
+        of p-values if used in the usual way.
+        """
+        p = self.model.exog.shape[1]
+        return np.sqrt(self.scale * np.diag(self.cov_params())[p:])
+
+
+    def ranef(self):
+        """
+        Returns posterior means of all random effects.
+
+        Returns:
+        --------
+        ranef_dict : dict
+            A dictionary mapping the distinct values of the `group`
+            variable to the conditional means of the random effects
+            given the data.
+        """
+
+        try:
+            cov_re_inv = np.linalg.inv(self.cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        ranef_dict = {}
+        for k in range(self.model.n_groups):
+
+            endog = self.model.endog_li[k]
+            exog = self.model.exog_li[k]
+            ex_r = self.model.exog_re_li[k]
+            label = self.model.group_labels[k]
+
+            # Get the residuals
+            expval = np.dot(exog, self.fe_params)
+            resid = endog - expval
+
+            vresid = _smw_solve(self.scale, ex_r, self.cov_re,
+                                cov_re_inv, resid)
+
+            ranef_dict[label] = np.dot(self.cov_re,
+                                       np.dot(ex_r.T, vresid))
+
+        return ranef_dict
+
+
+    def ranef_cov(self):
+        """
+        Returns the conditional covariance matrix of the random
+        effects for each group.
+
+        Returns:
+        --------
+        ranef_dict : dict
+            A dictionary mapping the distinct values of the `group`
+            variable to the conditional covariance matrix of the
+            random effects given the data.
+        """
+
+        try:
+            cov_re_inv = np.linalg.inv(self.cov_re)
+        except np.linalg.LinAlgError:
+            cov_re_inv = None
+
+        ranef_dict = {}
+        for k in range(self.model.n_groups):
+
+            endog = self.model.endog_li[k]
+            exog = self.model.exog_li[k]
+            ex_r = self.model.exog_re_li[k]
+            label = self.model.group_labels[k]
+
+            mat1 = np.dot(ex_r, self.cov_re)
+            mat2 = _smw_solve(self.scale, ex_r, self.cov_re, cov_re_inv,
+                             mat1)
+            mat2 = np.dot(mat1.T, mat2)
+
+            ranef_dict[label] = self.cov_re - mat2
+
+        return ranef_dict
+
+
+    def summary(self, yname=None, xname_fe=None, xname_re=None,
+                title=None, alpha=.05):
+        """Summarize the Regression Results
+
+        Parameters
+        -----------
+        yname : string, optional
+            Default is `y`
+        xname_fe : list of strings, optional
+            Fixed effects covariate names
+        xname_re : list of strings, optional
+            Random effects covariate names
+        title : string, optional
+            Title for the top table. If not None, then this replaces
+            the default title
+        alpha : float
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be
+            printed or converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary
+            results
+
+        """
+
+        from statsmodels.iolib import summary2
+        smry = summary2.Summary()
+
+        info = OrderedDict()
+        info["Model:"] = "MixedLM"
+        if yname is None:
+            yname = self.model.endog_names
+        info["No. Observations:"] = str(self.model.n_totobs)
+        info["No. Groups:"] = str(self.model.n_groups)
+
+        gs = np.array([len(x) for x in self.model.endog_li])
+        info["Min. group size:"] = "%.0f" % min(gs)
+        info["Max. group size:"] = "%.0f" % max(gs)
+        info["Mean group size:"] = "%.1f" % np.mean(gs)
+
+        info["Dependent Variable:"] = yname
+        info["Method:"] = self.method
+        info["Scale:"] = self.scale
+        info["Likelihood:"] = self.likeval
+        info["Converged:"] = "Yes" if self.converged else "No"
+        smry.add_dict(info)
+        smry.add_title("Mixed Linear Model Regression Results")
+
+        float_fmt = "%.3f"
+
+        names = list(self.model.exog_names)
+        sdf = np.nan * np.ones((self.k_fe + self.k_re2, 6),
+                               dtype=np.float64)
+
+        # Coefficient estimates
+        sdf[0:self.k_fe, 0] = self.fe_params
+
+        # Standard errors
+        sdf[0:self.k_fe, 1] =\
+                      np.sqrt(np.diag(self.cov_params()[0:self.k_fe]))
+
+        # Z-scores
+        sdf[0:self.k_fe, 2] = sdf[0:self.k_fe, 0] / sdf[0:self.k_fe, 1]
+
+        # p-values
+        sdf[0:self.k_fe, 3] = 2 * norm.cdf(-np.abs(sdf[0:self.k_fe, 2]))
+
+        # Confidence intervals
+        qm = -norm.ppf(alpha / 2)
+        sdf[0:self.k_fe, 4] = sdf[0:self.k_fe, 0] - qm * sdf[0:self.k_fe, 1]
+        sdf[0:self.k_fe, 5] = sdf[0:self.k_fe, 0] + qm * sdf[0:self.k_fe, 1]
+
+        # Names for all pairs of random effects
+        jj = self.k_fe
+        for i in range(self.k_re):
+            for j in range(i + 1):
+                if i == j:
+                    names.append(self.model.exog_re_names[i] + " RE")
+                else:
+                    names.append(self.model.exog_re_names[j] + " x " +
+                                 self.model.exog_re_names[i] + " RE")
+                sdf[jj, 0] = self.cov_re[i, j]
+                sdf[jj, 1] = np.sqrt(self.scale) * self.bse[jj]
+                jj += 1
+
+        sdf = pd.DataFrame(index=names, data=sdf)
+        sdf.columns = ['Coef.', 'Std.Err.', 'z', 'P>|z|',
+                          '[' + str(alpha/2), str(1-alpha/2) + ']']
+        for col in sdf.columns:
+            sdf[col] = [float_fmt % x if np.isfinite(x) else ""
+                        for x in sdf[col]]
+
+        smry.add_df(sdf, align='r')
+
+        return smry
+
+
+    def profile_re(self, re_ix, num_low=5, dist_low=1., num_high=5,
+                   dist_high=1.):
+        """
+        Calculate a series of values along a 1-dimensional profile
+        likelihood.
+
+        Arguments:
+        ----------
+        re_ix : integer
+            The index of the variance parameter for which to construct
+            a profile likelihood.
+        num_low : integer
+            The number of points at which to calculate the likelihood
+            below the MLE of the parameter of interest.
+        dist_low : float
+            The distance below the MLE of the parameter of interest to
+            begin calculating points on the profile likelihood.
+        num_high : integer
+            The number of points at which to calculate the likelihood
+            abov the MLE of the parameter of interest.
+        dist_high : float
+            The distance above the MLE of the parameter of interest to
+            begin calculating points on the profile likelihood.
+
+        Result
+        ------
+        A matrix with two columns.  The first column contains the
+        values to which the parameter of interest is constrained.  The
+        second column contains the corresponding likelihood values.
+        """
+
+        model = self.model
+        p = model.exog.shape[1]
+        pr = model.exog_re.shape[1]
+
+        # Need to permute the variables so that the profiled variable
+        # is first.
+        exog_re_li_save = [x.copy() for x in model.exog_re_li]
+        ix = list(range(pr))
+        ix[0] = re_ix
+        ix[re_ix] = 0
+        for k in range(len(model.exog_re_li)):
+           model.exog_re_li[k] = model.exog_re_li[k][:,ix]
+
+        # Permute the covariance structure to match the permuted data.
+        ru = self.params[p:]
+        ik = np.tril_indices(pr)
+        mat = np.zeros((pr ,pr), dtype=np.float64)
+        mat[ik] = ru
+        mat = np.dot(mat, mat.T)
+        mat = mat[ix,:][:,ix]
+        ix = np.tril_indices(pr)
+        re_params = np.linalg.cholesky(mat)[ix]
+
+        # Define the values to which the parameter of interest will be
+        # constrained.
+        ru0 = re_params[0]
+        left = np.linspace(ru0 - dist_low, ru0, num_low + 1)
+        right = np.linspace(ru0, ru0 + dist_high, num_high+1)[1:]
+        rvalues = np.concatenate((left, right))
+
+        # Indicators of which parameters are free and fixed.
+        free_slopes = np.ones(p, dtype=np.float64)
+        free_cov_re = np.ones((pr, pr), dtype=np.float64)
+        free_cov_re[0] = 0
+
+        start = {"fe": self.fe_params}
+
+        likev = []
+        for x in rvalues:
+            re_params[0] = x
+            start["cov_re_sqrt_unscaled"] = re_params
+            md1 = model.fit(start=start,
+                            free=(free_slopes, free_cov_re),
+                            reml=self.reml, cov_pen=self.cov_pen)
+            likev.append([md1.cov_re[0,0], md1.likeval])
+        likev = np.asarray(likev)
+
+        model.exog_re = exog_re_li_save
+
+        return likev
diff --git a/statsmodels/regression/quantile_regression.py b/statsmodels/regression/quantile_regression.py
index 57b12e8a229..c0e3730cd91 100644
--- a/statsmodels/regression/quantile_regression.py
+++ b/statsmodels/regression/quantile_regression.py
@@ -17,17 +17,20 @@
 Prinoth (christian at prinoth dot name).
 '''
 
+from statsmodels.compat.python import range
 import numpy as np
 import warnings
 import scipy.stats as stats
 from scipy.linalg import pinv
 from scipy.stats import norm
 from statsmodels.tools.tools import chain_dot
+from statsmodels.compat.numpy import np_matrix_rank
 from statsmodels.tools.decorators import cache_readonly
 from statsmodels.regression.linear_model import (RegressionModel,
                                                  RegressionResults,
                                                  RegressionResultsWrapper)
-
+from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
+                                             IterationLimitWarning)
 
 class QuantReg(RegressionModel):
     '''Quantile Regression
@@ -136,11 +139,14 @@ def fit(self, q=.5, vcov='robust', kernel='epa', bandwidth='hsheather',
         endog = self.endog
         exog = self.exog
         nobs = self.nobs
-        rank = self.rank
+        exog_rank = np_matrix_rank(self.exog)
+        self.rank = exog_rank
+        self.df_model = float(self.rank - self.k_constant)
+        self.df_resid = self.nobs - self.rank
         n_iter = 0
         xstar = exog
 
-        beta = np.ones(rank)
+        beta = np.ones(exog_rank)
         # TODO: better start, initial beta is used only for convergence check
 
         # Note the following doesn't work yet,
@@ -180,10 +186,11 @@ def fit(self, q=.5, vcov='robust', kernel='epa', bandwidth='hsheather',
                     if np.all(beta == history['params'][-ii]):
                         cycle = True
                         break
-                warnings.warn("Convergence cycle detected")
+                warnings.warn("Convergence cycle detected", ConvergenceWarning)
 
         if n_iter == max_iter:
-            warnings.warn("Maximum number of iterations (1000) reached.")
+            warnings.warn("Maximum number of iterations (1000) reached.",
+                          IterationLimitWarning)
 
         e = endog - np.dot(exog, beta)
         # Greene (2008, p.407) writes that Stata 6 uses this bandwidth:
@@ -365,12 +372,8 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
         jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
         omni, omnipv = omni_normtest(self.wresid)
 
-        #TODO: reuse condno from somewhere else ?
-        #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog))
-        wexog = self.model.wexog
-        eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog))
-        eigvals = np.sort(eigvals) #in increasing order
-        condno = np.sqrt(eigvals[-1]/eigvals[0])
+        eigvals = self.eigenvals
+        condno = self.condition_number
 
         self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
                           omni=omni, omnipv=omnipv, condno=condno,
@@ -420,13 +423,13 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05):
                           #title="")
 
         #add warnings/notes, added to text format only
-        etext =[]
-        if eigvals[0] < 1e-10:
+        etext = []
+        if eigvals[-1] < 1e-10:
             wstr = "The smallest eigenvalue is %6.3g. This might indicate "
             wstr += "that there are\n"
-            wstr = "strong multicollinearity problems or that the design "
+            wstr += "strong multicollinearity problems or that the design "
             wstr += "matrix is singular."
-            wstr = wstr % eigvals[0]
+            wstr = wstr % eigvals[-1]
             etext.append(wstr)
         elif condno > 1000:  #TODO: what is recommended
             wstr = "The condition number is large, %6.3g. This might "
diff --git a/statsmodels/regression/tests/generate_lasso.py b/statsmodels/regression/tests/generate_lasso.py
new file mode 100644
index 00000000000..da4f5323b49
--- /dev/null
+++ b/statsmodels/regression/tests/generate_lasso.py
@@ -0,0 +1,27 @@
+import numpy as np
+
+"""
+Generate data sets for testing OLS.fit_regularized
+
+After running this script, rerun lasso_r_results.R in R to rebuild the
+results file "glmnet_r_results.py".
+
+Currently only tests OLS.  Our implementation covers GLS, but it's not
+clear if glmnet does.
+"""
+
+n = 300
+p = 5
+
+np.random.seed(83423)
+
+exog = np.random.normal(size=(n,p))
+params = (-1.)**np.arange(p)
+params[::3] = 0
+expval = np.dot(exog, params)
+endog = expval + np.random.normal(size=n)
+data = np.concatenate((endog[:,None], exog), axis=1)
+data = np.around(100*data)
+
+fname = "results/lasso_data.csv"
+np.savetxt(fname, data, fmt="%.0f", delimiter=",")
diff --git a/statsmodels/regression/tests/generate_lme.py b/statsmodels/regression/tests/generate_lme.py
new file mode 100644
index 00000000000..cd02b2f2914
--- /dev/null
+++ b/statsmodels/regression/tests/generate_lme.py
@@ -0,0 +1,76 @@
+"""
+Generate test data sets for lme.
+
+After running this script, run lme_results.R with R
+to update the output.
+"""
+
+import numpy as np
+import os
+
+np.random.seed(348491)
+
+# Number of groups
+ngroup = 100
+
+# Sample size range per group
+n_min = 1
+n_max = 5
+
+dsix = 0
+
+# Number of random effects
+for pr in 1,2:
+
+    re_sd = np.linspace(-0.5, 1.5, pr)
+
+    # Number of fixed effects
+    for pf in 1,2,3:
+
+        # Error standard deviation
+        for sig in 0.5,2:
+
+            params = np.linspace(-1, 1, pf)
+
+            endog = []
+            exog_fe = []
+            exog_re = []
+            groups = []
+            for i in range(ngroup):
+
+                n = np.random.randint(n_min, n_max, 1)
+                x_fe = np.random.normal(size=(n, pf))
+                x_re = np.zeros((n, pr))
+                u = np.linspace(-1, 1, n)
+                for j in range(pr):
+                    x_re[:,j] = u**j
+
+                re = np.random.normal(size=pr) * re_sd
+
+                expval = np.dot(x_fe, params) + np.dot(x_re, re)
+
+                endog.append(expval + sig*np.random.normal(size=n))
+                exog_fe.append(x_fe)
+                exog_re.append(x_re)
+                groups.append(i*np.ones(n))
+
+            endog = np.concatenate(endog)
+            exog_fe = np.concatenate(exog_fe, axis=0)
+            exog_re = np.concatenate(exog_re, axis=0)
+            groups = np.concatenate(groups, axis=0)
+
+            data = np.concatenate((groups[:,None], endog[:,None],
+                                   exog_fe, exog_re), axis=1)
+
+            header = ["groups,endog",] +\
+                     ["exog_fe_%d" % k for k in range(pf)] +\
+                     ["exog_re_%d" % k for k in range(pr)]
+            header = ",".join(header)
+
+            cur_dir = os.path.dirname(os.path.abspath(__file__))
+
+            fname = os.path.join(cur_dir, "results",
+                                 "lme%02d.csv" % dsix)
+            np.savetxt(fname, data, fmt="%.3f", header=header,
+                       delimiter=",", comments="")
+            dsix += 1
diff --git a/statsmodels/regression/tests/glmnet_r_results.py b/statsmodels/regression/tests/glmnet_r_results.py
new file mode 100644
index 00000000000..d3b00c5ea35
--- /dev/null
+++ b/statsmodels/regression/tests/glmnet_r_results.py
@@ -0,0 +1,164 @@
+import numpy as np
+
+rslt_0 = np.array([100,2,0,38.51677,-0.0002828592,-0.01440669])
+
+rslt_1 = np.array([100,2,0,5.991962,-0.00144282,-0.08156386])
+
+rslt_2 = np.array([100,2,0,0.9321552,-0.003400212,-0.2968083])
+
+rslt_3 = np.array([100,2,0.5,0.2352493,0,-0.4099712])
+
+rslt_4 = np.array([100,2,0.5,0.07703354,0,-0.5185569])
+
+rslt_5 = np.array([100,2,0.5,0.02522501,0,-0.5578565])
+
+rslt_6 = np.array([100,2,1,0.1416793,0,-0.4346288])
+
+rslt_7 = np.array([100,2,1,0.04639359,0,-0.530877])
+
+rslt_8 = np.array([100,2,1,0.01829859,0,-0.5592558])
+
+rslt_9 = np.array([100,3,0,38.51677,-0.0002931891,-0.01441805,0.01019003])
+
+rslt_10 = np.array([100,3,0,5.991962,-0.00177573,-0.08193033,0.05812813])
+
+rslt_11 = np.array([100,3,0,0.9321552,-0.007886374,-0.3017527,0.2167204])
+
+rslt_12 = np.array([100,3,0.5,0.2143504,0,-0.4350311,0.2878706])
+
+rslt_13 = np.array([100,3,0.5,0.07019009,0,-0.540186,0.3830582])
+
+rslt_14 = np.array([100,3,0.5,0.02298409,-0.007207869,-0.5779581,0.417658])
+
+rslt_15 = np.array([100,3,1,0.1176247,0,-0.4728138,0.3103276])
+
+rslt_16 = np.array([100,3,1,0.03851677,0,-0.5564642,0.393978])
+
+rslt_17 = np.array([100,3,1,0.01384221,-0.004948496,-0.5824816,0.4202673])
+
+rslt_18 = np.array([100,5,0,38.51677,-0.0002957436,-0.01439344,0.01022347,-0.001361073,0.01156754])
+
+rslt_19 = np.array([100,5,0,5.991962,-0.001844459,-0.08115691,0.05920275,-0.006436703,0.06552873])
+
+rslt_20 = np.array([100,5,0,0.9321552,-0.008226096,-0.2921193,0.2311589,-0.008479325,0.2404427])
+
+rslt_21 = np.array([100,5,0.5,0.2143504,0,-0.4132961,0.3222149,0,0.3262246])
+
+rslt_22 = np.array([100,5,0.5,0.0639546,0,-0.5145012,0.4359585,0,0.4294001])
+
+rslt_23 = np.array([100,5,0.5,0.02094225,-0.00787628,-0.5466386,0.4728884,0.0106367,0.4627422])
+
+rslt_24 = np.array([100,5,1,0.1176247,0,-0.4469856,0.3513112,0,0.3519996])
+
+rslt_25 = np.array([100,5,1,0.03851677,0,-0.5243974,0.4448608,0,0.4370223])
+
+rslt_26 = np.array([100,5,1,0.01261251,-0.00600524,-0.5501652,0.4761259,0.009292623,0.4655236])
+
+rslt_27 = np.array([200,2,0,37.37243,-0.0006197982,-0.01442844])
+
+rslt_28 = np.array([200,2,0,5.813939,-0.002965157,-0.0813082])
+
+rslt_29 = np.array([200,2,0,0.9044607,-0.0046755,-0.2916905])
+
+rslt_30 = np.array([200,2,0.5,0.207982,0,-0.4102587])
+
+rslt_31 = np.array([200,2,0.5,0.06810473,0,-0.5061677])
+
+rslt_32 = np.array([200,2,0.5,0.02230123,0,-0.5404787])
+
+rslt_33 = np.array([200,2,1,0.11413,0,-0.443054])
+
+rslt_34 = np.array([200,2,1,0.03737243,0,-0.5201972])
+
+rslt_35 = np.array([200,2,1,0.01343095,0,-0.544259])
+
+rslt_36 = np.array([200,3,0,37.37243,-0.0006182692,-0.01444479,0.01107838])
+
+rslt_37 = np.array([200,3,0,5.813939,-0.002912734,-0.08183274,0.06300276])
+
+rslt_38 = np.array([200,3,0,0.9044607,-0.003838731,-0.298655,0.2325274])
+
+rslt_39 = np.array([200,3,0.5,0.207982,0,-0.426489,0.3142956])
+
+rslt_40 = np.array([200,3,0.5,0.06810473,0,-0.5287444,0.4093069])
+
+rslt_41 = np.array([200,3,0.5,0.02230123,0,-0.5654985,0.4434813])
+
+rslt_42 = np.array([200,3,1,0.1252575,0,-0.450539,0.3272248])
+
+rslt_43 = np.array([200,3,1,0.04101619,0,-0.540326,0.4170119])
+
+rslt_44 = np.array([200,3,1,0.01343095,0,-0.5697273,0.4464131])
+
+rslt_45 = np.array([200,5,0,37.37243,-0.0006213086,-0.01441334,0.01110832,-0.00159415,0.01237059])
+
+rslt_46 = np.array([200,5,0,5.813939,-0.003007826,-0.08084357,0.06395108,-0.008286656,0.06957285])
+
+rslt_47 = np.array([200,5,0,0.9044607,-0.004970054,-0.2862855,0.2446989,-0.02121838,0.2488685])
+
+rslt_48 = np.array([200,5,0.5,0.207982,0,-0.398901,0.3436805,0,0.3354236])
+
+rslt_49 = np.array([200,5,0.5,0.06810473,0,-0.4912754,0.4492343,0,0.4280285])
+
+rslt_50 = np.array([200,5,0.5,0.02230123,0,-0.5240797,0.4869532,-0.007872715,0.4611676])
+
+rslt_51 = np.array([200,5,1,0.11413,0,-0.4298238,0.3738058,0,0.3604706])
+
+rslt_52 = np.array([200,5,1,0.03737243,0,-0.5044899,0.4632316,0,0.4395307])
+
+rslt_53 = np.array([200,5,1,0.01343095,0,-0.5276608,0.4907892,-0.005343287,0.4641237])
+
+rslt_54 = np.array([300,2,0,34.28346,-0.0003952878,-0.01440603])
+
+rslt_55 = np.array([300,2,0,5.333396,-0.001501322,-0.08029644])
+
+rslt_56 = np.array([300,2,0,0.8297037,0.002123792,-0.2786158])
+
+rslt_57 = np.array([300,2,0.5,0.1907915,0,-0.3787717])
+
+rslt_58 = np.array([300,2,0.5,0.06247562,0,-0.4648646])
+
+rslt_59 = np.array([300,2,0.5,0.02045795,0.008282189,-0.4960003])
+
+rslt_60 = np.array([300,2,1,0.1046967,0,-0.4057543])
+
+rslt_61 = np.array([300,2,1,0.03428346,0,-0.476403])
+
+rslt_62 = np.array([300,2,1,0.01232084,0.006463487,-0.4988773])
+
+rslt_63 = np.array([300,3,0,34.28346,-0.000389794,-0.01441521,0.01276503])
+
+rslt_64 = np.array([300,3,0,5.333396,-0.001327446,-0.08058424,0.07142295])
+
+rslt_65 = np.array([300,3,0,0.8297037,0.004333533,-0.2821765,0.2505531])
+
+rslt_66 = np.array([300,3,0.5,0.2093935,0,-0.3745606,0.3227598])
+
+rslt_67 = np.array([300,3,0.5,0.06856692,0,-0.4707627,0.4155033])
+
+rslt_68 = np.array([300,3,0.5,0.02045795,0.01586083,-0.5078635,0.4505092])
+
+rslt_69 = np.array([300,3,1,0.1149045,0,-0.4043541,0.347238])
+
+rslt_70 = np.array([300,3,1,0.03762605,0,-0.483917,0.4268009])
+
+rslt_71 = np.array([300,3,1,0.01232084,0.01417268,-0.5109371,0.4530943])
+
+rslt_72 = np.array([300,5,0,34.28346,-0.0003887545,-0.01441065,0.01279253,-0.001336729,0.01332977])
+
+rslt_73 = np.array([300,5,0,5.333396,-0.001294265,-0.08044256,0.07228947,-0.006888477,0.07488032])
+
+rslt_74 = np.array([300,5,0,0.8297037,0.00476926,-0.2804704,0.2614478,-0.01761104,0.2661808])
+
+rslt_75 = np.array([300,5,0.5,0.2093935,0,-0.3705814,0.347887,0,0.351998])
+
+rslt_76 = np.array([300,5,0.5,0.06856692,0,-0.465371,0.449883,0,0.4508697])
+
+rslt_77 = np.array([300,5,0.5,0.02045795,0.01744528,-0.5021737,0.4881574,-0.007930221,0.4878982])
+
+rslt_78 = np.array([300,5,1,0.1149045,0,-0.3996701,0.3772643,0,0.3807041])
+
+rslt_79 = np.array([300,5,1,0.03762605,0,-0.4782089,0.4633918,0,0.4639368])
+
+rslt_80 = np.array([300,5,1,0.01232084,0.01578941,-0.5051176,0.4915133,-0.005703398,0.4910846])
+
diff --git a/statsmodels/regression/tests/lasso_r_results.R b/statsmodels/regression/tests/lasso_r_results.R
new file mode 100644
index 00000000000..8d7e3dd6e79
--- /dev/null
+++ b/statsmodels/regression/tests/lasso_r_results.R
@@ -0,0 +1,42 @@
+library(glmnet)
+library(R2nparray)
+
+# Run the glmnet lasso (elastic net) on all the test data sets
+
+data = read.csv("results/lasso_data.csv", header=FALSE)
+
+ik = 0
+rslt = list()
+
+for (n in c(100, 200, 300)) {
+    for (p in c(2, 3, 5)) {
+
+        endog = data[1:n, 1]
+        exog = data[1:n, 2:(p+1)]
+        exog = as.matrix(exog)
+
+        endog = (endog - mean(endog)) / sd(endog)
+        for (k in 1:p) {
+            exog[,k] = exog[,k] - mean(exog[,k])
+            exog[,k] = exog[,k] / sd(exog[,k])
+        }
+
+        for (alpha in c(0, 0.5, 1)) {
+
+            fit = glmnet(exog, endog, intercept=FALSE, standardize=FALSE, alpha=alpha)
+            ii = length(fit$lambda) * c(0.3, 0.5, 0.7)
+            ii = round(ii)
+
+            for (q in c(0.3, 0.5, 0.7)) {
+                ii = round(q * length(fit$lambda))
+                coefs = coef(fit, s=fit$lambda[ii])
+                coefs = coefs[2:length(coefs)]
+                rname = sprintf("rslt_%d", ik)
+                ik = ik + 1
+                rslt[[rname]] = c(n, p, alpha, fit$lambda[ii], coefs)
+            }
+        }
+    }
+}
+
+R2nparray(rslt, fname="glmnet_r_results.py")
diff --git a/statsmodels/regression/tests/lme_examples.R b/statsmodels/regression/tests/lme_examples.R
new file mode 100644
index 00000000000..e92a617c5be
--- /dev/null
+++ b/statsmodels/regression/tests/lme_examples.R
@@ -0,0 +1,6 @@
+library(lme4)
+library(MASS)
+
+md = lmer(size ~ Time + (1 | tree), Sitka)
+
+md = lmer(size ~ Time + (1 + Time | tree), Sitka)
diff --git a/statsmodels/regression/tests/lme_r_results.py b/statsmodels/regression/tests/lme_r_results.py
new file mode 100644
index 00000000000..9c81f541c80
--- /dev/null
+++ b/statsmodels/regression/tests/lme_r_results.py
@@ -0,0 +1,458 @@
+import numpy as np
+
+coef_ml_drf_0 = np.array([-0.9887517])
+
+vcov_ml_drf_0 = np.array([0.001317148]).reshape(1,1, order='F')
+
+cov_re_ml_drf_0 = np.array([0.2522485]).reshape(1,1, order='F')
+
+scale_ml_drf_0 = np.array([0.2718486])
+
+loglike_ml_drf_0 = np.array([-240.1254])
+
+ranef_mean_ml_drf_0 = np.array([0.04977167])
+
+ranef_condvar_ml_drf_0 = np.array([0.130841])
+
+coef_reml_drf_0 = np.array([-0.9887533])
+
+vcov_reml_drf_0 = np.array([0.001323559]).reshape(1,1, order='F')
+
+cov_re_reml_drf_0 = np.array([0.2524129]).reshape(1,1, order='F')
+
+scale_reml_drf_0 = np.array([0.2733467])
+
+loglike_reml_drf_0 = np.array([-242.5214])
+
+ranef_mean_reml_drf_0 = np.array([0.04964696])
+
+ranef_condvar_reml_drf_0 = np.array([0.1312315])
+
+coef_ml_drf_1 = np.array([-0.9115929])
+
+vcov_ml_drf_1 = np.array([0.01340632]).reshape(1,1, order='F')
+
+cov_re_ml_drf_1 = np.array([0]).reshape(1,1, order='F')
+
+scale_ml_drf_1 = np.array([4.050921])
+
+loglike_ml_drf_1 = np.array([-538.0763])
+
+ranef_mean_ml_drf_1 = np.array([0])
+
+ranef_condvar_ml_drf_1 = np.array([0])
+
+coef_reml_drf_1 = np.array([-0.9115929])
+
+vcov_reml_drf_1 = np.array([0.01345931]).reshape(1,1, order='F')
+
+cov_re_reml_drf_1 = np.array([2.839777e-14]).reshape(1,1, order='F')
+
+scale_reml_drf_1 = np.array([4.066932])
+
+loglike_reml_drf_1 = np.array([-539.3124])
+
+ranef_mean_reml_drf_1 = np.array([2.424384e-14])
+
+ranef_condvar_reml_drf_1 = np.array([2.839777e-14])
+
+coef_ml_drf_2 = np.array([-1.012044,0.9789052])
+
+vcov_ml_drf_2 = np.array([0.00117849,1.458744e-05,1.458744e-05,0.001054926]).reshape(2,2, order='F')
+
+cov_re_ml_drf_2 = np.array([0.1596058]).reshape(1,1, order='F')
+
+scale_ml_drf_2 = np.array([0.2129146])
+
+loglike_ml_drf_2 = np.array([-200.319])
+
+ranef_mean_ml_drf_2 = np.array([0.3197174])
+
+ranef_condvar_ml_drf_2 = np.array([0.09122291])
+
+coef_reml_drf_2 = np.array([-1.012137,0.9790792])
+
+vcov_reml_drf_2 = np.array([0.001190455,1.482666e-05,1.482666e-05,0.001066002]).reshape(2,2, order='F')
+
+cov_re_reml_drf_2 = np.array([0.1595015]).reshape(1,1, order='F')
+
+scale_reml_drf_2 = np.array([0.2154276])
+
+loglike_reml_drf_2 = np.array([-205.275])
+
+ranef_mean_reml_drf_2 = np.array([0.3172978])
+
+ranef_condvar_reml_drf_2 = np.array([0.09164674])
+
+coef_ml_drf_3 = np.array([-1.028053,0.8602685])
+
+vcov_ml_drf_3 = np.array([0.01398831,0.001592619,0.001592619,0.01602274]).reshape(2,2, order='F')
+
+cov_re_ml_drf_3 = np.array([0.8130996]).reshape(1,1, order='F')
+
+scale_ml_drf_3 = np.array([3.100447])
+
+loglike_ml_drf_3 = np.array([-477.1707])
+
+ranef_mean_ml_drf_3 = np.array([-0.2641747])
+
+ranef_condvar_ml_drf_3 = np.array([0.6441656])
+
+coef_reml_drf_3 = np.array([-1.027583,0.8605714])
+
+vcov_reml_drf_3 = np.array([0.01411922,0.001607343,0.001607343,0.01617574]).reshape(2,2, order='F')
+
+cov_re_reml_drf_3 = np.array([0.8117898]).reshape(1,1, order='F')
+
+scale_reml_drf_3 = np.array([3.13369])
+
+loglike_reml_drf_3 = np.array([-479.5354])
+
+ranef_mean_reml_drf_3 = np.array([-0.2614875])
+
+ranef_condvar_reml_drf_3 = np.array([0.6447625])
+
+coef_ml_drf_4 = np.array([-1.005151,-0.003657404,1.054786])
+
+vcov_ml_drf_4 = np.array([0.001190639,-5.327162e-05,5.992985e-05,-5.327162e-05,0.001460303,-2.662532e-05,5.992985e-05,-2.662532e-05,0.00148609]).reshape(3,3, order='F')
+
+cov_re_ml_drf_4 = np.array([0.1703249]).reshape(1,1, order='F')
+
+scale_ml_drf_4 = np.array([0.251763])
+
+loglike_ml_drf_4 = np.array([-231.6389])
+
+ranef_mean_ml_drf_4 = np.array([-0.2063164])
+
+ranef_condvar_ml_drf_4 = np.array([0.0459578])
+
+coef_reml_drf_4 = np.array([-1.005067,-0.003496032,1.054666])
+
+vcov_reml_drf_4 = np.array([0.001206925,-5.4182e-05,6.073475e-05,-5.4182e-05,0.001479871,-2.723494e-05,6.073475e-05,-2.723494e-05,0.001506198]).reshape(3,3, order='F')
+
+cov_re_reml_drf_4 = np.array([0.1705659]).reshape(1,1, order='F')
+
+scale_reml_drf_4 = np.array([0.2556394])
+
+loglike_reml_drf_4 = np.array([-238.761])
+
+ranef_mean_reml_drf_4 = np.array([-0.2055303])
+
+ranef_condvar_reml_drf_4 = np.array([0.04649027])
+
+coef_ml_drf_5 = np.array([-0.8949725,0.08141558,1.052529])
+
+vcov_ml_drf_5 = np.array([0.01677563,0.0008077524,-0.001255011,0.0008077524,0.01719346,0.0009266736,-0.001255011,0.0009266736,0.01608435]).reshape(3,3, order='F')
+
+cov_re_ml_drf_5 = np.array([0.3444677]).reshape(1,1, order='F')
+
+scale_ml_drf_5 = np.array([4.103944])
+
+loglike_ml_drf_5 = np.array([-579.4568])
+
+ranef_mean_ml_drf_5 = np.array([0.08254713])
+
+ranef_condvar_ml_drf_5 = np.array([0.3177935])
+
+coef_reml_drf_5 = np.array([-0.8946164,0.08134261,1.052486])
+
+vcov_reml_drf_5 = np.array([0.0169698,0.0008162714,-0.001268635,0.0008162714,0.01739219,0.0009345538,-0.001268635,0.0009345538,0.01627074]).reshape(3,3, order='F')
+
+cov_re_reml_drf_5 = np.array([0.3420993]).reshape(1,1, order='F')
+
+scale_reml_drf_5 = np.array([4.155737])
+
+loglike_reml_drf_5 = np.array([-582.8377])
+
+ranef_mean_reml_drf_5 = np.array([0.08111449])
+
+ranef_condvar_reml_drf_5 = np.array([0.3160797])
+
+coef_ml_drf_6 = np.array([-0.8885425])
+
+vcov_ml_drf_6 = np.array([0.002443738]).reshape(1,1, order='F')
+
+cov_re_ml_drf_6 = np.array([0.2595201,0.04591071,0.04591071,2.204612]).reshape(2,2, order='F')
+
+scale_ml_drf_6 = np.array([0.243133])
+
+loglike_ml_drf_6 = np.array([-382.551])
+
+ranef_mean_ml_drf_6 = np.array([-0.0597406,0.6037288])
+
+ranef_condvar_ml_drf_6 = np.array([0.2420741,0.2222169,0.2222169,0.4228908]).reshape(2,2, order='F')
+
+coef_reml_drf_6 = np.array([-0.8883881])
+
+vcov_reml_drf_6 = np.array([0.002461581]).reshape(1,1, order='F')
+
+cov_re_reml_drf_6 = np.array([0.2595767,0.04590012,0.04590012,2.204822]).reshape(2,2, order='F')
+
+scale_reml_drf_6 = np.array([0.2453537])
+
+loglike_reml_drf_6 = np.array([-384.6373])
+
+ranef_mean_reml_drf_6 = np.array([-0.05969892,0.6031793])
+
+ranef_condvar_reml_drf_6 = np.array([0.2421365,0.2221108,0.2221108,0.4244443]).reshape(2,2, order='F')
+
+coef_ml_irf_6 = np.array([-0.8874992])
+
+vcov_ml_irf_6 = np.array([0.002445505]).reshape(1,1, order='F')
+
+cov_re_ml_irf_6 = np.array([0.2587624,0,0,2.188653]).reshape(2,2, order='F')
+
+scale_ml_irf_6 = np.array([0.2432694])
+
+loglike_ml_irf_6 = np.array([-382.6581])
+
+coef_reml_irf_6 = np.array([-0.8873394])
+
+vcov_reml_irf_6 = np.array([0.002463375]).reshape(1,1, order='F')
+
+cov_re_reml_irf_6 = np.array([0.2588157,0,0,2.188876]).reshape(2,2, order='F')
+
+scale_reml_irf_6 = np.array([0.2454935])
+
+loglike_reml_irf_6 = np.array([-384.7441])
+
+coef_ml_drf_7 = np.array([-0.9645281])
+
+vcov_ml_drf_7 = np.array([0.01994]).reshape(1,1, order='F')
+
+cov_re_ml_drf_7 = np.array([0.2051329,0.0734377,0.0734377,3.285381]).reshape(2,2, order='F')
+
+scale_ml_drf_7 = np.array([3.423247])
+
+loglike_ml_drf_7 = np.array([-587.7101])
+
+ranef_mean_ml_drf_7 = np.array([0.07007965,-0.2920284])
+
+ranef_condvar_ml_drf_7 = np.array([0.1823183,0.02247519,0.02247519,1.125011]).reshape(2,2, order='F')
+
+coef_reml_drf_7 = np.array([-0.9647862])
+
+vcov_reml_drf_7 = np.array([0.02002546]).reshape(1,1, order='F')
+
+cov_re_reml_drf_7 = np.array([0.2056226,0.0726139,0.0726139,3.2876]).reshape(2,2, order='F')
+
+scale_reml_drf_7 = np.array([3.440244])
+
+loglike_reml_drf_7 = np.array([-588.7476])
+
+ranef_mean_reml_drf_7 = np.array([0.07000628,-0.2916737])
+
+ranef_condvar_reml_drf_7 = np.array([0.1828266,0.02229138,0.02229138,1.128947]).reshape(2,2, order='F')
+
+coef_ml_irf_7 = np.array([-0.9665524])
+
+vcov_ml_irf_7 = np.array([0.01998144]).reshape(1,1, order='F')
+
+cov_re_ml_irf_7 = np.array([0.2021561,0,0,3.270735]).reshape(2,2, order='F')
+
+scale_ml_irf_7 = np.array([3.423186])
+
+loglike_ml_irf_7 = np.array([-587.7456])
+
+coef_reml_irf_7 = np.array([-0.9667854])
+
+vcov_reml_irf_7 = np.array([0.02006657]).reshape(1,1, order='F')
+
+cov_re_reml_irf_7 = np.array([0.2026938,0,0,3.273129]).reshape(2,2, order='F')
+
+scale_reml_irf_7 = np.array([3.440197])
+
+loglike_reml_irf_7 = np.array([-588.782])
+
+coef_ml_drf_8 = np.array([-1.083882,0.8955623])
+
+vcov_ml_drf_8 = np.array([0.002491643,0.0001693531,0.0001693531,0.00253309]).reshape(2,2, order='F')
+
+cov_re_ml_drf_8 = np.array([0.1506188,0.126091,0.126091,2.485462]).reshape(2,2, order='F')
+
+scale_ml_drf_8 = np.array([0.2586519])
+
+loglike_ml_drf_8 = np.array([-363.6234])
+
+ranef_mean_ml_drf_8 = np.array([0.2852326,-0.5047804])
+
+ranef_condvar_ml_drf_8 = np.array([0.05400391,0.002330104,0.002330104,0.122761]).reshape(2,2, order='F')
+
+coef_reml_drf_8 = np.array([-1.083938,0.8956893])
+
+vcov_reml_drf_8 = np.array([0.002528969,0.0001712206,0.0001712206,0.002573335]).reshape(2,2, order='F')
+
+cov_re_reml_drf_8 = np.array([0.1505098,0.1256311,0.1256311,2.484219]).reshape(2,2, order='F')
+
+scale_reml_drf_8 = np.array([0.2635901])
+
+loglike_reml_drf_8 = np.array([-367.7667])
+
+ranef_mean_reml_drf_8 = np.array([0.2829798,-0.5042857])
+
+ranef_condvar_reml_drf_8 = np.array([0.05463632,0.002393538,0.002393538,0.1249828]).reshape(2,2, order='F')
+
+coef_ml_irf_8 = np.array([-1.079481,0.898216])
+
+vcov_ml_irf_8 = np.array([0.002511536,0.0001812511,0.0001812511,0.002573405]).reshape(2,2, order='F')
+
+cov_re_ml_irf_8 = np.array([0.1498568,0,0,2.403849]).reshape(2,2, order='F')
+
+scale_ml_irf_8 = np.array([0.2605245])
+
+loglike_ml_irf_8 = np.array([-364.4824])
+
+coef_reml_irf_8 = np.array([-1.07952,0.8983678])
+
+vcov_reml_irf_8 = np.array([0.002549354,0.0001833386,0.0001833386,0.002614672]).reshape(2,2, order='F')
+
+cov_re_reml_irf_8 = np.array([0.1497193,0,0,2.403076]).reshape(2,2, order='F')
+
+scale_reml_irf_8 = np.array([0.2655558])
+
+loglike_reml_irf_8 = np.array([-368.6141])
+
+coef_ml_drf_9 = np.array([-1.272698,0.8617471])
+
+vcov_ml_drf_9 = np.array([0.02208544,0.001527479,0.001527479,0.02597528]).reshape(2,2, order='F')
+
+cov_re_ml_drf_9 = np.array([0.510175,0.08826114,0.08826114,3.342888]).reshape(2,2, order='F')
+
+scale_ml_drf_9 = np.array([3.722112])
+
+loglike_ml_drf_9 = np.array([-589.8274])
+
+ranef_mean_ml_drf_9 = np.array([0.03253644,0.224043])
+
+ranef_condvar_ml_drf_9 = np.array([0.3994872,0.02478884,0.02478884,1.195077]).reshape(2,2, order='F')
+
+coef_reml_drf_9 = np.array([-1.272483,0.861814])
+
+vcov_reml_drf_9 = np.array([0.02228589,0.001535598,0.001535598,0.0262125]).reshape(2,2, order='F')
+
+cov_re_reml_drf_9 = np.array([0.5123204,0.08897376,0.08897376,3.341722]).reshape(2,2, order='F')
+
+scale_reml_drf_9 = np.array([3.764058])
+
+loglike_reml_drf_9 = np.array([-591.7188])
+
+ranef_mean_reml_drf_9 = np.array([0.03239688,0.2230525])
+
+ranef_condvar_reml_drf_9 = np.array([0.401762,0.02521271,0.02521271,1.203536]).reshape(2,2, order='F')
+
+coef_ml_irf_9 = np.array([-1.277018,0.86395])
+
+vcov_ml_irf_9 = np.array([0.02205706,0.001509887,0.001509887,0.02599941]).reshape(2,2, order='F')
+
+cov_re_ml_irf_9 = np.array([0.5086816,0,0,3.312757]).reshape(2,2, order='F')
+
+scale_ml_irf_9 = np.array([3.72105])
+
+loglike_ml_irf_9 = np.array([-589.8628])
+
+coef_reml_irf_9 = np.array([-1.276822,0.8640243])
+
+vcov_reml_irf_9 = np.array([0.02225705,0.001517774,0.001517774,0.02623682]).reshape(2,2, order='F')
+
+cov_re_reml_irf_9 = np.array([0.5107725,0,0,3.31152]).reshape(2,2, order='F')
+
+scale_reml_irf_9 = np.array([3.762967])
+
+loglike_reml_irf_9 = np.array([-591.7543])
+
+coef_ml_drf_10 = np.array([-0.9419566,-0.02359824,1.085796])
+
+vcov_ml_drf_10 = np.array([0.001963536,-0.0003221793,0.0001950186,-0.0003221793,0.002534251,0.0004107718,0.0001950186,0.0004107718,0.002580736]).reshape(3,3, order='F')
+
+cov_re_ml_drf_10 = np.array([0.2040541,0.09038325,0.09038325,2.218903]).reshape(2,2, order='F')
+
+scale_ml_drf_10 = np.array([0.2558286])
+
+loglike_ml_drf_10 = np.array([-379.6591])
+
+ranef_mean_ml_drf_10 = np.array([0.03876325,-0.725853])
+
+ranef_condvar_ml_drf_10 = np.array([0.1988816,0.1872403,0.1872403,0.4052274]).reshape(2,2, order='F')
+
+coef_reml_drf_10 = np.array([-0.9426367,-0.02336203,1.085733])
+
+vcov_reml_drf_10 = np.array([0.002011348,-0.0003300612,0.0002002948,-0.0003300612,0.002589149,0.000418987,0.0002002948,0.000418987,0.002637433]).reshape(3,3, order='F')
+
+cov_re_reml_drf_10 = np.array([0.2034827,0.09063836,0.09063836,2.219191]).reshape(2,2, order='F')
+
+scale_reml_drf_10 = np.array([0.2630213])
+
+loglike_reml_drf_10 = np.array([-386.0008])
+
+ranef_mean_reml_drf_10 = np.array([0.03838686,-0.7240812])
+
+ranef_condvar_reml_drf_10 = np.array([0.1983981,0.1865469,0.1865469,0.4100937]).reshape(2,2, order='F')
+
+coef_ml_irf_10 = np.array([-0.9441033,-0.01755913,1.088568])
+
+vcov_ml_irf_10 = np.array([0.001960114,-0.0003215658,0.0001944005,-0.0003215658,0.00253441,0.0004061179,0.0001944005,0.0004061179,0.002589158]).reshape(3,3, order='F')
+
+cov_re_ml_irf_10 = np.array([0.2032228,0,0,2.192893]).reshape(2,2, order='F')
+
+scale_ml_irf_10 = np.array([0.2553399])
+
+loglike_ml_irf_10 = np.array([-380.162])
+
+coef_reml_irf_10 = np.array([-0.9448257,-0.01722993,1.088557])
+
+vcov_reml_irf_10 = np.array([0.00200783,-0.0003294349,0.0001996613,-0.0003294349,0.00258937,0.0004141667,0.0001996613,0.0004141667,0.002646242]).reshape(3,3, order='F')
+
+cov_re_reml_irf_10 = np.array([0.2026653,0,0,2.193124]).reshape(2,2, order='F')
+
+scale_reml_irf_10 = np.array([0.2625147])
+
+loglike_reml_irf_10 = np.array([-386.5024])
+
+coef_ml_drf_11 = np.array([-1.36971,0.1596278,0.8588724])
+
+vcov_ml_drf_11 = np.array([0.0232326,0.00172214,0.002275343,0.00172214,0.02318941,0.0004755663,0.002275343,0.0004755663,0.02123474]).reshape(3,3, order='F')
+
+cov_re_ml_drf_11 = np.array([0.3719096,0.332198,0.332198,1.120588]).reshape(2,2, order='F')
+
+scale_ml_drf_11 = np.array([4.849781])
+
+loglike_ml_drf_11 = np.array([-601.6432])
+
+ranef_mean_ml_drf_11 = np.array([-0.4256917,-0.3907759])
+
+ranef_condvar_ml_drf_11 = np.array([0.2987928,0.1992074,0.1992074,0.7477486]).reshape(2,2, order='F')
+
+coef_reml_drf_11 = np.array([-1.370236,0.1597671,0.8585994])
+
+vcov_reml_drf_11 = np.array([0.02351795,0.001749756,0.002301599,0.001749756,0.02346869,0.0004785668,0.002301599,0.0004785668,0.02149093]).reshape(3,3, order='F')
+
+cov_re_reml_drf_11 = np.array([0.3680346,0.3324419,0.3324419,1.118623]).reshape(2,2, order='F')
+
+scale_reml_drf_11 = np.array([4.922222])
+
+loglike_reml_drf_11 = np.array([-604.5746])
+
+ranef_mean_reml_drf_11 = np.array([-0.4168539,-0.3879533])
+
+ranef_condvar_reml_drf_11 = np.array([0.2965372,0.2010191,0.2010191,0.7503986]).reshape(2,2, order='F')
+
+coef_ml_irf_11 = np.array([-1.370117,0.1414964,0.8466083])
+
+vcov_ml_irf_11 = np.array([0.02319951,0.001705996,0.002265252,0.001705996,0.02345623,0.000514879,0.002265252,0.000514879,0.02153162]).reshape(3,3, order='F')
+
+cov_re_ml_irf_11 = np.array([0.4004789,0,0,1.108087]).reshape(2,2, order='F')
+
+scale_ml_irf_11 = np.array([4.78776])
+
+loglike_ml_irf_11 = np.array([-602.308])
+
+coef_reml_irf_11 = np.array([-1.370663,0.1417561,0.8464232])
+
+vcov_reml_irf_11 = np.array([0.02348548,0.001734072,0.002291519,0.001734072,0.02373715,0.0005177618,0.002291519,0.0005177618,0.02178966]).reshape(3,3, order='F')
+
+cov_re_reml_irf_11 = np.array([0.3966454,0,0,1.106551]).reshape(2,2, order='F')
+
+scale_reml_irf_11 = np.array([4.860342])
+
+loglike_reml_irf_11 = np.array([-605.2274])
+
diff --git a/statsmodels/regression/tests/lme_results.R b/statsmodels/regression/tests/lme_results.R
new file mode 100644
index 00000000000..3aef78a6522
--- /dev/null
+++ b/statsmodels/regression/tests/lme_results.R
@@ -0,0 +1,90 @@
+library(lme4)
+library(R2nparray)
+
+files = list.files(path="results", pattern="lme...csv")
+
+rslt = list()
+
+# Check the code with a sequence of data files with different
+# dimensions and random effects structures.
+for (file in files) {
+
+    # Fit a model with independent random effects (irf=TRUE) or
+    # dependent random effects (irf=FALSE).
+    for (irf in c(FALSE, TRUE)) {
+
+        if (irf) {
+            rf = "irf"
+        } else {
+            rf = "drf"
+        }
+
+        # Use REML or ML estimation
+        for (reml in c(FALSE, TRUE)) {
+
+            if (reml) {
+                meth = "reml"
+            } else {
+                meth = "ml"
+            }
+
+            data = read.csv(paste("results", file, sep="/"))
+
+            exog_fe_ix = grep("exog_fe", names(data))
+            exog_re_ix = grep("exog_re", names(data))
+
+            # No need to check independent random effects when there is
+            # only one of them.
+            if (irf & (length(exog_re_ix) == 1)) {
+                next
+            }
+
+            pr = length(exog_re_ix)
+            fml_fe = paste(names(data)[exog_fe_ix], collapse="+")
+
+            if (irf) {
+                st = NULL
+                for (ik in exog_re_ix) {
+                    st[length(st) + 1] = sprintf("(0 + %s | groups)", names(data)[ik])
+                }
+                fml_re = paste(st, collapse="+")
+
+                fml = sprintf("endog ~ 0 + %s + %s", fml_fe, fml_re)
+            } else {
+                fml_re = paste(names(data)[exog_re_ix], collapse="+")
+                fml = sprintf("endog ~ 0 + %s + (0 + %s | groups)", fml_fe, fml_re)
+            }
+
+            md = lmer(as.formula(fml), data=data, REML=reml)
+
+            ds_ix = as.integer(substr(file, 4, 6))
+            rslt[[sprintf("coef_%s_%s_%d", meth, rf, ds_ix)]] = as.vector(fixef(md))
+            rslt[[sprintf("vcov_%s_%s_%d", meth, rf, ds_ix)]] = as.matrix(vcov(md))
+            if (irf) {
+                rev = NULL
+                for (k in 1:length(exog_re_ix)) {
+                    rev[k] = as.numeric(VarCorr(md)[[k]])
+                }
+                rev = diag(rev)
+                rslt[[sprintf("cov_re_%s_%s_%d", meth, rf, ds_ix)]] = rev
+            } else {
+                rslt[[sprintf("cov_re_%s_%s_%d", meth, rf, ds_ix)]] = array(as.numeric(VarCorr(md)$groups),
+                        c(pr, pr))
+            }
+            rslt[[sprintf("scale_%s_%s_%d", meth, rf, ds_ix)]] = attr(VarCorr(md), "sc")^2
+            rslt[[sprintf("loglike_%s_%s_%d", meth, rf, ds_ix)]] = as.numeric(logLik(md))
+
+            # Apparently lmer does not support these things when the random effects
+            # are independent.
+            if (!irf) {
+                reo = ranef(md, condVar=TRUE)
+                re = as.matrix(reo$groups)
+                condvar = attr(reo$groups, "postVar")
+                rslt[[sprintf("ranef_mean_%s_%s_%d", meth, rf, ds_ix)]] = re[1,]
+                rslt[[sprintf("ranef_condvar_%s_%s_%d", meth, rf, ds_ix)]] = condvar[,,1]
+            }
+        }
+    }
+}
+
+R2nparray(rslt, fname="lme_r_results.py")
diff --git a/statsmodels/regression/tests/results/lasso_data.csv b/statsmodels/regression/tests/results/lasso_data.csv
new file mode 100644
index 00000000000..12e133f5380
--- /dev/null
+++ b/statsmodels/regression/tests/results/lasso_data.csv
@@ -0,0 +1,300 @@
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diff --git a/statsmodels/regression/tests/results/lme02.csv b/statsmodels/regression/tests/results/lme02.csv
new file mode 100644
index 00000000000..eb277b1ec78
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diff --git a/statsmodels/regression/tests/results/lme03.csv b/statsmodels/regression/tests/results/lme03.csv
new file mode 100644
index 00000000000..a55bf82b982
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diff --git a/statsmodels/regression/tests/results/lme04.csv b/statsmodels/regression/tests/results/lme04.csv
new file mode 100644
index 00000000000..a222e2bd477
--- /dev/null
+++ b/statsmodels/regression/tests/results/lme04.csv
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diff --git a/statsmodels/regression/tests/results/lme05.csv b/statsmodels/regression/tests/results/lme05.csv
new file mode 100644
index 00000000000..05f94eee6e5
--- /dev/null
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new file mode 100644
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new file mode 100644
index 00000000000..512c82022b3
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new file mode 100644
index 00000000000..a57a8881b4f
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diff --git a/statsmodels/regression/tests/results/lme09.csv b/statsmodels/regression/tests/results/lme09.csv
new file mode 100644
index 00000000000..0fb53f29dbf
--- /dev/null
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diff --git a/statsmodels/regression/tests/results/lme10.csv b/statsmodels/regression/tests/results/lme10.csv
new file mode 100644
index 00000000000..835233e8066
--- /dev/null
+++ b/statsmodels/regression/tests/results/lme10.csv
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diff --git a/statsmodels/regression/tests/results/lme11.csv b/statsmodels/regression/tests/results/lme11.csv
new file mode 100644
index 00000000000..178cedd824b
--- /dev/null
+++ b/statsmodels/regression/tests/results/lme11.csv
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+73.000,-0.258,1.144,0.818,1.790,1.000,0.333
+73.000,2.504,-1.825,-1.582,0.262,1.000,1.000
+74.000,1.416,-0.428,0.456,0.490,1.000,-1.000
+74.000,3.877,-0.198,-1.393,-0.547,1.000,1.000
+75.000,-1.958,0.467,0.323,1.252,1.000,-1.000
+75.000,2.353,-1.233,0.778,0.379,1.000,0.000
+75.000,-3.190,-0.715,-1.180,-1.238,1.000,1.000
+76.000,1.281,-0.355,-1.494,0.620,1.000,-1.000
+76.000,-1.045,0.942,0.586,-0.730,1.000,0.000
+76.000,2.971,-1.343,-0.189,0.665,1.000,1.000
+77.000,1.803,-0.336,-0.717,-0.417,1.000,-1.000
+77.000,-1.283,-1.062,0.760,0.980,1.000,-0.333
+77.000,4.764,-0.713,0.323,-0.691,1.000,0.333
+77.000,7.235,-1.857,0.647,1.059,1.000,1.000
+78.000,1.077,1.096,0.785,0.285,1.000,-1.000
+78.000,-1.462,1.185,-1.439,0.402,1.000,0.000
+78.000,-3.405,-0.956,-0.038,-1.044,1.000,1.000
+79.000,-2.323,-0.082,-0.901,-1.307,1.000,-1.000
+79.000,1.501,-0.678,-0.672,0.781,1.000,0.000
+79.000,-2.355,0.286,0.164,-0.902,1.000,1.000
+80.000,0.154,-0.518,1.182,-1.140,1.000,-1.000
+80.000,-1.169,-1.044,-0.963,0.545,1.000,0.000
+80.000,3.371,0.168,1.705,1.405,1.000,1.000
+81.000,5.661,-1.153,0.365,1.072,1.000,-1.000
+82.000,3.924,-0.775,1.796,0.777,1.000,-1.000
+82.000,3.692,-1.654,1.673,0.034,1.000,0.000
+82.000,4.973,-1.558,-0.239,-0.802,1.000,1.000
+83.000,-4.433,0.097,0.176,0.124,1.000,-1.000
+84.000,-0.331,-0.494,-0.611,2.648,1.000,-1.000
+84.000,2.324,0.593,1.263,-0.725,1.000,1.000
+85.000,-1.634,-1.538,-0.959,-1.966,1.000,-1.000
+86.000,2.046,0.317,2.039,0.365,1.000,-1.000
+86.000,2.597,-0.977,-0.161,1.815,1.000,0.000
+86.000,-1.517,2.003,-0.515,0.253,1.000,1.000
+87.000,1.314,0.606,-0.870,-1.089,1.000,-1.000
+87.000,-0.151,0.461,0.025,1.103,1.000,1.000
+88.000,1.555,0.366,0.819,-0.510,1.000,-1.000
+88.000,0.147,-0.037,0.804,-0.647,1.000,0.000
+88.000,-3.459,-0.306,0.029,-0.788,1.000,1.000
+89.000,-0.088,0.469,-0.174,0.533,1.000,-1.000
+89.000,-5.634,1.586,-0.568,0.168,1.000,0.000
+89.000,6.470,-1.374,1.194,-0.177,1.000,1.000
+90.000,1.156,-0.821,0.961,0.681,1.000,-1.000
+91.000,1.282,0.291,-1.246,0.711,1.000,-1.000
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+92.000,-1.910,-0.183,-0.110,0.398,1.000,-1.000
+92.000,5.424,-1.609,-0.652,-1.474,1.000,1.000
+93.000,1.777,-0.149,1.462,0.618,1.000,-1.000
+93.000,1.602,0.401,-1.128,-0.205,1.000,-0.333
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+93.000,-0.641,-0.209,0.439,-0.833,1.000,1.000
+94.000,-0.742,-0.002,-0.303,0.014,1.000,-1.000
+94.000,6.648,-0.767,-0.476,0.413,1.000,1.000
+95.000,-3.939,-0.028,1.455,-1.046,1.000,-1.000
+95.000,-3.992,-0.936,0.525,-0.691,1.000,1.000
+96.000,1.316,0.016,0.367,-1.287,1.000,-1.000
+96.000,-2.157,0.885,1.678,-1.062,1.000,-0.333
+96.000,1.075,-1.454,0.652,0.976,1.000,0.333
+96.000,3.665,-0.563,0.246,0.395,1.000,1.000
+97.000,3.121,-2.871,-1.058,0.355,1.000,-1.000
+97.000,-1.160,0.497,0.645,-0.296,1.000,1.000
+98.000,-7.100,0.912,0.010,-0.685,1.000,-1.000
+98.000,-2.823,0.769,0.148,-1.593,1.000,0.000
+98.000,-3.731,1.655,-0.693,-1.300,1.000,1.000
+99.000,-0.579,0.527,1.014,0.243,1.000,-1.000
+99.000,0.314,1.084,-0.030,0.904,1.000,-0.333
+99.000,0.493,-1.464,-1.787,-1.039,1.000,0.333
+99.000,3.419,0.787,-0.341,-0.586,1.000,1.000
diff --git a/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py b/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py
new file mode 100644
index 00000000000..a528fdda57c
--- /dev/null
+++ b/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py
@@ -0,0 +1,823 @@
+import numpy as np
+
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+est = dict(
+           N_clust = 10,
+           N = 200,
+           df_m = 2,
+           df_r = 9,
+           F = 51.59060716590177,
+           r2 = .8124080178314147,
+           rmse = 94.40840193979599,
+           mss = 7604093.484267689,
+           rss = 1755850.432294737,
+           r2_a = .8105035307027997,
+           ll = -1191.80235741801,
+           ll_0 = -1359.150955647688,
+           rank = 3,
+           cmdline = "regress invest mvalue kstock, vce(cluster company)",
+           title = "Linear regression",
+           marginsok = "XB default",
+           vce = "cluster",
+           depvar = "invest",
+           cmd = "regress",
+           properties = "b V",
+           predict = "regres_p",
+           model = "ols",
+           estat_cmd = "regress_estat",
+           vcetype = "Robust",
+           clustvar = "company",
+          )
+
+params_table = np.array([
+     .11556215606596,  .01589433647768,  7.2706499090564,  .00004710548549,
+     .07960666895505,  .15151764317688,                9,  2.2621571627982,
+                   0,  .23067848754982,  .08496711097464,  2.7149150406994,
+     .02380515903536,  .03846952885627,  .42288744624337,                9,
+     2.2621571627982,                0, -42.714369016733,  20.425202580078,
+    -2.0912580352272,  .06604843284516, -88.919387334862,  3.4906493013959,
+                   9,  2.2621571627982,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00025262993207, -.00065043385106,  .20961897960949, -.00065043385106,
+     .00721940994738, -1.2171040967615,  .20961897960949, -1.2171040967615,
+     417.18890043724]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           N_clust = 10,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .8124080178314146,
+           rmse = 93.69766358599176,
+           rss = 1755850.432294737,
+           mss = 7604093.484267682,
+           r2_a = .8105035307027995,
+           F = 51.59060716590192,
+           Fp = .0000117341240941,
+           Fdf1 = 2,
+           Fdf2 = 9,
+           yy = 13620706.07273678,
+           yyc = 9359943.916562419,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -1191.802357418011,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .8124080178314146,
+           r2u = .8710896173136538,
+           clustvar = "company",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on company",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock, cluster(company)",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           vce = "robust cluster",
+           partialsmall = "small",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,  .01500272788516,  7.7027429245215,  1.331761148e-14,
+     .08615734974119,  .14496696239074, np.nan,  1.9599639845401,
+                   0,  .23067848754982,  .08020079648691,  2.8762618035529,
+     .00402415789383,  .07348781490405,  .38786916019559, np.nan,
+     1.9599639845401,                0, -42.714369016733,   19.27943055305,
+    -2.2155410088072,  .02672295281194, -80.501358543152, -4.9273794903145,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+       .000225081844, -.00057950714469,   .1867610305767, -.00057950714469,
+     .00643216775713, -1.0843847053056,   .1867610305767, -1.0843847053056,
+     371.69644244987]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           N_g = 10,
+           df_m = 2,
+           df_r = 9,
+           F = 97.97910905239282,
+           r2 = .8124080178314147,
+           rmse = 94.40840193979599,
+           lag = 4,
+           cmd = "xtscc",
+           predict = "xtscc_p",
+           method = "Pooled OLS",
+           depvar = "invest",
+           vcetype = "Drisc/Kraay",
+           title = "Regression with Driscoll-Kraay standard errors",
+           groupvar = "company",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,   .0134360177573,  8.6009231420662,  .00001235433261,
+     .08516777225681,  .14595653987512,                9,  2.2621571627982,
+                   0,  .23067848754982,  .04930800664089,   4.678317037431,
+     .00115494570515,  .11913602714384,   .3422209479558,                9,
+     2.2621571627982,                0, -42.714369016733,  12.190347184209,
+    -3.5039501641153,   .0066818746948, -70.290850216489, -15.137887816977,
+                   9,  2.2621571627982,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00018052657317, -.00035661054613, -.06728261073866, -.00035661054613,
+      .0024312795189, -.32394785247278, -.06728261073866, -.32394785247278,
+     148.60456447156]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_nw_groupsum4 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           df_m = 2,
+           df_r = 197,
+           F = 73.07593045506036,
+           N = 200,
+           lag = 4,
+           rank = 3,
+           title = "Regression with Newey-West standard errors",
+           cmd = "newey",
+           cmdline = "newey invest mvalue kstock, lag(4) force",
+           estat_cmd = "newey_estat",
+           predict = "newey_p",
+           vcetype = "Newey-West",
+           depvar = "invest",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,  .01142785251475,  10.112324771147,  1.251631065e-19,
+      .0930255277205,  .13809878441142,              197,  1.9720790337785,
+                   0,  .23067848754982,  .06842168281423,  3.3714237660029,
+     .00089998163666,  .09574552141602,  .36561145368361,              197,
+     1.9720790337785,                0, -42.714369016733,  16.179042041128,
+    -2.6401049523298,  .00895205094219, -74.620718612662, -10.808019420804,
+                 197,  1.9720790337785,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+      .0001305958131, -.00022910455176,  .00889686530849, -.00022910455176,
+     .00468152667913, -.88403667445531,  .00889686530849, -.88403667445531,
+     261.76140136858]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_nw_panel4 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           df_r = 9,
+           N_clust = 10,
+           N_clust1 = 10,
+           N_clust2 = 20,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .8124080178314146,
+           rmse = 94.40840193979601,
+           rss = 1755850.432294737,
+           mss = 7604093.484267682,
+           r2_a = .8105035307027995,
+           F = 57.99124535923564,
+           Fp = 7.21555935862e-06,
+           Fdf1 = 2,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -1191.802357418011,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .8124080178314146,
+           r2u = .8710896173136538,
+           yyc = 9359943.916562419,
+           yy = 13620706.07273678,
+           Fdf2 = 9,
+           clustvar = "company time",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on company and time",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock, cluster(company time) small",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           clustvar2 = "time",
+           clustvar1 = "company",
+           vce = "robust two-way cluster",
+           partialsmall = "small",
+           small = "small",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,  .01635175387097,  7.0672636695645,  .00005873628221,
+     .07857191892244,  .15255239320949,                9,  2.2621571627982,
+                   0,  .23067848754982,  .07847391274682,  2.9395563375824,
+     .01649863150032,  .05315816373679,  .40819881136285,                9,
+     2.2621571627982,                0, -42.714369016733,  19.505607409785,
+     -2.189850750062,  .05626393734425, -86.839118533508,  1.4103805000422,
+                   9,  2.2621571627982,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00026737985466, -.00070163493529,  .19641438763743, -.00070163493529,
+      .0061581549818, -.99627581152391,  .19641438763743, -.99627581152391,
+     380.46872042467]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster_2groups_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           N_clust = 10,
+           N_clust1 = 10,
+           N_clust2 = 20,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .8124080178314146,
+           rmse = 93.69766358599176,
+           rss = 1755850.432294737,
+           mss = 7604093.484267682,
+           r2_a = .8105035307027995,
+           F = 57.99124535923565,
+           Fp = 7.21555935862e-06,
+           Fdf1 = 2,
+           Fdf2 = 9,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -1191.802357418011,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .8124080178314146,
+           r2u = .8710896173136538,
+           yyc = 9359943.916562419,
+           yy = 13620706.07273678,
+           clustvar = "company time",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on company and time",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock, cluster(company time)",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           clustvar2 = "time",
+           clustvar1 = "company",
+           vce = "robust two-way cluster",
+           partialsmall = "small",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,  .01543448599542,   7.487269488613,  7.032121917e-14,
+     .08531111939505,  .14581319273688, np.nan,  1.9599639845401,
+                   0,  .23067848754982,  .07407184066336,  3.1142534799181,
+     .00184410987255,  .08550034758104,   .3758566275186, np.nan,
+     1.9599639845401,                0, -42.714369016733,  18.411420987265,
+     -2.319993065515,  .02034125246974, -78.800091055978, -6.6286469774879,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00023822335794, -.00062512499511,  .17499633632219, -.00062512499511,
+     .00548663757926, -.88763669036779,  .17499633632219, -.88763669036779,
+     338.98042277032]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster_2groups_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           bw = 5,
+           N_clust = 20,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .8124080178314146,
+           rmse = 93.69766358599176,
+           rss = 1755850.432294737,
+           mss = 7604093.484267682,
+           r2_a = .8105035307027995,
+           F = 92.14467466912147,
+           Fp = 1.66368179227e-10,
+           Fdf1 = 2,
+           Fdf2 = 19,
+           yy = 13620706.07273678,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -1191.802357418011,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .8124080178314146,
+           r2u = .8710896173136538,
+           yyc = 9359943.916562419,
+           clustvar = "year",
+           hacsubtitleV2 = "and kernel-robust to common correlated disturbances (Driscoll-Kraay)",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on year",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock, dkraay(5)",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           vce = "cluster ac bartlett bw=5",
+           partialsmall = "small",
+           ivar = "company",
+           tvar = "year",
+           kernel = "Bartlett",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,   .0134360177573,  8.6009231420662,  7.907743030e-18,
+     .08922804516602,  .14189626696591, np.nan,  1.9599639845401,
+                   0,  .23067848754982,  .04930800664089,   4.678317037431,
+     2.892390940e-06,  .13403657038422,  .32732040471542, np.nan,
+     1.9599639845401,                0, -42.714369016733,  12.190347184209,
+    -3.5039501641153,  .00045841113727, -66.607010456823, -18.821727576643,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00018052657317, -.00035661054613, -.06728261073866, -.00035661054613,
+      .0024312795189, -.32394785247278, -.06728261073866, -.32394785247278,
+     148.60456447156]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_nw_groupsum4_ivreg_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           bw = 5,
+           df_r = 19,
+           N_clust = 20,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .8124080178314146,
+           rmse = 94.40840193979601,
+           rss = 1755850.432294737,
+           mss = 7604093.484267682,
+           r2_a = .8105035307027995,
+           F = 92.14467466912149,
+           Fp = 1.66368179227e-10,
+           Fdf1 = 2,
+           Fdf2 = 19,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -1191.802357418011,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .8124080178314146,
+           r2u = .8710896173136538,
+           yyc = 9359943.916562419,
+           yy = 13620706.07273678,
+           clustvar = "year",
+           hacsubtitleV2 = "and kernel-robust to common correlated disturbances (Driscoll-Kraay)",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on year",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock, dkraay(5) small",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           vce = "cluster ac bartlett bw=5",
+           partialsmall = "small",
+           small = "small",
+           ivar = "company",
+           tvar = "year",
+           kernel = "Bartlett",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11556215606596,   .0138548615926,  8.3409101775303,  8.967911239e-08,
+     .08656359748216,  .14456071464977,               19,  2.0930240544083,
+                   0,  .23067848754982,   .0508450956047,  4.5368876743442,
+     .00022550505646,  .12425847940049,  .33709849569915,               19,
+     2.0930240544083,                0, -42.714369016733,  12.570359466158,
+    -3.3980228752988,  .00301793225123,  -69.02443375196, -16.404304281506,
+                  19,  2.0930240544083,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00019195718975, -.00037919048186, -.07154282413568, -.00037919048186,
+     .00258522374705, -.34445964542925, -.07154282413568, -.34445964542925,
+     158.01393710842]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_nw_groupsum4_ivreg_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+
+#########################   WLS
+
+est = dict(
+           N = 200,
+           df_m = 2,
+           df_r = 197,
+           F = 158.2726503915062,
+           r2 = .7728224625923459,
+           rmse = 35.1783035325949,
+           mss = 829335.6968772264,
+           rss = 243790.0687679817,
+           r2_a = .7705160916541971,
+           ll = -994.3622459900876,
+           ll_0 = -1142.564592396746,
+           rank = 3,
+           cmdline = "regress invest mvalue kstock [aw=1/mvalue], robust",
+           title = "Linear regression",
+           marginsok = "XB default",
+           vce = "robust",
+           depvar = "invest",
+           cmd = "regress",
+           properties = "b V",
+           predict = "regres_p",
+           model = "ols",
+           estat_cmd = "regress_estat",
+           wexp = "= 1/mvalue",
+           wtype = "aweight",
+           vcetype = "Robust",
+          )
+
+params_table = np.array([
+     .11694307068216,  .00768545583365,    15.2161528494,  4.371656843e-35,
+     .10178674436759,  .13209939699674,              197,  1.9720790337785,
+                   0,  .10410756769914,  .00986959606725,  10.548310892334,
+     6.565731752e-21,  .08464394422305,  .12357119117523,              197,
+     1.9720790337785,                0, -9.2723336171089,  2.3458404391932,
+    -3.9526702081656,  .00010767530575, -13.898516363832, -4.6461508703863,
+                 197,  1.9720790337785,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00005906623137,  6.805470065e-06, -.01210153268743,  6.805470065e-06,
+     .00009740892653, -.01511046663892, -.01210153268743, -.01511046663892,
+      5.502967366154]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_hc1_wls_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N_clust = 10,
+           N = 200,
+           df_m = 2,
+           df_r = 9,
+           F = 22.90591346432732,
+           r2 = .7728224625923459,
+           rmse = 35.1783035325949,
+           mss = 829335.6968772264,
+           rss = 243790.0687679817,
+           r2_a = .7705160916541971,
+           ll = -994.3622459900876,
+           ll_0 = -1142.564592396746,
+           rank = 3,
+           cmdline = "regress invest mvalue kstock[aw=1/mvalue], vce(cluster company)",
+           title = "Linear regression",
+           marginsok = "XB default",
+           vce = "cluster",
+           depvar = "invest",
+           cmd = "regress",
+           properties = "b V",
+           predict = "regres_p",
+           model = "ols",
+           estat_cmd = "regress_estat",
+           wexp = "= 1/mvalue",
+           wtype = "aweight",
+           vcetype = "Robust",
+           clustvar = "company",
+          )
+
+params_table = np.array([
+     .11694307068216,  .02609630113434,  4.4812124936848,  .00152974827456,
+     .05790913614858,  .17597700521575,                9,  2.2621571627982,
+                   0,  .10410756769914,  .02285882773869,  4.5543703679489,
+     .00137730504553,  .05239730679689,  .15581782860139,                9,
+     2.2621571627982,                0, -9.2723336171089,  5.7204731422962,
+    -1.6209032690934,  .13948922172294, -22.212942910549,  3.6682756763312,
+                   9,  2.2621571627982,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00068101693289, -.00006496077364, -.08926939086077, -.00006496077364,
+     .00052252600559,  -.0697116307149, -.08926939086077,  -.0697116307149,
+     32.723812971732]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster_wls_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 200,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           N_clust = 10,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .772822462592346,
+           rmse = 34.91346937558495,
+           rss = 243790.0687679817,
+           mss = 829335.6968772268,
+           r2_a = .7705160916541972,
+           F = 22.9059134643273,
+           Fp = .000294548654088,
+           Fdf1 = 2,
+           Fdf2 = 9,
+           yy = 1401938.856802022,
+           yyc = 1073125.765645209,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -994.3622459900874,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .772822462592346,
+           r2u = .8261050632949187,
+           clustvar = "company",
+           hacsubtitleV = "Statistics robust to heteroskedasticity and clustering on company",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "03.1.07",
+           cmdline = "ivreg2 invest mvalue kstock [aw=1/mvalue], cluster(company)",
+           cmd = "ivreg2",
+           wtype = "aweight",
+           wexp = "=1/mvalue",
+           model = "ols",
+           depvar = "invest",
+           vcetype = "Robust",
+           vce = "robust cluster",
+           partialsmall = "small",
+           inexog = "mvalue kstock",
+           insts = "mvalue kstock",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .11694307068216,  .02463240320082,  4.7475298990826,  2.059159576e-06,
+     .06866444755588,  .16522169380844, np.nan,  1.9599639845401,
+                   0,  .10410756769914,  .02157653909108,  4.8250355286218,
+     1.399783125e-06,  .06181832816961,  .14639680722867, np.nan,
+     1.9599639845401,                0, -9.2723336171089,  5.3995775192484,
+    -1.7172331694572,  .08593657730569, -19.855311086568,    1.31064385235,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'mvalue kstock _cons'.split()
+
+cov = np.array([
+     .00060675528745, -.00005787711139, -.07953498994782, -.00005787711139,
+     .00046554703915, -.06210991017966, -.07953498994782, -.06210991017966,
+     29.155437386372]).reshape(3,3)
+
+cov_colnames = 'mvalue kstock _cons'.split()
+
+cov_rownames = 'mvalue kstock _cons'.split()
+
+
+results_cluster_wls_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/regression/tests/results/results_macro_ols_robust.py b/statsmodels/regression/tests/results/results_macro_ols_robust.py
new file mode 100644
index 00000000000..b214fb75e5f
--- /dev/null
+++ b/statsmodels/regression/tests/results/results_macro_ols_robust.py
@@ -0,0 +1,455 @@
+'''autogenerated and edited by hand
+
+'''
+
+import numpy as np
+
+est = dict(
+           N = 202,
+           df_m = 2,
+           df_r = 199,
+           F = 92.94502024547633,
+           r2 = .6769775594319385,
+           rmse = 10.7037959322668,
+           mss = 47782.65712176046,
+           rss = 22799.67822456265,
+           r2_a = .6737311027428123,
+           ll = -763.9752181602238,
+           ll_0 = -878.1085999159409,
+           rank = 3,
+           cmdline = "regress g_realinv g_realgdp L.realint, vce(robust)",
+           title = "Linear regression",
+           marginsok = "XB default",
+           vce = "robust",
+           depvar = "g_realinv",
+           cmd = "regress",
+           properties = "b V",
+           predict = "regres_p",
+           model = "ols",
+           estat_cmd = "regress_estat",
+           vcetype = "Robust",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .32355452428856,  13.519272136038,  5.703151404e-30,
+     3.7361862031101,  5.0122571262963,              199,  1.9719565442518,
+                   0, -.61399696947899,  .32772840315987, -1.8734933059173,
+     .06246625509181, -1.2602631388273,   .0322691998693,              199,
+     1.9719565442518,                0, -9.4816727746549,  1.3690593206013,
+    -6.9256843965613,  5.860240898e-11, -12.181398261383, -6.7819472879264,
+                 199,  1.9719565442518,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+      .1046875301876, -.00084230205782, -.34205013876828, -.00084230205782,
+     .10740590623772, -.14114426417778, -.34205013876828, -.14114426417778,
+     1.8743234233252]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_hc0 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+######################
+
+est = dict(
+           df_m = 2,
+           df_r = 199,
+           F = 89.45120275471848,
+           N = 202,
+           lag = 4,
+           rank = 3,
+           title = "Regression with Newey-West standard errors",
+           cmd = "newey",
+           cmdline = "newey g_realinv g_realgdp L.realint, lag(4)",
+           estat_cmd = "newey_estat",
+           predict = "newey_p",
+           vcetype = "Newey-West",
+           depvar = "g_realinv",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .33125644884286,  13.204940401864,  5.282334606e-29,
+     3.7209983425819,  5.0274449868245,              199,  1.9719565442518,
+                   0, -.61399696947899,  .29582347593197, -2.0755518727668,
+     .03922090940364, -1.1973480087863, -.03064593017165,              199,
+     1.9719565442518,                0, -9.4816727746549,  1.1859338087713,
+    -7.9951112823729,  1.036821797e-13, -11.820282709911, -7.1430628393989,
+                 199,  1.9719565442518,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+     .10973083489998,   .0003953117603, -.31803287070833,   .0003953117603,
+     .08751152891247, -.06062111121649, -.31803287070833, -.06062111121649,
+     1.4064389987868]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+results_newey4 = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+
+
+est = dict(
+           N = 202,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           bw = 5,
+           df_r = 199,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .6769775594319388,
+           rmse = 10.7037959322668,
+           rss = 22799.67822456265,
+           mss = 47782.65712176055,
+           r2_a = .6737311027428126,
+           F = 89.45120275471867,
+           Fp = 1.93466284646e-28,
+           Fdf1 = 2,
+           Fdf2 = 199,
+           yy = 72725.68049533673,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -763.9752181602239,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .6769775594319388,
+           r2u = .6864975608440735,
+           yyc = 70582.33534632321,
+           hacsubtitleV = "Statistics robust to heteroskedasticity and autocorrelation",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 g_realinv g_realgdp L.realint, robust bw(5) small",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "g_realinv",
+           vcetype = "Robust",
+           partialsmall = "small",
+           small = "small",
+           tvar = "qu",
+           kernel = "Bartlett",
+           inexog = "g_realgdp L.realint",
+           insts = "g_realgdp L.realint",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .33125644884286,  13.204940401864,  5.282334606e-29,
+     3.7209983425819,  5.0274449868245,              199,  1.9719565442518,
+                   0, -.61399696947899,  .29582347593197, -2.0755518727668,
+     .03922090940364, -1.1973480087863, -.03064593017165,              199,
+     1.9719565442518,                0, -9.4816727746549,  1.1859338087713,
+    -7.9951112823729,  1.036821797e-13, -11.820282709911, -7.1430628393989,
+                 199,  1.9719565442518,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+     .10973083489998,   .0003953117603, -.31803287070833,   .0003953117603,
+     .08751152891247, -.06062111121649, -.31803287070833, -.06062111121649,
+     1.4064389987868]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+results_ivhac4_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+est = dict(
+           N = 202,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           bw = 5,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .6769775594319388,
+           rmse = 10.6240149746225,
+           rss = 22799.67822456265,
+           mss = 47782.65712176055,
+           r2_a = .6737311027428126,
+           F = 89.45120275471867,
+           Fp = 1.93466284646e-28,
+           Fdf1 = 2,
+           Fdf2 = 199,
+           yy = 72725.68049533673,
+           yyc = 70582.33534632321,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -763.9752181602239,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .6769775594319388,
+           r2u = .6864975608440735,
+           hacsubtitleV = "Statistics robust to heteroskedasticity and autocorrelation",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 g_realinv g_realgdp L.realint, robust bw(5)",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "g_realinv",
+           vcetype = "Robust",
+           partialsmall = "small",
+           tvar = "qu",
+           kernel = "Bartlett",
+           inexog = "g_realgdp L.realint",
+           insts = "g_realgdp L.realint",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .32878742225811,  13.304102798888,  2.191074740e-40,
+     3.7298101585076,  5.0186331708989, np.nan,  1.9599639845401,
+                   0, -.61399696947899,  .29361854972141, -2.0911382133777,
+     .03651567605333, -1.1894787521258, -.03851518683214, np.nan,
+     1.9599639845401,                0, -9.4816727746549,  1.1770944273439,
+     -8.055150508231,  7.938107001e-16, -11.788735458652, -7.1746100906581,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+     .10810116903513,  .00038944079356, -.31330961025227,  .00038944079356,
+      .0862118527405, -.05972079768357, -.31330961025227, -.05972079768357,
+      1.385551290884]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+results_ivhac4_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+######################
+
+est = dict(
+           N = 202,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           df_r = 199,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .6769775594319388,
+           rmse = 10.7037959322668,
+           rss = 22799.67822456265,
+           mss = 47782.65712176055,
+           r2_a = .6737311027428126,
+           F = 92.94502024547634,
+           Fp = 3.12523087723e-29,
+           Fdf1 = 2,
+           Fdf2 = 199,
+           yy = 72725.68049533673,
+           yyc = 70582.33534632321,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -763.9752181602239,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .6769775594319388,
+           r2u = .6864975608440735,
+           hacsubtitleV = "Statistics robust to heteroskedasticity",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 g_realinv g_realgdp L.realint, robust small",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "g_realinv",
+           vcetype = "Robust",
+           partialsmall = "small",
+           small = "small",
+           inexog = "g_realgdp L.realint",
+           insts = "g_realgdp L.realint",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .32355452428856,  13.519272136038,  5.703151404e-30,
+     3.7361862031101,  5.0122571262963,              199,  1.9719565442518,
+                   0, -.61399696947899,  .32772840315987, -1.8734933059173,
+     .06246625509181, -1.2602631388273,   .0322691998693,              199,
+     1.9719565442518,                0, -9.4816727746549,  1.3690593206013,
+    -6.9256843965613,  5.860240898e-11, -12.181398261383, -6.7819472879264,
+                 199,  1.9719565442518,                0]).reshape(3,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+      .1046875301876, -.00084230205782, -.34205013876828, -.00084230205782,
+     .10740590623772, -.14114426417778, -.34205013876828, -.14114426417778,
+     1.8743234233252]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+results_ivhc0_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+###################
+
+est = dict(
+           N = 202,
+           inexog_ct = 2,
+           exexog_ct = 0,
+           endog_ct = 0,
+           partial_ct = 0,
+           df_m = 2,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .6769775594319388,
+           rmse = 10.6240149746225,
+           rss = 22799.67822456265,
+           mss = 47782.65712176055,
+           r2_a = .6737311027428126,
+           F = 92.94502024547633,
+           Fp = 3.12523087723e-29,
+           Fdf1 = 2,
+           Fdf2 = 199,
+           yy = 72725.68049533673,
+           yyc = 70582.33534632321,
+           r2u = .6864975608440735,
+           partialcons = 0,
+           cons = 1,
+           jdf = 0,
+           j = 0,
+           ll = -763.9752181602239,
+           rankV = 3,
+           rankS = 3,
+           rankxx = 3,
+           rankzz = 3,
+           r2c = .6769775594319388,
+           hacsubtitleV = "Statistics robust to heteroskedasticity",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "OLS estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 g_realinv g_realgdp L.realint, robust",
+           cmd = "ivreg2",
+           model = "ols",
+           depvar = "g_realinv",
+           vcetype = "Robust",
+           partialsmall = "small",
+           inexog = "g_realgdp L.realint",
+           insts = "g_realgdp L.realint",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     4.3742216647032,  .32114290415293,  13.620795004769,  3.012701837e-42,
+     3.7447931386729,  5.0036501907336, np.nan,  1.9599639845401,
+                   0, -.61399696947899,  .32528567293437, -1.8875622892954,
+     .05908473670106, -1.2515451731172,  .02355123415926, np.nan,
+     1.9599639845401,                0, -9.4816727746549,  1.3588550094989,
+    -6.9776927695558,  3.000669464e-12, -12.144979653484, -6.8183658958253,
+    np.nan,  1.9599639845401,                0]).reshape(3,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'g_realgdp L.realint _cons'.split()
+
+cov = np.array([
+     .10313276488778, -.00082979262132, -.33697018621231, -.00082979262132,
+     .10581076901637, -.13904806223455, -.33697018621231, -.13904806223455,
+     1.8464869368401]).reshape(3,3)
+
+cov_colnames = 'g_realgdp L.realint _cons'.split()
+
+cov_rownames = 'g_realgdp L.realint _cons'.split()
+
+results_ivhc0_large = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/regression/tests/results/results_regression.py b/statsmodels/regression/tests/results/results_regression.py
index 427a928c8a5..0e2610a038b 100644
--- a/statsmodels/regression/tests/results/results_regression.py
+++ b/statsmodels/regression/tests/results/results_regression.py
@@ -43,6 +43,14 @@ def __init__(self):
             -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,
             -206.75783))
 
+        # Obtained from R using m$residuals / sqrt(sum(m$residuals * m$residuals) / m$df.residual)
+        self.resid_pearson = np.array((0.87694426, -0.30838998,
+                                    0.15183385, -1.34528175, 1.01594375,
+                                    -0.81780510, -0.53812289, -0.04323497,
+                                    0.04692334, 1.49381010, -0.05664654,
+                                    -0.12811061, -0.51024404, -0.28102399,
+                                    1.12162357, -0.67821900))
+
     def conf_int(self): # a method to be consistent with sm
         return [(-177.0291,207.1524), (-.111581,.0399428),(-3.125065,
                 -.9153928),(-1.517948,-.5485049),(-.5625173,.4603083),
@@ -130,6 +138,20 @@ def __init__(self):
                       -100.632293701, 34.9342498779, -96.6685943604,
                       -367.32925415, -40.1300048828, -72.8692245483,
                       -60.8728256226, -35.9937324524, -222.944747925]
+        self.resid_pearson = \
+            [-0.90569581, -0.75496938, -1.28663890, -0.11309411, -0.24746253, -0.47181831,
+             -1.02062293, -0.31403683,  1.62862142, -0.84973225, -0.42919669, -0.78007426,
+             -0.63913772,  0.29787637, -0.38364568, -0.21381846, -0.85577361,  0.54156452,
+             -0.48496031, -1.15374603,  0.41145856,  0.23996158, 2.70305838, -0.53171027,
+              0.79057028,  1.82433320, -0.04150362,  0.97048328,  0.13667658,  0.26750667,
+             -0.12690810,  0.11703354, -0.72689772,  0.34160874, 0.71332338, -0.75079661,
+              1.73137185, -0.39477348,  0.04107215, -0.11332274, 0.22952063, -0.88580496,
+             -0.67239515, -0.17656300,  4.48867723,  2.61499898, -0.16988320, -0.63136893,
+             -0.68135396,  0.06351572, -0.64467367, -0.37800911, -0.64304809,  1.88607184,
+              0.57624742,  0.60875207, -0.78636761, -0.17897383, -0.21716827, -0.07885570,
+             -0.57566752,  0.25202879, -0.29176531, -0.54378274,  0.30203654, -0.57460072,
+             -0.72378394, -0.23853382, -0.17325464, -0.24121979, -0.10269489, -0.57826451]
+
 
     def conf_int(self): # a method to be consistent with sm
         return [( -10.2936,  4.90523), ( 5.949595, 310.9044),
@@ -165,6 +187,13 @@ def __init__(self):
                     201.321121216, 215.908889771, 73.0936813354, 913.216918945,
                     424.824859619, -8.56475830078, -361.329742432,
                     27.3456058502, 151.28956604, -492.499359131]
+        # Obtained from R using m$residuals / sqrt(sum(m$residuals * m$residuals) / m$df.residual)
+        self.resid_pearson = [0.58906369, -0.27427213, 0.19094881,
+                            -0.84457419, -0.92697740, -1.14390695,
+                            0.42368630, 0.45438671, 0.15382784,
+                            1.92189233, 0.89405658, -0.01802479,
+                            -0.76042924, 0.05754964, 0.31839340,
+                            -1.03647964]
 
     def conf_int(self):
         return [(-341.6373,   235.6502), ( .0038583,   .1382881),
diff --git a/statsmodels/regression/tests/test_glsar_gretl.py b/statsmodels/regression/tests/test_glsar_gretl.py
index df4de2441a8..05bd4f7bc0c 100644
--- a/statsmodels/regression/tests/test_glsar_gretl.py
+++ b/statsmodels/regression/tests/test_glsar_gretl.py
@@ -360,7 +360,7 @@ def test_all(self):
         #TODO
 
         assert_almost_equal(res.ssr, result_gretl_g1['ssr'][1], decimal=2)
-        #assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=7) #not in gretl
+        assert_almost_equal(res.llf, result_gretl_g1['llf'][1], decimal=4) #not in gretl
         assert_almost_equal(res.rsquared, result_gretl_g1['rsquared'][1], decimal=6) #FAIL
         assert_almost_equal(res.rsquared_adj, result_gretl_g1['rsquared_adj'][1], decimal=6) #FAIL
         assert_almost_equal(np.sqrt(res.mse_resid), result_gretl_g1['mse_resid_sqrt'][1], decimal=5)
diff --git a/statsmodels/regression/tests/test_glsar_stata.py b/statsmodels/regression/tests/test_glsar_stata.py
index b914617abd8..6db52a7e181 100644
--- a/statsmodels/regression/tests/test_glsar_stata.py
+++ b/statsmodels/regression/tests/test_glsar_stata.py
@@ -46,12 +46,14 @@ def setup_class(self):
         mod1 = GLSAR(g_inv, exogg, 1)
         self.res = mod1.iterative_fit(5)
 
-        from results.macro_gr_corc_stata import results
+        from .results.macro_gr_corc_stata import results
         self.results = results
 
     def test_rho(self):
         assert_almost_equal(self.res.model.rho, self.results.rho, 3)  # pylint: disable-msg=E1101
 
+        assert_almost_equal(self.res.llf, self.results.ll, 4)
+
 
 if __name__=="__main__":
     import nose
diff --git a/statsmodels/regression/tests/test_lme.py b/statsmodels/regression/tests/test_lme.py
new file mode 100644
index 00000000000..5fecaf421ff
--- /dev/null
+++ b/statsmodels/regression/tests/test_lme.py
@@ -0,0 +1,292 @@
+import numpy as np
+import pandas as pd
+from statsmodels.regression.mixed_linear_model import MixedLM
+from numpy.testing import assert_almost_equal
+from . import lme_r_results
+from scipy.misc import derivative
+from statsmodels.base import _penalties as penalties
+import os
+import csv
+
+
+class R_Results(object):
+    """
+    A class for holding various results obtained from fitting one data
+    set using lmer in R.
+
+    Parameters
+    ----------
+    meth : string
+        Either "ml" or "reml".
+    irfs : string
+        Either "irf", for independent random effects, or "drf" for
+        dependent random effects.
+    ds_ix : integer
+        The number of the data set
+    """
+
+    def __init__(self, meth, irfs, ds_ix):
+
+        bname = "_%s_%s_%d" % (meth, irfs, ds_ix)
+
+        self.coef = getattr(lme_r_results, "coef" + bname)
+        self.vcov_r = getattr(lme_r_results, "vcov" + bname)
+        self.cov_re_r = getattr(lme_r_results, "cov_re" + bname)
+        self.scale_r = getattr(lme_r_results, "scale" + bname)
+        self.loglike = getattr(lme_r_results, "loglike" + bname)
+
+        if hasattr(lme_r_results, "ranef_mean" + bname):
+            self.ranef_postmean = getattr(lme_r_results, "ranef_mean"
+                                          + bname)
+            self.ranef_condvar = getattr(lme_r_results,
+                                         "ranef_condvar" + bname)
+            self.ranef_condvar = np.atleast_2d(self.ranef_condvar)
+
+        # Load the data file
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        rdir = os.path.join(cur_dir, 'results')
+        fname = os.path.join(rdir, "lme%02d.csv" % ds_ix)
+        fid = open(fname)
+        rdr = csv.reader(fid)
+        header = next(rdr)
+        data = [[float(x) for x in line] for line in rdr]
+        data = np.asarray(data)
+
+        # Split into exog, endog, etc.
+        self.endog = data[:,header.index("endog")]
+        self.groups = data[:,header.index("groups")]
+        ii = [i for i,x in enumerate(header) if
+              x.startswith("exog_fe")]
+        self.exog_fe = data[:,ii]
+        ii = [i for i,x in enumerate(header) if
+              x.startswith("exog_re")]
+        self.exog_re = data[:,ii]
+
+
+
+
+class TestMixedLM(object):
+
+    # Test analytic scores using numeric differentiation
+    # TODO: should also do this with the hessian
+    def test_score(self):
+
+        n = 200
+        m = 5
+        p = 3
+        pr = 2
+
+        for jl in 0,1:
+            for reml in False,True:
+                for cov_pen_wt in 0,10:
+
+                    cov_pen = penalties.PSD(cov_pen_wt)
+
+                    np.random.seed(3558)
+                    exog_fe = np.random.normal(size=(n*m, p))
+                    exog_re = np.random.normal(size=(n*m, pr))
+                    endog = exog_fe.sum(1) + np.random.normal(size=n*m)
+                    groups = np.kron(range(n), np.ones(m))
+
+                    md = MixedLM(endog, exog_fe, groups, exog_re)
+                    md.reml = reml
+                    md.cov_pen = cov_pen
+                    if jl == 0:
+                        like = lambda x: -md.loglike_sqrt(x)
+                        score = lambda x: -md.score_sqrt(x)
+                    else:
+                        like = lambda x: -md.loglike(x)
+                        score = lambda x: -md.score(x)
+
+                    for kr in range(5):
+                        fe_params = np.random.normal(size=p)
+                        cov_re = np.random.normal(size=(pr,pr))
+                        cov_re = np.dot(cov_re.T, cov_re)
+                        params_prof = md._pack(fe_params, cov_re)
+                        gr = score(params_prof)
+
+                        ngr = np.zeros_like(gr)
+                        for k in range(len(ngr)):
+                            def f(x):
+                                pp = params_prof.copy()
+                                pp[k] = x
+                                return like(pp)
+                            ngr[k] = derivative(f, params_prof[k],
+                                                dx=1e-6)
+
+                        assert_almost_equal(gr / ngr, np.ones(len(gr)),
+                                            decimal=3)
+
+    def test_default_re(self):
+
+        np.random.seed(3235)
+        exog = np.random.normal(size=(300,4))
+        groups = np.kron(np.arange(100), [1,1,1])
+        g_errors = np.kron(np.random.normal(size=100), [1,1,1])
+        endog = exog.sum(1) + g_errors + np.random.normal(size=300)
+        mdf1 = MixedLM(endog, exog, groups).fit()
+        mdf2 = MixedLM(endog, exog, groups, np.ones(300)).fit()
+        assert_almost_equal(mdf1.params, mdf2.params, decimal=8)
+
+    def test_EM(self):
+
+        np.random.seed(3298)
+        exog = np.random.normal(size=(300,4))
+        groups = np.kron(np.arange(100), [1,1,1])
+        g_errors = np.kron(np.random.normal(size=100), [1,1,1])
+        endog = exog.sum(1) + g_errors + np.random.normal(size=300)
+        mdf1 = MixedLM(endog, exog, groups).fit(niter_em=10)
+
+    def test_profile(self):
+
+        np.random.seed(9814)
+        exog = np.random.normal(size=(300,4))
+        groups = np.kron(np.arange(100), [1,1,1])
+        g_errors = np.kron(np.random.normal(size=100), [1,1,1])
+        endog = exog.sum(1) + g_errors + np.random.normal(size=300)
+        mdf1 = MixedLM(endog, exog, groups).fit(niter_em=10)
+        mdf1.profile_re(0, dist_low=0.1, num_low=1, dist_high=0.1,
+                        num_high=1)
+
+    def test_formulas(self):
+
+        np.random.seed(2410)
+        exog = np.random.normal(size=(300,4))
+        exog_re = np.random.normal(size=300)
+        groups = np.kron(np.arange(100), [1,1,1])
+        g_errors = exog_re * np.kron(np.random.normal(size=100),
+                                     [1,1,1])
+        endog = exog.sum(1) + g_errors + np.random.normal(size=300)
+
+        mod1 = MixedLM(endog, exog, groups, exog_re)
+        rslt1 = mod1.fit()
+
+        # Fit with a formula, passing groups as the actual values.
+        df = pd.DataFrame({"endog": endog})
+        for k in range(exog.shape[1]):
+            df["exog%d" % k] = exog[:,k]
+        df["exog_re"] = exog_re
+        fml = "endog ~ 0 + exog0 + exog1 + exog2 + exog3"
+        re_fml = "0 + exog_re"
+        mod2 = MixedLM.from_formula(fml, df, re_formula=re_fml,
+                                    groups=groups)
+        rslt2 = mod2.fit()
+        assert_almost_equal(rslt1.params, rslt2.params)
+
+        # Fit with a formula, passing groups as the variable name.
+        df["groups"] = groups
+        mod3 = MixedLM.from_formula(fml, df, re_formula=re_fml,
+                                    groups="groups")
+        rslt3 = mod3.fit(start_params=rslt2.params)
+        assert_almost_equal(rslt1.params, rslt3.params, decimal=5)
+
+        # Check default variance structure with formula.api
+        exog_re = np.ones(len(endog), dtype=np.float64)
+        mod4 = MixedLM(endog, exog, groups, exog_re)
+        rslt4 = mod4.fit(start_params=rslt2.params)
+        from statsmodels.formula.api import mixedlm
+        mod5 = mixedlm(fml, df, groups="groups")
+        rslt5 = mod5.fit(start_params=rslt2.params)
+        assert_almost_equal(rslt4.params, rslt5.params)
+
+    def test_regularized(self):
+
+        np.random.seed(3453)
+        exog = np.random.normal(size=(400,5))
+        groups = np.kron(np.arange(100), np.ones(4))
+        expected_endog = exog[:,0] - exog[:,2]
+        endog = expected_endog +\
+                np.kron(np.random.normal(size=100), np.ones(4)) +\
+                np.random.normal(size=400)
+
+        # L1 regularization
+        md = MixedLM(endog, exog, groups)
+        mdf1 = md.fit_regularized(alpha=1.)
+        mdf1.summary()
+
+        # L1 regularization
+        md = MixedLM(endog, exog, groups)
+        mdf2 = md.fit_regularized(alpha=10*np.ones(5))
+        mdf2.summary()
+
+        # L2 regularization
+        pen = penalties.L2()
+        mdf3 = md.fit_regularized(method=pen, alpha=0.)
+        mdf3.summary()
+
+        # L2 regularization
+        pen = penalties.L2()
+        mdf4 = md.fit_regularized(method=pen, alpha=100.)
+        mdf4.summary()
+
+        # Pseudo-Huber regularization
+        pen = penalties.PseudoHuber(0.3)
+        mdf4 = md.fit_regularized(method=pen, alpha=1.)
+        mdf4.summary()
+
+
+    def do1(self, reml, irf, ds_ix):
+
+        # No need to check independent random effects when there is
+        # only one of them.
+        if irf and ds_ix < 6:
+            return
+
+        irfs = "irf" if irf else "drf"
+        meth = "reml" if reml else "ml"
+
+        rslt = R_Results(meth, irfs, ds_ix)
+
+        # Fit the model
+        md = MixedLM(rslt.endog, rslt.exog_fe, rslt.groups,
+                     rslt.exog_re)
+        if not irf: # Free random effects covariance
+            mdf = md.fit(gtol=1e-7, reml=reml)
+        else: # Independent random effects
+            k_fe = rslt.exog_fe.shape[1]
+            k_re = rslt.exog_re.shape[1]
+            mdf = md.fit(reml=reml, gtol=1e-7,
+                         free=(np.ones(k_fe), np.eye(k_re)))
+
+        assert_almost_equal(mdf.fe_params, rslt.coef, decimal=4)
+        assert_almost_equal(mdf.cov_re, rslt.cov_re_r, decimal=4)
+        assert_almost_equal(mdf.scale, rslt.scale_r, decimal=4)
+
+        pf = rslt.exog_fe.shape[1]
+        assert_almost_equal(rslt.vcov_r, mdf.cov_params()[0:pf,0:pf],
+                            decimal=3)
+
+        assert_almost_equal(mdf.likeval, rslt.loglike[0], decimal=2)
+
+        # Not supported in R
+        if not irf:
+            assert_almost_equal(mdf.ranef()[0], rslt.ranef_postmean,
+                                decimal=3)
+            assert_almost_equal(mdf.ranef_cov()[0],
+                                rslt.ranef_condvar,
+                                decimal=3)
+
+    # Run all the tests against R
+    def test_r(self):
+
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        rdir = os.path.join(cur_dir, 'results')
+        fnames = os.listdir(rdir)
+        fnames = [x for x in fnames if x.startswith("lme")
+                  and x.endswith(".csv")]
+
+        for fname in fnames:
+            for reml in False,True:
+                for irf in False,True:
+                    ds_ix = int(fname[3:5])
+
+                    yield self.do1, reml, irf, ds_ix
+
+
+
+if  __name__=="__main__":
+
+    import nose
+
+    nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
+                   exit=False)
diff --git a/statsmodels/regression/tests/test_quantile_regression.py b/statsmodels/regression/tests/test_quantile_regression.py
index f62a1f0be50..09a23fb01fa 100644
--- a/statsmodels/regression/tests/test_quantile_regression.py
+++ b/statsmodels/regression/tests/test_quantile_regression.py
@@ -5,7 +5,7 @@
 from numpy.testing import assert_allclose, assert_equal, assert_almost_equal
 from patsy import dmatrices   # pylint: disable=E0611
 from statsmodels.regression.quantile_regression import QuantReg
-from results_quantile_regression import (
+from .results_quantile_regression import (
       biweight_chamberlain, biweight_hsheather, biweight_bofinger,
       cosine_chamberlain, cosine_hsheather, cosine_bofinger,
       gaussian_chamberlain, gaussian_hsheather, gaussian_bofinger,
diff --git a/statsmodels/regression/tests/test_regression.py b/statsmodels/regression/tests/test_regression.py
index 927e9843b23..e0ebd607824 100644
--- a/statsmodels/regression/tests/test_regression.py
+++ b/statsmodels/regression/tests/test_regression.py
@@ -1,13 +1,16 @@
 """
 Test functions for models.regression
 """
+# TODO: Test for LM
+from statsmodels.compat.python import long, lrange
 import warnings
 import pandas
 import numpy as np
 from numpy.testing import (assert_almost_equal, assert_approx_equal,
-                            assert_raises, assert_equal)
+                            assert_raises, assert_equal, assert_allclose)
 from scipy.linalg import toeplitz
 from statsmodels.tools.tools import add_constant, categorical
+from statsmodels.compat.numpy import np_matrix_rank
 from statsmodels.regression.linear_model import OLS, WLS, GLS, yule_walker
 from statsmodels.datasets import longley
 from scipy.stats import t as student_t
@@ -21,10 +24,10 @@
 
 
 class CheckRegressionResults(object):
-    '''
+    """
     res2 contains results from Rmodelwrap or were obtained from a statistical
     packages such as R, Stata, or SAS and were written to model_results
-    '''
+    """
 
     decimal_params = DECIMAL_4
     def test_params(self):
@@ -99,7 +102,7 @@ def test_mse_model(self):
     decimal_mse_total = DECIMAL_4
     def test_mse_total(self):
         assert_almost_equal(self.res1.mse_total, self.res2.mse_total,
-                    self.decimal_mse_total)
+                    self.decimal_mse_total, err_msg="Test class %s" % self)
 
     decimal_fvalue = DECIMAL_4
     def test_fvalue(self):
@@ -135,12 +138,17 @@ def test_resids(self):
         assert_almost_equal(self.res1.resid, self.res2.resid,
             self.decimal_resids)
 
+    decimal_norm_resids = DECIMAL_4
+    def test_norm_resids(self):
+        assert_almost_equal(self.res1.resid_pearson, self.res2.resid_pearson,
+                            self.decimal_norm_resids)
+
 #TODO: test fittedvalues and what else?
 
 class TestOLS(CheckRegressionResults):
     @classmethod
     def setupClass(cls):
-        from results.results_regression import Longley
+        from .results.results_regression import Longley
         data = longley.load()
         data.exog = add_constant(data.exog, prepend=False)
         res1 = OLS(data.endog, data.exog).fit()
@@ -150,7 +158,23 @@ def setupClass(cls):
         cls.res2 = res2
 
         res_qr = OLS(data.endog, data.exog).fit(method="qr")
+
+        model_qr = OLS(data.endog, data.exog)
+        Q, R = np.linalg.qr(data.exog)
+        model_qr.exog_Q, model_qr.exog_R  = Q, R
+        model_qr.normalized_cov_params = np.linalg.inv(np.dot(R.T, R))
+        model_qr.rank = np_matrix_rank(R)
+        res_qr2 = model_qr.fit(method="qr")
+
         cls.res_qr = res_qr
+        cls.res_qr_manual = res_qr2
+
+    def test_eigenvalues(self):
+        eigenval_perc_diff = (self.res_qr.eigenvals - self.res_qr_manual.eigenvals)
+        eigenval_perc_diff /= self.res_qr.eigenvals
+        zeros = np.zeros_like(eigenval_perc_diff)
+        assert_almost_equal(eigenval_perc_diff, zeros, DECIMAL_7)
+
 
     #  Robust error tests.  Compare values computed with SAS
     def test_HC0_errors(self):
@@ -204,10 +228,28 @@ def test_rsquared_adj_overfit(self):
             rsquared_adj = results.rsquared_adj
             assert_equal(rsquared_adj, np.nan)
 
+    def test_qr_alternatives(self):
+        assert_allclose(self.res_qr.params, self.res_qr_manual.params,
+                        rtol=5e-12)
+
+    def test_norm_resid(self):
+        resid = self.res1.wresid
+        norm_resid = resid / np.sqrt(np.sum(resid**2.0) / self.res1.df_resid)
+        model_norm_resid = self.res1.resid_pearson
+        assert_almost_equal(model_norm_resid, norm_resid, DECIMAL_7)
+
+    def test_norm_resid_zero_variance(self):
+        with warnings.catch_warnings(record=True):
+            y = self.res1.model.endog
+            res = OLS(y,y).fit()
+            assert_allclose(res.scale, 0, atol=1e-20)
+            assert_allclose(res.wresid, res.resid_pearson, atol=5e-11)
+
+
 class TestRTO(CheckRegressionResults):
     @classmethod
     def setupClass(cls):
-        from results.results_regression import LongleyRTO
+        from .results.results_regression import LongleyRTO
         data = longley.load()
         res1 = OLS(data.endog, data.exog).fit()
         res2 = LongleyRTO()
@@ -243,13 +285,13 @@ def test_Df_num(self):
         assert_equal(self.Ftest.df_num, 6)
 
 class TestFTest2(object):
-    '''
+    """
     A joint test that the coefficient on
     GNP = the coefficient on UNEMP  and that the coefficient on
     POP = the coefficient on YEAR for the Longley dataset.
 
     Ftest1 is from statsmodels.  Results are from Rpy using R's car library.
-    '''
+    """
     @classmethod
     def setupClass(cls):
         data = longley.load()
@@ -307,11 +349,11 @@ def test_df_num(self):
         assert_equal(self.Ftest1.df_num, 5)
 
 class TestTtest(object):
-    '''
+    """
     Test individual t-tests.  Ie., are the coefficients significantly
     different than zero.
 
-        '''
+        """
     @classmethod
     def setupClass(cls):
         data = longley.load()
@@ -343,12 +385,12 @@ def test_effect(self):
         assert_almost_equal(self.Ttest.effect, self.res1.params)
 
 class TestTtest2(object):
-    '''
+    """
     Tests the hypothesis that the coefficients on POP and YEAR
     are equal.
 
     Results from RPy using 'car' package.
-    '''
+    """
     @classmethod
     def setupClass(cls):
         R = np.zeros(7)
@@ -376,12 +418,12 @@ def test_effect(self):
         assert_almost_equal(self.Ttest1.effect, -1829.2025687186533, DECIMAL_4)
 
 class TestGLS(object):
-    '''
+    """
     These test results were obtained by replication with R.
-    '''
+    """
     @classmethod
     def setupClass(cls):
-        from results.results_regression import LongleyGls
+        from .results.results_regression import LongleyGls
 
         data = longley.load()
         exog = add_constant(np.column_stack((data.exog[:,1],
@@ -438,28 +480,221 @@ def test_missing(self):
         assert_equal(mod.exog.shape[0], 13)
         assert_equal(mod.sigma.shape, (13,13))
 
-class TestGLS_nosigma(CheckRegressionResults):
-    '''
+class TestGLS_alt_sigma(CheckRegressionResults):
+    """
     Test that GLS with no argument is equivalent to OLS.
-    '''
+    """
     @classmethod
     def setupClass(cls):
         data = longley.load()
         data.exog = add_constant(data.exog, prepend=False)
         ols_res = OLS(data.endog, data.exog).fit()
         gls_res = GLS(data.endog, data.exog).fit()
+        gls_res_scalar = GLS(data.endog, data.exog, sigma=1)
+        cls.endog = data.endog
+        cls.exog = data.exog
         cls.res1 = gls_res
         cls.res2 = ols_res
+        cls.res3 = gls_res_scalar
 #        self.res2.conf_int = self.res2.conf_int()
 
+    def test_wrong_size_sigma_1d(self):
+        n = len(self.endog)
+        assert_raises(ValueError, GLS, self.endog, self.exog, sigma=np.ones(n-1))
+
+    def test_wrong_size_sigma_2d(self):
+        n = len(self.endog)
+        assert_raises(ValueError, GLS, self.endog, self.exog, sigma=np.ones((n-1,n-1)))
+
 #    def check_confidenceintervals(self, conf1, conf2):
 #        assert_almost_equal(conf1, conf2, DECIMAL_4)
 
+
+class TestLM(object):
+
+    @classmethod
+    def setupClass(cls):
+        # TODO: Test HAC method
+        X = np.random.randn(100,3)
+        b = np.ones((3,1))
+        e = np.random.randn(100,1)
+        y = np.dot(X,b) + e
+        # Cases?
+        # Homoskedastic
+        # HC0
+        cls.res1_full = OLS(y,X).fit()
+        cls.res1_restricted = OLS(y,X[:,0]).fit()
+
+        cls.res2_full = cls.res1_full.get_robustcov_results('HC0')
+        cls.res2_restricted = cls.res1_restricted.get_robustcov_results('HC0')
+
+        cls.X = X
+        cls.Y = y
+
+
+    def test_LM_homoskedastic(self):
+        resid = self.res1_restricted.wresid
+        n = resid.shape[0]
+        X = self.X
+        S = np.dot(resid,resid) / n * np.dot(X.T,X) / n
+        Sinv = np.linalg.inv(S)
+        s = np.mean(X * resid[:,None], 0)
+        LMstat = n * np.dot(np.dot(s,Sinv),s.T)
+        LMstat_OLS = self.res1_full.compare_lm_test(self.res1_restricted)
+        LMstat2 = LMstat_OLS[0]
+        assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
+
+    def test_LM_heteroskedastic_nodemean(self):
+        resid = self.res1_restricted.wresid
+        n = resid.shape[0]
+        X = self.X
+        scores = X * resid[:,None]
+        S = np.dot(scores.T,scores) / n
+        Sinv = np.linalg.inv(S)
+        s = np.mean(scores, 0)
+        LMstat = n * np.dot(np.dot(s,Sinv),s.T)
+        LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted, demean=False)
+        LMstat2 = LMstat_OLS[0]
+        assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
+
+    def test_LM_heteroskedastic_demean(self):
+        resid = self.res1_restricted.wresid
+        n = resid.shape[0]
+        X = self.X
+        scores = X * resid[:,None]
+        scores_demean = scores - scores.mean(0)
+        S = np.dot(scores_demean.T,scores_demean) / n
+        Sinv = np.linalg.inv(S)
+        s = np.mean(scores, 0)
+        LMstat = n * np.dot(np.dot(s,Sinv),s.T)
+        LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted)
+        LMstat2 = LMstat_OLS[0]
+        assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
+
+    def test_LM_heteroskedastic_LRversion(self):
+        resid = self.res1_restricted.wresid
+        resid_full = self.res1_full.wresid
+        n = resid.shape[0]
+        X = self.X
+        scores = X * resid[:,None]
+        s = np.mean(scores, 0)
+        scores = X * resid_full[:,None]
+        S = np.dot(scores.T,scores) / n
+        Sinv = np.linalg.inv(S)
+        LMstat = n * np.dot(np.dot(s,Sinv),s.T)
+        LMstat_OLS = self.res2_full.compare_lm_test(self.res2_restricted, use_lr = True)
+        LMstat2 = LMstat_OLS[0]
+        assert_almost_equal(LMstat, LMstat2, DECIMAL_7)
+
+    def test_LM_nonnested(self):
+        assert_raises(ValueError, self.res2_restricted.compare_lm_test, self.res2_full)
+
+
+class TestOLS_GLS_WLS_equivalence(object):
+
+    @classmethod
+    def setupClass(cls):
+        data = longley.load()
+        data.exog = add_constant(data.exog, prepend=False)
+        y = data.endog
+        X = data.exog
+        n = y.shape[0]
+        w = np.ones(n)
+        cls.results = []
+        cls.results.append(OLS(y, X).fit())
+        cls.results.append(WLS(y, X, w).fit())
+        cls.results.append(GLS(y, X, 100*w).fit())
+        cls.results.append(GLS(y, X, np.diag(0.1*w)).fit())
+
+    def test_ll(self):
+        llf = np.array([r.llf for r in self.results])
+        llf_1 = np.ones_like(llf) * self.results[0].llf
+        assert_almost_equal(llf, llf_1, DECIMAL_7)
+
+        ic = np.array([r.aic for r in self.results])
+        ic_1 = np.ones_like(ic) * self.results[0].aic
+        assert_almost_equal(ic, ic_1, DECIMAL_7)
+
+        ic = np.array([r.bic for r in self.results])
+        ic_1 = np.ones_like(ic) * self.results[0].bic
+        assert_almost_equal(ic, ic_1, DECIMAL_7)
+
+    def test_params(self):
+        params = np.array([r.params for r in self.results])
+        params_1 = np.array([self.results[0].params] * len(self.results))
+        assert_allclose(params, params_1)
+
+    def test_ss(self):
+        bse = np.array([r.bse for r in self.results])
+        bse_1 = np.array([self.results[0].bse] * len(self.results))
+        assert_allclose(bse, bse_1)
+
+    def test_rsquared(self):
+        rsquared = np.array([r.rsquared for r in self.results])
+        rsquared_1 = np.array([self.results[0].rsquared] * len(self.results))
+        assert_almost_equal(rsquared, rsquared_1, DECIMAL_7)
+
+
+class TestGLS_WLS_equivalence(TestOLS_GLS_WLS_equivalence):
+    # reuse test methods
+
+    @classmethod
+    def setupClass(cls):
+        data = longley.load()
+        data.exog = add_constant(data.exog, prepend=False)
+        y = data.endog
+        X = data.exog
+        n = y.shape[0]
+        np.random.seed(5)
+        w = np.random.uniform(0.5, 1, n)
+        w_inv = 1. / w
+        cls.results = []
+        cls.results.append(WLS(y, X, w).fit())
+        cls.results.append(WLS(y, X, 0.01 * w).fit())
+        cls.results.append(GLS(y, X, 100 * w_inv).fit())
+        cls.results.append(GLS(y, X, np.diag(0.1 * w_inv)).fit())
+
+    def test_rsquared(self):
+        # TODO: WLS rsquared is ok, GLS might have wrong centered_tss
+        # We only check that WLS and GLS rsquared is invariant to scaling
+        # WLS and GLS have different rsquared
+        assert_almost_equal(self.results[1].rsquared, self.results[0].rsquared,
+                            DECIMAL_7)
+        assert_almost_equal(self.results[3].rsquared, self.results[2].rsquared,
+                            DECIMAL_7)
+
+
+class TestNonFit(object):
+    @classmethod
+    def setupClass(cls):
+        data = longley.load()
+        data.exog = add_constant(data.exog, prepend=False)
+        cls.endog = data.endog
+        cls.exog = data.exog
+        cls.ols_model = OLS(data.endog, data.exog)
+
+    def test_df_resid(self):
+        df_resid = self.endog.shape[0] - self.exog.shape[1]
+        assert_equal(self.ols_model.df_resid, long(9))
+
+class TestWLS_CornerCases(object):
+    @classmethod
+    def setupClass(cls):
+        cls.exog = np.ones((1,))
+        cls.endog = np.ones((1,))
+        weights = 1
+        cls.wls_res = WLS(cls.endog, cls.exog, weights=weights).fit()
+
+    def test_wrong_size_weights(self):
+        weights = np.ones((10,10))
+        assert_raises(ValueError, WLS, self.endog, self.exog, weights=weights)
+
+
 class TestWLSExogWeights(CheckRegressionResults):
     #Test WLS with Greene's credit card data
     #reg avgexp age income incomesq ownrent [aw=1/incomesq]
     def __init__(self):
-        from results.results_regression import CCardWLS
+        from .results.results_regression import CCardWLS
         from statsmodels.datasets.ccard import load
         dta = load()
 
@@ -474,13 +709,20 @@ def __init__(self):
         self.res2 = CCardWLS()
         self.res2.wresid = scaled_weights ** .5 * self.res2.resid
 
+        # correction because we use different definition for loglike/llf
+        corr_ic = 2 * (self.res1.llf - self.res2.llf)
+        self.res2.aic -= corr_ic
+        self.res2.bic -= corr_ic
+        self.res2.llf += 0.5 * np.sum(np.log(self.res1.model.weights))
+
+
 def test_wls_example():
     #example from the docstring, there was a note about a bug, should
     #be fixed now
     Y = [1,3,4,5,2,3,4]
-    X = range(1,8)
+    X = lrange(1,8)
     X = add_constant(X, prepend=False)
-    wls_model = WLS(Y,X, weights=range(1,8)).fit()
+    wls_model = WLS(Y,X, weights=lrange(1,8)).fit()
     #taken from R lm.summary
     assert_almost_equal(wls_model.fvalue, 0.127337843215, 6)
     assert_almost_equal(wls_model.scale, 2.44608530786**2, 6)
@@ -596,7 +838,6 @@ def setupClass(cls):
         cls.degen_exog = cls.exog_n_one[:-1]
         cls.mod1 = OLS(cls.endog_n_one, cls.exog_n_one)
         cls.mod1.df_model += 1
-        #cls.mod1.df_resid -= 1
         cls.res1 = cls.mod1.fit()
         # Note that these are created for every subclass..
         # A little extra overhead probably
@@ -607,6 +848,29 @@ def setupClass(cls):
     def check_confidenceintervals(self, conf1, conf2):
         assert_almost_equal(conf1, conf2(), DECIMAL_4)
 
+class TestGLS_large_data(TestDataDimensions):
+    @classmethod
+    def setupClass(cls):
+        nobs = 1000
+        y = np.random.randn(nobs,1)
+        X = np.random.randn(nobs,20)
+        sigma = np.ones_like(y)
+        cls.gls_res = GLS(y, X, sigma=sigma).fit()
+        cls.gls_res_scalar = GLS(y, X, sigma=1).fit()
+        cls.gls_res_none= GLS(y, X).fit()
+        cls.ols_res = OLS(y, X).fit()
+
+    def test_large_equal_params(self):
+        assert_almost_equal(self.ols_res.params, self.gls_res.params, DECIMAL_7)
+
+    def test_large_equal_loglike(self):
+        assert_almost_equal(self.ols_res.llf, self.gls_res.llf, DECIMAL_7)
+
+    def test_large_equal_params_none(self):
+        assert_almost_equal(self.gls_res.params, self.gls_res_none.params,
+                            DECIMAL_7)
+
+
 class TestNxNx(TestDataDimensions):
     @classmethod
     def setupClass(cls):
@@ -615,6 +879,7 @@ def setupClass(cls):
         cls.mod2.df_model += 1
         cls.res2 = cls.mod2.fit()
 
+
 class TestNxOneNx(TestDataDimensions):
     @classmethod
     def setupClass(cls):
@@ -623,6 +888,7 @@ def setupClass(cls):
         cls.mod2.df_model += 1
         cls.res2 = cls.mod2.fit()
 
+
 class TestNxNxOne(TestDataDimensions):
     @classmethod
     def setupClass(cls):
@@ -631,6 +897,7 @@ def setupClass(cls):
         cls.mod2.df_model += 1
         cls.res2 = cls.mod2.fit()
 
+
 def test_bad_size():
     np.random.seed(54321)
     data = np.random.uniform(0,20,31)
@@ -681,6 +948,7 @@ def test_summary():
 \\textbf{Time:}             &     13:46:07     & \\textbf{  Log-Likelihood:    } &   -109.62   \\\\
 \\textbf{No. Observations:} &          16      & \\textbf{  AIC:               } &     233.2   \\\\
 \\textbf{Df Residuals:}     &           9      & \\textbf{  BIC:               } &     238.6   \\\\
+\\textbf{Df Model:}         &           6      & \\textbf{                     } &             \\\\
 \\bottomrule
 \\end{tabular}
 \\begin{tabular}{lccccc}
@@ -706,7 +974,43 @@ def test_summary():
 \\end{center}"""
     assert_equal(table, expected)
 
+class TestRegularizedFit(object):
+
+    def test_regularized(self):
+
+        import os
+        from . import glmnet_r_results
+
+        cur_dir = os.path.dirname(os.path.abspath(__file__))
+        data = np.loadtxt(os.path.join(cur_dir, "results", "lasso_data.csv"),
+                          delimiter=",")
+
+        tests = [x for x in dir(glmnet_r_results) if x.startswith("rslt_")]
+
+        for test in tests:
+
+            vec = getattr(glmnet_r_results, test)
+
+            n = vec[0]
+            p = vec[1]
+            L1_wt = float(vec[2])
+            lam = float(vec[3])
+            params = vec[4:].astype(np.float64)
+
+            endog = data[0:n, 0]
+            exog = data[0:n, 1:(p+1)]
+
+            endog = endog - endog.mean()
+            endog /= endog.std(ddof=1)
+            exog = exog - exog.mean(0)
+            exog /= exog.std(0, ddof=1)
+
+            mod = OLS(endog, exog)
+            rslt = mod.fit_regularized(L1_wt=L1_wt, alpha=lam)
+            assert_almost_equal(rslt.params, params, decimal=3)
 
+            # Smoke test for summary
+            smry = rslt.summary()
 
 
 if __name__=="__main__":
diff --git a/statsmodels/regression/tests/test_robustcov.py b/statsmodels/regression/tests/test_robustcov.py
new file mode 100644
index 00000000000..a9e39553eb3
--- /dev/null
+++ b/statsmodels/regression/tests/test_robustcov.py
@@ -0,0 +1,638 @@
+# -*- coding: utf-8 -*-
+"""Testing OLS robust covariance matrices against STATA
+
+Created on Mon Oct 28 15:25:14 2013
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from scipy import stats
+
+from numpy.testing import assert_allclose, assert_equal, assert_warns
+
+from statsmodels.regression.linear_model import OLS, WLS
+import statsmodels.stats.sandwich_covariance as sw
+from statsmodels.tools.tools import add_constant
+from statsmodels.datasets import macrodata
+from statsmodels.tools.sm_exceptions import InvalidTestWarning
+
+from .results import results_macro_ols_robust as res
+from .results import results_grunfeld_ols_robust_cluster as res2
+#test_hac_simple():
+
+class CheckOLSRobust(object):
+
+    def test_basic(self):
+        res1 = self.res1
+        res2 = self.res2
+        rtol = getattr(self, 'rtol', 1e-10)
+        assert_allclose(res1.params, res2.params, rtol=rtol)
+        assert_allclose(self.bse_robust, res2.bse, rtol=rtol)
+        assert_allclose(self.cov_robust, res2.cov, rtol=rtol)
+
+    def test_tests(self):
+        # Note: differences between small (t-distribution, ddof) and large (normal)
+        # F statistic has no ddof correction in large, but uses F distribution (?)
+        res1 = self.res1
+        res2 = self.res2
+        rtol = getattr(self, 'rtol', 1e-10)
+        rtolh = getattr(self, 'rtolh', 1e-12)
+        mat = np.eye(len(res1.params))
+        tt = res1.t_test(mat, cov_p=self.cov_robust)
+        # has 'effect', 'pvalue', 'sd', 'tvalue'
+        # TODO confint missing
+        assert_allclose(tt.effect, res2.params, rtol=rtol)
+        assert_allclose(tt.sd, res2.bse, rtol=rtol)
+        assert_allclose(tt.tvalue, res2.tvalues, rtol=rtol)
+        if self.small:
+            assert_allclose(tt.pvalue, res2.pvalues, rtol=5 * rtol)
+        else:
+            pval = stats.norm.sf(np.abs(tt.tvalue)) * 2
+            assert_allclose(pval, res2.pvalues, rtol=5 * rtol, atol=1e-25)
+
+        ft = res1.f_test(mat[:-1], cov_p=self.cov_robust)
+        if self.small:
+            #'df_denom', 'df_num', 'fvalue', 'pvalue'
+            assert_allclose(ft.fvalue, res2.F, rtol=rtol)
+            # f-pvalue is not directly available in Stata results, but is in ivreg2
+            if hasattr(res2, 'Fp'):
+                assert_allclose(ft.pvalue, res2.Fp, rtol=rtol)
+        else:
+            if not getattr(self, 'skip_f', False):
+                dof_corr = res1.df_resid * 1. / res1.nobs
+                assert_allclose(ft.fvalue * dof_corr, res2.F, rtol=rtol)
+
+        if hasattr(res2, 'df_r'):
+            assert_equal(ft.df_num, res2.df_m)
+            assert_equal(ft.df_denom, res2.df_r)
+        else:
+            # ivreg2
+            assert_equal(ft.df_num, res2.Fdf1)
+            assert_equal(ft.df_denom, res2.Fdf2)
+
+        # SMOKE
+        tt.summary()
+        ft.summary()
+        tt.summary_frame()
+
+
+
+class TestOLSRobust1(CheckOLSRobust):
+    # compare with regress robust
+
+    def setup(self):
+        res_ols = self.res1
+        self.bse_robust = res_ols.HC1_se
+        self.cov_robust = res_ols.cov_HC1
+        self.small = True
+        self.res2 = res.results_hc0
+
+    @classmethod
+    def setup_class(cls):
+        d2 = macrodata.load().data
+        g_gdp = 400*np.diff(np.log(d2['realgdp']))
+        g_inv = 400*np.diff(np.log(d2['realinv']))
+        exogg = add_constant(np.c_[g_gdp, d2['realint'][:-1]], prepend=False)
+
+        cls.res1 = res_ols = OLS(g_inv, exogg).fit()
+
+
+class TestOLSRobust2(TestOLSRobust1):
+    # compare with ivreg robust small
+
+    def setup(self):
+        res_ols = self.res1
+        self.bse_robust = res_ols.HC1_se
+        self.cov_robust = res_ols.cov_HC1
+        self.small = True
+
+        self.res2 = res.results_ivhc0_small
+
+
+
+class TestOLSRobust3(TestOLSRobust1):
+    # compare with ivreg robust   (not small)
+
+    def setup(self):
+        res_ols = self.res1
+        self.bse_robust = res_ols.HC0_se
+        self.cov_robust = res_ols.cov_HC0
+        self.small = False
+
+        self.res2 = res.results_ivhc0_large
+
+
+class TestOLSRobustHacSmall(TestOLSRobust1):
+    # compare with ivreg robust small
+
+    def setup(self):
+        res_ols = self.res1
+        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=True)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust = se1
+        self.cov_robust = cov1
+        self.small = True
+
+        self.res2 = res.results_ivhac4_small
+
+
+
+class TestOLSRobustHacLarge(TestOLSRobust1):
+    # compare with ivreg robust (not small)
+
+    def setup(self):
+        res_ols = self.res1
+        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=False)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust = se1
+        self.cov_robust = cov1
+        self.small = False
+
+        self.res2 = res.results_ivhac4_large
+
+
+class CheckOLSRobustNewMixin(object):
+    # This uses the robust covariance as default covariance
+
+    def test_compare(self):
+        rtol = getattr(self, 'rtol', 1e-10)
+        assert_allclose(self.cov_robust, self.cov_robust2, rtol=rtol)
+        assert_allclose(self.bse_robust, self.bse_robust2, rtol=rtol)
+
+
+    def test_fvalue(self):
+        if not getattr(self, 'skip_f', False):
+            rtol = getattr(self, 'rtol', 1e-10)
+            assert_allclose(self.res1.fvalue, self.res2.F, rtol=rtol)
+            if hasattr(self.res2, 'Fp'):
+                #only available with ivreg2
+                assert_allclose(self.res1.f_pvalue, self.res2.Fp, rtol=rtol)
+
+
+    def test_confint(self):
+        rtol = getattr(self, 'rtol', 1e-10)
+        ci1 = self.res1.conf_int()
+        ci2 = self.res2.params_table[:,4:6]
+        assert_allclose(ci1, ci2, rtol=rtol)
+
+        # check critical value
+        crit1 = np.diff(ci1, 1).ravel() / 2 / self.res1.bse
+        crit2 = np.diff(ci1, 1).ravel() / 2 / self.res1.bse
+        assert_allclose(crit1, crit2, rtol=12)
+
+
+    def test_ttest(self):
+        res1 = self.res1
+        res2 = self.res2
+        rtol = getattr(self, 'rtol', 1e-10)
+        rtolh = getattr(self, 'rtol', 1e-12)
+
+        mat = np.eye(len(res1.params))
+        tt = res1.t_test(mat, cov_p=self.cov_robust)
+        # has 'effect', 'pvalue', 'sd', 'tvalue'
+        # TODO confint missing
+        assert_allclose(tt.effect, res2.params, rtol=rtolh)
+        assert_allclose(tt.sd, res2.bse, rtol=rtol)
+        assert_allclose(tt.tvalue, res2.tvalues, rtol=rtolh)
+        assert_allclose(tt.pvalue, res2.pvalues, rtol=5 * rtol)
+        ci1 = tt.conf_int()
+        ci2 = self.res2.params_table[:,4:6]
+        assert_allclose(ci1, ci2, rtol=rtol)
+
+
+    def test_smoke(self):
+        self.res1.summary()
+
+
+
+class TestOLSRobust2SmallNew(TestOLSRobust1, CheckOLSRobustNewMixin):
+    # compare with ivreg robust small
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('HC1', use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        self.bse_robust2 = res_ols.HC1_se
+        self.cov_robust2 = res_ols.cov_HC1
+        self.small = True
+        self.res2 = res.results_ivhc0_small
+
+    def test_compare(self):
+        #check that we get a warning using the nested compare methods
+        res1 = self.res1
+        endog = res1.model.endog
+        exog = res1.model.exog[:, [0, 2]]  # drop one variable
+        res_ols2 = OLS(endog, exog).fit()
+        # results from Stata
+        r_pval =  .0307306938402991
+        r_chi2 =  4.667944083588736
+        r_df =  1
+        assert_warns(InvalidTestWarning, res1.compare_lr_test, res_ols2)
+        import warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            chi2, pval, df = res1.compare_lr_test(res_ols2)
+        assert_allclose(chi2, r_chi2, rtol=1e-11)
+        assert_allclose(pval, r_pval, rtol=1e-11)
+        assert_equal(df, r_df)
+
+        assert_warns(InvalidTestWarning, res1.compare_f_test, res_ols2)
+        #fva, pval, df = res1.compare_f_test(res_ols2)
+
+
+
+class TestOLSRobustHACSmallNew(TestOLSRobust1, CheckOLSRobustNewMixin):
+    # compare with ivreg robust small
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('HAC', maxlags=4,
+                                            use_correction=True, use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_hac_simple(res_ols, nlags=4, use_correction=True)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res.results_ivhac4_small
+
+
+class TestOLSRobust2LargeNew(TestOLSRobust1, CheckOLSRobustNewMixin):
+    # compare with ivreg robust small
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('HC0')
+        res_ols.use_t = False
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        self.bse_robust2 = res_ols.HC0_se
+        self.cov_robust2 = res_ols.cov_HC0
+        self.small = False
+        self.res2 = res.results_ivhc0_large
+
+
+    # TODO: skipping next two for now, not refactored yet for `large`
+    def test_fvalue(self):
+        pass
+
+
+    def test_confint(self):
+        pass
+
+#######################################################
+#    cluster robust standard errors
+#######################################################
+
+
+class CheckOLSRobustCluster(CheckOLSRobust):
+    # compare with regress robust
+
+
+    @classmethod
+    def setup_class(cls):
+        #import pandas as pa
+        from statsmodels.datasets import grunfeld
+
+        dtapa = grunfeld.data.load_pandas()
+        #Stata example/data seems to miss last firm
+        dtapa_endog = dtapa.endog[:200]
+        dtapa_exog = dtapa.exog[:200]
+        exog = add_constant(dtapa_exog[['value', 'capital']], prepend=False)
+        #asserts don't work for pandas
+        cls.res1 = OLS(dtapa_endog, exog).fit()
+
+        firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'),
+                                    return_inverse=True)
+        cls.groups = firm_id
+        #time indicator in range(max Ti)
+        time = np.asarray(dtapa_exog[['year']])
+        time -= time.min()
+        cls.time = np.squeeze(time).astype(int)
+        # nw_panel function requires interval bounds
+        cls.tidx = [(i*20, 20*(i+1)) for i in range(10)]
+
+
+
+class TestOLSRobustCluster2(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=self.groups,
+                                                  use_correction=True,
+                                                  use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_cluster
+
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+
+class TestOLSRobustCluster2Large(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=self.groups,
+                                                  use_correction=False,
+                                                  use_t=False,
+                                                  df_correction=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = False
+        self.res2 = res2.results_cluster_large
+
+        self.skip_f = True
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+    # skipping see https://github.com/statsmodels/statsmodels/pull/1189#issuecomment-29141741
+    def test_fvalue(self):
+        pass
+
+
+
+class TestOLSRobustClusterGS(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('nw-groupsum',
+                                                  time=self.time,
+                                                  maxlags=4,
+                                                  use_correction=False,
+                                                  use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_nw_groupsum(self.res1, 4, self.time, use_correction=False)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_nw_groupsum4
+
+        self.skip_f = True
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+
+class TestOLSRobustClusterNWP(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('nw-panel',
+                                                  time=self.time,
+                                                  maxlags=4,
+                                                  use_correction='hac',
+                                                  use_t=True,
+                                                  df_correction=False)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_nw_panel(self.res1, 4, self.tidx)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_nw_panel4
+
+        self.skip_f = True
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+
+# TODO: low precision/agreement
+class TestOLSRobustCluster2G(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=(self.groups, self.time),
+                                                  use_correction=True,
+                                                  use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster_2groups(self.res1, self.groups, group2=self.time,
+                                       use_correction=True)[0]
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_cluster_2groups_small
+
+        self.rtol = 0.35 # only f_pvalue and confint for constant differ >rtol=0.05
+        self.rtolh = 1e-10
+
+
+
+class TestOLSRobustCluster2GLarge(CheckOLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=(self.groups, self.time),
+                                                  use_correction=False, #True,
+                                                  use_t=False)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster_2groups(self.res1, self.groups, group2=self.time,
+                                       use_correction=False)[0]
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = False
+        self.res2 = res2.results_cluster_2groups_large
+
+        self.skip_f = True
+        self.rtol = 1e-7
+        self.rtolh = 1e-10
+
+
+######################################
+#                 WLS
+######################################
+
+class CheckWLSRobustCluster(CheckOLSRobust):
+    # compare with regress robust
+
+
+    @classmethod
+    def setup_class(cls):
+        #import pandas as pa
+        from statsmodels.datasets import grunfeld
+
+        dtapa = grunfeld.data.load_pandas()
+        #Stata example/data seems to miss last firm
+        dtapa_endog = dtapa.endog[:200]
+        dtapa_exog = dtapa.exog[:200]
+        exog = add_constant(dtapa_exog[['value', 'capital']], prepend=False)
+        #asserts don't work for pandas
+        cls.res1 = WLS(dtapa_endog, exog, weights=1/dtapa_exog['value']).fit()
+
+        firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'),
+                                    return_inverse=True)
+        cls.groups = firm_id
+        #time indicator in range(max Ti)
+        time = np.asarray(dtapa_exog[['year']])
+        time -= time.min()
+        cls.time = np.squeeze(time).astype(int)
+        # nw_panel function requires interval bounds
+        cls.tidx = [(i*20, 20*(i+1)) for i in range(10)]
+
+
+# not available yet for WLS
+class T_estWLSRobustCluster2(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=self.groups,
+                                                  use_correction=True,
+                                                  use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=True)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_cluster_wls_small
+
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+
+# not available yet for WLS
+class T_estWLSRobustCluster2Large(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('cluster',
+                                                  groups=self.groups,
+                                                  use_correction=False,
+                                                  use_t=False,
+                                                  df_correction=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = False
+        self.res2 = res2.results_cluster_wls_large
+
+        self.skip_f = True
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+class TestWLSRobustSmall(CheckWLSRobustCluster, CheckOLSRobustNewMixin):
+    # compare with `reg cluster`
+
+    def setup(self):
+        res_ols = self.res1.get_robustcov_results('HC1',
+                                                  use_t=True)
+        self.res3 = self.res1
+        self.res1 = res_ols
+        self.bse_robust = res_ols.bse
+        self.cov_robust = res_ols.cov_params()
+        #TODO: check standalone function
+        #cov1 = sw.cov_cluster(self.res1, self.groups, use_correction=False)
+        cov1 = res_ols.cov_HC1
+        se1 =  sw.se_cov(cov1)
+        self.bse_robust2 = se1
+        self.cov_robust2 = cov1
+        self.small = True
+        self.res2 = res2.results_hc1_wls_small
+
+        self.skip_f = True
+        self.rtol = 1e-6
+        self.rtolh = 1e-10
+
+
+class TestWLSOLSRobustSmall(object):
+
+    @classmethod
+    def setup_class(cls):
+        #import pandas as pa
+        from statsmodels.datasets import grunfeld
+
+        dtapa = grunfeld.data.load_pandas()
+        #Stata example/data seems to miss last firm
+        dtapa_endog = dtapa.endog[:200]
+        dtapa_exog = dtapa.exog[:200]
+        exog = add_constant(dtapa_exog[['value', 'capital']], prepend=False)
+        #asserts don't work for pandas
+        cls.res_wls = WLS(dtapa_endog, exog, weights=1/dtapa_exog['value']).fit()
+        w_sqrt = 1 / np.sqrt(np.asarray(dtapa_exog['value']))
+        cls.res_ols = OLS(dtapa_endog * w_sqrt,
+                          np.asarray(exog) * w_sqrt[:, None]).fit() # hasconst=True ?
+
+        firm_names, firm_id = np.unique(np.asarray(dtapa_exog[['firm']], 'S20'),
+                                    return_inverse=True)
+        cls.groups = firm_id
+        #time indicator in range(max Ti)
+        time = np.asarray(dtapa_exog[['year']])
+        time -= time.min()
+        cls.time = np.squeeze(time).astype(int)
+        # nw_panel function requires interval bounds
+        cls.tidx = [(i*20, 20*(i+1)) for i in range(10)]
+
+
+    def test_all(self):
+        all_cov = [('HC0', dict(use_t=True)),
+                   ('HC1', dict(use_t=True)),
+                   ('HC2', dict(use_t=True)),
+                   ('HC3', dict(use_t=True))]
+
+        # fvalue are not the same
+        #res_ols = self.res_ols
+        #res_wls = self.res_wls
+        #assert_allclose(res_ols.fvalue, res_wls.fvalue, rtol=1e-13)
+
+        for cov_type, kwds in all_cov:
+            res1 = self.res_ols.get_robustcov_results(cov_type, **kwds)
+            res2 = self.res_wls.get_robustcov_results(cov_type, **kwds)
+            assert_allclose(res1.params, res2.params, rtol=1e-13)
+            assert_allclose(res1.cov_params(), res2.cov_params(), rtol=1e-13)
+            assert_allclose(res1.bse, res2.bse, rtol=1e-13)
+            assert_allclose(res1.pvalues, res2.pvalues, rtol=1e-13)
+            #Note: Fvalue doesn't match up, difference in calculation ?
+            #      The only difference should be in the constant detection
+            #assert_allclose(res1.fvalue, res2.fvalue, rtol=1e-13)
+            #assert_allclose(res1.f_value, res2.f_pvalue, rtol=1e-13)
+            mat = np.eye(len(res1.params))
+            ft1 = res1.f_test(mat)
+            ft2 = res2.f_test(mat)
+            assert_allclose(ft1.fvalue, ft2.fvalue, rtol=1e-13)
+            assert_allclose(ft1.pvalue, ft2.pvalue, rtol=1e-12)
diff --git a/statsmodels/regression/tests/tests_predict.py b/statsmodels/regression/tests/tests_predict.py
new file mode 100644
index 00000000000..2048990e1c1
--- /dev/null
+++ b/statsmodels/regression/tests/tests_predict.py
@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Apr 20 17:12:53 2014
+
+author: Josef Perktold
+
+"""
+
+import numpy as np
+from statsmodels.regression.linear_model import OLS, WLS
+from statsmodels.sandbox.regression.predstd import wls_prediction_std
+
+
+def test_predict_se():
+    # this test doesn't use reference values
+    # checks conistency across options, and compares to direct calculation
+
+    # generate dataset
+    nsample = 50
+    x1 = np.linspace(0, 20, nsample)
+    x = np.c_[x1, (x1 - 5)**2, np.ones(nsample)]
+    np.random.seed(0)#9876789) #9876543)
+    beta = [0.5, -0.01, 5.]
+    y_true2 = np.dot(x, beta)
+    w = np.ones(nsample)
+    w[nsample * 6. / 10:] = 3
+    sig = 0.5
+    y2 = y_true2 + sig * w * np.random.normal(size=nsample)
+    x2 = x[:,[0,2]]
+
+    # estimate OLS
+    res2 = OLS(y2, x2).fit()
+
+    #direct calculation
+    covb = res2.cov_params()
+    predvar = res2.mse_resid + (x2 * np.dot(covb, x2.T).T).sum(1)
+    predstd = np.sqrt(predvar)
+
+    prstd, iv_l, iv_u = wls_prediction_std(res2)
+    np.testing.assert_almost_equal(prstd, predstd, 15)
+
+    #stats.t.isf(0.05/2., 50 - 2)
+    q = 2.0106347546964458
+    ci_half = q * predstd
+    np.testing.assert_allclose(iv_u, res2.fittedvalues + ci_half, rtol=1e-12)
+    np.testing.assert_allclose(iv_l, res2.fittedvalues - ci_half, rtol=1e-12)
+
+    prstd, iv_l, iv_u = wls_prediction_std(res2, x2[:3,:])
+    np.testing.assert_equal(prstd, prstd[:3])
+    np.testing.assert_allclose(iv_u, res2.fittedvalues[:3] + ci_half[:3],
+                               rtol=1e-12)
+    np.testing.assert_allclose(iv_l, res2.fittedvalues[:3] - ci_half[:3],
+                               rtol=1e-12)
+
+
+    # check WLS
+    res3 = WLS(y2, x2, 1. / w).fit()
+
+    #direct calculation
+    covb = res3.cov_params()
+    predvar = res3.mse_resid * w + (x2 * np.dot(covb, x2.T).T).sum(1)
+    predstd = np.sqrt(predvar)
+
+    prstd, iv_l, iv_u = wls_prediction_std(res3)
+    np.testing.assert_almost_equal(prstd, predstd, 15)
+
+    #stats.t.isf(0.05/2., 50 - 2)
+    q = 2.0106347546964458
+    ci_half = q * predstd
+    np.testing.assert_allclose(iv_u, res3.fittedvalues + ci_half, rtol=1e-12)
+    np.testing.assert_allclose(iv_l, res3.fittedvalues - ci_half, rtol=1e-12)
+
+    # testing shapes of exog
+    prstd, iv_l, iv_u = wls_prediction_std(res3, x2[-1:,:], weights=3.)
+    np.testing.assert_equal(prstd, prstd[-1])
+    prstd, iv_l, iv_u = wls_prediction_std(res3, x2[-1,:], weights=3.)
+    np.testing.assert_equal(prstd, prstd[-1])
+
+    prstd, iv_l, iv_u = wls_prediction_std(res3, x2[-2:,:], weights=3.)
+    np.testing.assert_equal(prstd, prstd[-2:])
+
+    prstd, iv_l, iv_u = wls_prediction_std(res3, x2[-2:,:], weights=[3, 3])
+    np.testing.assert_equal(prstd, prstd[-2:])
+
+    prstd, iv_l, iv_u = wls_prediction_std(res3, x2[:3,:])
+    np.testing.assert_equal(prstd, prstd[:3])
+    np.testing.assert_allclose(iv_u, res3.fittedvalues[:3] + ci_half[:3],
+                               rtol=1e-12)
+    np.testing.assert_allclose(iv_l, res3.fittedvalues[:3] - ci_half[:3],
+                               rtol=1e-12)
+
+
+    #use wrong size for exog
+    #prstd, iv_l, iv_u = wls_prediction_std(res3, x2[-1,0], weights=3.)
+    np.testing.assert_raises(ValueError, wls_prediction_std, res3, x2[-1,0],
+                             weights=3.)
+
+    # check some weight values
+    sew1 = wls_prediction_std(res3, x2[-3:,:])[0]**2
+    for wv in np.linspace(0.5, 3, 5):
+
+        sew = wls_prediction_std(res3, x2[-3:,:], weights=1. / wv)[0]**2
+        np.testing.assert_allclose(sew, sew1 + res3.scale * (wv - 1))
diff --git a/statsmodels/robust/__init__.py b/statsmodels/robust/__init__.py
index d755e79fe55..0ddeffd09c7 100644
--- a/statsmodels/robust/__init__.py
+++ b/statsmodels/robust/__init__.py
@@ -1,7 +1,7 @@
 """
 Robust statistical models
 """
-import norms
+from . import norms
 from .scale import mad, stand_mad, Huber, HuberScale, hubers_scale
 
 from statsmodels import NoseWrapper as Tester
diff --git a/statsmodels/robust/norms.py b/statsmodels/robust/norms.py
index 17ca183e1a8..db34d3836ff 100644
--- a/statsmodels/robust/norms.py
+++ b/statsmodels/robust/norms.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import range
 import numpy as np
 
 #TODO: add plots to weighting functions for online docs.
diff --git a/statsmodels/robust/robust_linear_model.py b/statsmodels/robust/robust_linear_model.py
index 43bdf6b33b2..6ae7375d54c 100644
--- a/statsmodels/robust/robust_linear_model.py
+++ b/statsmodels/robust/robust_linear_model.py
@@ -13,17 +13,18 @@
 R Venables, B Ripley. 'Modern Applied Statistics in S'  Springer, New York,
     2002.
 """
+from statsmodels.compat.python import string_types
 import numpy as np
 import scipy.stats as stats
 
 from statsmodels.tools.decorators import (cache_readonly,
                                                   resettable_cache)
-from statsmodels.tools.tools import rank
 import statsmodels.regression.linear_model as lm
 import statsmodels.robust.norms as norms
 import statsmodels.robust.scale as scale
 import statsmodels.base.model as base
 import statsmodels.base.wrapper as wrap
+from statsmodels.compat.numpy import np_matrix_rank
 
 __all__ = ['RLM']
 
@@ -126,8 +127,9 @@ def _initialize(self):
         self.pinv_wexog = np.linalg.pinv(self.exog)
         self.normalized_cov_params = np.dot(self.pinv_wexog,
                                         np.transpose(self.pinv_wexog))
-        self.df_resid = np.float(self.exog.shape[0] - rank(self.exog))
-        self.df_model = np.float(rank(self.exog)-1)
+        self.df_resid = (np.float(self.exog.shape[0] -
+                         np_matrix_rank(self.exog)))
+        self.df_model = np.float(np_matrix_rank(self.exog)-1)
         self.nobs = float(self.endog.shape[0])
 
     def score(self, params):
@@ -187,9 +189,12 @@ def _estimate_scale(self, resid):
         """
         if isinstance(self.scale_est, str):
             if self.scale_est.lower() == 'mad':
-                return scale.mad(resid)
+                return scale.mad(resid, center=0)
             if self.scale_est.lower() == 'stand_mad':
-                return scale.stand_mad(resid)
+                return scale.mad(resid)
+            else:
+                raise ValueError("Option %s for scale_est not understood" %
+                                 self.scale_est)
         elif isinstance(self.scale_est, scale.HuberScale):
             return self.scale_est(self.df_resid, self.nobs, resid)
         else:
@@ -222,15 +227,13 @@ def fit(self, maxiter=50, tol=1e-8, scale_est='mad', init=None, cov='H1',
         maxiter : int
             The maximum number of iterations to try. Default is 50.
         scale_est : string or HuberScale()
-            'mad', 'stand_mad', or HuberScale()
+            'mad' or HuberScale()
             Indicates the estimate to use for scaling the weights in the IRLS.
             The default is 'mad' (median absolute deviation.  Other options are
-            use 'stand_mad' for the median absolute deviation standardized
-            around the median and 'HuberScale' for Huber's proposal 2.
-            Huber's proposal 2 has optional keyword arguments d, tol, and
-            maxiter for specifying the tuning constant, the convergence
-            tolerance, and the maximum number of iterations.
-            See models.robust.scale for more information.
+            'HuberScale' for Huber's proposal 2. Huber's proposal 2 has
+            optional keyword arguments d, tol, and maxiter for specifying the
+            tuning constant, the convergence tolerance, and the maximum number
+            of iterations. See statsmodels.robust.scale for more information.
         tol : float
             The convergence tolerance of the estimate.  Default is 1e-8.
         update_scale : Bool
@@ -252,6 +255,12 @@ def fit(self, maxiter=50, tol=1e-8, scale_est='mad', init=None, cov='H1',
             raise ValueError("Convergence argument %s not understood" \
                 % conv)
         self.scale_est = scale_est
+        if (isinstance(scale_est,
+                       string_types) and scale_est.lower() == "stand_mad"):
+            from warnings import warn
+            warn("stand_mad is deprecated and will be removed in 0.7.0",
+                 FutureWarning)
+
         wls_results = lm.WLS(self.endog, self.exog).fit()
         if not init:
             self.scale = self._estimate_scale(wls_results.resid)
@@ -394,6 +403,7 @@ def __init__(self, model, params, normalized_cov_params, scale):
         #for remove_data
         self.data_in_cache = ['sresid']
 
+        self.cov_params_default = self.bcov_scaled
         #TODO: "pvals" should come from chisq on bse?
 
     @cache_readonly
@@ -410,7 +420,7 @@ def sresid(self):
 
     @cache_readonly
     def bcov_unscaled(self):
-        return self.cov_params(scale=1.)
+        return self.normalized_cov_params
 
     @cache_readonly
     def weights(self):
@@ -507,7 +517,7 @@ def summary(self, yname=None, xname=None, title=0, alpha=.05,
         smry.add_table_2cols(self, gleft=top_left, gright=top_right, #[],
                           yname=yname, xname=xname, title=title)
         smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
-                             use_t=False)
+                             use_t=self.use_t)
 
         #diagnostic table is not used yet
 #        smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
@@ -591,9 +601,6 @@ class RLMResultsWrapper(lm.RegressionResultsWrapper):
 #    model_ols = models.regression.OLS(endog, exog)
 #    results_ols = model_ols.fit()
 
-#    model_huber = RLM(endog, exog, M=norms.HuberT(t=2.))
-#    results_huber = model_huber.fit(scale_est="stand_mad", update_scale=False)
-
 #    model_ramsaysE = RLM(endog, exog, M=norms.RamsayE())
 #    results_ramsaysE = model_ramsaysE.fit(update_scale=False)
 
diff --git a/statsmodels/robust/scale.py b/statsmodels/robust/scale.py
index b3df376dd7a..2d246475e26 100644
--- a/statsmodels/robust/scale.py
+++ b/statsmodels/robust/scale.py
@@ -8,13 +8,14 @@
 R Venables, B Ripley. 'Modern Applied Statistics in S'
     Springer, New York, 2002.
 """
-
+from statsmodels.compat.python import callable, range
 import numpy as np
 from scipy.stats import norm as Gaussian
-import norms
+from . import norms
 from statsmodels.tools import tools
 
-def mad(a, c=Gaussian.ppf(3/4.), axis=0):  # c \approx .6745
+def mad(a, c=Gaussian.ppf(3/4.), axis=0, center=np.median):
+    # c \approx .6745
     """
     The Median Absolute Deviation along given axis of an array
 
@@ -26,40 +27,27 @@ def mad(a, c=Gaussian.ppf(3/4.), axis=0):  # c \approx .6745
         The normalization constant.  Defined as scipy.stats.norm.ppf(3/4.),
         which is approximately .6745.
     axis : int, optional
-        The defaul is 0.
+        The defaul is 0. Can also be None.
+    center : callable or float
+        If a callable is provided, such as the default `np.median` then it
+        is expected to be called center(a). The axis argument will be applied
+        via np.apply_over_axes. Otherwise, provide a float.
 
     Returns
     -------
     mad : float
-        `mad` = median(abs(`a`))/`c`
+        `mad` = median(abs(`a` - center))/`c`
     """
     a = np.asarray(a)
-    return np.median((np.fabs(a))/c, axis=axis)
+    if callable(center):
+        center = np.apply_over_axes(center, a, axis)
+    return np.median((np.fabs(a-center))/c, axis=axis)
 
 def stand_mad(a, c=Gaussian.ppf(3/4.), axis=0):
-    """
-    The standardized Median Absolute Deviation along given axis of an array.
-
-    Parameters
-    ----------
-    a : array-like
-        Input array.
-    c : float, optional
-        The normalization constant.  Defined as scipy.stats.norm.ppf(3/4.),
-        which is approximately .6745.
-    axis : int, optional
-        The defaul is 0.
-
-    Returns
-    -------
-    mad : float
-        `mad` = median(abs(`a`-median(`a`))/`c`
-    """
-
-    a = np.asarray(a)
-    d = np.median(a, axis = axis)
-    d = tools.unsqueeze(d, axis, a.shape)
-    return np.median(np.fabs(a - d)/c, axis = axis)
+    from warnings import warn
+    warn("stand_mad is deprecated and will be removed in 0.7.0. Use mad "
+         "instead.", FutureWarning)
+    return mad(a, c=c, axis=axis)
 
 class Huber(object):
     """
@@ -138,7 +126,7 @@ def __call__(self, a, mu=None, initscale=None, axis=0):
             est_mu = False
 
         if initscale is None:
-            scale = stand_mad(a, axis=axis)
+            scale = mad(a, axis=axis)
         else:
             scale = initscale
         scale = tools.unsqueeze(scale, axis, a.shape)
@@ -235,7 +223,7 @@ def __call__(self, df_resid, nobs, resid):
         h = (df_resid)/nobs*(self.d**2 + (1-self.d**2)*\
                     Gaussian.cdf(self.d)-.5 - self.d/(np.sqrt(2*np.pi))*\
                     np.exp(-.5*self.d**2))
-        s = stand_mad(resid)
+        s = mad(resid)
         subset = lambda x: np.less(np.fabs(resid/x),self.d)
         chi = lambda s: subset(s)*(resid/s)**2/2+(1-subset(s))*(self.d**2/2)
         scalehist = [np.inf,s]
diff --git a/statsmodels/robust/tests/test_rlm.py b/statsmodels/robust/tests/test_rlm.py
index 4e831384758..63605953cf8 100644
--- a/statsmodels/robust/tests/test_rlm.py
+++ b/statsmodels/robust/tests/test_rlm.py
@@ -116,7 +116,7 @@ def setup(self):
 #        r.library('MASS')
 #        self.res2 = RModel(self.data.endog, self.data.exog,
 #                        r.rlm, psi="psi.huber")
-        from results.results_rlm import Huber
+        from .results.results_rlm import Huber
         self.res2 = Huber()
 
     def test_summary(self):
@@ -143,7 +143,7 @@ def __init__(self):
     def setup(self):
 #        self.res2 = RModel(self.data.endog[:,None], self.data.exog,
 #        r.rlm, psi="psi.hampel") #, init="lts")
-        from results.results_rlm import Hampel
+        from .results.results_rlm import Hampel
         self.res2 = Hampel()
 
 
@@ -168,7 +168,7 @@ def __init__(self):
     def setup(self):
 #        self.res2 = RModel(self.data.endog, self.data.exog,
 #                        r.rlm, psi="psi.bisquare")
-        from results.results_rlm import BiSquare
+        from .results.results_rlm import BiSquare
         self.res2 = BiSquare()
 
 
@@ -187,7 +187,7 @@ def __init__(self):
         self.res1.h3 = h3
 
     def setup(self):
-        from results.results_rlm import Andrews
+        from .results.results_rlm import Andrews
         self.res2 = Andrews()
 
 ### tests with Huber scaling
@@ -211,7 +211,7 @@ def __init__(self):
         self.res1.h3 = h3
 
     def setup(self):
-        from results.results_rlm import HuberHuber
+        from .results.results_rlm import HuberHuber
         self.res2 = HuberHuber()
 
 class TestHampelHuber(TestRlm):
@@ -232,7 +232,7 @@ def __init__(self):
         self.res1.h3 = h3
 
     def setup(self):
-        from results.results_rlm import HampelHuber
+        from .results.results_rlm import HampelHuber
         self.res2 = HampelHuber()
 
 class TestRlmBisquareHuber(TestRlm):
@@ -254,7 +254,7 @@ def __init__(self):
         self.res1.h3 = h3
 
     def setup(self):
-        from results.results_rlm import BisquareHuber
+        from .results.results_rlm import BisquareHuber
         self.res2 = BisquareHuber()
 
 class TestRlmAndrewsHuber(TestRlm):
@@ -275,7 +275,7 @@ def __init__(self):
         self.res1.h3 = h3
 
     def setup(self):
-        from results.results_rlm import AndrewsHuber
+        from .results.results_rlm import AndrewsHuber
         self.res2 = AndrewsHuber()
 
 class TestRlmSresid(CheckRlmResultsMixin):
@@ -303,5 +303,5 @@ def setup(self):
 #        r.library('MASS')
 #        self.res2 = RModel(self.data.endog, self.data.exog,
 #                        r.rlm, psi="psi.huber")
-        from results.results_rlm import Huber
+        from .results.results_rlm import Huber
         self.res2 = Huber()
diff --git a/statsmodels/robust/tests/test_scale.py b/statsmodels/robust/tests/test_scale.py
index 1453e7a7134..fe4a226cfea 100644
--- a/statsmodels/robust/tests/test_scale.py
+++ b/statsmodels/robust/tests/test_scale.py
@@ -25,8 +25,8 @@ def test_mean(self):
     def test_median(self):
         assert_almost_equal(np.median(self.chem), 3.385, DECIMAL)
 
-    def test_stand_mad(self):
-        assert_almost_equal(scale.stand_mad(self.chem), 0.52632, DECIMAL)
+    def test_mad(self):
+        assert_almost_equal(scale.mad(self.chem), 0.52632, DECIMAL)
 
     def test_huber_scale(self):
         assert_almost_equal(scale.huber(self.chem)[0], 3.20549, DECIMAL)
@@ -51,36 +51,36 @@ def __init__(self):
         np.random.seed(54321)
         self.X = standard_normal((40,10))
 
-    def test_stand_mad(self):
-        m = scale.stand_mad(self.X)
+    def test_mad(self):
+        m = scale.mad(self.X)
         assert_equal(m.shape, (10,))
 
-    def test_mad(self):
-        n = scale.mad(self.X)
+    def test_mad_center(self):
+        n = scale.mad(self.X, center=0)
         assert_equal(n.shape, (10,))
 
-class TestMadAxes():
+class TestMadAxes(object):
     def __init__(self):
         np.random.seed(54321)
         self.X = standard_normal((40,10,30))
 
     def test_axis0(self):
-        m = scale.stand_mad(self.X, axis=0)
+        m = scale.mad(self.X, axis=0)
         assert_equal(m.shape, (10,30))
 
     def test_axis1(self):
-        m = scale.stand_mad(self.X, axis=1)
+        m = scale.mad(self.X, axis=1)
         assert_equal(m.shape, (40,30))
 
     def test_axis2(self):
-        m = scale.stand_mad(self.X, axis=2)
+        m = scale.mad(self.X, axis=2)
         assert_equal(m.shape, (40,10))
 
     def test_axisneg1(self):
-        m = scale.stand_mad(self.X, axis=-1)
+        m = scale.mad(self.X, axis=-1)
         assert_equal(m.shape, (40,10))
 
-class TestHuber():
+class TestHuber(object):
     def __init__(self):
         np.random.seed(54321)
         self.X = standard_normal((40,10))
diff --git a/statsmodels/sandbox/archive/linalg_covmat.py b/statsmodels/sandbox/archive/linalg_covmat.py
index 3266cdf0041..be066d24fe4 100644
--- a/statsmodels/sandbox/archive/linalg_covmat.py
+++ b/statsmodels/sandbox/archive/linalg_covmat.py
@@ -1,9 +1,9 @@
-
+from __future__ import print_function
 import math
 import numpy as np
 from scipy import linalg, stats
 
-from linalg_decomp_1 import tiny2zero
+from .linalg_decomp_1 import tiny2zero
 
 #univariate standard normal distribution
 #following from scipy.stats.distributions with adjustments
@@ -58,7 +58,7 @@ def __init__(self, const, tmat, dist):
 
     def rvs(self, size):
         #size can only be integer not yet tuple
-        print (size,)+(self.nrv,)
+        print((size,)+(self.nrv,))
         return self.transform(self.dist.rvs(size=(size,)+(self.nrv,)))
 
     def transform(self, x):
@@ -75,7 +75,7 @@ def logpdf(self, x):
         return - self.logabsdet + self.dist.logpdf(self.invtransform(x))
 
 
-from linalg_decomp_1 import SvdArray, OneTimeProperty
+from .linalg_decomp_1 import SvdArray, OneTimeProperty
 
 class MultivariateNormal(object):
     '''multivariate normal distribution with plain linalg
@@ -251,32 +251,32 @@ def mvn_nloglike_obs(x, sigma):
 
 ll, ls = mvn_nloglike_obs(x, sigma)
 #the following are all the same for diagonal sigma
-print ll.sum(), 'll.sum()'
-print llike.sum(), 'llike.sum()'
-print np.log(stats.norm._pdf(x_whitened)).sum() - 0.5 * logdetsigma,
-print 'stats whitened'
-print np.log(stats.norm.pdf(x,scale=np.sqrt(np.diag(sigma)))).sum(),
-print 'stats scaled'
-print 0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma, lower=False)[0].T,
+print(ll.sum(), 'll.sum()')
+print(llike.sum(), 'llike.sum()')
+print(np.log(stats.norm._pdf(x_whitened)).sum() - 0.5 * logdetsigma,)
+print('stats whitened')
+print(np.log(stats.norm.pdf(x,scale=np.sqrt(np.diag(sigma)))).sum(),)
+print('stats scaled')
+print(0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma, lower=False)[0].T,
                                     False),x.T), x)
            + nobs*np.log(2*np.pi)
-           - 2.* np.log(np.diagonal(cholsigmainv)).sum())
-print 0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma)[0].T, False),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum())
-print 0.5*(np.dot(linalg.cho_solve(linalg.cho_factor(sigma),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum())
-print mvn_loglike(x, sigma)
+           - 2.* np.log(np.diagonal(cholsigmainv)).sum()))
+print(0.5*(np.dot(linalg.cho_solve((linalg.cho_factor(sigma)[0].T, False),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum()))
+print(0.5*(np.dot(linalg.cho_solve(linalg.cho_factor(sigma),x.T), x) + nobs*np.log(2*np.pi)- 2.* np.log(np.diagonal(cholsigmainv)).sum()))
+print(mvn_loglike(x, sigma))
 
 
 normtransf = AffineTransform(np.zeros(nobs), cholsigma, StandardNormal())
-print normtransf.logpdf(x_whitened).sum()
-#print normtransf.rvs(5)
-print loglike_ar1(x, 0.8)
+print(normtransf.logpdf(x_whitened).sum())
+#print(normtransf.rvs(5)
+print(loglike_ar1(x, 0.8))
 
 mch = MultivariateNormalChol(np.zeros(nobs), sigma)
-print mch.logpdf(x)
+print(mch.logpdf(x))
 
-#print tiny2zero(mch.cholsigmainv / mch.cholsigmainv[-1,-1])
+#print(tiny2zero(mch.cholsigmainv / mch.cholsigmainv[-1,-1])
 
 xw = mch.whiten(x)
-print 'xSigmax', np.dot(xw,xw)
-print 'xSigmax', np.dot(x,linalg.cho_solve(linalg.cho_factor(mch.sigma),x))
-print 'xSigmax', np.dot(x,linalg.cho_solve((mch.cholsigma, False),x))
+print('xSigmax', np.dot(xw,xw))
+print('xSigmax', np.dot(x,linalg.cho_solve(linalg.cho_factor(mch.sigma),x)))
+print('xSigmax', np.dot(x,linalg.cho_solve((mch.cholsigma, False),x)))
diff --git a/statsmodels/sandbox/archive/linalg_decomp_1.py b/statsmodels/sandbox/archive/linalg_decomp_1.py
index ff1b7bc3395..83248ea54e2 100644
--- a/statsmodels/sandbox/archive/linalg_decomp_1.py
+++ b/statsmodels/sandbox/archive/linalg_decomp_1.py
@@ -22,7 +22,8 @@
 Created on 2010-10-20
 '''
 
-
+from __future__ import print_function
+from statsmodels.compat.python import get_function_name
 import numpy as np
 from scipy import linalg
 
@@ -50,7 +51,7 @@ def __init__(self,func):
              the value of this computation.
              """
         self.getter = func
-        self.name = func.func_name
+        self.name = get_function_name(func)
 
     def __get__(self,obj,type=None):
         """This will be called on attribute access on the class or instance. """
@@ -59,11 +60,11 @@ def __get__(self,obj,type=None):
             # Being called on the class, return the original function. This way,
             # introspection works on the class.
             #return func
-            print 'class access'
+            print('class access')
             return self.getter
 
         val = self.getter(obj)
-        #print "** auto_attr - loading '%s'" % self.name  # dbg
+        #print("** auto_attr - loading '%s'" % self.name  # dbg)
         setattr(obj, self.name, val)
         return val
 
@@ -280,17 +281,17 @@ def maxabs(x):
     sigma = linalg.toeplitz(autocov)
 
     mat = PlainMatrixArray(sym=sigma)
-    print tiny2zero(mat.mhalf)
+    print(tiny2zero(mat.mhalf))
     mih = mat.minvhalf
-    print tiny2zero(mih) #for nicer printing
+    print(tiny2zero(mih)) #for nicer printing
 
     mat2 = PlainMatrixArray(data=x)
-    print maxabs(mat2.yt_minv_y(np.dot(x.T, x)) - mat2.m)
-    print tiny2zero(mat2.minv_y(mat2.m))
+    print(maxabs(mat2.yt_minv_y(np.dot(x.T, x)) - mat2.m))
+    print(tiny2zero(mat2.minv_y(mat2.m)))
 
     mat3 = SvdArray(data=x)
-    print mat3.meigh[0]
-    print mat2.meigh[0]
+    print(mat3.meigh[0])
+    print(mat2.meigh[0])
 
     testcompare(mat2, mat3)
 
diff --git a/statsmodels/sandbox/bspline.py b/statsmodels/sandbox/bspline.py
index 4ff2e37b668..c28d9f6b621 100644
--- a/statsmodels/sandbox/bspline.py
+++ b/statsmodels/sandbox/bspline.py
@@ -15,6 +15,7 @@
     Numerische Mathematik, 47(1), 99-106.
 '''
 
+from statsmodels.compat.python import range
 import numpy as np
 import numpy.linalg as L
 
@@ -24,9 +25,11 @@
 
 # Issue warning regarding heavy development status of this module
 import warnings
-_msg = "The bspline code is technology preview and requires significant work\
-on the public API and documentation. The API will likely change in the future"
-warnings.warn(_msg, UserWarning)
+_msg = """
+The bspline code is technology preview and requires significant work
+on the public API and documentation. The API will likely change in the future
+"""
+warnings.warn(_msg, FutureWarning)
 
 
 def _band2array(a, lower=0, symmetric=False, hermitian=False):
diff --git a/statsmodels/sandbox/contrast_old.py b/statsmodels/sandbox/contrast_old.py
index 9699778a37c..087ccd5aa03 100644
--- a/statsmodels/sandbox/contrast_old.py
+++ b/statsmodels/sandbox/contrast_old.py
@@ -32,10 +32,10 @@ def __array__(self):
     def __str__(self):
         if hasattr(self, 'F'):
             return '<F contrast: F=%s, df_denom=%d, df_num=%d>' % \
-                   (`self.F`, self.df_denom, self.df_num)
+                   (repr(self.F), self.df_denom, self.df_num)
         else:
             return '<T contrast: effect=%s, sd=%s, t=%s, df_denom=%d>' % \
-                   (`self.effect`, `self.sd`, `self.t`, self.df_denom)
+                   (repr(self.effect), repr(self.sd), repr(self.t), self.df_denom)
 
 
 class Contrast(object):
@@ -72,7 +72,7 @@ def __init__(self, term, formula, name=''):
 
     def __str__(self):
         return '<contrast:%s>' % \
-               `{'term':str(self.term), 'formula':str(self.formula)}`
+               repr({'term':str(self.term), 'formula':str(self.formula)})
 
     def compute_matrix(self, *args, **kw):
         """
@@ -146,7 +146,7 @@ def contrastfromcols(L, D, pseudo=None):
     n, p = D.shape
 
     if L.shape[0] != n and L.shape[1] != p:
-        raise ValueError, 'shape of L and D mismatched'
+        raise ValueError('shape of L and D mismatched')
 
     if pseudo is None:
         pseudo = pinv(D)
diff --git a/statsmodels/sandbox/cox.py b/statsmodels/sandbox/cox.py
index d21b0581f40..555d2a5c8b8 100644
--- a/statsmodels/sandbox/cox.py
+++ b/statsmodels/sandbox/cox.py
@@ -24,8 +24,8 @@
 need testcase before trying to fix
 
 '''
-
-
+from __future__ import print_function
+from statsmodels.compat.python import iterkeys, range
 import shutil
 import tempfile
 
@@ -33,7 +33,7 @@
 
 
 from statsmodels.base import model
-import survival
+from . import survival
 
 class Discrete(object):
 
@@ -99,7 +99,7 @@ def __init__(self, subjects, formula, time_dependent=False):
         self.initialize(self.subjects)
 
     def initialize(self, subjects):
-        print 'called initialize'
+        print('called initialize')
         self.failures = {}
         for i in range(len(subjects)):
             s = subjects[i]
@@ -109,7 +109,7 @@ def initialize(self, subjects):
                 else:
                     self.failures[s.time].append(i)
 
-        self.failure_times = self.failures.keys()
+        self.failure_times = list(iterkeys(self.failures))
         self.failure_times.sort()
 
     def cache(self):
@@ -120,7 +120,7 @@ def cache(self):
         self.risk = {}
         first = True
 
-        for t in self.failures.keys():
+        for t in iterkeys(self.failures):
             if self.time_dependent:
                 d = np.array([s(self.formula, time=t)
                              for s in self.subjects]).astype(float)[:,None]
@@ -145,13 +145,13 @@ def __del__(self):
         try:
             shutil.rmtree(self.cachedir, ignore_errors=True)
         except AttributeError:
-            print "AttributeError: 'CoxPH' object has no attribute 'cachedir'"
+            print("AttributeError: 'CoxPH' object has no attribute 'cachedir'")
             pass
 
     def loglike(self, b, ties='breslow'):
 
         logL = 0
-        for t in self.failures.keys():
+        for t in iterkeys(self.failures):
             fail = self.failures[t]
             d = len(fail)
             risk = self.risk[t]
@@ -177,7 +177,7 @@ def loglike(self, b, ties='breslow'):
     def score(self, b, ties='breslow'):
 
         score = 0
-        for t in self.failures.keys():
+        for t in iterkeys(self.failures):
             fail = self.failures[t]
             d = len(fail)
             risk = self.risk[t]
@@ -208,7 +208,7 @@ def information(self, b, ties='breslow'):
 
         info = 0 #np.zeros((len(b),len(b))) #0
         score = 0
-        for t in self.failures.keys():
+        for t in iterkeys(self.failures):
             fail = self.failures[t]
             d = len(fail)
             risk = self.risk[t]
@@ -261,8 +261,8 @@ def information(self, b, ties='breslow'):
 
     #res = c.newton([0.4])  #doesn't work anymore
     res=c.fit([0.4],method="bfgs")
-    print res.params
-    print dir(c)
+    print(res.params)
+    print(dir(c))
     #print c.fit(Y)
     #c.information(res.params)  #raises exception
 
diff --git a/statsmodels/sandbox/datarich/factormodels.py b/statsmodels/sandbox/datarich/factormodels.py
index 5fad557b53d..3c1b0e39c86 100644
--- a/statsmodels/sandbox/datarich/factormodels.py
+++ b/statsmodels/sandbox/datarich/factormodels.py
@@ -6,7 +6,7 @@
 License: BSD (3-clause)
 """
 
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_array_almost_equal
 import statsmodels.api as sm
@@ -184,10 +184,10 @@ def summary_find_nfact(self):
         y0 = ytrue + 0.1*np.random.normal(size=ytrue.shape)
 
         mod = FactorModelUnivariate(y0, x0)
-        print mod.summary_find_nfact()
-        print "with cross validation - slower"
+        print(mod.summary_find_nfact())
+        print("with cross validation - slower")
         mod.fit_find_nfact(maxfact=None, skip_crossval=False, cv_iter=None)
-        print mod.summary_find_nfact()
+        print(mod.summary_find_nfact())
 
 
 
diff --git a/statsmodels/sandbox/descstats.py b/statsmodels/sandbox/descstats.py
index bc57b69d0b5..1291f078cd5 100644
--- a/statsmodels/sandbox/descstats.py
+++ b/statsmodels/sandbox/descstats.py
@@ -132,7 +132,7 @@ def descstats(data, cols=None, axis=0):
                 'stddev': x[:,var].std(), 'range': '('+str(x[:,var].min())+', '+\
                 str(x[:,var].max())+')'+os.linesep}
     else:
-        raise ValueError, "data not understood"
+        raise ValueError("data not understood")
 
     return desc
 
diff --git a/statsmodels/sandbox/distributions/estimators.py b/statsmodels/sandbox/distributions/estimators.py
index 7fc2f7756bd..2933ed01b3e 100644
--- a/statsmodels/sandbox/distributions/estimators.py
+++ b/statsmodels/sandbox/distributions/estimators.py
@@ -87,7 +87,7 @@
 
 '''
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats, optimize, special
 
@@ -423,8 +423,8 @@ def fit_mps(dist, data, x0=None):
     if x0 is None:
         x0 = getstartparams(dist, xsorted)
     args = (xsorted, dist)
-    print x0
-    #print args
+    print(x0)
+    #print(args)
     return optimize.fmin(logmps, x0, args=args)
 
 
@@ -434,29 +434,29 @@ def fit_mps(dist, data, x0=None):
     #Example: gamma - distribution
     #-----------------------------
 
-    print '\n\nExample: gamma Distribution'
-    print     '---------------------------'
+    print('\n\nExample: gamma Distribution')
+    print(    '---------------------------')
 
     alpha = 2
     xq = [0.5, 4]
     pq = [0.1, 0.9]
-    print stats.gamma.ppf(pq, alpha)
+    print(stats.gamma.ppf(pq, alpha))
     xq = stats.gamma.ppf(pq, alpha)
-    print np.diff((stats.gamma.ppf(pq, np.linspace(0.01,4,10)[:,None])*xq[::-1]))
+    print(np.diff((stats.gamma.ppf(pq, np.linspace(0.01,4,10)[:,None])*xq[::-1])))
     #optimize.bisect(lambda alpha: np.diff((stats.gamma.ppf(pq, alpha)*xq[::-1])))
-    print optimize.fsolve(lambda alpha: np.diff((stats.gamma.ppf(pq, alpha)*xq[::-1])), 3.)
+    print(optimize.fsolve(lambda alpha: np.diff((stats.gamma.ppf(pq, alpha)*xq[::-1])), 3.))
 
     distfn = stats.gamma
     mcond = gammamomentcond(distfn, [5.,10], mom2=stats.gamma.stats(alpha, 0.,1.), quantile=None)
-    print optimize.fsolve(mcond, [1.,2.])
+    print(optimize.fsolve(mcond, [1.,2.]))
     mom2 = stats.gamma.stats(alpha, 0.,1.)
-    print optimize.fsolve(lambda params:gammamomentcond2(distfn, params, mom2), [1.,2.])
+    print(optimize.fsolve(lambda params:gammamomentcond2(distfn, params, mom2), [1.,2.]))
 
     grvs = stats.gamma.rvs(alpha, 0.,2., size=1000)
     mom2 = np.array([grvs.mean(), grvs.var()])
     alphaestq = optimize.fsolve(lambda params:gammamomentcond2(distfn, params, mom2), [1.,3.])
-    print alphaestq
-    print 'scale = ', xq/stats.gamma.ppf(pq, alphaestq)
+    print(alphaestq)
+    print('scale = ', xq/stats.gamma.ppf(pq, alphaestq))
 
 
     #Example beta - distribution
@@ -464,8 +464,8 @@ def fit_mps(dist, data, x0=None):
 
     #Warning: this example had cut-and-paste errors
 
-    print '\n\nExample: beta Distribution'
-    print     '--------------------------'
+    print('\n\nExample: beta Distribution')
+    print(    '--------------------------')
 
     #monkey patching :
 ##    if hasattr(stats.beta, '_fitstart'):
@@ -477,21 +477,21 @@ def fit_mps(dist, data, x0=None):
     pq = np.array([0.01, 0.05,0.1,0.4,0.6,0.9,0.95,0.99])
     #rvsb = stats.beta.rvs(0.5,0.15,size=200)
     rvsb = stats.beta.rvs(10,15,size=2000)
-    print 'true params', 10, 15, 0, 1
-    print stats.beta.fit(rvsb)
+    print('true params', 10, 15, 0, 1)
+    print(stats.beta.fit(rvsb))
     xqsb = [stats.scoreatpercentile(rvsb, p) for p in pq*100]
     mom2s = np.array([rvsb.mean(), rvsb.var()])
     betaparest_gmmquantile = optimize.fmin(lambda params:np.sum(momentcondquant(stats.beta, params, mom2s,(pq,xqsb), shape=None)**2),
                                            [10,10, 0., 1.], maxiter=2000)
-    print 'betaparest_gmmquantile',  betaparest_gmmquantile
+    print('betaparest_gmmquantile',  betaparest_gmmquantile)
     #result sensitive to initial condition
 
 
     #Example t - distribution
     #------------------------
 
-    print '\n\nExample: t Distribution'
-    print     '-----------------------'
+    print('\n\nExample: t Distribution')
+    print(    '-----------------------')
 
     nobs = 1000
     distfn = stats.t
@@ -502,10 +502,10 @@ def fit_mps(dist, data, x0=None):
     mom2th = distfn.stats(*paramsdgp)
     mom2s = np.array([trvs.mean(), trvs.var()])
     tparest_gmm3quantilefsolve = optimize.fsolve(lambda params:momentcondunbound(distfn,params, mom2s,(pq,xqs)), [10,1.,2.])
-    print 'tparest_gmm3quantilefsolve', tparest_gmm3quantilefsolve
+    print('tparest_gmm3quantilefsolve', tparest_gmm3quantilefsolve)
     tparest_gmm3quantile = optimize.fmin(lambda params:np.sum(momentcondunbound(distfn,params, mom2s,(pq,xqs))**2), [10,1.,2.])
-    print 'tparest_gmm3quantile', tparest_gmm3quantile
-    print distfn.fit(trvs)
+    print('tparest_gmm3quantile', tparest_gmm3quantile)
+    print(distfn.fit(trvs))
 
     ##
 
@@ -516,11 +516,11 @@ def fit_mps(dist, data, x0=None):
     ##xqs = [stats.scoreatpercentile(trvs, p) for p in pq*100]
     ##mom2th = distfn.stats(*paramsdgp)
     ##mom2s = np.array([trvs.mean(), trvs.var()])
-    print optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,shape=5), [1.,2.])
-    print optimize.fmin(lambda params:np.sum(momentcondunboundls(distfn, params, mom2s,shape=5)**2), [1.,2.])
-    print distfn.fit(trvs)
+    print(optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,shape=5), [1.,2.]))
+    print(optimize.fmin(lambda params:np.sum(momentcondunboundls(distfn, params, mom2s,shape=5)**2), [1.,2.]))
+    print(distfn.fit(trvs))
     #loc, scale, based on quantiles
-    print optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,(pq,xqs),shape=5), [1.,2.])
+    print(optimize.fsolve(lambda params:momentcondunboundls(distfn, params, mom2s,(pq,xqs),shape=5), [1.,2.]))
 
     ##
 
@@ -528,9 +528,9 @@ def fit_mps(dist, data, x0=None):
     #paramsdgp = (5, 0, 1)
     xqs = [stats.scoreatpercentile(trvs, p) for p in pq*100]
     tparest_gmmquantile = optimize.fmin(lambda params:np.sum(momentcondquant(distfn, params, mom2s,(pq,xqs), shape=None)**2), [10, 1.,2.])
-    print 'tparest_gmmquantile', tparest_gmmquantile
+    print('tparest_gmmquantile', tparest_gmmquantile)
     tparest_gmmquantile2 = fitquantilesgmm(distfn, trvs, start=[10, 1.,2.], pquant=None, frozen=None)
-    print 'tparest_gmmquantile2', tparest_gmmquantile2
+    print('tparest_gmmquantile2', tparest_gmmquantile2)
 
 
     ##
@@ -539,11 +539,11 @@ def fit_mps(dist, data, x0=None):
     #use trvs from before
     bt = stats.t.ppf(np.linspace(0,1,21),5)
     ft,bt = np.histogram(trvs,bins=bt)
-    print 'fitbinned t-distribution'
+    print('fitbinned t-distribution')
     tparest_mlebinew = fitbinned(stats.t, ft, bt, [10, 0, 1])
     tparest_gmmbinewidentity = fitbinnedgmm(stats.t, ft, bt, [10, 0, 1])
     tparest_gmmbinewoptimal = fitbinnedgmm(stats.t, ft, bt, [10, 0, 1], weightsoptimal=False)
-    print paramsdgp
+    print(paramsdgp)
 
     #Note: this can be used for chisquare test and then has correct asymptotic
     #   distribution for a distribution with estimated parameters, find ref again
@@ -558,21 +558,21 @@ def fit_mps(dist, data, x0=None):
     tparest_mle = stats.t.fit(trvs)
 
     np.set_printoptions(precision=6)
-    print 'sample size', nobs
-    print 'true (df, loc, scale)      ', paramsdgp
-    print 'parest_mle                 ', tparest_mle
+    print('sample size', nobs)
+    print('true (df, loc, scale)      ', paramsdgp)
+    print('parest_mle                 ', tparest_mle)
     print
-    print 'tparest_mlebinel           ', tparest_mlebinel
-    print 'tparest_gmmbinelidentity   ', tparest_gmmbinelidentity
-    print 'tparest_gmmbineloptimal    ', tparest_gmmbineloptimal
+    print('tparest_mlebinel           ', tparest_mlebinel)
+    print('tparest_gmmbinelidentity   ', tparest_gmmbinelidentity)
+    print('tparest_gmmbineloptimal    ', tparest_gmmbineloptimal)
     print
-    print 'tparest_mlebinew           ', tparest_mlebinew
-    print 'tparest_gmmbinewidentity   ', tparest_gmmbinewidentity
-    print 'tparest_gmmbinewoptimal    ', tparest_gmmbinewoptimal
+    print('tparest_mlebinew           ', tparest_mlebinew)
+    print('tparest_gmmbinewidentity   ', tparest_gmmbinewidentity)
+    print('tparest_gmmbinewoptimal    ', tparest_gmmbinewoptimal)
     print
-    print 'tparest_gmmquantileidentity', tparest_gmmquantile
-    print 'tparest_gmm3quantilefsolve ', tparest_gmm3quantilefsolve
-    print 'tparest_gmm3quantile       ', tparest_gmm3quantile
+    print('tparest_gmmquantileidentity', tparest_gmmquantile)
+    print('tparest_gmm3quantilefsolve ', tparest_gmm3quantilefsolve)
+    print('tparest_gmm3quantile       ', tparest_gmm3quantile)
 
     ''' example results:
     standard error for df estimate looks large
@@ -606,22 +606,22 @@ def fit_mps(dist, data, x0=None):
     #tough problem for MLE according to JKB
     #but not sure for which parameters
 
-    print '\n\nExample: Lognormal Distribution'
-    print     '-------------------------------'
+    print('\n\nExample: Lognormal Distribution')
+    print(    '-------------------------------')
 
     sh = np.exp(10)
     sh = 0.01
-    print sh
+    print(sh)
     x = stats.lognorm.rvs(sh,loc=100, scale=10,size=200)
 
-    print x.min()
-    print stats.lognorm.fit(x,  1.,loc=x.min()-1,scale=1)
+    print(x.min())
+    print(stats.lognorm.fit(x,  1.,loc=x.min()-1,scale=1))
 
     xsorted = np.sort(x)
 
     x0 = [1., x.min()-1, 1]
     args = (xsorted, stats.lognorm)
-    print optimize.fmin(logmps,x0,args=args)
+    print(optimize.fmin(logmps,x0,args=args))
 
 
     #Example: Lomax, Pareto, Generalized Pareto Distributions
@@ -633,10 +633,10 @@ def fit_mps(dist, data, x0=None):
 
     # current results:
     # doesn't look very good yet sensitivity to starting values
-    # Pareto and Generalized Pareto look like a tough estimation problemprint '\n\nExample: Lognormal Distribution'
+    # Pareto and Generalized Pareto look like a tough estimation problemprint('\n\nExample: Lognormal Distribution'
 
-    print '\n\nExample: Lomax, Pareto, Generalized Pareto Distributions'
-    print     '--------------------------------------------------------'
+    print('\n\nExample: Lomax, Pareto, Generalized Pareto Distributions')
+    print(    '--------------------------------------------------------')
 
 
 
@@ -646,28 +646,28 @@ def fit_mps(dist, data, x0=None):
     p2rvssorted = np.sort(p2rvs)
     argsp = (p2rvssorted, stats.pareto)
     x0p = [1., p2rvs.min()-5, 1]
-    print optimize.fmin(logmps,x0p,args=argsp)
-    print stats.pareto.fit(p2rvs, 0.5, loc=-20, scale=0.5)
-    print 'gpdparest_ mle', stats.genpareto.fit(p2rvs)
+    print(optimize.fmin(logmps,x0p,args=argsp))
+    print(stats.pareto.fit(p2rvs, 0.5, loc=-20, scale=0.5))
+    print('gpdparest_ mle', stats.genpareto.fit(p2rvs))
     parsgpd = fit_mps(stats.genpareto, p2rvs)
-    print 'gpdparest_ mps', parsgpd
+    print('gpdparest_ mps', parsgpd)
     argsgpd = (p2rvssorted, stats.genpareto)
     options = dict(stepFix=1e-7)
     #hess_ndt(fun, pars, argsgdp, options)
     #the results for the following look strange, maybe refactoring error
     he, h = hess_ndt(logmps, parsgpd, argsgpd, options)
-    print np.linalg.eigh(he)[0]
+    print(np.linalg.eigh(he)[0])
     f = lambda params: logmps(params, *argsgpd)
-    print f(parsgpd)
+    print(f(parsgpd))
     #add binned
     fp2, bp2 = np.histogram(p2rvs, bins=50)
     'fitbinned t-distribution'
     gpdparest_mlebinel = fitbinned(stats.genpareto, fp2, bp2, x0p)
     gpdparest_gmmbinelidentity = fitbinnedgmm(stats.genpareto, fp2, bp2, x0p)
-    print 'gpdparest_mlebinel', gpdparest_mlebinel
-    print 'gpdparest_gmmbinelidentity', gpdparest_gmmbinelidentity
+    print('gpdparest_mlebinel', gpdparest_mlebinel)
+    print('gpdparest_gmmbinelidentity', gpdparest_gmmbinelidentity)
     gpdparest_gmmquantile2 = fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=None, frozen=None)
-    print 'gpdparest_gmmquantile2', gpdparest_gmmquantile2
+    print('gpdparest_gmmquantile2', gpdparest_gmmquantile2)
     #something wrong : something hard coded ?
     '''
     >>> fitquantilesgmm(stats.genpareto, p2rvs, start=x0p, pquant=np.linspace(0.5,0.95,10), frozen=None)
@@ -686,9 +686,9 @@ def fit_mps(dist, data, x0=None):
         cdfdiff = distfn.cdf(xq, *params) - pq
     ValueError: shape mismatch: objects cannot be broadcast to a single shape
     '''
-    print fitquantilesgmm(stats.genpareto, p2rvs, start=x0p,
-                          pquant=np.linspace(0.01,0.99,10), frozen=None)
+    print(fitquantilesgmm(stats.genpareto, p2rvs, start=x0p,
+                          pquant=np.linspace(0.01,0.99,10), frozen=None))
     fp2, bp2 = np.histogram(p2rvs,
                     bins=stats.genpareto(2).ppf(np.linspace(0,0.99,10)))
-    print 'fitbinnedgmm equal weight bins',
-    print fitbinnedgmm(stats.genpareto, fp2, bp2, x0p)
+    print('fitbinnedgmm equal weight bins')
+    print(fitbinnedgmm(stats.genpareto, fp2, bp2, x0p))
diff --git a/statsmodels/sandbox/distributions/examples/ex_extras.py b/statsmodels/sandbox/distributions/examples/ex_extras.py
new file mode 100644
index 00000000000..9ab22c84783
--- /dev/null
+++ b/statsmodels/sandbox/distributions/examples/ex_extras.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Wed Feb 19 12:39:49 2014
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from scipy import stats
+
+from statsmodels.sandbox.distributions.extras import (SkewNorm_gen, skewnorm,
+                                ACSkewT_gen,
+                                NormExpan_gen, pdf_moments,
+                                ExpTransf_gen, LogTransf_gen)
+from statsmodels.stats.moment_helpers import mc2mvsk, mnc2mc, mvsk2mnc
+
+
+def example_n():
+
+    print(skewnorm.pdf(1,0), stats.norm.pdf(1), skewnorm.pdf(1,0) - stats.norm.pdf(1))
+    print(skewnorm.pdf(1,1000), stats.chi.pdf(1,1), skewnorm.pdf(1,1000) - stats.chi.pdf(1,1))
+    print(skewnorm.pdf(-1,-1000), stats.chi.pdf(1,1), skewnorm.pdf(-1,-1000) - stats.chi.pdf(1,1))
+    rvs = skewnorm.rvs(0,size=500)
+    print('sample mean var: ', rvs.mean(), rvs.var())
+    print('theoretical mean var', skewnorm.stats(0))
+    rvs = skewnorm.rvs(5,size=500)
+    print('sample mean var: ', rvs.mean(), rvs.var())
+    print('theoretical mean var', skewnorm.stats(5))
+    print(skewnorm.cdf(1,0), stats.norm.cdf(1), skewnorm.cdf(1,0) - stats.norm.cdf(1))
+    print(skewnorm.cdf(1,1000), stats.chi.cdf(1,1), skewnorm.cdf(1,1000) - stats.chi.cdf(1,1))
+    print(skewnorm.sf(0.05,1000), stats.chi.sf(0.05,1), skewnorm.sf(0.05,1000) - stats.chi.sf(0.05,1))
+
+
+def example_T():
+    skewt = ACSkewT_gen()
+    rvs = skewt.rvs(10,0,size=500)
+    print('sample mean var: ', rvs.mean(), rvs.var())
+    print('theoretical mean var', skewt.stats(10,0))
+    print('t mean var', stats.t.stats(10))
+    print(skewt.stats(10,1000)) # -> folded t distribution, as alpha -> inf
+    rvs = np.abs(stats.t.rvs(10,size=1000))
+    print(rvs.mean(), rvs.var())
+
+
+
+def examples_normexpand():
+    skewnorm = SkewNorm_gen()
+    rvs = skewnorm.rvs(5,size=100)
+    normexpan = NormExpan_gen(rvs, mode='sample')
+
+    smvsk = stats.describe(rvs)[2:]
+    print('sample: mu,sig,sk,kur')
+    print(smvsk)
+
+    dmvsk = normexpan.stats(moments='mvsk')
+    print('normexpan: mu,sig,sk,kur')
+    print(dmvsk)
+    print('mvsk diff distribution - sample')
+    print(np.array(dmvsk) - np.array(smvsk))
+    print('normexpan attributes mvsk')
+    print(mc2mvsk(normexpan.cnt))
+    print(normexpan.mvsk)
+
+    mnc = mvsk2mnc(dmvsk)
+    mc = mnc2mc(mnc)
+    print('central moments')
+    print(mc)
+    print('non-central moments')
+    print(mnc)
+
+
+    pdffn = pdf_moments(mc)
+    print('\npdf approximation from moments')
+    print('pdf at', mc[0]-1,mc[0]+1)
+    print(pdffn([mc[0]-1,mc[0]+1]))
+    print(normexpan.pdf([mc[0]-1,mc[0]+1]))
+
+
+def examples_transf():
+    ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
+    ##print(lognormal.cdf(1))
+    ##print(stats.lognorm.cdf(1,1))
+    ##print(lognormal.stats())
+    ##print(stats.lognorm.stats(1))
+    ##print(lognormal.rvs(size=10))
+
+    print('Results for lognormal')
+    lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
+    print(lognormalg.cdf(1))
+    print(stats.lognorm.cdf(1,1))
+    print(lognormalg.stats())
+    print(stats.lognorm.stats(1))
+    print(lognormalg.rvs(size=5))
+
+    ##print('Results for loggamma')
+    ##loggammag = ExpTransf_gen(stats.gamma)
+    ##print(loggammag._cdf(1,10))
+    ##print(stats.loggamma.cdf(1,10))
+
+    print('Results for expgamma')
+    loggammaexpg = LogTransf_gen(stats.gamma)
+    print(loggammaexpg._cdf(1,10))
+    print(stats.loggamma.cdf(1,10))
+    print(loggammaexpg._cdf(2,15))
+    print(stats.loggamma.cdf(2,15))
+
+
+    # this requires change in scipy.stats.distribution
+    #print(loggammaexpg.cdf(1,10))
+
+    print('Results for loglaplace')
+    loglaplaceg = LogTransf_gen(stats.laplace)
+    print(loglaplaceg._cdf(2))
+    print(stats.loglaplace.cdf(2,1))
+    loglaplaceexpg = ExpTransf_gen(stats.laplace)
+    print(loglaplaceexpg._cdf(2))
+    stats.loglaplace.cdf(3,3)
+    #0.98148148148148151
+    loglaplaceexpg._cdf(3,0,1./3)
+    #0.98148148148148151
+
+if __name__ == '__main__':
+    example_n()
+    example_T()
+    examples_normexpand()
+    examples_transf()
diff --git a/statsmodels/sandbox/distributions/examples/ex_fitfr.py b/statsmodels/sandbox/distributions/examples/ex_fitfr.py
index b84e42c0068..d98ad4c4007 100644
--- a/statsmodels/sandbox/distributions/examples/ex_fitfr.py
+++ b/statsmodels/sandbox/distributions/examples/ex_fitfr.py
@@ -7,6 +7,7 @@
 
 '''
 
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 #Note the following import attaches methods to scipy.stats.distributions
@@ -18,11 +19,11 @@
 x = stats.gamma.rvs(2.5, loc=0, scale=1.2, size=200)
 
 #estimate all parameters
-print stats.gamma.fit(x)
-print stats.gamma.fit_fr(x, frozen=[np.nan, np.nan, np.nan])
+print(stats.gamma.fit(x))
+print(stats.gamma.fit_fr(x, frozen=[np.nan, np.nan, np.nan]))
 #estimate shape parameter only
-print stats.gamma.fit_fr(x, frozen=[np.nan, 0., 1.2])
+print(stats.gamma.fit_fr(x, frozen=[np.nan, 0., 1.2]))
 
 np.random.seed(12345)
 x = stats.lognorm.rvs(2, loc=0, scale=2, size=200)
-print stats.lognorm.fit_fr(x, frozen=[np.nan, 0., np.nan])
+print(stats.lognorm.fit_fr(x, frozen=[np.nan, 0., np.nan]))
diff --git a/statsmodels/sandbox/distributions/examples/ex_gof.py b/statsmodels/sandbox/distributions/examples/ex_gof.py
index c64572d9eab..408b5c724a8 100644
--- a/statsmodels/sandbox/distributions/examples/ex_gof.py
+++ b/statsmodels/sandbox/distributions/examples/ex_gof.py
@@ -1,4 +1,4 @@
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 from statsmodels.stats import gof
@@ -7,7 +7,7 @@
 
 freq, expfreq, histsupp = gof.gof_binning_discrete(poissrvs, stats.poisson, (0.6,), nsupp=20)
 (chi2val, pval) = stats.chisquare(freq, expfreq)
-print chi2val, pval
+print(chi2val, pval)
 
-print gof.gof_chisquare_discrete(stats.poisson, (0.6,), poissrvs, 0.05,
-                                     'Poisson')
+print(gof.gof_chisquare_discrete(stats.poisson, (0.6,), poissrvs, 0.05,
+                                     'Poisson'))
diff --git a/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py b/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py
index ae05cc44ec0..7b85ab28c3b 100644
--- a/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py
+++ b/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py
@@ -10,7 +10,7 @@
 for comparison I used R mvtnorm version 0.9-96
 
 """
-
+from __future__ import print_function
 import numpy as np
 import statsmodels.sandbox.distributions.mv_normal as mvd
 
@@ -43,12 +43,12 @@
 n_cdf = [mvn3.cdf(a) for a in xli]
 assert_array_almost_equal(r_cdf, n_cdf, decimal=4)
 
-print n_cdf
-print
-print (x<np.array(xli[0])).all(-1).mean(0)
-print (x[...,None]<xliarr).all(1).mean(0)
-print  mvn3.expect_mc(lambda x: (x<xli[0]).all(-1), size=100000)
-print  mvn3.expect_mc(lambda x: (x[...,None]<xliarr).all(1), size=100000)
+print(n_cdf)
+print('')
+print((x<np.array(xli[0])).all(-1).mean(0))
+print((x[...,None]<xliarr).all(1).mean(0))
+print(mvn3.expect_mc(lambda x: (x<xli[0]).all(-1), size=100000))
+print(mvn3.expect_mc(lambda x: (x[...,None]<xliarr).all(1), size=100000))
 
 #other methods
 mvn3n = mvn3.normalized()
@@ -74,27 +74,27 @@
 assert_array_almost_equal(np.zeros(3), xs.mean(0), decimal=2)
 
 mv2m = mvn3.marginal(np.array([0,1]))
-print mv2m.mean
-print mv2m.cov
+print(mv2m.mean)
+print(mv2m.cov)
 
 mv2c = mvn3.conditional(np.array([0,1]), [0])
-print mv2c.mean
-print mv2c.cov
+print(mv2c.mean)
+print(mv2c.cov)
 
 mv2c = mvn3.conditional(np.array([0]), [0, 0])
-print mv2c.mean
-print mv2c.cov
+print(mv2c.mean)
+print(mv2c.cov)
 
 import statsmodels.api as sm
 
 mod = sm.OLS(x[:,0], sm.add_constant(x[:,1:], prepend=True))
 res = mod.fit()
-print res.model.predict(np.array([1,0,0]))
+print(res.model.predict(np.array([1,0,0])))
 mv2c = mvn3.conditional(np.array([0]), [0, 0])
-print mv2c.mean
+print(mv2c.mean)
 mv2c = mvn3.conditional(np.array([0]), [1, 1])
-print res.model.predict(np.array([1,1,1]))
-print mv2c.mean
+print(res.model.predict(np.array([1,1,1])))
+print(mv2c.mean)
 
 #the following wrong input doesn't raise an exception but produces wrong numbers
 #mv2c = mvn3.conditional(np.array([0]), [[1, 1],[2,2]])
@@ -128,18 +128,18 @@
 
 a = [0.0, 1.0, 1.5]
 mvt3_cdf0 = mvt3.cdf(a)
-print mvt3_cdf0
-print (xt<np.array(a)).all(-1).mean(0)
-print 'R', 0.3026741 # "error": 0.0004832187
-print 'R', 0.3026855 # error 3.444375e-06   with smaller abseps
-print 'diff', mvt3_cdf0 - 0.3026855
+print(mvt3_cdf0)
+print((xt<np.array(a)).all(-1).mean(0))
+print('R', 0.3026741) # "error": 0.0004832187
+print('R', 0.3026855) # error 3.444375e-06   with smaller abseps
+print('diff', mvt3_cdf0 - 0.3026855)
 a = [0.0, 0.5, 1.0]
 mvt3_cdf1 = mvt3.cdf(a)
-print mvt3_cdf1
-print (xt<np.array(a)).all(-1).mean(0)
-print 'R', 0.1946621 # "error": 0.0002524817
-print 'R', 0.1946217 # "error:"2.748699e-06    with smaller abseps
-print 'diff', mvt3_cdf1 - 0.1946217
+print(mvt3_cdf1)
+print((xt<np.array(a)).all(-1).mean(0))
+print('R', 0.1946621) # "error": 0.0002524817)
+print('R', 0.1946217) # "error:"2.748699e-06    with smaller abseps)
+print('diff', mvt3_cdf1 - 0.1946217)
 
 assert_array_almost_equal(mvt3_cdf0, 0.3026855, decimal=5)
 assert_array_almost_equal(mvt3_cdf1, 0.1946217, decimal=5)
diff --git a/statsmodels/sandbox/distributions/examples/ex_transf2.py b/statsmodels/sandbox/distributions/examples/ex_transf2.py
index 7e15afbe9fc..25cb7366395 100644
--- a/statsmodels/sandbox/distributions/examples/ex_transf2.py
+++ b/statsmodels/sandbox/distributions/examples/ex_transf2.py
@@ -4,7 +4,7 @@
 Author: josef-pktd
 Licese: BSD
 """
-
+from __future__ import print_function
 import numpy as np
 
 from numpy.testing import assert_almost_equal
@@ -152,12 +152,12 @@ def test_squared_normal_chi2():
     assert_almost_equal(cdfst, cdftr, 14)
     assert_almost_equal(cdfst, sfctr, 14)
 
-#    print 'sqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s)
-#    print 'chi2    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
-#    print 'sqnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s)
-#    print 'chi2    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1)
-#    print 'sqnorm  cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20)
-#    print 'chi2    cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20)
+#    print('sqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s)
+#    print('chi2    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
+#    print('sqnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s)
+#    print('chi2    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1)
+#    print('sqnorm  cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20)
+#    print('chi2    cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20)
 
 
 
@@ -170,81 +170,81 @@ def test_squared_normal_chi2():
     xx = [0.95, 1.0, 1.1]
     nxx = [-0.95, -1.0, -1.1]
     print
-    #print invnormalg.__doc__
-    print '\nsquare of standard normal random variable is chisquare with dof=1 distributed'
-    print 'sqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.cdf(xx,loc=l, scale=s)
-    print 'sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squarenormalg.sf(xx,loc=l, scale=s)
-    print 'chi2    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1)
-    print 'sqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s)
-    print 'chi2    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
-    print 'sqnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s)
-    print 'chi2    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1)
-    print 'sqnorm  cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20)
-    print 'chi2    cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20)
-#    print 'cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s)
-#    print 'chi square distribution'
-#    print 'chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
-#    print 'cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1)
-
-    print '\nabsolute value of standard normal random variable is foldnorm(0) and '
-    print 'halfnorm distributed:'
-    print 'absnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.cdf(xx,loc=l, scale=s)
-    print 'absnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-absnormalg.sf(xx,loc=l, scale=s)
-    print 'foldn    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.cdf(xx,1e-5)
-    print 'halfn    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.halfnorm.cdf(xx)
-    print 'absnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.pdf(xx,loc=l, scale=s)
-    print 'foldn    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.pdf(xx,1e-5)
-    print 'halfn    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.halfnorm.pdf(xx)
-    print 'absnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), absnormalg.ppf(ppfq,loc=l, scale=s)
-    print 'foldn    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.foldnorm.ppf(ppfq,1e-5)
-    print 'halfn    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.halfnorm.ppf(ppfq)
-#    print 'cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s)
-#    print 'chi square distribution'
-#    print 'chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
-#    print 'cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1)
-
-    print '\nnegative square of standard normal random variable is'
-    print '1-chisquare with dof=1 distributed'
-    print 'this is mainly for testing'
-    print 'the following should be outside of the support - returns nan'
-    print 'nsqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), negsquarenormalg.cdf(xx,loc=l, scale=s)
-    print 'nsqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-negsquarenormalg.sf(xx,loc=l, scale=s)
-    print 'nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), negsquarenormalg.pdf(xx,loc=l, scale=s)
-
-    print 'nsqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.cdf(nxx,loc=l, scale=s)
-    print 'nsqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), 1-negsquarenormalg.sf(nxx,loc=l, scale=s)
-    print 'chi2      sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.sf(xx,1)
-    print 'nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.pdf(nxx,loc=l, scale=s)
-    print 'chi2     pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
-    print 'nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.pdf(nxx,loc=l, scale=s)
-
-
-
-    print '\nsquare of a t distributed random variable with dof=10 is'
-    print '        F with dof=1,10 distributed'
-    print 'sqt  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squaretg.cdf(xx,10)
-    print 'sqt 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squaretg.sf(xx,10)
-    print 'f    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.f.cdf(xx,1,10)
-    print 'sqt  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squaretg.pdf(xx,10)
-    print 'f    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.f.pdf(xx,1,10)
-    print 'sqt  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), squaretg.ppf(ppfq,10)
-    print 'f    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.f.ppf(ppfq,1,10)
-    print 'sqt  cdf for 100:', squaretg.cdf(100,10)
-    print 'f    cdf for 100:', stats.f.cdf(100,1,10)
-    print 'sqt  stats:', squaretg.stats(10, moments='mvsk')
-    print 'f    stats:', stats.f.stats(1,10, moments='mvsk')
+    #print(invnormalg.__doc__
+    print('\nsquare of standard normal random variable is chisquare with dof=1 distributed')
+    print('sqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.cdf(xx,loc=l, scale=s))
+    print('sqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squarenormalg.sf(xx,loc=l, scale=s))
+    print('chi2    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
+    print('sqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.pdf(xx,loc=l, scale=s))
+    print('chi2    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
+    print('sqnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squarenormalg.ppf(ppfq,loc=l, scale=s))
+    print('chi2    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.ppf(ppfq,1))
+    print('sqnorm  cdf with loc scale', squarenormalg.cdf(xx,loc=-10, scale=20))
+    print('chi2    cdf with loc scale', stats.chi2.cdf(xx,1,loc=-10, scale=20))
+#    print('cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s))
+#    print('chi square distribution')
+#    print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
+#    print('cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1))
+
+    print('\nabsolute value of standard normal random variable is foldnorm(0) and ')
+    print('halfnorm distributed:')
+    print('absnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.cdf(xx,loc=l, scale=s))
+    print('absnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-absnormalg.sf(xx,loc=l, scale=s))
+    print('foldn    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.cdf(xx,1e-5))
+    print('halfn    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.halfnorm.cdf(xx))
+    print('absnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), absnormalg.pdf(xx,loc=l, scale=s))
+    print('foldn    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.foldnorm.pdf(xx,1e-5))
+    print('halfn    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.halfnorm.pdf(xx))
+    print('absnorm  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), absnormalg.ppf(ppfq,loc=l, scale=s))
+    print('foldn    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.foldnorm.ppf(ppfq,1e-5))
+    print('halfn    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.halfnorm.ppf(ppfq))
+#    print('cdf for [0.5]:', squarenormalg.cdf(0.5,loc=l, scale=s)
+#    print('chi square distribution'
+#    print('chi2 pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1)
+#    print('cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.cdf(xx,1)
+
+    print('\nnegative square of standard normal random variable is')
+    print('1-chisquare with dof=1 distributed')
+    print('this is mainly for testing')
+    print('the following should be outside of the support - returns nan')
+    print('nsqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), negsquarenormalg.cdf(xx,loc=l, scale=s))
+    print('nsqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-negsquarenormalg.sf(xx,loc=l, scale=s))
+    print('nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), negsquarenormalg.pdf(xx,loc=l, scale=s))
+
+    print('nsqnorm  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.cdf(nxx,loc=l, scale=s))
+    print('nsqnorm 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), 1-negsquarenormalg.sf(nxx,loc=l, scale=s))
+    print('chi2      sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.sf(xx,1))
+    print('nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.pdf(nxx,loc=l, scale=s))
+    print('chi2     pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.chi2.pdf(xx,1))
+    print('nsqnorm  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(nxx), negsquarenormalg.pdf(nxx,loc=l, scale=s))
+
+
+
+    print('\nsquare of a t distributed random variable with dof=10 is')
+    print('        F with dof=1,10 distributed')
+    print('sqt  cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squaretg.cdf(xx,10))
+    print('sqt 1-sf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), 1-squaretg.sf(xx,10))
+    print('f    cdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.f.cdf(xx,1,10))
+    print('sqt  pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), squaretg.pdf(xx,10))
+    print('f    pdf for (%3.2f, %3.2f, %3.2f):' % tuple(xx), stats.f.pdf(xx,1,10))
+    print('sqt  ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), squaretg.ppf(ppfq,10))
+    print('f    ppf for (%3.2f, %3.2f, %3.2f):' % tuple(ppfq), stats.f.ppf(ppfq,1,10))
+    print('sqt  cdf for 100:', squaretg.cdf(100,10))
+    print('f    cdf for 100:', stats.f.cdf(100,1,10))
+    print('sqt  stats:', squaretg.stats(10, moments='mvsk'))
+    print('f    stats:', stats.f.stats(1,10, moments='mvsk'))
     #Note the results differ for skew and kurtosis. I think the 3rd and 4th moment
     #    in the scipy.stats.f distribution is incorrect.
     # I corrected it now in stats.distributions.py in bzr branch
     v1=1; v2=10;
     g1 = 2*(v2+2*v1-2.)/(v2-6.)*np.sqrt(2*(v2-4.)/(v1*(v2+v1-2.)))
     g2 = 3/(2.*v2-16)*(8+g1*g1*(v2-6.))
-    print 'corrected skew, kurtosis of f(1,10) is', g1, g2
-    print squarenormalg.rvs()
-    print squarenormalg.rvs(size=(2,4))
-    print 'sqt random variables'
-    print stats.f.rvs(1,10,size=4)
-    print squaretg.rvs(10,size=4)
+    print('corrected skew, kurtosis of f(1,10) is', g1, g2)
+    print(squarenormalg.rvs())
+    print(squarenormalg.rvs(size=(2,4)))
+    print('sqt random variables')
+    print(stats.f.rvs(1,10,size=4))
+    print(squaretg.rvs(10,size=4))
 
     #a large number check:
     np.random.seed(464239857)
@@ -276,7 +276,7 @@ def test_squared_normal_chi2():
     # fraction of observations in each decile
     dec = squaretg.ppf(np.linspace(0.,1,11),10)
     freq,edges = np.histogram(rvstsq, bins=dec)
-    print freq/float(len(rvstsq))
+    print(freq/float(len(rvstsq)))
 
     import matplotlib.pyplot as plt
     freq,edges,_ = plt.hist(rvstsq, bins=50, range=(0,4),normed=True)
diff --git a/statsmodels/sandbox/distributions/examples/matchdist.py b/statsmodels/sandbox/distributions/examples/matchdist.py
index 19d722a410c..fcf703fe1a4 100644
--- a/statsmodels/sandbox/distributions/examples/matchdist.py
+++ b/statsmodels/sandbox/distributions/examples/matchdist.py
@@ -15,7 +15,7 @@
 *
 
 '''
-
+from __future__ import print_function
 from scipy import stats
 import numpy as np
 import matplotlib.mlab as mlab
@@ -29,13 +29,13 @@ def plothist(x,distfn, args, loc, scale, right=1):
     # the histogram of the data
     n, bins, patches = plt.hist(x, 25, normed=1, facecolor='green', alpha=0.75)
     maxheight = max([p.get_height() for p in patches])
-    print maxheight
+    print(maxheight)
     axlim = list(plt.axis())
-    #print axlim
+    #print(axlim)
     axlim[-1] = maxheight*1.05
     #plt.axis(tuple(axlim))
-##    print bins
-##    print 'args in plothist', args
+##    print(bins)
+##    print('args in plothist', args)
     # add a 'best fit' line
     #yt = stats.norm.pdf( bins, loc=loc, scale=scale)
     yt = distfn.pdf( bins, loc=loc, scale=scale, *args)
@@ -167,8 +167,8 @@ def plothist(x,distfn, args, loc, scale, right=1):
         rvs_pos = rvs_orig[rvs_orig>0]
         rightfactor = 1
         rvs_right = rvs_pos
-        print '='*50
-        print 'samplesize = ', n
+        print('='*50)
+        print('samplesize = ', n)
         for distname in targetdist:
             distfn = getattr(stats,distname)
             if distname in right_all:
@@ -178,8 +178,8 @@ def plothist(x,distfn, args, loc, scale, right=1):
             else:
                 rvs = rvs_orig
                 rind = 1
-            print '-'*30
-            print 'target = %s' % distname
+            print('-'*30)
+            print('target = %s' % distname)
             sm = rvs.mean()
             sstd = np.sqrt(rvs.var())
             ssupp = (rvs.min(), rvs.max())
@@ -206,17 +206,17 @@ def plothist(x,distfn, args, loc, scale, right=1):
                 par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd))
 
 
-            print 'fit', par_est
+            print('fit', par_est)
             arg_est = par_est[:-2]
             loc_est = par_est[-2]
             scale_est = par_est[-1]
             rvs_normed = (rvs-loc_est)/scale_est
             ks_stat, ks_pval = stats.kstest(rvs_normed,distname, arg_est)
-            print 'kstest', ks_stat, ks_pval
+            print('kstest', ks_stat, ks_pval)
             quant = 0.1
             crit = distfn.ppf(1-quant*float(rind), loc=loc_est, scale=scale_est,*par_est)
             tail_prob = stats.t.sf(crit,dgp_arg,scale=dgp_scale)
-            print 'crit, prob', quant, crit, tail_prob
+            print('crit, prob', quant, crit, tail_prob)
             #if distname == 'norm':
                 #plothist(rvs,loc_est,scale_est)
                 #args = tuple()
@@ -235,7 +235,7 @@ def plothist(x,distfn, args, loc, scale, right=1):
 
     res_sort.reverse()  #kstest statistic: smaller is better, pval larger is better
 
-    print 'number of distributions', len(res_sort)
+    print('number of distributions', len(res_sort))
     imagedir = 'matchresults'
     import os
     if not os.path.exists(imagedir):
@@ -252,10 +252,10 @@ def plothist(x,distfn, args, loc, scale, right=1):
             rvs = rvs_orig
             ri = ''
             rind = 1
-        print '%s ks-stat = %f, ks-pval = %f tail_prob = %f)' % \
-              (distname, ks_stat, ks_pval, tail_prob)
-    ##    print 'arg_est = %s, loc_est = %f scale_est = %f)' % \
-    ##          (repr(arg_est),loc_est,scale_est)
+        print('%s ks-stat = %f, ks-pval = %f tail_prob = %f)' % \
+              (distname, ks_stat, ks_pval, tail_prob))
+    ##    print('arg_est = %s, loc_est = %f scale_est = %f)' % \
+    ##          (repr(arg_est),loc_est,scale_est))
         plothist(rvs,distfn,arg_est,loc_est,scale_est,right = rind)
         plt.savefig(os.path.join(imagedir,'%s%s%02d_%s.png'% (prefix, ri,ii, distname)))
     ##plt.show()
diff --git a/statsmodels/sandbox/distributions/extras.py b/statsmodels/sandbox/distributions/extras.py
index aca2cf709a5..0b084e030bc 100644
--- a/statsmodels/sandbox/distributions/extras.py
+++ b/statsmodels/sandbox/distributions/extras.py
@@ -52,6 +52,8 @@
 
 #note copied from distr_skewnorm_0.py
 
+from __future__ import print_function
+from statsmodels.compat.python import range, iteritems
 from scipy import stats, special, integrate  # integrate is for scipy 0.6.0 ???
 from scipy.stats import distributions
 from statsmodels.stats.moment_helpers import mvsk2mc, mc2mvsk
@@ -95,20 +97,7 @@ def _stats_skip(self,x,alpha,moments='mvsk'):
         pass
 
 skewnorm = SkewNorm_gen()
-def example_n():
-
-    print skewnorm.pdf(1,0), stats.norm.pdf(1), skewnorm.pdf(1,0) - stats.norm.pdf(1)
-    print skewnorm.pdf(1,1000), stats.chi.pdf(1,1), skewnorm.pdf(1,1000) - stats.chi.pdf(1,1)
-    print skewnorm.pdf(-1,-1000), stats.chi.pdf(1,1), skewnorm.pdf(-1,-1000) - stats.chi.pdf(1,1)
-    rvs = skewnorm.rvs(0,size=500)
-    print 'sample mean var: ', rvs.mean(), rvs.var()
-    print 'theoretical mean var', skewnorm.stats(0)
-    rvs = skewnorm.rvs(5,size=500)
-    print 'sample mean var: ', rvs.mean(), rvs.var()
-    print 'theoretical mean var', skewnorm.stats(5)
-    print skewnorm.cdf(1,0), stats.norm.cdf(1), skewnorm.cdf(1,0) - stats.norm.cdf(1)
-    print skewnorm.cdf(1,1000), stats.chi.cdf(1,1), skewnorm.cdf(1,1000) - stats.chi.cdf(1,1)
-    print skewnorm.sf(0.05,1000), stats.chi.sf(0.05,1), skewnorm.sf(0.05,1000) - stats.chi.sf(0.05,1)
+
 
 # generated the same way as distributions in stats.distributions
 class SkewNorm2_gen(distributions.rv_continuous):
@@ -179,15 +168,6 @@ def _pdf(self, x, df, alpha):
         # 2*normpdf(x)*normcdf(alpha*x)
         return 2.0*distributions.t._pdf(x, df) * special.stdtr(df+1, alpha*x*np.sqrt((1+df)/(x**2+df)))
 
-def example_T():
-    skewt = ACSkewT_gen()
-    rvs = skewt.rvs(10,0,size=500)
-    print 'sample mean var: ', rvs.mean(), rvs.var()
-    print 'theoretical mean var', skewt.stats(10,0)
-    print 't mean var', stats.t.stats(10)
-    print skewt.stats(10,1000) # -> folded t distribution, as alpha -> inf
-    rvs = np.abs(stats.t.rvs(10,size=1000))
-    print rvs.mean(), rvs.var()
 
 ##
 ##def mvsk2cm(*args):
@@ -253,8 +233,8 @@ def pdf_moments_st(cnt):
 
     N = len(cnt)
     if N < 2:
-        raise ValueError, "At least two moments must be given to" + \
-              "approximate the pdf."
+        raise ValueError("At least two moments must be given to "
+                         "approximate the pdf.")
 
     totp = poly1d(1)
     sig = sqrt(cnt[1])
@@ -272,9 +252,9 @@ def pdf_moments_st(cnt):
                 momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1)
             Ck += Dvals[k][m] / sig**m * momdiff
         # Add to totp
-        raise
-        print Dvals
-        print Ck
+        raise SystemError
+        print(Dvals)
+        print(Ck)
         totp = totp +  Ck*Dvals[k]
 
     def thisfunc(x):
@@ -324,8 +304,8 @@ def pdf_mvsk(mvsk):
     """
     N = len(mvsk)
     if N < 4:
-        raise ValueError, "Four moments must be given to" + \
-              "approximate the pdf."
+        raise ValueError("Four moments must be given to "
+                         "approximate the pdf.")
 
     mu, mc2, skew, kurt = mvsk
 
@@ -372,8 +352,8 @@ def pdf_moments(cnt):
     """
     N = len(cnt)
     if N < 2:
-        raise ValueError, "At least two moments must be given to" + \
-              "approximate the pdf."
+        raise ValueError("At least two moments must be given to "
+                         "approximate the pdf.")
 
 
 
@@ -420,7 +400,7 @@ class follows scipy.stats.distributions pattern
     def __init__(self,args, **kwds):
         #todo: replace with super call
         distributions.rv_continuous.__init__(self,
-            name = 'Normal Expansion distribution', shapes = 'alpha',
+            name = 'Normal Expansion distribution', shapes = ' ',
             extradoc = '''
         The distribution is defined as the Gram-Charlier expansion of
         the normal distribution using the first four moments. The pdf
@@ -452,7 +432,7 @@ def __init__(self,args, **kwds):
             cnt = args
             self.mvsk = mc2mvsk(cnt)
         else:
-            raise ValueError, "mode must be 'mvsk' or centmom"
+            raise ValueError("mode must be 'mvsk' or centmom")
 
         self.cnt = cnt
         #self.mvsk = (mu,sig,sk,kur)
@@ -469,40 +449,6 @@ def _stats_skip(self):
         return self.mvsk
 
 
-def examples_normexpand():
-    skewnorm = SkewNorm_gen()
-    rvs = skewnorm.rvs(5,size=100)
-    normexpan = NormExpan_gen(rvs, mode='sample')
-
-    smvsk = stats.describe(rvs)[2:]
-    print 'sample: mu,sig,sk,kur'
-    print smvsk
-
-    dmvsk = normexpan.stats(moments='mvsk')
-    print 'normexpan: mu,sig,sk,kur'
-    print dmvsk
-    print 'mvsk diff distribution - sample'
-    print np.array(dmvsk) - np.array(smvsk)
-    print 'normexpan attributes mvsk'
-    print mc2mvsk(normexpan.cnt)
-    print normexpan.mvsk
-
-    from statsmodels.stats.momenthelpers import mvsk2mnc, mnc2mc
-    mnc = mvsk2mnc(dmvsk)
-    mc = mnc2mc(mnc)
-    print 'central moments'
-    print mc
-    print 'non-central moments'
-    print mnc
-
-
-    pdffn = pdf_moments(mc)
-    print '\npdf approximation from moments'
-    print 'pdf at', mc[0]-1,mc[0]+1
-    print pdffn([mc[0]-1,mc[0]+1])
-    print normexpan.pdf([mc[0]-1,mc[0]+1])
-
-
 ## copied from nonlinear_transform_gen.py
 
 ''' A class for the distribution of a non-linear monotonic transformation of a continuous random variable
@@ -550,7 +496,7 @@ def examples_normexpand():
 
 def get_u_argskwargs(**kwargs):
     #Todo: What's this? wrong spacing, used in Transf_gen TransfTwo_gen
-    u_kwargs = dict((k.replace('u_','',1),v) for k,v in kwargs.items()
+    u_kwargs = dict((k.replace('u_','',1),v) for k,v in iteritems(kwargs)
                     if k.startswith('u_'))
     u_args = u_kwargs.pop('u_args',None)
     return u_args, u_kwargs
@@ -704,52 +650,6 @@ def _cdf(self,x, *args):
     def _ppf(self, q, *args):
         return np.log(self.kls._ppf(q,*args))
 
-def examples_transf():
-    ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
-    ##print lognormal.cdf(1)
-    ##print stats.lognorm.cdf(1,1)
-    ##print lognormal.stats()
-    ##print stats.lognorm.stats(1)
-    ##print lognormal.rvs(size=10)
-
-    print 'Results for lognormal'
-    lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
-    print lognormalg.cdf(1)
-    print stats.lognorm.cdf(1,1)
-    print lognormalg.stats()
-    print stats.lognorm.stats(1)
-    print lognormalg.rvs(size=5)
-
-    ##print 'Results for loggamma'
-    ##loggammag = ExpTransf_gen(stats.gamma)
-    ##print loggammag._cdf(1,10)
-    ##print stats.loggamma.cdf(1,10)
-
-    print 'Results for expgamma'
-    loggammaexpg = LogTransf_gen(stats.gamma)
-    print loggammaexpg._cdf(1,10)
-    print stats.loggamma.cdf(1,10)
-    print loggammaexpg._cdf(2,15)
-    print stats.loggamma.cdf(2,15)
-
-
-    # this requires change in scipy.stats.distribution
-    #print loggammaexpg.cdf(1,10)
-
-    print 'Results for loglaplace'
-    loglaplaceg = LogTransf_gen(stats.laplace)
-    print loglaplaceg._cdf(2)
-    print stats.loglaplace.cdf(2,1)
-    loglaplaceexpg = ExpTransf_gen(stats.laplace)
-    print loglaplaceexpg._cdf(2)
-    stats.loglaplace.cdf(3,3)
-    #0.98148148148148151
-    loglaplaceexpg._cdf(3,0,1./3)
-    #0.98148148148148151
-
-
-
-
 
 ## copied from transformtwo.py
 
@@ -849,7 +749,7 @@ def _pdf(self,x,*args, **kwargs):
         elif self.shape == 'hump':
             signpdf = -1
         else:
-            raise ValueError, 'shape can only be `u` or `hump`'
+            raise ValueError('shape can only be `u` or `hump`')
 
         return signpdf * (self.derivplus(x)*self.kls._pdf(self.funcinvplus(x),*args, **kwargs) -
                    self.derivminus(x)*self.kls._pdf(self.funcinvminus(x),*args, **kwargs))
@@ -1129,9 +1029,9 @@ def mvstdnormcdf(lower, upper, corrcoef, **kwds):
     correl = np.zeros(n*(n-1)/2.0)  #dtype necessary?
 
     if (lower.ndim != 1) or (upper.ndim != 1):
-        raise ValueError, 'can handle only 1D bounds'
+        raise ValueError('can handle only 1D bounds')
     if len(upper) != n:
-        raise ValueError, 'bounds have different lengths'
+        raise ValueError('bounds have different lengths')
     if n==2 and corrcoef.size==1:
         correl = corrcoef
         #print 'case scalar rho', n
@@ -1145,7 +1045,7 @@ def mvstdnormcdf(lower, upper, corrcoef, **kwds):
 #            for jj in range(ii):
 #                correl[ jj + ((ii-2)*(ii-1))/2] = corrcoef[ii,jj]
     else:
-        raise ValueError, 'corrcoef has incorrect dimension'
+        raise ValueError('corrcoef has incorrect dimension')
 
     if not 'maxpts' in kwds:
         if n >2:
@@ -1169,7 +1069,7 @@ def mvstdnormcdf(lower, upper, corrcoef, **kwds):
     #print kwds.items()
     error, cdfvalue, inform = scipy.stats.kde.mvn.mvndst(lower,upper,infin,correl,**kwds)
     if inform:
-        print 'something wrong', informcode[inform], error
+        print('something wrong', informcode[inform], error)
     return cdfvalue
 
 
diff --git a/statsmodels/sandbox/distributions/genpareto.py b/statsmodels/sandbox/distributions/genpareto.py
index 479e77e4a50..87d36e61532 100644
--- a/statsmodels/sandbox/distributions/genpareto.py
+++ b/statsmodels/sandbox/distributions/genpareto.py
@@ -6,9 +6,10 @@
 
 Author: josef-pktd
 """
+from __future__ import print_function
 import numpy as np
 from scipy import stats
-from scipy import comb
+from scipy.misc import comb
 from scipy.stats.distributions import rv_continuous
 
 from numpy import where, inf
@@ -60,13 +61,13 @@ def _entropy(self, c):
                   ('sf', rv),
                   ('ppf', quant),
                   ('isf', quant)]:
-    print getattr(genpareto2, method)(x, shape, loc, scale)
-    print getattr(stats.genpareto, method)(x, -shape, loc, scale)
+    print(getattr(genpareto2, method)(x, shape, loc, scale))
+    print(getattr(stats.genpareto, method)(x, -shape, loc, scale))
 
-print genpareto2.stats(shape, loc, scale, moments='mvsk')
-print stats.genpareto.stats(-shape, loc, scale, moments='mvsk')
-print genpareto2.entropy(shape, loc, scale)
-print stats.genpareto.entropy(-shape, loc, scale)
+print(genpareto2.stats(shape, loc, scale, moments='mvsk'))
+print(stats.genpareto.stats(-shape, loc, scale, moments='mvsk'))
+print(genpareto2.entropy(shape, loc, scale))
+print(stats.genpareto.entropy(-shape, loc, scale))
 
 
 def paramstopot(thresh, shape, scale):
@@ -83,7 +84,7 @@ def paramsfrompot(thresh, shape, scalepot):
 
 def warnif(cond, msg):
     if not cond:
-        print msg, 'does not hold'
+        print(msg, 'does not hold')
 
 def meanexcess(thresh, shape, scale):
     '''mean excess function of genpareto
@@ -117,8 +118,8 @@ def meanexcess_plot(data, params=None, lidx=100, uidx=10, method='emp', plot=0):
     return datasorted, meanexcess
 
 
-print meanexcess(5, -0.5, 10)
-print meanexcess(5, -2, 10)
+print(meanexcess(5, -0.5, 10))
+print(meanexcess(5, -2, 10))
 import matplotlib.pyplot as plt
 
 data = genpareto2.rvs(-0.75, scale=5, size=1000)
@@ -126,8 +127,8 @@ def meanexcess_plot(data, params=None, lidx=100, uidx=10, method='emp', plot=0):
 #data = stats.norm.rvs(0, np.sqrt(50), size=1000)
 #data = stats.pareto.rvs(1.5, np.sqrt(50), size=1000)
 tmp = meanexcess_plot(data, params=(-0.75, 5), plot=1)
-print tmp[1][-20:]
-print tmp[0][-20:]
+print(tmp[1][-20:])
+print(tmp[0][-20:])
 #plt.show()
 
 def meanexcess_emp(data):
@@ -147,15 +148,15 @@ def meanexcess_dist(self, lb, *args, **kwds):
                     lbb in lb])
 
 ds, me, mc = meanexcess_emp(1.*np.arange(1,10))
-print ds
-print me
-print mc
+print(ds)
+print(me)
+print(mc)
 
-print meanexcess_dist(stats.norm, lb=0.5)
-print meanexcess_dist(stats.norm, lb=[-np.inf, -0.5, 0, 0.5])
+print(meanexcess_dist(stats.norm, lb=0.5))
+print(meanexcess_dist(stats.norm, lb=[-np.inf, -0.5, 0, 0.5]))
 rvs = stats.norm.rvs(size=100000)
 rvs = rvs - rvs.mean()
-print rvs.mean(), rvs[rvs>-0.5].mean(), rvs[rvs>0].mean(), rvs[rvs>0.5].mean()
+print(rvs.mean(), rvs[rvs>-0.5].mean(), rvs[rvs>0].mean(), rvs[rvs>0.5].mean())
 
 
 
diff --git a/statsmodels/sandbox/distributions/gof_new.py b/statsmodels/sandbox/distributions/gof_new.py
index a8ad60c84e5..15816420f0b 100644
--- a/statsmodels/sandbox/distributions/gof_new.py
+++ b/statsmodels/sandbox/distributions/gof_new.py
@@ -17,7 +17,8 @@
 ----------
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import range, lmap, string_types, callable
 import numpy as np
 
 from scipy.stats import distributions
@@ -100,7 +101,7 @@ def ks_2samp(data1, data2):
     (0.07999999999999996, 0.41126949729859719)
 
     """
-    data1, data2 = map(np.asarray, (data1, data2))
+    data1, data2 = lmap(np.asarray, (data1, data2))
     n1 = data1.shape[0]
     n2 = data2.shape[0]
     n1 = len(data1)
@@ -239,7 +240,7 @@ def kstest(rvs, cdf, args=(), N=20, alternative = 'two_sided', mode='approx',**k
     (0.131016895759829, 0.058826222555312224)
 
     """
-    if isinstance(rvs, basestring):
+    if isinstance(rvs, string_types):
         #cdf = getattr(stats, rvs).cdf
         if (not cdf) or (cdf == rvs):
             cdf = getattr(distributions, rvs).cdf
@@ -248,7 +249,7 @@ def kstest(rvs, cdf, args=(), N=20, alternative = 'two_sided', mode='approx',**k
             raise AttributeError('if rvs is string, cdf has to be the same distribution')
 
 
-    if isinstance(cdf, basestring):
+    if isinstance(cdf, string_types):
         cdf = getattr(distributions, cdf).cdf
     if callable(rvs):
         kwds = {'size':N}
@@ -390,7 +391,7 @@ class GOF(object):
 
 
     def __init__(self, rvs, cdf, args=(), N=20):
-        if isinstance(rvs, basestring):
+        if isinstance(rvs, string_types):
             #cdf = getattr(stats, rvs).cdf
             if (not cdf) or (cdf == rvs):
                 cdf = getattr(distributions, rvs).cdf
@@ -399,7 +400,7 @@ def __init__(self, rvs, cdf, args=(), N=20):
                 raise AttributeError('if rvs is string, cdf has to be the same distribution')
 
 
-        if isinstance(cdf, basestring):
+        if isinstance(cdf, string_types):
             cdf = getattr(distributions, cdf).cdf
         if callable(rvs):
             kwds = {'size':N}
@@ -461,7 +462,7 @@ def a(self):
 
         #one loop instead of large array
         msum = 0
-        for j in xrange(1,nobs):
+        for j in range(1,nobs):
             mj = cdfvals[j] - cdfvals[:j]
             mask = (mj > 0.5)
             mj[mask] = 1 - mj[mask]
@@ -505,17 +506,17 @@ def gof_mc(randfn, distr, nobs=100):
     from collections import defaultdict
 
     results = defaultdict(list)
-    for i in xrange(1000):
+    for i in range(1000):
         rvs = randfn(nobs)
         goft = GOF(rvs, distr)
         for ti in all_gofs:
             results[ti].append(goft.get_test(ti, 'stephens70upp')[0][1])
 
     resarr = np.array([results[ti] for ti in all_gofs])
-    print '         ', '      '.join(all_gofs)
-    print 'at 0.01:', (resarr < 0.01).mean(1)
-    print 'at 0.05:', (resarr < 0.05).mean(1)
-    print 'at 0.10:', (resarr < 0.1).mean(1)
+    print('         ', '      '.join(all_gofs))
+    print('at 0.01:', (resarr < 0.01).mean(1))
+    print('at 0.05:', (resarr < 0.05).mean(1))
+    print('at 0.10:', (resarr < 0.1).mean(1))
 
 def asquare(cdfvals, axis=0):
     '''vectorized Anderson Darling A^2, Stephens 1974'''
@@ -567,10 +568,10 @@ def bootstrap(distr, args=(), nobs=200, nrep=100, value=None, batch_size=None):
             raise ValueError('using batching requires a value')
         n_batch = int(np.ceil(nrep/float(batch_size)))
         count = 0
-        for irep in xrange(n_batch):
+        for irep in range(n_batch):
             rvs = distr.rvs(args, **{'size':(batch_size, nobs)})
             params = distr.fit_vec(rvs, axis=1)
-            params = map(lambda x: np.expand_dims(x, 1), params)
+            params = lmap(lambda x: np.expand_dims(x, 1), params)
             cdfvals = np.sort(distr.cdf(rvs, params), axis=1)
             stat = asquare(cdfvals, axis=1)
             count += (stat >= value).sum()
@@ -579,7 +580,7 @@ def bootstrap(distr, args=(), nobs=200, nrep=100, value=None, batch_size=None):
         #rvs = distr.rvs(args, **kwds)  #extension to distribution kwds ?
         rvs = distr.rvs(args, **{'size':(nrep, nobs)})
         params = distr.fit_vec(rvs, axis=1)
-        params = map(lambda x: np.expand_dims(x, 1), params)
+        params = lmap(lambda x: np.expand_dims(x, 1), params)
         cdfvals = np.sort(distr.cdf(rvs, params), axis=1)
         stat = asquare(cdfvals, axis=1)
         if value is None:           #return all bootstrap results
@@ -609,7 +610,7 @@ def bootstrap2(value, distr, args=(), nobs=200, nrep=100):
 
 
     count = 0
-    for irep in xrange(nrep):
+    for irep in range(nrep):
         #rvs = distr.rvs(args, **kwds)  #extension to distribution kwds ?
         rvs = distr.rvs(args, **{'size':nobs})
         params = distr.fit_vec(rvs)
@@ -641,31 +642,31 @@ def rvs(self, args, size):
     from scipy import stats
     #rvs = np.random.randn(1000)
     rvs = stats.t.rvs(3, size=200)
-    print 'scipy kstest'
-    print kstest(rvs, 'norm')
+    print('scipy kstest')
+    print(kstest(rvs, 'norm'))
     goft = GOF(rvs, 'norm')
-    print goft.get_test()
+    print(goft.get_test())
 
     all_gofs = ['d', 'd_plus', 'd_minus', 'v', 'wsqu', 'usqu', 'a']
     for ti in all_gofs:
-        print ti, goft.get_test(ti, 'stephens70upp')
+        print(ti, goft.get_test(ti, 'stephens70upp'))
 
-    print '\nIs it correctly sized?'
+    print('\nIs it correctly sized?')
     from collections import defaultdict
 
     results = defaultdict(list)
     nobs = 200
-    for i in xrange(100):
+    for i in range(100):
         rvs = np.random.randn(nobs)
         goft = GOF(rvs, 'norm')
         for ti in all_gofs:
             results[ti].append(goft.get_test(ti, 'stephens70upp')[0][1])
 
     resarr = np.array([results[ti] for ti in all_gofs])
-    print '         ', '      '.join(all_gofs)
-    print 'at 0.01:', (resarr < 0.01).mean(1)
-    print 'at 0.05:', (resarr < 0.05).mean(1)
-    print 'at 0.10:', (resarr < 0.1).mean(1)
+    print('         ', '      '.join(all_gofs))
+    print('at 0.01:', (resarr < 0.01).mean(1))
+    print('at 0.05:', (resarr < 0.05).mean(1))
+    print('at 0.10:', (resarr < 0.1).mean(1))
 
     gof_mc(lambda nobs: stats.t.rvs(3, size=nobs), 'norm', nobs=200)
 
@@ -673,7 +674,7 @@ def rvs(self, args, size):
     nrep = 100
     bt = bootstrap(NewNorm(), args=(0,1), nobs=nobs, nrep=nrep, value=None)
     quantindex = np.floor(nrep * np.array([0.99, 0.95, 0.9])).astype(int)
-    print bt[quantindex]
+    print(bt[quantindex])
 
     #the bootstrap results match Stephens pretty well for nobs=100, but not so well for
     #large (1000) or small (20) nobs
diff --git a/statsmodels/sandbox/distributions/multivariate.py b/statsmodels/sandbox/distributions/multivariate.py
index d6c84fc45d5..69381821026 100644
--- a/statsmodels/sandbox/distributions/multivariate.py
+++ b/statsmodels/sandbox/distributions/multivariate.py
@@ -14,11 +14,12 @@
 Genz and Bretz for formula
 
 '''
+from __future__ import print_function
 import numpy as np
 from scipy import integrate, stats, special
 from scipy.stats import chi,chi2
 
-from extras import mvnormcdf, mvstdnormcdf, mvnormcdf
+from .extras import mvnormcdf, mvstdnormcdf, mvnormcdf
 
 from numpy import exp as np_exp
 from numpy import log as np_log
@@ -133,25 +134,25 @@ def multivariate_t_rvs(m, S, df=np.inf, n=1):
     b[:] = 3
     df = 10.
     sqrt_df = np.sqrt(df)
-    print mvstdnormcdf(a, b, corr, abseps=1e-6)
+    print(mvstdnormcdf(a, b, corr, abseps=1e-6))
 
     #print integrate.quad(funbgh, 0, np.inf, args=(a,b,R,df))
-    print (stats.t.cdf(b[0], df) - stats.t.cdf(a[0], df))**3
+    print((stats.t.cdf(b[0], df) - stats.t.cdf(a[0], df))**3)
 
     s = 1
-    print mvstdnormcdf(s*a/sqrt_df, s*b/sqrt_df, R)
+    print(mvstdnormcdf(s*a/sqrt_df, s*b/sqrt_df, R))
 
 
     df=4
-    print mvstdtprob(a, b, R, df)
+    print(mvstdtprob(a, b, R, df))
 
     S = np.array([[1.,.5],[.5,1.]])
-    print multivariate_t_rvs([10.,20.], S, 2, 5)
+    print(multivariate_t_rvs([10.,20.], S, 2, 5))
 
     nobs = 10000
     rvst = multivariate_t_rvs([10.,20.], S, 2, nobs)
-    print np.sum((rvst<[10.,20.]).all(1),0) * 1. / nobs
-    print mvstdtprob(-np.inf*np.ones(2), np.zeros(2), R[:2,:2], 2)
+    print(np.sum((rvst<[10.,20.]).all(1),0) * 1. / nobs)
+    print(mvstdtprob(-np.inf*np.ones(2), np.zeros(2), R[:2,:2], 2))
 
 
     '''
diff --git a/statsmodels/sandbox/distributions/mv_measures.py b/statsmodels/sandbox/distributions/mv_measures.py
index aab08a56913..69182e2eafe 100644
--- a/statsmodels/sandbox/distributions/mv_measures.py
+++ b/statsmodels/sandbox/distributions/mv_measures.py
@@ -62,7 +62,8 @@ def mutualinfo_kde_2sample(y, x, normed=True):
     #kde_yx = gaussian_kde(yx)(yx)
 
     mi_obs = np.log(kde_x) - np.log(kde_y)
-    if len(mi_obs) != nobs: raise
+    if len(mi_obs) != nobs:
+        raise ValueError("Wrong number of observations")
     mi = mi_obs.mean()
     if normed:
         mi_normed = np.sqrt(1. - np.exp(-2 * mi))
@@ -151,30 +152,30 @@ def mutualinfo_binned(y, x, bins, normed=True):
     if funtype == 'linear':
         y = 0 + x + sig * np.random.randn(nobs)
 
-    print 'correlation'
-    print np.corrcoef(y,x)[0, 1]
-    print 'pearsonr', stats.pearsonr(y,x)
-    print 'spearmanr', stats.spearmanr(y,x)
-    print 'kendalltau', stats.kendalltau(y,x)
+    print('correlation')
+    print(np.corrcoef(y,x)[0, 1])
+    print('pearsonr', stats.pearsonr(y,x))
+    print('spearmanr', stats.spearmanr(y,x))
+    print('kendalltau', stats.kendalltau(y,x))
 
     pxy, binsx, binsy = np.histogram2d(x,y, bins=5)
     px, binsx_ = np.histogram(x, bins=binsx)
     py, binsy_ = np.histogram(y, bins=binsy)
-    print 'mutualinfo', infotheo.mutualinfo(px*1./nobs, py*1./nobs,
-                                            1e-15+pxy*1./nobs, logbase=np.e)
+    print('mutualinfo', infotheo.mutualinfo(px*1./nobs, py*1./nobs,
+                                            1e-15+pxy*1./nobs, logbase=np.e))
 
-    print 'mutualinfo_kde normed', mutualinfo_kde(y,x)
-    print 'mutualinfo_kde       ', mutualinfo_kde(y,x, normed=False)
+    print('mutualinfo_kde normed', mutualinfo_kde(y,x))
+    print('mutualinfo_kde       ', mutualinfo_kde(y,x, normed=False))
     mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \
                mutualinfo_binned(y, x, 5, normed=True)
-    print 'mutualinfo_binned normed', mi_normed
-    print 'mutualinfo_binned       ', mi_obs.sum()
+    print('mutualinfo_binned normed', mi_normed)
+    print('mutualinfo_binned       ', mi_obs.sum())
 
     mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \
                mutualinfo_binned(y, x, 'auto', normed=True)
-    print 'auto'
-    print 'mutualinfo_binned normed', mi_normed
-    print 'mutualinfo_binned       ', mi_obs.sum()
+    print('auto')
+    print('mutualinfo_binned normed', mi_normed)
+    print('mutualinfo_binned       ', mi_obs.sum())
 
     ys = np.sort(y)
     xs = np.sort(x)
@@ -182,9 +183,9 @@ def mutualinfo_binned(y, x, bins, normed=True):
     bx = xs[((nobs-1)*np.array([0, 0.25, 0.4, 0.6, 0.75, 1])).astype(int)]
     mi_normed, (pyx2, py2, px2, binsy2, binsx2), mi_obs = \
                mutualinfo_binned(y, x, (by,bx), normed=True)
-    print 'quantiles'
-    print 'mutualinfo_binned normed', mi_normed
-    print 'mutualinfo_binned       ', mi_obs.sum()
+    print('quantiles')
+    print('mutualinfo_binned normed', mi_normed)
+    print('mutualinfo_binned       ', mi_obs.sum())
 
     doplot = 1#False
     if doplot:
diff --git a/statsmodels/sandbox/distributions/mv_normal.py b/statsmodels/sandbox/distributions/mv_normal.py
index 2a9a831fc82..b3f8a027b6c 100644
--- a/statsmodels/sandbox/distributions/mv_normal.py
+++ b/statsmodels/sandbox/distributions/mv_normal.py
@@ -144,7 +144,7 @@
 'standardize', 'standardized', 'std', 'std_sigma', 'whiten']
 
 """
-
+from __future__ import print_function
 import numpy as np
 
 from statsmodels.sandbox.distributions.multivariate import (
@@ -288,10 +288,10 @@ def fun(x):
         used += rvsok.shape[0]
 
         rvsli.append(rvsok)   #[:remain]) use extras instead
-        print used
+        print(used)
         if used >= size: break
     rvs = np.vstack(rvsli)
-    print rvs.shape
+    print(rvs.shape)
     assert used == rvs.shape[0] #saftey check
     mean_conditional = fun(rvs).mean(0)
     if conditional:
@@ -1056,7 +1056,7 @@ def rvs(self, size=1):
 
 
         '''
-        from multivariate import multivariate_t_rvs
+        from .multivariate import multivariate_t_rvs
         return multivariate_t_rvs(self.mean, self.sigma, df=self.df, n=size)
 
 
@@ -1201,17 +1201,17 @@ def fun(x, y):
     if 'mvn' in examples:
         bvn = BivariateNormal(mu, covx)
         rvs = bvn.rvs(size=1000)
-        print rvs.mean(0)
-        print np.cov(rvs, rowvar=0)
-        print bvn.expect()
-        print bvn.cdf([0,0])
+        print(rvs.mean(0))
+        print(np.cov(rvs, rowvar=0))
+        print(bvn.expect())
+        print(bvn.cdf([0,0]))
         bvn1 = BivariateNormal(mu, np.eye(2))
         bvn2 = BivariateNormal(mu, 4*np.eye(2))
-        fun = lambda(x) : np.log(bvn1.pdf(x)) - np.log(bvn.pdf(x))
-        print bvn1.expect(fun)
-        print bvn1.kl(bvn2), bvn1.kl_mc(bvn2)
-        print bvn2.kl(bvn1), bvn2.kl_mc(bvn1)
-        print bvn1.kl(bvn), bvn1.kl_mc(bvn)
+        fun = lambda x : np.log(bvn1.pdf(x)) - np.log(bvn.pdf(x))
+        print(bvn1.expect(fun))
+        print(bvn1.kl(bvn2), bvn1.kl_mc(bvn2))
+        print(bvn2.kl(bvn1), bvn2.kl_mc(bvn1))
+        print(bvn1.kl(bvn), bvn1.kl_mc(bvn))
         mvn = MVNormal(mu, covx)
         mvn.pdf([0,0])
         mvn.pdf(np.zeros((2,2)))
@@ -1247,9 +1247,9 @@ def fun(x, y):
         assert_array_almost_equal( mvn3c.pdf(cov3), r_val, decimal = 16)
 
         mvn3b = MVNormal((0,0,0), 1)
-        fun = lambda(x) : np.log(mvn3.pdf(x)) - np.log(mvn3b.pdf(x))
-        print mvn3.expect_mc(fun)
-        print mvn3.expect_mc(fun, size=200000)
+        fun = lambda x : np.log(mvn3.pdf(x)) - np.log(mvn3b.pdf(x))
+        print(mvn3.expect_mc(fun))
+        print(mvn3.expect_mc(fun, size=200000))
 
 
     mvt = MVT((0,0), 1, 5)
diff --git a/statsmodels/sandbox/distributions/otherdist.py b/statsmodels/sandbox/distributions/otherdist.py
index a25c4b26920..3ff5c83fae4 100644
--- a/statsmodels/sandbox/distributions/otherdist.py
+++ b/statsmodels/sandbox/distributions/otherdist.py
@@ -21,7 +21,7 @@
 '''
 
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats
 
@@ -263,10 +263,10 @@ def plot(self, x, *args, **kwds):
 
 
     pd = ParametricMixtureD(mdist, bdist, bd_args_fn, bd_kwds_fn)
-    print pd.pdf(1)
+    print(pd.pdf(1))
     p, bp = pd.pdf(np.linspace(0,20,21))
     pc, bpc = pd.cdf(np.linspace(0,20,21))
-    print pd.rvs()
+    print(pd.rvs())
     rvs, m = pd.rvs(size=1000)
 
 
@@ -281,8 +281,8 @@ def plot(self, x, *args, **kwds):
     clip_lower_ = 0. #-0.5
     cnorm = ClippedContinuous(bdist, clip_lower_)
     x = np.linspace(1e-8, 4, 11)
-    print cnorm.pdf(x)
-    print cnorm.cdf(x)
+    print(cnorm.pdf(x))
+    print(cnorm.cdf(x))
 
     if doplots:
         #plt.figure()
diff --git a/statsmodels/sandbox/distributions/quantize.py b/statsmodels/sandbox/distributions/quantize.py
index 75282d392f7..b566b2a98e7 100644
--- a/statsmodels/sandbox/distributions/quantize.py
+++ b/statsmodels/sandbox/distributions/quantize.py
@@ -2,8 +2,8 @@
 
 Author: josef-pktd
 '''
-
-
+from __future__ import print_function
+from statsmodels.compat.python import range, lmap
 import numpy as np
 
 def prob_bv_rectangle(lower, upper, cdf):
@@ -38,12 +38,12 @@ def prob_mv_grid(bins, cdf, axis=-1):
 
     '''
     if not isinstance(bins, np.ndarray):
-        bins = map(np.asarray, bins)
+        bins = lmap(np.asarray, bins)
         n_dim = len(bins)
         bins_ = []
         #broadcast if binedges are 1d
-        if all(map(np.ndim, bins) == np.ones(n_dim)):
-            for d in xrange(n_dim):
+        if all(lmap(np.ndim, bins) == np.ones(n_dim)):
+            for d in range(n_dim):
                 sl = [None]*n_dim
                 sl[d] = slice(None)
                 bins_.append(bins[d][sl])
@@ -51,10 +51,10 @@ def prob_mv_grid(bins, cdf, axis=-1):
         n_dim = bins.shape[0]
         bins_ = bins
 
-    print len(bins)
+    print(len(bins))
     cdf_values = cdf(bins_)
     probs = cdf_values.copy()
-    for d in xrange(n_dim):
+    for d in range(n_dim):
         probs = np.diff(probs, axis=d)
 
     return probs
diff --git a/statsmodels/sandbox/distributions/sppatch.py b/statsmodels/sandbox/distributions/sppatch.py
index d41c1a5339a..04df3c20d59 100644
--- a/statsmodels/sandbox/distributions/sppatch.py
+++ b/statsmodels/sandbox/distributions/sppatch.py
@@ -10,7 +10,8 @@
 Author: josef-pktd
 License: Simplified BSD
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import range, lmap
 import numpy as np
 from scipy import stats, optimize, integrate
 
@@ -182,7 +183,7 @@ def nnlf_fr(self, thetash, x, frmask):
         scale = theta[-1]
         args = tuple(theta[:-2])
     except IndexError:
-        raise ValueError, "Not enough input arguments."
+        raise ValueError("Not enough input arguments.")
     if not self._argcheck(*args) or scale <= 0:
         return np.inf
     x = np.array((x-loc) / scale)
@@ -252,13 +253,13 @@ def fit_fr(self, data, *args, **kwds):
     * more input checking, args is list ? might also apply to current fit method
 
     '''
-    loc0, scale0 = map(kwds.get, ['loc', 'scale'],[0.0, 1.0])
+    loc0, scale0 = lmap(kwds.get, ['loc', 'scale'],[0.0, 1.0])
     Narg = len(args)
 
     if Narg == 0 and hasattr(self, '_fitstart'):
         x0 = self._fitstart(data)
     elif Narg > self.numargs:
-        raise ValueError, "Too many input arguments."
+        raise ValueError("Too many input arguments.")
     else:
         args += (1.0,)*(self.numargs-Narg)
         # location and scale are at the end
@@ -267,15 +268,15 @@ def fit_fr(self, data, *args, **kwds):
     if 'frozen' in kwds:
         frmask = np.array(kwds['frozen'])
         if len(frmask) != self.numargs+2:
-            raise ValueError, "Incorrect number of frozen arguments."
+            raise ValueError("Incorrect number of frozen arguments.")
         else:
             # keep starting values for not frozen parameters
             x0  = np.array(x0)[np.isnan(frmask)]
     else:
         frmask = None
 
-    #print x0
-    #print frmask
+    #print(x0
+    #print(frmask
     return optimize.fmin(self.nnlf_fr, x0,
                 args=(np.ravel(data), frmask), disp=0)
 
@@ -501,7 +502,7 @@ def fun(x):
     low = max(min(-suppnmin, low), lb)
     upp = min(max(suppnmin, upp), ub)
     supp = np.arange(low, upp+1, self.inc) #check limits
-    #print 'low, upp', low, upp
+    #print('low, upp', low, upp
     tot = np.sum(fun(supp))
     diff = 1e100
     pos = upp + self.inc
@@ -525,7 +526,7 @@ def fun(x):
             count += 1
     if count > maxcount:
         # replace with proper warning
-        print 'sum did not converge'
+        print('sum did not converge')
     return tot/invfac
 
 stats.distributions.rv_continuous.fit_fr = fit_fr
@@ -560,7 +561,7 @@ def distfitbootstrap(sample, distr, nrepl=100):
     '''
     nobs = len(sample)
     res = np.zeros(nrepl)
-    for ii in xrange(nrepl):
+    for ii in range(nrepl):
         rvsind = np.random.randint(nobs, size=nobs)
         x = sample[rvsind]
         res[ii] = distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0])
@@ -589,14 +590,14 @@ def distfitmc(sample, distr, nrepl=100, distkwds={}):
     arg = distkwds.pop('arg')
     nobs = len(sample)
     res = np.zeros(nrepl)
-    for ii in xrange(nrepl):
+    for ii in range(nrepl):
         x = distr.rvs(arg, size=nobs, **distkwds)
         res[ii] = distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0])
     return res
 
 
 def printresults(sample, arg, bres, kind='bootstrap'):
-    '''calculate and print Bootstrap or Monte Carlo result
+    '''calculate and print(Bootstrap or Monte Carlo result
 
     Parameters
     ----------
@@ -626,31 +627,31 @@ def printresults(sample, arg, bres, kind='bootstrap'):
     todo: return results and string instead of printing
 
     '''
-    print 'true parameter value'
-    print arg
-    print 'MLE estimate of parameters using sample (nobs=%d)'% (nobs)
+    print('true parameter value')
+    print(arg)
+    print('MLE estimate of parameters using sample (nobs=%d)'% (nobs))
     argest = distr.fit_fr(sample, frozen=[np.nan, 0.0, 1.0])
-    print argest
+    print(argest)
     if kind == 'bootstrap':
         #bootstrap compares to estimate from sample
         argorig = arg
         arg = argest
 
-    print '%s distribution of parameter estimate (nrepl=%d)'% (kind, nrepl)
-    print 'mean = %f, bias=%f' % (bres.mean(0), bres.mean(0)-arg)
-    print 'median', np.median(bres, axis=0)
-    print 'var and std', bres.var(0), np.sqrt(bres.var(0))
+    print('%s distribution of parameter estimate (nrepl=%d)'% (kind, nrepl))
+    print('mean = %f, bias=%f' % (bres.mean(0), bres.mean(0)-arg))
+    print('median', np.median(bres, axis=0))
+    print('var and std', bres.var(0), np.sqrt(bres.var(0)))
     bmse = ((bres - arg)**2).mean(0)
-    print 'mse, rmse', bmse, np.sqrt(bmse)
+    print('mse, rmse', bmse, np.sqrt(bmse))
     bressorted = np.sort(bres)
-    print '%s confidence interval (90%% coverage)' % kind
-    print bressorted[np.floor(nrepl*0.05)], bressorted[np.floor(nrepl*0.95)]
-    print '%s confidence interval (90%% coverage) normal approximation' % kind
-    print stats.norm.ppf(0.05, loc=bres.mean(), scale=bres.std()),
-    print stats.norm.isf(0.05, loc=bres.mean(), scale=bres.std())
-    print 'Kolmogorov-Smirnov test for normality of %s distribution' % kind
-    print ' - estimated parameters, p-values not really correct'
-    print stats.kstest(bres, 'norm', (bres.mean(), bres.std()))
+    print('%s confidence interval (90%% coverage)' % kind)
+    print(bressorted[np.floor(nrepl*0.05)], bressorted[np.floor(nrepl*0.95)])
+    print('%s confidence interval (90%% coverage) normal approximation' % kind)
+    print(stats.norm.ppf(0.05, loc=bres.mean(), scale=bres.std()),)
+    print(stats.norm.isf(0.05, loc=bres.mean(), scale=bres.std()))
+    print('Kolmogorov-Smirnov test for normality of %s distribution' % kind)
+    print(' - estimated parameters, p-values not really correct')
+    print(stats.kstest(bres, 'norm', (bres.mean(), bres.std())))
 
 
 if __name__ == '__main__':
@@ -659,35 +660,35 @@ def printresults(sample, arg, bres, kind='bootstrap'):
 
     if 'largenumber' in examplecases:
 
-        print '\nDistribution: vonmises'
+        print('\nDistribution: vonmises')
 
         for nobs in [200]:#[20000, 1000, 100]:
             x = stats.vonmises.rvs(1.23, loc=0, scale=1, size=nobs)
-            print '\nnobs:', nobs
-            print 'true parameter'
-            print '1.23, loc=0, scale=1'
-            print 'unconstraint'
-            print stats.vonmises.fit(x)
-            print stats.vonmises.fit_fr(x, frozen=[np.nan, np.nan, np.nan])
-            print 'with fixed loc and scale'
-            print stats.vonmises.fit_fr(x, frozen=[np.nan, 0.0, 1.0])
-
-        print '\nDistribution: gamma'
+            print('\nnobs:', nobs)
+            print('true parameter')
+            print('1.23, loc=0, scale=1')
+            print('unconstraint')
+            print(stats.vonmises.fit(x))
+            print(stats.vonmises.fit_fr(x, frozen=[np.nan, np.nan, np.nan]))
+            print('with fixed loc and scale')
+            print(stats.vonmises.fit_fr(x, frozen=[np.nan, 0.0, 1.0]))
+
+        print('\nDistribution: gamma')
         distr = stats.gamma
         arg, loc, scale = 2.5, 0., 20.
 
         for nobs in [200]:#[20000, 1000, 100]:
             x = distr.rvs(arg, loc=loc, scale=scale, size=nobs)
-            print '\nnobs:', nobs
-            print 'true parameter'
-            print '%f, loc=%f, scale=%f' % (arg, loc, scale)
-            print 'unconstraint'
-            print distr.fit(x)
-            print distr.fit_fr(x, frozen=[np.nan, np.nan, np.nan])
-            print 'with fixed loc and scale'
-            print distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0])
-            print 'with fixed loc'
-            print distr.fit_fr(x, frozen=[np.nan, 0.0, np.nan])
+            print('\nnobs:', nobs)
+            print('true parameter')
+            print('%f, loc=%f, scale=%f' % (arg, loc, scale))
+            print('unconstraint')
+            print(distr.fit(x))
+            print(distr.fit_fr(x, frozen=[np.nan, np.nan, np.nan]))
+            print('with fixed loc and scale')
+            print(distr.fit_fr(x, frozen=[np.nan, 0.0, 1.0]))
+            print('with fixed loc')
+            print(distr.fit_fr(x, frozen=[np.nan, 0.0, np.nan]))
 
 
     ex = ['gamma', 'vonmises'][0]
@@ -706,14 +707,14 @@ def printresults(sample, arg, bres, kind='bootstrap'):
 
     sample = distr.rvs(arg, loc=loc, scale=scale, size=nobs)
 
-    print '\nDistribution:', distr
+    print('\nDistribution:', distr)
     if 'bootstrap' in examplecases:
-        print '\nBootstrap'
+        print('\nBootstrap')
         bres = distfitbootstrap(sample, distr, nrepl=nrepl )
         printresults(sample, arg, bres)
 
     if 'montecarlo' in examplecases:
-        print '\nMonteCarlo'
+        print('\nMonteCarlo')
         mcres = distfitmc(sample, distr, nrepl=nrepl,
                           distkwds=dict(arg=arg, loc=loc, scale=scale))
         printresults(sample, arg, mcres, kind='montecarlo')
diff --git a/statsmodels/sandbox/distributions/tests/_est_fit.py b/statsmodels/sandbox/distributions/tests/_est_fit.py
index 031e596e862..cc0029b6113 100644
--- a/statsmodels/sandbox/distributions/tests/_est_fit.py
+++ b/statsmodels/sandbox/distributions/tests/_est_fit.py
@@ -12,7 +12,7 @@
 
 from scipy import stats
 
-from distparams import distcont
+from .distparams import distcont
 
 # this is not a proper statistical test for convergence, but only
 # verifies that the estimate and true values don't differ by too much
diff --git a/statsmodels/sandbox/distributions/tests/check_moments.py b/statsmodels/sandbox/distributions/tests/check_moments.py
index 26d2d5881f8..419cc87bfaf 100644
--- a/statsmodels/sandbox/distributions/tests/check_moments.py
+++ b/statsmodels/sandbox/distributions/tests/check_moments.py
@@ -4,13 +4,13 @@
 
 
 '''
-
+from statsmodels.compat.python import iteritems
 import numpy as np
 from scipy import stats
 #from statsmodels.stats.moment_helpers import mnc2mvsk
 from statsmodels.sandbox.distributions.sppatch import expect_v2
 
-from distparams import distcont, distdiscrete#, distslow
+from .distparams import distcont, distdiscrete#, distslow
 
 
 specialcases = {'ncf':{'ub':1000} # diverges if it's too large, checked for mean
@@ -75,7 +75,7 @@ def check_cont_basic():
         mask = np.isfinite(st)
         if mask.sum() < 4:
             distnonfinite.append(distname)
-        print distname
+        print(distname)
         #print 'stats ', m,v,s,k
         expect = distfn.expect
         expect = lambda *args, **kwds : expect_v2(distfn, *args, **kwds)
@@ -92,7 +92,7 @@ def check_cont_basic():
         try:
             me, ve, se, ke = mnc2mvsk((mnc1, mnc2, mnc3, mnc4))
         except:
-            print 'exception', mnc1, mnc2, mnc3, mnc4, st
+            print('exception', mnc1, mnc2, mnc3, mnc4, st)
             me, ve, se, ke = [np.nan]*4
             if mask.size > 0:
                 distex.append(distname)
@@ -102,7 +102,7 @@ def check_cont_basic():
         em = np.array([me, ve, se, ke])
 
         diff = st[mask] - em[mask]
-        print diff, mnc1_lc - (1 + 2*mnc1)
+        print(diff, mnc1_lc - (1 + 2*mnc1))
         if np.size(diff)>0 and np.max(np.abs(diff)) > 1e-3:
             distlow.append(distname)
         else:
@@ -136,24 +136,24 @@ def nct_kurt_bug():
 if __name__ == '__main__':
 
     check_cont_basic()
-    #print [(k, v[0]) for k,v in res.items() if np.abs(v[0]-1)>1e-3]
-    #print [(k, v[2][0], 1+2*v[2][0]) for k,v in res.items() if np.abs(v[-1]-(1+2*v[2][0]))>1e-3]
-    mean_ = [(k, v[1][0], v[2][0]) for k,v in res.items()
+    #print [(k, v[0]) for k,v in iteritems(res) if np.abs(v[0]-1)>1e-3]
+    #print [(k, v[2][0], 1+2*v[2][0]) for k,v in iteritems(res) if np.abs(v[-1]-(1+2*v[2][0]))>1e-3]
+    mean_ = [(k, v[1][0], v[2][0]) for k,v in iteritems(res)
              if np.abs(v[1][0] - v[2][0])>1e-6 and np.isfinite(v[1][0])]
-    var_ = [(k, v[1][1], v[2][1]) for k,v in res.items()
+    var_ = [(k, v[1][1], v[2][1]) for k,v in iteritems(res)
             if np.abs(v[1][1] - v[2][1])>1e-2 and np.isfinite(v[1][1])]
-    skew = [(k, v[1][2], v[2][2]) for k,v in res.items()
+    skew = [(k, v[1][2], v[2][2]) for k,v in iteritems(res)
             if np.abs(v[1][2] - v[2][2])>1e-2 and np.isfinite(v[1][1])]
-    kurt = [(k, v[1][3], v[2][3]) for k,v in res.items()
+    kurt = [(k, v[1][3], v[2][3]) for k,v in iteritems(res)
             if np.abs(v[1][3] - v[2][3])>1e-2 and np.isfinite(v[1][1])]
 
     from statsmodels.iolib import SimpleTable
     if len(mean_) > 0:
-        print '\nMean difference at least 1e-6'
-        print SimpleTable(mean_, headers=['distname', 'diststats', 'expect'])
-    print '\nVariance difference at least 1e-2'
-    print SimpleTable(var_, headers=['distname', 'diststats', 'expect'])
-    print '\nSkew difference at least 1e-2'
-    print SimpleTable(skew, headers=['distname', 'diststats', 'expect'])
-    print '\nKurtosis difference at least 1e-2'
-    print SimpleTable(kurt, headers=['distname', 'diststats', 'expect'])
+        print('\nMean difference at least 1e-6')
+        print(SimpleTable(mean_, headers=['distname', 'diststats', 'expect']))
+    print('\nVariance difference at least 1e-2')
+    print(SimpleTable(var_, headers=['distname', 'diststats', 'expect']))
+    print('\nSkew difference at least 1e-2')
+    print(SimpleTable(skew, headers=['distname', 'diststats', 'expect']))
+    print('\nKurtosis difference at least 1e-2')
+    print(SimpleTable(kurt, headers=['distname', 'diststats', 'expect']))
diff --git a/statsmodels/sandbox/distributions/tests/test_extras.py b/statsmodels/sandbox/distributions/tests/test_extras.py
index 499768333f2..c71d72d1ef5 100644
--- a/statsmodels/sandbox/distributions/tests/test_extras.py
+++ b/statsmodels/sandbox/distributions/tests/test_extras.py
@@ -108,7 +108,7 @@ def test_skewt():
     nose.runmodule(argv=['__main__','-vvs','-x','--pdb', '--pdb-failure'],
                    exit=False)
 
-    print ('Done')
+    print('Done')
 
 
 '''
diff --git a/statsmodels/sandbox/distributions/tests/test_multivariate.py b/statsmodels/sandbox/distributions/tests/test_multivariate.py
index f879c6785f6..c3d18e66c5a 100644
--- a/statsmodels/sandbox/distributions/tests/test_multivariate.py
+++ b/statsmodels/sandbox/distributions/tests/test_multivariate.py
@@ -171,4 +171,4 @@ def test_mvt_pdf(self):
     nose.runmodule(argv=['__main__','-vvs','-x'],#,'--pdb', '--pdb-failure'],
                    exit=False)
 
-    print ('Done')
+    print('Done')
diff --git a/statsmodels/sandbox/distributions/tests/test_norm_expan.py b/statsmodels/sandbox/distributions/tests/test_norm_expan.py
new file mode 100644
index 00000000000..5bc60414fb0
--- /dev/null
+++ b/statsmodels/sandbox/distributions/tests/test_norm_expan.py
@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+"""Unit tests for Gram-Charlier exansion
+
+No reference results, test based on consistency and normal case.
+
+Created on Wed Feb 19 12:39:49 2014
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from scipy import stats
+
+from numpy.testing import assert_allclose, assert_array_less
+
+from statsmodels.sandbox.distributions.extras import (SkewNorm_gen,
+                                       NormExpan_gen, pdf_moments)
+from statsmodels.stats.moment_helpers import mc2mvsk, mnc2mc
+
+
+class CheckDistribution(object):
+
+    def test_smoke(self):
+        self.dist1.rvs(size=10)
+        self.dist1.pdf(np.linspace(-4, 4, 11))
+
+        # round trip
+        probs = np.linspace(0.001, 0.999, 6)
+        ppf = self.dist2.ppf(probs)
+        cdf = self.dist2.cdf(ppf)
+        assert_allclose(cdf, probs, rtol=1e-6)
+        sf = self.dist2.sf(ppf)
+        assert_allclose(sf, 1 - probs, rtol=1e-6)
+
+
+class CheckExpandNorm(CheckDistribution):
+
+    def test_pdf(self):
+        scale = getattr(self, 'scale', 1)
+        x = np.linspace(-4, 4, 11) * scale
+        pdf2 = self.dist2.pdf(x)
+        pdf1 = self.dist1.pdf(x)
+        atol_pdf = getattr(self, 'atol_pdf', 0)
+        assert_allclose(((pdf2 - pdf1)**2).mean(), 0, rtol=1e-6, atol=atol_pdf)
+        assert_allclose(pdf2, pdf1, rtol=1e-6, atol=atol_pdf)
+
+    def test_mvsk(self):
+        #compare defining mvsk with numerical integration, generic stats
+        mvsk2 = self.dist2.mvsk
+        mvsk1 = self.dist2.stats(moments='mvsk')
+        assert_allclose(mvsk2, mvsk1, rtol=1e-6, atol=1e-13)
+
+        # check mvsk that was used to generate distribution
+        assert_allclose(self.dist2.mvsk, self.mvsk, rtol=1e-12)
+
+
+
+class TestExpandNormMom(CheckExpandNorm):
+    # compare with normal, skew=0, excess_kurtosis=0
+
+    @classmethod
+    def setup_class(kls):
+        kls.scale = 2
+        kls.dist1 = stats.norm(1, 2)
+        kls.mvsk = [1., 2**2, 0, 0]
+        kls.dist2 = NormExpan_gen(kls.mvsk, mode='mvsk')
+
+
+class TestExpandNormSample(object):
+    # don't subclass CheckExpandNorm,
+    # precision not high enough because of mvsk from data
+
+    @classmethod
+    def setup_class(kls):
+        kls.dist1 = dist1 = stats.norm(1, 2)
+        np.random.seed(5999)
+        kls.rvs = dist1.rvs(size=200)
+        #rvs = np.concatenate([rvs, -rvs])
+        # fix mean and std of sample
+        #rvs = (rvs - rvs.mean())/rvs.std(ddof=1) * np.sqrt(2) + 1
+        kls.dist2 = NormExpan_gen(kls.rvs, mode='sample')
+
+        kls.scale = 2
+        kls.atol_pdf = 1e-3
+
+    def test_ks(self):
+        # cdf is slow
+        # Kolmogorov-Smirnov test against generating sample
+        stat, pvalue = stats.kstest(self.rvs, self.dist2.cdf)
+        assert_array_less(0.25, pvalue)
+
+    def test_mvsk(self):
+        mvsk = stats.describe(self.rvs)[-4:]
+        assert_allclose(self.dist2.mvsk, mvsk, rtol=1e-12)
diff --git a/statsmodels/sandbox/distributions/tests/testtransf.py b/statsmodels/sandbox/distributions/tests/testtransf.py
index f7e66f6bef9..2e713265959 100644
--- a/statsmodels/sandbox/distributions/tests/testtransf.py
+++ b/statsmodels/sandbox/distributions/tests/testtransf.py
@@ -15,7 +15,7 @@
   the best which can be obtained.
 array(2981.0032380193438)
 """
-
+from __future__ import print_function
 import warnings # for silencing, see above...
 import numpy as np
 from numpy.testing import assert_almost_equal
@@ -24,7 +24,8 @@
     lognormalg, squarenormalg, absnormalg, negsquarenormalg, squaretg)
 
 
-#some patches to scipy.stats.distributions so tests work and pass
+# some patches to scipy.stats.distributions so tests work and pass
+# this should be necessary only for older scipy
 
 #patch frozen distributions with a name
 stats.distributions.rv_frozen.name = property(lambda self: self.dist.name)
@@ -45,7 +46,8 @@ def f_stats(self, dfn, dfd):
     g2 = where(v2 > 8, g2, nan)
     return mu, mu2, g1, g2
 
-stats.distributions.f_gen._stats = f_stats
+#stats.distributions.f_gen._stats = f_stats
+stats.f.__class__._stats = f_stats
 
 #correct kurtosis by subtracting 3 (Fisher)
 #after this it matches halfnorm for arg close to zero
@@ -69,7 +71,8 @@ def foldnorm_stats(self, c):
     g2 -= 3.
     return mu, mu2, g1, g2
 
-stats.distributions.foldnorm_gen._stats = foldnorm_stats
+#stats.distributions.foldnorm_gen._stats = foldnorm_stats
+stats.foldnorm.__class__._stats = foldnorm_stats
 
 
 #-----------------------------
@@ -165,11 +168,11 @@ def test_equivalent_negsq(self):
 
     debug = 0
     if debug:
-        print negsquarenormalg.ppf([0.1,0.5,0.9])
-        print stats.chi2.ppf([0.1,0.5,0.9],1)
-        print negsquarenormalg.a
-        print negsquarenormalg.b
-
-        print absnormalg.stats( moments='mvsk')
-        print stats.foldnorm(1e-10).stats( moments='mvsk')
-        print stats.halfnorm.stats( moments='mvsk')
+        print(negsquarenormalg.ppf([0.1,0.5,0.9]))
+        print(stats.chi2.ppf([0.1,0.5,0.9],1))
+        print(negsquarenormalg.a)
+        print(negsquarenormalg.b)
+
+        print(absnormalg.stats( moments='mvsk'))
+        print(stats.foldnorm(1e-10).stats( moments='mvsk'))
+        print(stats.halfnorm.stats( moments='mvsk'))
diff --git a/statsmodels/sandbox/distributions/transform_functions.py b/statsmodels/sandbox/distributions/transform_functions.py
index 511655f1206..11ab765f784 100644
--- a/statsmodels/sandbox/distributions/transform_functions.py
+++ b/statsmodels/sandbox/distributions/transform_functions.py
@@ -7,7 +7,7 @@
 Author: Josef Perktold
 License : BSD
 """
-
+from __future__ import print_function
 import numpy as np
 
 
@@ -184,8 +184,8 @@ def deriv(self, x):
     absf.inverseminus(5) == -5
 
     chainf = ChainFunc(AffineFunc(1,2), BoxCoxNonzeroFunc(2))
-    print chainf.func(3.)
+    print(chainf.func(3.))
     chainf2 = ChainFunc(BoxCoxNonzeroFunc(2), AffineFunc(1,2))
-    print chainf.func(3.)
+    print(chainf.func(3.))
 
 
diff --git a/statsmodels/sandbox/distributions/transformed.py b/statsmodels/sandbox/distributions/transformed.py
index d4be989a742..6c6d5387c6d 100644
--- a/statsmodels/sandbox/distributions/transformed.py
+++ b/statsmodels/sandbox/distributions/transformed.py
@@ -38,7 +38,8 @@
 License: BSD
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import iteritems
 from scipy import integrate # for scipy 0.6.0
 
 from scipy import stats, info
@@ -47,7 +48,7 @@
 
 def get_u_argskwargs(**kwargs):
     #Todo: What's this? wrong spacing, used in Transf_gen TransfTwo_gen
-    u_kwargs = dict((k.replace('u_','',1),v) for k,v in kwargs.items()
+    u_kwargs = dict((k.replace('u_','',1),v) for k,v in iteritems(kwargs)
                     if k.startswith('u_'))
     u_args = u_kwargs.pop('u_args',None)
     return u_args, u_kwargs
@@ -57,15 +58,15 @@ class Transf_gen(distributions.rv_continuous):
 
     '''
     def __init__(self, kls, func, funcinv, *args, **kwargs):
-        #print args
-        #print kwargs
+        #print(args
+        #print(kwargs
 
         self.func = func
         self.funcinv = funcinv
         #explicit for self.__dict__.update(kwargs)
         #need to set numargs because inspection does not work
         self.numargs = kwargs.pop('numargs', 0)
-        #print self.numargs
+        #print(self.numargs
         name = kwargs.pop('name','transfdist')
         longname = kwargs.pop('longname','Non-linear transformed distribution')
         extradoc = kwargs.pop('extradoc',None)
@@ -86,7 +87,7 @@ def __init__(self, kls, func, funcinv, *args, **kwargs):
                                         extradoc = extradoc)
 
     def _cdf(self,x,*args, **kwargs):
-        #print args
+        #print(args
         if not self.decr:
             return self.kls._cdf(self.funcinv(x),*args, **kwargs)
             #note scipy _cdf only take *args not *kwargs
@@ -143,8 +144,8 @@ class ExpTransf_gen(distributions.rv_continuous):
 
     '''
     def __init__(self, kls, *args, **kwargs):
-        #print args
-        #print kwargs
+        #print(args
+        #print(kwargs
         #explicit for self.__dict__.update(kwargs)
         if 'numargs' in kwargs:
             self.numargs = kwargs['numargs']
@@ -161,7 +162,7 @@ def __init__(self, kls, *args, **kwargs):
         super(ExpTransf_gen,self).__init__(a=a, name=name)
         self.kls = kls
     def _cdf(self,x,*args):
-        #print args
+        #print(args
         return self.kls._cdf(np.log(x),*args)
     def _ppf(self, q, *args):
         return np.exp(self.kls._ppf(q,*args))
@@ -192,49 +193,49 @@ def __init__(self, kls, *args, **kwargs):
         self.kls = kls
 
     def _cdf(self,x, *args):
-        #print args
+        #print(args
         return self.kls._cdf(np.exp(x),*args)
     def _ppf(self, q, *args):
         return np.log(self.kls._ppf(q,*args))
 
 def examples_transf():
     ##lognormal = ExpTransf(a=0.0, xa=-10.0, name = 'Log transformed normal')
-    ##print lognormal.cdf(1)
-    ##print stats.lognorm.cdf(1,1)
-    ##print lognormal.stats()
-    ##print stats.lognorm.stats(1)
-    ##print lognormal.rvs(size=10)
+    ##print(lognormal.cdf(1)
+    ##print(stats.lognorm.cdf(1,1)
+    ##print(lognormal.stats()
+    ##print(stats.lognorm.stats(1)
+    ##print(lognormal.rvs(size=10)
 
-    print 'Results for lognormal'
+    print('Results for lognormal')
     lognormalg = ExpTransf_gen(stats.norm, a=0, name = 'Log transformed normal general')
-    print lognormalg.cdf(1)
-    print stats.lognorm.cdf(1,1)
-    print lognormalg.stats()
-    print stats.lognorm.stats(1)
-    print lognormalg.rvs(size=5)
+    print(lognormalg.cdf(1))
+    print(stats.lognorm.cdf(1,1))
+    print(lognormalg.stats())
+    print(stats.lognorm.stats(1))
+    print(lognormalg.rvs(size=5))
 
-    ##print 'Results for loggamma'
+    ##print('Results for loggamma'
     ##loggammag = ExpTransf_gen(stats.gamma)
-    ##print loggammag._cdf(1,10)
-    ##print stats.loggamma.cdf(1,10)
+    ##print(loggammag._cdf(1,10)
+    ##print(stats.loggamma.cdf(1,10)
 
-    print 'Results for expgamma'
+    print('Results for expgamma')
     loggammaexpg = LogTransf_gen(stats.gamma)
-    print loggammaexpg._cdf(1,10)
-    print stats.loggamma.cdf(1,10)
-    print loggammaexpg._cdf(2,15)
-    print stats.loggamma.cdf(2,15)
+    print(loggammaexpg._cdf(1,10))
+    print(stats.loggamma.cdf(1,10))
+    print(loggammaexpg._cdf(2,15))
+    print(stats.loggamma.cdf(2,15))
 
 
     # this requires change in scipy.stats.distribution
-    #print loggammaexpg.cdf(1,10)
+    #print(loggammaexpg.cdf(1,10)
 
-    print 'Results for loglaplace'
+    print('Results for loglaplace')
     loglaplaceg = LogTransf_gen(stats.laplace)
-    print loglaplaceg._cdf(2,10)
-    print stats.loglaplace.cdf(2,10)
+    print(loglaplaceg._cdf(2,10))
+    print(stats.loglaplace.cdf(2,10))
     loglaplaceexpg = ExpTransf_gen(stats.laplace)
-    print loglaplaceexpg._cdf(2,10)
+    print(loglaplaceexpg._cdf(2,10))
 
 
 
@@ -296,8 +297,8 @@ class TransfTwo_gen(distributions.rv_continuous):
     #a class for non-linear non-monotonic transformation of a continuous random variable
     def __init__(self, kls, func, funcinvplus, funcinvminus, derivplus,
                  derivminus, *args, **kwargs):
-        #print args
-        #print kwargs
+        #print(args
+        #print(kwargs
 
         self.func = func
         self.funcinvplus = funcinvplus
@@ -307,7 +308,7 @@ def __init__(self, kls, func, funcinvplus, funcinvminus, derivplus,
         #explicit for self.__dict__.update(kwargs)
         #need to set numargs because inspection does not work
         self.numargs = kwargs.pop('numargs', 0)
-        #print self.numargs
+        #print(self.numargs
         name = kwargs.pop('name','transfdist')
         longname = kwargs.pop('longname','Non-linear transformed distribution')
         extradoc = kwargs.pop('extradoc',None)
@@ -333,20 +334,20 @@ def _rvs(self, *args):
         return self.func(self.kls._rvs(*args))
 
     def _pdf(self,x,*args, **kwargs):
-        #print args
+        #print(args
         if self.shape == 'u':
             signpdf = 1
         elif self.shape == 'hump':
             signpdf = -1
         else:
-            raise ValueError, 'shape can only be `u` or `hump`'
+            raise ValueError('shape can only be `u` or `hump`')
 
         return signpdf * (self.derivplus(x)*self.kls._pdf(self.funcinvplus(x),*args, **kwargs) -
                    self.derivminus(x)*self.kls._pdf(self.funcinvminus(x),*args, **kwargs))
             #note scipy _cdf only take *args not *kwargs
 
     def _cdf(self,x,*args, **kwargs):
-        #print args
+        #print(args
         if self.shape == 'u':
             return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \
                    self.kls._cdf(self.funcinvminus(x),*args, **kwargs)
@@ -355,7 +356,7 @@ def _cdf(self,x,*args, **kwargs):
             return 1.0 - self._sf(x,*args, **kwargs)
 
     def _sf(self,x,*args, **kwargs):
-        #print args
+        #print(args
         if self.shape == 'hump':
             return self.kls._cdf(self.funcinvplus(x),*args, **kwargs) - \
                    self.kls._cdf(self.funcinvminus(x),*args, **kwargs)
diff --git a/statsmodels/sandbox/distributions/try_max.py b/statsmodels/sandbox/distributions/try_max.py
index 1d7135a7e8f..6e1bf8c29ea 100644
--- a/statsmodels/sandbox/distributions/try_max.py
+++ b/statsmodels/sandbox/distributions/try_max.py
@@ -46,8 +46,8 @@ def _ppf(self, q, *args, **kw):
 
 maxdistr = MaxDist(stats.norm, 10)
 
-print maxdistr.rvs(size=10)
-print maxdistr.stats(moments = 'mvsk')
+print(maxdistr.rvs(size=10))
+print(maxdistr.stats(moments = 'mvsk'))
 
 '''
 >>> print maxdistr.stats(moments = 'mvsk')
diff --git a/statsmodels/sandbox/distributions/try_pot.py b/statsmodels/sandbox/distributions/try_pot.py
index 26cec4230a5..0c023ec6027 100644
--- a/statsmodels/sandbox/distributions/try_pot.py
+++ b/statsmodels/sandbox/distributions/try_pot.py
@@ -4,7 +4,7 @@
 
 @author: josef
 """
-
+from __future__ import print_function
 import numpy as np
 
 def mean_residual_life(x, frac=None, alpha=0.05):
@@ -53,9 +53,9 @@ def mean_residual_life(x, frac=None, alpha=0.05):
 if __name__ == "__main__":
     rvs = np.random.standard_t(5, size= 10)
     res = mean_residual_life(rvs)
-    print res
+    print(res)
     rmean = [rvs[i:].mean() for i in range(len(rvs))]
-    print res[:,2] - rmean[1:]
+    print(res[:,2] - rmean[1:])
 
 '''
 >>> mean_residual_life(rvs, frac= 0.5)
diff --git a/statsmodels/sandbox/examples/bayesprior.py b/statsmodels/sandbox/examples/bayesprior.py
index f5f02f7180f..bbac9142c1e 100644
--- a/statsmodels/sandbox/examples/bayesprior.py
+++ b/statsmodels/sandbox/examples/bayesprior.py
@@ -7,7 +7,7 @@
     import pymc
     pymc_installed = 1
 except:
-    print "pymc not imported"
+    print("pymc not imported")
     pymc_installed = 0
 from scipy.stats import gamma, beta, invgamma
 import numpy as np
@@ -61,17 +61,17 @@ def integ(x):
 # in inverted gamma
 
 palpha = np.random.gamma(400.,.005, size=10000)
-print "First moment: %s\nSecond moment: %s" % (palpha.mean(),palpha.std())
+print("First moment: %s\nSecond moment: %s" % (palpha.mean(),palpha.std()))
 palpha = palpha[0]
 
 prho = np.random.beta(49.5,49.5, size=1e5)
-print "Beta Distribution"
-print "First moment: %s\nSecond moment: %s" % (prho.mean(),prho.std())
+print("Beta Distribution")
+print("First moment: %s\nSecond moment: %s" % (prho.mean(),prho.std()))
 prho = prho[0]
 
 psigma = igamma.rvs(1.,4.**2/2, size=1e5)
-print "Inverse Gamma Distribution"
-print "First moment: %s\nSecond moment: %s" % (psigma.mean(),psigma.std())
+print("Inverse Gamma Distribution")
+print("First moment: %s\nSecond moment: %s" % (psigma.mean(),psigma.std()))
 
 # First do the univariate case
 # y_t = theta_t + epsilon_t
diff --git a/statsmodels/sandbox/examples/ex_cusum.py b/statsmodels/sandbox/examples/ex_cusum.py
index 7e0875fdcc6..2586dcf0137 100644
--- a/statsmodels/sandbox/examples/ex_cusum.py
+++ b/statsmodels/sandbox/examples/ex_cusum.py
@@ -66,8 +66,8 @@
 
 rresid, rparams, rypred, rresid_standardized, rresid_scaled, rcusum, rcusumci = \
             recursive_olsresiduals(res1, skip)
-print rcusum
-print rresid_scaled[skip-1:]
+print(rcusum)
+print(rresid_scaled[skip-1:])
 
 assert_almost_equal(rparams[-1], res1.params)
 
@@ -79,8 +79,8 @@
 plt.plot(rresid)
 plt.plot(np.abs(rresid))
 
-print 'cusum test reject:'
-print ((rcusum[1:]>rcusumci[1])|(rcusum[1:]<rcusumci[0])).any()
+print('cusum test reject:')
+print(((rcusum[1:]>rcusumci[1])|(rcusum[1:]<rcusumci[0])).any())
 
 rresid2, rparams2, rypred2, rresid_standardized2, rresid_scaled2, rcusum2, rcusumci2 = \
             recursive_olsresiduals2(res1, skip)
@@ -94,11 +94,11 @@
 from statsmodels.sandbox.stats.diagnostic import breaks_hansen, \
         breaks_cusumolsresid#, breaks_cusum
 H, crit95, ft, s = breaks_hansen(res1)
-print H
-print crit95
+print(H)
+print(crit95)
 
 supb, pval, crit = breaks_cusumolsresid(res1.resid)
-print supb, pval, crit
+print(supb, pval, crit)
 
 ##check whether this works directly: Ploberger/Kramer framing of standard cusum
 ##no, it's different, there is another denominator
diff --git a/statsmodels/sandbox/examples/ex_formula.py b/statsmodels/sandbox/examples/ex_formula.py
index 9fb68bd7910..8c1702ff2d5 100644
--- a/statsmodels/sandbox/examples/ex_formula.py
+++ b/statsmodels/sandbox/examples/ex_formula.py
@@ -3,7 +3,7 @@
 some work, some things don't
 
 '''
-
+from statsmodels.compat.python import iterkeys, zip
 import string
 import numpy as np
 
@@ -56,22 +56,22 @@
   File "<pyshell#21>", line 1, in <module>
     form.termcolumns('C')
   File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental\scikits\statsmodels\sandbox\formula.py", line 494, in termcolumns
-    raise ValueError, 'term not in formula'
+    raise ValueError('term not in formula')
 ValueError: term not in formula
 
 
 '''
-print form.hasterm('C')
-print form.termcolumns(formula.Term('C'))  #doesn't work with string argument
+print(form.hasterm('C'))
+print(form.termcolumns(formula.Term('C')))  #doesn't work with string argument
 
 #Example: use two columns and get contrast
 
 f2 = (form['A']+form['B'])
-print f2
-print repr(f2)
-f2.namespace.keys()   #namespace is still empty
+print(f2)
+print(repr(f2))
+list(iterkeys(f2.namespace))   #namespace is still empty
 f2.namespace = namespace  #associate data
-f2.namespace.keys()
+iterkeys(f2.namespace)
 f2.design().shape
 contrast.Contrast(formula.Term('A'), f2).matrix
 
@@ -120,7 +120,7 @@
 f4 = formula.interactions([form['A'],form['B']])
 f4.namespace
 f4.namespace = namespace
-print f4
+print(f4)
 f4.names()
 f4.design().shape
 
diff --git a/statsmodels/sandbox/examples/ex_formula_factor.py b/statsmodels/sandbox/examples/ex_formula_factor.py
index 35753ea5a18..559fb18899b 100644
--- a/statsmodels/sandbox/examples/ex_formula_factor.py
+++ b/statsmodels/sandbox/examples/ex_formula_factor.py
@@ -18,7 +18,7 @@
 f = ['a']*4 + ['b']*3 + ['c']*4
 fac = formula.Factor('ff', f)
 fac.namespace = {'ff':f}
-fac.values()
+list(fac.values())
 [f for f in dir(fac) if f[0] != '_']
 
 #create dummy variable
diff --git a/statsmodels/sandbox/examples/ex_kaplan_meier.py b/statsmodels/sandbox/examples/ex_kaplan_meier.py
index 8da1f0651c9..0aed40fc3f1 100644
--- a/statsmodels/sandbox/examples/ex_kaplan_meier.py
+++ b/statsmodels/sandbox/examples/ex_kaplan_meier.py
@@ -1,4 +1,6 @@
 #An example for the Kaplan-Meier estimator
+from __future__ import print_function
+from statsmodels.compat.python import lrange
 import statsmodels.api as sm
 import matplotlib.pyplot as plt
 import numpy as np
@@ -6,11 +8,11 @@
 
 #Getting the strike data as an array
 dta = sm.datasets.strikes.load()
-print 'basic data'
-print '\n'
-dta = dta.values()[-1]
-print dta[range(5),:]
-print '\n'
+print('basic data')
+print('\n')
+dta = list(dta.values()[-1])
+print(dta[lrange(5),:])
+print('\n')
 
 #Create the KaplanMeier object and fit the model
 
@@ -20,19 +22,19 @@
 #show the results
 
 km.plot()
-print 'basic  model'
-print '\n'
+print('basic  model')
+print('\n')
 km.summary()
-print '\n'
+print('\n')
 
 #Mutiple survival curves
 
 km2 = KaplanMeier(dta,0,exog=1)
 km2.fit()
-print 'more than one curve'
-print '\n'
+print('more than one curve')
+print('\n')
 km2.summary()
-print '\n'
+print('\n')
 km2.plot()
 
 #with censoring
@@ -40,23 +42,23 @@
 censoring = np.ones_like(dta[:,0])
 censoring[dta[:,0] > 80] = 0
 dta = np.c_[dta,censoring]
-print 'with censoring'
-print '\n'
-print dta[range(5),:]
-print '\n'
+print('with censoring')
+print('\n')
+print(dta[lrange(5),:])
+print('\n')
 km3 = KaplanMeier(dta,0,exog=1,censoring=2)
 km3.fit()
 km3.summary()
-print '\n'
+print('\n')
 km3.plot()
 
 #Test for difference of survival curves
 
 log_rank = km3.test_diff([0.0645,-0.03957])
-print 'log rank test'
-print '\n'
-print log_rank
-print '\n'
+print('log rank test')
+print('\n')
+print(log_rank)
+print('\n')
 
 #The zeroth element of log_rank is the chi-square test statistic
 #for the difference between the survival curves for exog = 0.0645
@@ -64,10 +66,10 @@
 #the test, and the index two element is the p-value for the test
 
 wilcoxon = km3.test_diff([0.0645,-0.03957], rho=1)
-print 'Wilcoxon'
-print '\n'
-print wilcoxon
-print '\n'
+print('Wilcoxon')
+print('\n')
+print(wilcoxon)
+print('\n')
 
 #Same info as log_rank, but for Peto and Peto modification to the
 #Gehan-Wilcoxon test
@@ -92,10 +94,10 @@ def weights(t):
 #internally, so the weighting function must accept one arguement
 
 test = km3.test_diff([0.0645,-0.03957], weight=weights)
-print 'user specified weights'
-print '\n'
-print test
-print '\n'
+print('user specified weights')
+print('\n')
+print(test)
+print('\n')
 
 #Groups with nan names
 
@@ -107,14 +109,14 @@ def weights(t):
 groups[dta[:,1] <= 0] = 'low'
 dta = dta.astype('S4')
 dta[:,1] = groups
-print 'with nan group names'
-print '\n'
-print dta[range(5),:]
-print '\n'
+print('with nan group names')
+print('\n')
+print(dta[lrange(5),:])
+print('\n')
 km4 = KaplanMeier(dta,0,exog=1,censoring=2)
 km4.fit()
 km4.summary()
-print '\n'
+print('\n')
 km4.plot()
 
 #show all the plots
diff --git a/statsmodels/sandbox/examples/ex_mixed_lls_0.py b/statsmodels/sandbox/examples/ex_mixed_lls_0.py
index a724317fe3a..6e53f4bb767 100644
--- a/statsmodels/sandbox/examples/ex_mixed_lls_0.py
+++ b/statsmodels/sandbox/examples/ex_mixed_lls_0.py
@@ -11,7 +11,7 @@
 
 
 """
-
+from __future__ import print_function
 import numpy as np
 
 from statsmodels.sandbox.panel.mixed import OneWayMixed, Unit
@@ -73,38 +73,38 @@
     m.initialize()
     res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6)
     t1 = time.time()
-    print 'time for initialize and fit', t1-t0
-    print 'number of iterations', m.iterations
-    #print dir(m)
-    #print vars(m)
-    print '\nestimates for fixed effects'
-    print m.a
-    print m.params
+    print('time for initialize and fit', t1-t0)
+    print('number of iterations', m.iterations)
+    #print(dir(m)
+    #print(vars(m)
+    print('\nestimates for fixed effects')
+    print(m.a)
+    print(m.params)
     bfixed_cov = m.cov_fixed()
-    print 'beta fixed standard errors'
-    print np.sqrt(np.diag(bfixed_cov))
+    print('beta fixed standard errors')
+    print(np.sqrt(np.diag(bfixed_cov)))
 
-    print m.bse
+    print(m.bse)
     b_re = m.params_random_units
-    print 'RE mean:', b_re.mean(0)
-    print 'RE columns std', b_re.std(0)
-    print 'np.cov(b_re, rowvar=0), sample statistic'
-    print np.cov(b_re, rowvar=0)
-    print 'std of above'
-    print np.sqrt(np.diag(np.cov(b_re, rowvar=0)))
-    print 'm.cov_random()'
-    print m.cov_random()
-    print 'std of above'
-    print res.std_random()
-    print np.sqrt(np.diag(m.cov_random()))
-
-    print '\n(non)convergence of llf'
-    print m.history['llf'][-4:]
-    print 'convergence of parameters'
-    #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0)
-    print np.diff(np.vstack(m.history['params'][-4:]),axis=0)
-    print 'convergence of D'
-    print np.diff(np.array(m.history['D'][-4:]), axis=0)
+    print('RE mean:', b_re.mean(0))
+    print('RE columns std', b_re.std(0))
+    print('np.cov(b_re, rowvar=0), sample statistic')
+    print(np.cov(b_re, rowvar=0))
+    print('std of above')
+    print(np.sqrt(np.diag(np.cov(b_re, rowvar=0))))
+    print('m.cov_random()')
+    print(m.cov_random())
+    print('std of above')
+    print(res.std_random())
+    print(np.sqrt(np.diag(m.cov_random())))
+
+    print('\n(non)convergence of llf')
+    print(m.history['llf'][-4:])
+    print('convergence of parameters')
+    #print(np.diff(np.vstack(m.history[-4:])[:,1:],axis=0)
+    print(np.diff(np.vstack(m.history['params'][-4:]),axis=0))
+    print('convergence of D')
+    print(np.diff(np.array(m.history['D'][-4:]), axis=0))
 
     #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units])
     zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units])
@@ -117,13 +117,13 @@
            [-0.00909159,  0.26846254]])
     >>> #note cov_random doesn't subtract mean!
     '''
-    print '\nchecking the random effects distribution and prediction'
+    print('\nchecking the random effects distribution and prediction')
     gamma_re_true = np.array(gamma_re_true)
-    print 'mean of random effect true', gamma_re_true.mean(0)
-    print 'mean from fixed effects   ', m.params[-2:]
-    print 'mean of estimated RE      ', b_re.mean(0)
+    print('mean of random effect true', gamma_re_true.mean(0))
+    print('mean from fixed effects   ', m.params[-2:])
+    print('mean of estimated RE      ', b_re.mean(0))
 
-    print
+    print('')
     absmean_true = np.abs(gamma_re_true).mean(0)
     mape = ((m.params[-2:] + b_re) / gamma_re_true - 1).mean(0)*100
     mean_abs_perc = np.abs((m.params[-2:] + b_re) - gamma_re_true).mean(0) \
@@ -132,22 +132,22 @@
                          / absmean_true*100
     rmse_perc = ((m.params[-2:] + b_re) - gamma_re_true).std(0) \
                   / absmean_true*100
-    print 'mape           ', mape
-    print 'mean_abs_perc  ', mean_abs_perc
-    print 'median_abs_perc', median_abs_perc
-    print 'rmse_perc (std)', rmse_perc
+    print('mape           ', mape)
+    print('mean_abs_perc  ', mean_abs_perc)
+    print('median_abs_perc', median_abs_perc)
+    print('rmse_perc (std)', rmse_perc)
     from numpy.testing import assert_almost_equal
     #assert is for n_units=100 in original example
     #I changed random number generation, so this won't work anymore
     #assert_almost_equal(rmse_perc, [ 34.14783884,  11.6031684 ], decimal=8)
 
     #now returns res
-    print res.llf  #based on MLE, does not include constant
-    print res.tvalues
-    print res.pvalues
-    print res.t_test([1,-1,0,0,0,0])
-    print 'test mean of both random effects variables is zero'
-    print res.f_test([[0,0,0,0,1,0], [0,0,0,0,0,1]])
+    print(res.llf)  #based on MLE, does not include constant
+    print(res.tvalues)
+    print(res.pvalues)
+    print(res.t_test([1,-1,0,0,0,0]))
+    print('test mean of both random effects variables is zero')
+    print(res.f_test([[0,0,0,0,1,0], [0,0,0,0,0,1]]))
     plots = res.plot_random_univariate(bins=50)
     fig = res.plot_scatter_pairs(0, 1)
     import matplotlib.pyplot as plt
diff --git a/statsmodels/sandbox/examples/ex_mixed_lls_re.py b/statsmodels/sandbox/examples/ex_mixed_lls_re.py
index 8b78f9892e9..c839df76f9d 100644
--- a/statsmodels/sandbox/examples/ex_mixed_lls_re.py
+++ b/statsmodels/sandbox/examples/ex_mixed_lls_re.py
@@ -76,39 +76,39 @@
     m.initialize()
     res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6)
     t1 = time.time()
-    print 'time for initialize and fit', t1-t0
-    print 'number of iterations', m.iterations
+    print('time for initialize and fit', t1-t0)
+    print('number of iterations', m.iterations)
     #print dir(m)
     #print vars(m)
-    print '\nestimates for fixed effects'
-    print m.a
-    print m.params
+    print('\nestimates for fixed effects')
+    print(m.a)
+    print(m.params)
     bfixed_cov = m.cov_fixed()
-    print 'beta fixed standard errors'
-    print np.sqrt(np.diag(bfixed_cov))
+    print('beta fixed standard errors')
+    print(np.sqrt(np.diag(bfixed_cov)))
 
-    print m.bse
+    print(m.bse)
     b_re = m.params_random_units
-    print 'RE mean:', b_re.mean(0)
-    print 'RE columns std', b_re.std(0)
-    print 'np.cov(b_re, rowvar=0), sample statistic'
-    print np.cov(b_re, rowvar=0)
-    print 'std of above'
+    print('RE mean:', b_re.mean(0))
+    print('RE columns std', b_re.std(0))
+    print('np.cov(b_re, rowvar=0), sample statistic')
+    print(np.cov(b_re, rowvar=0))
+    print('std of above')
     #need atleast_1d or diag raises exception
-    print np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0))))
-    print 'm.cov_random()'
-    print m.cov_random()
-    print 'std of above'
-    print res.std_random()
-    print np.sqrt(np.diag(m.cov_random()))
-
-    print '\n(non)convergence of llf'
-    print m.history['llf'][-4:]
-    print 'convergence of parameters'
+    print(np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0)))))
+    print('m.cov_random()')
+    print(m.cov_random())
+    print('std of above')
+    print(res.std_random())
+    print(np.sqrt(np.diag(m.cov_random())))
+
+    print('\n(non)convergence of llf')
+    print(m.history['llf'][-4:])
+    print('convergence of parameters')
     #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0)
-    print np.diff(np.vstack(m.history['params'][-4:]),axis=0)
-    print 'convergence of D'
-    print np.diff(np.array(m.history['D'][-4:]), axis=0)
+    print(np.diff(np.vstack(m.history['params'][-4:]),axis=0))
+    print('convergence of D')
+    print(np.diff(np.array(m.history['D'][-4:]), axis=0))
 
     #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units])
     zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units])
@@ -121,13 +121,13 @@
            [-0.00909159,  0.26846254]])
     >>> #note cov_random doesn't subtract mean!
     '''
-    print '\nchecking the random effects distribution and prediction'
+    print('\nchecking the random effects distribution and prediction')
     gamma_re_true = np.array(gamma_re_true)
-    print 'mean of random effect true', gamma_re_true.mean(0)
-    print 'mean from fixed effects   ', m.params[-2:]
-    print 'mean of estimated RE      ', b_re.mean(0)
+    print('mean of random effect true', gamma_re_true.mean(0))
+    print('mean from fixed effects   ', m.params[-2:])
+    print('mean of estimated RE      ', b_re.mean(0))
 
-    print
+    print()
     absmean_true = np.abs(gamma_re_true).mean(0)
     mape = ((m.params[-2:] + b_re) / gamma_re_true - 1).mean(0)*100
     mean_abs_perc = np.abs((m.params[-2:] + b_re) - gamma_re_true).mean(0) \
@@ -136,22 +136,22 @@
                          / absmean_true*100
     rmse_perc = ((m.params[-2:] + b_re) - gamma_re_true).std(0) \
                   / absmean_true*100
-    print 'mape           ', mape
-    print 'mean_abs_perc  ', mean_abs_perc
-    print 'median_abs_perc', median_abs_perc
-    print 'rmse_perc (std)', rmse_perc
+    print('mape           ', mape)
+    print('mean_abs_perc  ', mean_abs_perc)
+    print('median_abs_perc', median_abs_perc)
+    print('rmse_perc (std)', rmse_perc)
     from numpy.testing import assert_almost_equal
     #assert is for n_units=100 in original example
     #I changed random number generation, so this won't work anymore
     #assert_almost_equal(rmse_perc, [ 34.14783884,  11.6031684 ], decimal=8)
 
     #now returns res
-    print 'llf', res.llf  #based on MLE, does not include constant
-    print 'tvalues', res.tvalues
-    print 'pvalues', res.pvalues
-    print res.t_test([1])
-    print 'test mean of both random effects variables is zero'
-    print res.f_test([[1]])
+    print('llf', res.llf)  #based on MLE, does not include constant
+    print('tvalues', res.tvalues)
+    print('pvalues', res.pvalues)
+    print(res.t_test([1]))
+    print('test mean of both random effects variables is zero')
+    print(res.f_test([[1]]))
     plots = res.plot_random_univariate(bins=50)
     #fig = res.plot_scatter_pairs(0, 1) #no pairs
     import matplotlib.pyplot as plt
diff --git a/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py b/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py
index 847afbb668a..2652a670c11 100644
--- a/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py
+++ b/statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py
@@ -121,43 +121,43 @@
     m.initialize()
     res = m.fit(maxiter=100, rtol=1.0e-5, params_rtol=1e-6, params_atol=1e-6)
     t1 = time.time()
-    print 'time for initialize and fit', t1-t0
-    print 'number of iterations', m.iterations
+    print('time for initialize and fit', t1-t0)
+    print('number of iterations', m.iterations)
     #print dir(m)
     #print vars(m)
-    print '\nestimates for fixed effects'
-    print m.a
-    print m.params
+    print('\nestimates for fixed effects')
+    print(m.a)
+    print(m.params)
     bfixed_cov = m.cov_fixed()
-    print 'beta fixed standard errors'
-    print np.sqrt(np.diag(bfixed_cov))
+    print('beta fixed standard errors')
+    print(np.sqrt(np.diag(bfixed_cov)))
 
-    print m.bse
+    print(m.bse)
     b_re = m.params_random_units
-    print 'RE mean:', b_re.mean(0)
-    print 'RE columns std', b_re.std(0)
-    print 'np.cov(b_re, rowvar=0), sample statistic'
-    print np.cov(b_re, rowvar=0)
-    print 'sample correlation of estimated random effects'
-    print np.corrcoef(b_re, rowvar=0)
-    print 'std of above'
+    print('RE mean:', b_re.mean(0))
+    print('RE columns std', b_re.std(0))
+    print('np.cov(b_re, rowvar=0), sample statistic')
+    print(np.cov(b_re, rowvar=0))
+    print('sample correlation of estimated random effects')
+    print(np.corrcoef(b_re, rowvar=0))
+    print('std of above')
     #need atleast_1d or diag raises exception
-    print np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0))))
-    print 'm.cov_random()'
-    print m.cov_random()
-    print 'correlation from above'
-    print res.cov_random()/ res.std_random()[:,None] /res.std_random()
-    print 'std of above'
-    print res.std_random()
-    print np.sqrt(np.diag(m.cov_random()))
-
-    print '\n(non)convergence of llf'
-    print m.history['llf'][-4:]
-    print 'convergence of parameters'
+    print(np.sqrt(np.diag(np.atleast_1d(np.cov(b_re, rowvar=0)))))
+    print('m.cov_random()')
+    print(m.cov_random())
+    print('correlation from above')
+    print(res.cov_random()/ res.std_random()[:,None] /res.std_random())
+    print('std of above')
+    print(res.std_random())
+    print(np.sqrt(np.diag(m.cov_random())))
+
+    print('\n(non)convergence of llf')
+    print(m.history['llf'][-4:])
+    print('convergence of parameters')
     #print np.diff(np.vstack(m.history[-4:])[:,1:],axis=0)
-    print np.diff(np.vstack(m.history['params'][-4:]),axis=0)
-    print 'convergence of D'
-    print np.diff(np.array(m.history['D'][-4:]), axis=0)
+    print(np.diff(np.vstack(m.history['params'][-4:]),axis=0))
+    print('convergence of D')
+    print(np.diff(np.array(m.history['D'][-4:]), axis=0))
 
     #zdotb = np.array([np.dot(unit.Z, unit.b) for unit in m.units])
     zb = np.array([(unit.Z * unit.b[None,:]).sum(0) for unit in m.units])
@@ -170,13 +170,13 @@
            [-0.00909159,  0.26846254]])
     >>> #note cov_random doesn't subtract mean!
     '''
-    print '\nchecking the random effects distribution and prediction'
+    print('\nchecking the random effects distribution and prediction')
     gamma_re_true = np.array(gamma_re_true)
-    print 'mean of random effect true', gamma_re_true.mean(0)
-    print 'mean from fixed effects   ', m.params[-2:]
-    print 'mean of estimated RE      ', b_re.mean(0)
+    print('mean of random effect true', gamma_re_true.mean(0))
+    print('mean from fixed effects   ', m.params[-2:])
+    print('mean of estimated RE      ', b_re.mean(0))
 
-    print
+    print()
     absmean_true = np.abs(gamma_re_true).mean(0)
     mape = ((m.params[-nz:] + b_re) / gamma_re_true - 1).mean(0)*100
     mean_abs_perc = np.abs((m.params[-nz:] + b_re) - gamma_re_true).mean(0) \
@@ -185,25 +185,25 @@
                          / absmean_true*100
     rmse_perc = ((m.params[-nz:] + b_re) - gamma_re_true).std(0) \
                   / absmean_true*100
-    print 'mape           ', mape
-    print 'mean_abs_perc  ', mean_abs_perc
-    print 'median_abs_perc', median_abs_perc
-    print 'rmse_perc (std)', rmse_perc
+    print('mape           ', mape)
+    print('mean_abs_perc  ', mean_abs_perc)
+    print('median_abs_perc', median_abs_perc)
+    print('rmse_perc (std)', rmse_perc)
     from numpy.testing import assert_almost_equal
     #assert is for n_units=100 in original example
     #I changed random number generation, so this won't work anymore
     #assert_almost_equal(rmse_perc, [ 34.14783884,  11.6031684 ], decimal=8)
 
     #now returns res
-    print 'llf', res.llf  #based on MLE, does not include constant
-    print 'tvalues', res.tvalues
-    print 'pvalues', res.pvalues
+    print('llf', res.llf)  #based on MLE, does not include constant
+    print('tvalues', res.tvalues)
+    print('pvalues', res.pvalues)
     rmat = np.zeros(len(res.params))
     rmat[-nz:] = 1
-    print 't_test mean of random effects variables are zero'
-    print res.t_test(rmat)
-    print 'f_test mean of both random effects variables is zero (joint hypothesis)'
-    print res.f_test(rmat)
+    print('t_test mean of random effects variables are zero')
+    print(res.t_test(rmat))
+    print('f_test mean of both random effects variables is zero (joint hypothesis)')
+    print(res.f_test(rmat))
     plots = res.plot_random_univariate() #(bins=50)
     fig = res.plot_scatter_all_pairs()
     import matplotlib.pyplot as plt
diff --git a/statsmodels/sandbox/examples/ex_onewaygls.py b/statsmodels/sandbox/examples/ex_onewaygls.py
index 18155506a6c..72c0d55b086 100644
--- a/statsmodels/sandbox/examples/ex_onewaygls.py
+++ b/statsmodels/sandbox/examples/ex_onewaygls.py
@@ -61,7 +61,7 @@ def print_results(res):
     #res.fitjoint()  #not really necessary, because called by ftest_summary
     ft = res.ftest_summary()
     #print ft[0]  #skip because table is nicer
-    print '\nTable of F-tests for overall or pairwise equality of coefficients'
+    print('\nTable of F-tests for overall or pairwise equality of coefficients')
 ##    print 'hypothesis F-statistic         p-value  df_denom df_num  reject'
 ##    for row in ft[1]:
 ##        print row,
@@ -70,37 +70,37 @@ def print_results(res):
 ##        else:
 ##            print ''
     from statsmodels.iolib import SimpleTable
-    print SimpleTable([(['%r'%(row[0],)]
+    print(SimpleTable([(['%r'%(row[0],)]
                         + list(row[1])
                         + ['*']*(row[1][1]>0.5).item() ) for row in ft[1]],
                       headers=['pair', 'F-statistic','p-value','df_denom',
-                               'df_num'])
-
-    print 'Notes: p-values are not corrected for many tests'
-    print '       (no Bonferroni correction)'
-    print '       * : reject at 5% uncorrected confidence level'
-    print 'Null hypothesis: all or pairwise coefficient are the same'
-    print 'Alternative hypothesis: all coefficients are different'
-
-    print '\nComparison with stats.f_oneway'
-    print stats.f_oneway(*[y[groupind==gr] for gr in res.unique])
-    print '\nLikelihood Ratio Test'
-    print 'likelihood ratio    p-value       df'
-    print res.lr_test()
-    print 'Null model: pooled all coefficients are the same across groups,'
-    print 'Alternative model: all coefficients are allowed to be different'
-    print 'not verified but looks close to f-test result'
-
-    print '\nOls parameters by group from individual, separate ols regressions'
+                               'df_num']))
+
+    print('Notes: p-values are not corrected for many tests')
+    print('       (no Bonferroni correction)')
+    print('       * : reject at 5% uncorrected confidence level')
+    print('Null hypothesis: all or pairwise coefficient are the same')
+    print('Alternative hypothesis: all coefficients are different')
+
+    print('\nComparison with stats.f_oneway')
+    print(stats.f_oneway(*[y[groupind==gr] for gr in res.unique]))
+    print('\nLikelihood Ratio Test')
+    print('likelihood ratio    p-value       df')
+    print(res.lr_test())
+    print('Null model: pooled all coefficients are the same across groups,')
+    print('Alternative model: all coefficients are allowed to be different')
+    print('not verified but looks close to f-test result')
+
+    print('\nOls parameters by group from individual, separate ols regressions')
     for group in sorted(res.olsbygroup):
         r = res.olsbygroup[group]
-        print group, r.params
+        print(group, r.params)
 
-    print '\nCheck for heteroscedasticity, '
-    print 'variance and standard deviation for individual regressions'
-    print ' '*12, ' '.join('group %-10s' %(gr) for gr in res.unique)
-    print 'variance    ', res.sigmabygroup
-    print 'standard dev', np.sqrt(res.sigmabygroup)
+    print('\nCheck for heteroscedasticity, ')
+    print('variance and standard deviation for individual regressions')
+    print(' '*12, ' '.join('group %-10s' %(gr) for gr in res.unique))
+    print('variance    ', res.sigmabygroup)
+    print('standard dev', np.sqrt(res.sigmabygroup))
 
 #now added to class
 def print_results2(res):
@@ -173,24 +173,24 @@ def print_results2(res):
 #get results for example
 #-----------------------
 
-print '\nTest for equality of coefficients for all exogenous variables'
-print   '-------------------------------------------------------------'
+print('\nTest for equality of coefficients for all exogenous variables')
+print('-------------------------------------------------------------')
 res = OneWayLS(y,x, groups=groupind.astype(int))
 print_results(res)
 
-print '\n\nOne way ANOVA, constant is the only regressor'
-print   '---------------------------------------------'
+print('\n\nOne way ANOVA, constant is the only regressor')
+print('---------------------------------------------')
 
-print 'this is the same as scipy.stats.f_oneway'
+print('this is the same as scipy.stats.f_oneway')
 res = OneWayLS(y,np.ones(len(y)), groups=groupind)
 print_results(res)
 
 
-print '\n\nOne way ANOVA, constant is the only regressor with het is true'
-print   '--------------------------------------------------------------'
+print('\n\nOne way ANOVA, constant is the only regressor with het is true')
+print('--------------------------------------------------------------')
 
-print 'this is the similar to scipy.stats.f_oneway,'
-print 'but variance is not assumed to be the same across groups'
+print('this is the similar to scipy.stats.f_oneway,')
+print('but variance is not assumed to be the same across groups')
 res = OneWayLS(y,np.ones(len(y)), groups=groupind.astype(str), het=True)
 print_results(res)
-print res.print_summary() #(res)
+print(res.print_summary()) #(res)
diff --git a/statsmodels/sandbox/examples/ex_random_panel.py b/statsmodels/sandbox/examples/ex_random_panel.py
index 5721805d220..554212c8b1a 100644
--- a/statsmodels/sandbox/examples/ex_random_panel.py
+++ b/statsmodels/sandbox/examples/ex_random_panel.py
@@ -35,25 +35,25 @@
 #                      corr_args=([1, -0.95],))
     dgp = PanelSample(nobs, k_vars, n_groups, corr_structure=cs.corr_arma,
                       corr_args=([1], [1., -0.9],), seed=377769)
-    print 'seed', dgp.seed
+    print('seed', dgp.seed)
     y = dgp.generate_panel()
     noise = y - dgp.y_true
-    print np.corrcoef(y.reshape(-1,n_groups, order='F'))
-    print np.corrcoef(noise.reshape(-1,n_groups, order='F'))
+    print(np.corrcoef(y.reshape(-1,n_groups, order='F')))
+    print(np.corrcoef(noise.reshape(-1,n_groups, order='F')))
 
     mod = ShortPanelGLS2(y, dgp.exog, dgp.groups)
     res = mod.fit()
-    print res.params
-    print res.bse
+    print(res.params)
+    print(res.bse)
     #Now what?
     #res.resid is of transformed model
     #np.corrcoef(res.resid.reshape(-1,n_groups, order='F'))
     y_pred = np.dot(mod.exog, res.params)
     resid = y - y_pred
-    print np.corrcoef(resid.reshape(-1,n_groups, order='F'))
-    print resid.std()
+    print(np.corrcoef(resid.reshape(-1,n_groups, order='F')))
+    print(resid.std())
     err = y_pred - dgp.y_true
-    print err.std()
+    print(err.std())
     #OLS standard errors are too small
     mod.res_pooled.params
     mod.res_pooled.bse
@@ -61,26 +61,26 @@
     mod.res_pooled.HC1_se
     #compare with cluster robust se
 
-    print sw.se_cov(sw.cov_cluster(mod.res_pooled, dgp.groups.astype(int)))
+    print(sw.se_cov(sw.cov_cluster(mod.res_pooled, dgp.groups.astype(int))))
     #not bad, pretty close to panel estimator
     #and with Newey-West Hac
-    print sw.se_cov(sw.cov_nw_panel(mod.res_pooled, 4, mod.group.groupidx))
+    print(sw.se_cov(sw.cov_nw_panel(mod.res_pooled, 4, mod.group.groupidx)))
     #too small, assuming no bugs,
     #see Peterson assuming it refers to same kind of model
-    print dgp.cov
+    print(dgp.cov)
 
     mod2 = ShortPanelGLS(y, dgp.exog, dgp.groups)
     res2 = mod2.fit_iterative(2)
-    print res2.params
-    print res2.bse
+    print(res2.params)
+    print(res2.bse)
     #both implementations produce the same results:
     from numpy.testing import assert_almost_equal
     assert_almost_equal(res.params, res2.params, decimal=12)
     assert_almost_equal(res.bse, res2.bse, decimal=13)
     mod5 = ShortPanelGLS(y, dgp.exog, dgp.groups)
     res5 = mod5.fit_iterative(5)
-    print res5.params
-    print res5.bse
+    print(res5.params)
+    print(res5.bse)
     #fitting once is the same as OLS
     #note: I need to create new instance, otherwise it continuous fitting
     mod1 = ShortPanelGLS(y, dgp.exog, dgp.groups)
@@ -138,7 +138,7 @@
     se = se.append(pa.DataFrame(clbse[None,:], index=['OLSclu']))
     pnwse = sw.se_cov(sw.cov_nw_panel(mod.res_pooled, 4, mod.group.groupidx))
     se = se.append(pa.DataFrame(pnwse[None,:], index=['OLSpnw']))
-    print se
+    print(se)
     #list(se.index)
     from statsmodels.iolib.table import SimpleTable
     headers = [str(i) for i in se.columns]
@@ -146,6 +146,6 @@
 #    print SimpleTable(np.round(np.asarray(se), 4),
 #                      headers=headers,
 #                      stubs=stubs)
-    print SimpleTable(np.asarray(se), headers=headers, stubs=stubs,
+    print(SimpleTable(np.asarray(se), headers=headers, stubs=stubs,
                       txt_fmt=dict(data_fmts=['%10.4f']),
-                      title='Standard Errors')
+                      title='Standard Errors'))
diff --git a/statsmodels/sandbox/examples/example_crossval.py b/statsmodels/sandbox/examples/example_crossval.py
index d4d32580a30..eee1770084a 100644
--- a/statsmodels/sandbox/examples/example_crossval.py
+++ b/statsmodels/sandbox/examples/example_crossval.py
@@ -20,13 +20,13 @@
 
     resols = sm.OLS(data.endog, data.exog).fit()
 
-    print '\n OLS leave 1 out'
+    print('\n OLS leave 1 out')
     for inidx, outidx in cross_val.LeaveOneOut(len(data.endog)):
         res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit()
-        print data.endog[outidx], res.model.predict(res.params, data.exog[outidx,:]),
-        print data.endog[outidx] - res.model.predict(res.params, data.exog[outidx,:])
+        print(data.endog[outidx], res.model.predict(res.params, data.exog[outidx,:], end=' '))
+        print(data.endog[outidx] - res.model.predict(res.params, data.exog[outidx,:]))
 
-    print '\n OLS leave 2 out'
+    print('\n OLS leave 2 out')
     resparams = []
     for inidx, outidx in cross_val.LeavePOut(len(data.endog), 2):
         res = sm.OLS(data.endog[inidx], data.exog[inidx,:]).fit()
@@ -35,7 +35,7 @@
         resparams.append(res.params)
 
     resparams = np.array(resparams)
-    print resparams
+    print(resparams)
 
     doplots = 1
     if doplots:
@@ -61,7 +61,7 @@
 
     for inidx, outidx in cross_val.KStepAhead(20,2):
         #note the following were broken because KStepAhead returns now a slice by default
-        print inidx
-        print np.ones(20)[inidx].sum(), np.arange(20)[inidx][-4:]
-        print outidx
-        print np.nonzero(np.ones(20)[outidx])[0][()]
+        print(inidx)
+        print(np.ones(20)[inidx].sum(), np.arange(20)[inidx][-4:])
+        print(outidx)
+        print(np.nonzero(np.ones(20)[outidx])[0][()])
diff --git a/statsmodels/sandbox/examples/example_gam.py b/statsmodels/sandbox/examples/example_gam.py
index 5080eb7cf9c..7aace05457e 100644
--- a/statsmodels/sandbox/examples/example_gam.py
+++ b/statsmodels/sandbox/examples/example_gam.py
@@ -33,12 +33,12 @@
 
 
 if example == 1:
-    print "normal"
+    print("normal")
     m = AdditiveModel(d)
     m.fit(y)
     x = np.linspace(-2,2,50)
 
-    print m
+    print(m)
 
     y_pred = m.results.predict(d)
     plt.figure()
@@ -51,7 +51,7 @@
 import scipy.stats, time
 
 if example == 2:
-    print "binomial"
+    print("binomial")
     f = family.Binomial()
     b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)])
     b.shape = y.shape
@@ -59,11 +59,11 @@
     toc = time.time()
     m.fit(b)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
 
 
 if example == 3:
-    print "Poisson"
+    print("Poisson")
     f = family.Poisson()
     y = y/y.max() * 3
     yp = f.link.inverse(y)
@@ -73,7 +73,7 @@
     toc = time.time()
     m.fit(p)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
 
 
 plt.figure()
diff --git a/statsmodels/sandbox/examples/example_gam_0.py b/statsmodels/sandbox/examples/example_gam_0.py
index 93e752f1286..3e8e42638e8 100644
--- a/statsmodels/sandbox/examples/example_gam_0.py
+++ b/statsmodels/sandbox/examples/example_gam_0.py
@@ -11,6 +11,7 @@
 example = 2 #3  # 1,2 or 3
 
 import numpy as np
+from statsmodels.compat.python import zip
 import numpy.random as R
 import matplotlib.pyplot as plt
 
@@ -45,10 +46,10 @@
 z = f1(x1) + f2(x2)
 #z = demean(z)
 z -= np.median(z)
-print'z.std()', z.std()
+print('z.std()', z.std())
 #z = standardize(z) + 0.2
 # with standardize I get better values, but I don't know what the true params are
-print z.mean(), z.min(), z.max()
+print(z.mean(), z.min(), z.max())
 
 #y += z  #noise
 y = z
@@ -57,17 +58,17 @@
 
 
 if example == 1:
-    print "normal"
+    print("normal")
     m = AdditiveModel(d)
     m.fit(y)
     x = np.linspace(-2,2,50)
 
-    print m
+    print(m)
 
 import scipy.stats, time
 
 if example == 2:
-    print "binomial"
+    print("binomial")
     mod_name = 'Binomial'
     f = families.Binomial()
     #b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)])
@@ -77,14 +78,14 @@
     toc = time.time()
     m.fit(b)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
     #for plotting
     yp = f.link.inverse(y)
     p = b
 
 
 if example == 3:
-    print "Poisson"
+    print("Poisson")
     f = families.Poisson()
     #y = y/y.max() * 3
     yp = f.link.inverse(z)
@@ -95,7 +96,7 @@
     toc = time.time()
     m.fit(p)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
 
 if example > 1:
     y_pred = m.results.mu# + m.results.alpha#m.results.predict(d)
diff --git a/statsmodels/sandbox/examples/example_garch.py b/statsmodels/sandbox/examples/example_garch.py
index 420897f3000..b3edb43a4d9 100644
--- a/statsmodels/sandbox/examples/example_garch.py
+++ b/statsmodels/sandbox/examples/example_garch.py
@@ -24,7 +24,7 @@
 ggmod._start_params = np.array([-0.1, 0.1, 0.1, 0.1])
 ggres = ggmod.fit(start_params=np.array([-0.1, 0.1, 0.1, 0.0]),
                   maxiter=1000,method='bfgs')
-print 'ggres.params', ggres.params
+print('ggres.params', ggres.params)
 garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated')
 
 use_rpy = False
@@ -43,12 +43,12 @@
 start_params = np.array([-0.1, 0.1, ret.var()])
 ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
 ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)
-print 'ggres0.params', ggres0.params
+print('ggres0.params', ggres0.params)
 
 g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, ret - ret.mean())[0], [0.01, 0.1, 0.1])
-print g11res
+print(g11res)
 llf = loglike_GARCH11(g11res, ret - ret.mean())
-print llf[0]
+print(llf[0])
 
 
 ggmod0 = Garch0(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
@@ -57,7 +57,7 @@
 start_params = np.array([-0.1,-0.1, 0.1, 0.1, ret.var()])
 ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
 ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)#, method='ncg')
-print 'ggres0.params', ggres0.params
+print('ggres0.params', ggres0.params)
 
 ggmod = Garch(ret - ret.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
 ggmod.nar = 2
@@ -65,4 +65,4 @@
 start_params = np.array([-0.1,-0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
 ggmod._start_params = start_params
 ggres = ggmod.fit(start_params=start_params, maxiter=1000)#,method='bfgs')
-print 'ggres.params', ggres.params
+print('ggres.params', ggres.params)
diff --git a/statsmodels/sandbox/examples/example_maxent.py b/statsmodels/sandbox/examples/example_maxent.py
index af25931a15c..821a81bad7d 100644
--- a/statsmodels/sandbox/examples/example_maxent.py
+++ b/statsmodels/sandbox/examples/example_maxent.py
@@ -29,22 +29,22 @@ def meanp(x):
     model.fit(K[i])
 
     # Output the distribution
-    print "\nFitted model parameters are:\n" + str(model.params)
-    print "\nFitted distribution is:"
+    print("\nFitted model parameters are:\n" + str(model.params))
+    print("\nFitted distribution is:")
     p = model.probdist()
     for j in range(len(model.samplespace)):
         x = model.samplespace[j]
-        print "y = %-15s\tx = %-15s" %(str(K[i,1])+":",str(x) + ":") + \
-                " p(x) = "+str(p[j])
+        print("y = %-15s\tx = %-15s" %(str(K[i,1])+":",str(x) + ":") + \
+                " p(x) = "+str(p[j]))
 
     # Now show how well the constraints are satisfied:
-    print
-    print "Desired constraints:"
-    print "\tsum_{i}p_{i}= 1"
-    print "\tE[X] = %-15s" % str(K[i,1])
-    print
-    print "Actual expectations under the fitted model:"
-    print "\tsum_{i}p_{i} =", np.sum(p)
-    print "\tE[X]  = " + str(np.sum(p*np.arange(1,7)))
+    print()
+    print("Desired constraints:")
+    print("\tsum_{i}p_{i}= 1")
+    print("\tE[X] = %-15s" % str(K[i,1]))
+    print()
+    print("Actual expectations under the fitted model:")
+    print("\tsum_{i}p_{i} =", np.sum(p))
+    print("\tE[X]  = " + str(np.sum(p*np.arange(1,7))))
 
 
diff --git a/statsmodels/sandbox/examples/example_mle.py b/statsmodels/sandbox/examples/example_mle.py
index f170633fc57..7431e15a658 100644
--- a/statsmodels/sandbox/examples/example_mle.py
+++ b/statsmodels/sandbox/examples/example_mle.py
@@ -7,8 +7,8 @@
 import numpy as np
 import statsmodels.base.model as models
 
-print '\nExample 1: Artificial Data'
-print   '--------------------------\n'
+print('\nExample 1: Artificial Data')
+print('--------------------------\n')
 import statsmodels.api as sm
 
 np.random.seed(54321)
@@ -20,15 +20,15 @@
 res2 = mod2.fit()
 f2 = lambda params: -1*mod2.loglike(params)
 resfmin = optimize.fmin(f2, np.ones(3), ftol=1e-10)
-print 'OLS'
-print res2.params
-print 'MLE'
-print resfmin
+print('OLS')
+print(res2.params)
+print('MLE')
+print(resfmin)
 
 
 
-print '\nExample 2: Longley Data, high multicollinearity'
-print   '-----------------------------------------------\n'
+print('\nExample 2: Longley Data, high multicollinearity')
+print('-----------------------------------------------\n')
 
 from statsmodels.datasets.longley import load
 data = load()
@@ -42,27 +42,27 @@
 
 res = mod.fit()
 #print mod.results.params
-print 'OLS'
-print res.params
-print 'MLE'
+print('OLS')
+print(res.params)
+print('MLE')
 #resfmin2 = optimize.fmin(f, mod.results.params*0.9, maxfun=5000, maxiter=5000, xtol=1e-10, ftol= 1e-10)
 resfmin2 = optimize.fmin(f, np.ones(7), maxfun=5000, maxiter=5000, xtol=1e-10, ftol= 1e-10)
-print resfmin2
+print(resfmin2)
 # there isn't a unique solution?  Is this due to the multicollinearity? Improved with use of analytically
 # defined score function?
 
 #check X'X matrix
 xtxi = np.linalg.inv(np.dot(data.exog.T,data.exog))
 eval, evec = np.linalg.eig(xtxi)
-print 'Eigenvalues'
-print eval
+print('Eigenvalues')
+print(eval)
 # look at correlation
-print 'correlation matrix'
-print np.corrcoef(data.exog[:,:-1], rowvar=0) #exclude constant
+print('correlation matrix')
+print(np.corrcoef(data.exog[:,:-1], rowvar=0)) #exclude constant
 # --> conclusion high multicollinearity
 
 # compare
-print 'with matrix formula'
-print np.dot(xtxi,np.dot(data.exog.T, data.endog[:,np.newaxis])).ravel()
-print 'with pinv'
-print np.dot(np.linalg.pinv(data.exog), data.endog[:,np.newaxis]).ravel()
+print('with matrix formula')
+print(np.dot(xtxi,np.dot(data.exog.T, data.endog[:,np.newaxis])).ravel())
+print('with pinv')
+print(np.dot(np.linalg.pinv(data.exog), data.endog[:,np.newaxis]).ravel())
diff --git a/statsmodels/sandbox/examples/example_nbin.py b/statsmodels/sandbox/examples/example_nbin.py
index 5063a866e93..834a404048c 100644
--- a/statsmodels/sandbox/examples/example_nbin.py
+++ b/statsmodels/sandbox/examples/example_nbin.py
@@ -229,10 +229,10 @@ def _score_nbp(y, X, beta, thet, Q):
     return sc
 
 #### Tests ####
+from statsmodels.compat.python import urlopen
 from numpy.testing import assert_almost_equal
 import pandas
 import patsy
-from urllib2 import urlopen
 medpar = pandas.read_csv(urlopen('http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'))
 mdvis = pandas.read_csv(urlopen('http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/mdvis.csv'))
 
diff --git a/statsmodels/sandbox/examples/example_pca.py b/statsmodels/sandbox/examples/example_pca.py
index 903e12b6838..2d32c25f3b6 100644
--- a/statsmodels/sandbox/examples/example_pca.py
+++ b/statsmodels/sandbox/examples/example_pca.py
@@ -9,9 +9,9 @@
 
 #create the Pca object - requires a p x N array as the input
 p=Pca((x,y,z))
-print 'energies:',p.getEnergies()
-print 'vecs:',p.getEigenvectors()
-print 'projected data',p.project(vals=np.ones((3,10)))
+print('energies:',p.getEnergies())
+print('vecs:',p.getEigenvectors())
+print('projected data',p.project(vals=np.ones((3,10))))
 
 
 #p.plot2d() #requires matplotlib
diff --git a/statsmodels/sandbox/examples/example_pca_regression.py b/statsmodels/sandbox/examples/example_pca_regression.py
index 2c905d4088c..48fb4b1820f 100644
--- a/statsmodels/sandbox/examples/example_pca_regression.py
+++ b/statsmodels/sandbox/examples/example_pca_regression.py
@@ -38,17 +38,17 @@
 y0 = ytrue + 0.1*np.random.normal(size=ytrue.shape)
 
 xred, fact, eva, eve  = pca(x0, keepdim=0)
-print eve
-print fact[:5]
-print f0[:5]
+print(eve)
+print(fact[:5])
+print(f0[:5])
 
 import statsmodels.api as sm
 
 res = sm.OLS(y0, sm.add_constant(x0, prepend=False)).fit()
-print 'OLS on original data'
-print res.params
-print res.aic
-print res.rsquared
+print('OLS on original data')
+print(res.params)
+print(res.aic)
+print(res.rsquared)
 
 #print 'OLS on Factors'
 #for k in range(x0.shape[1]):
@@ -63,7 +63,7 @@
 #    print 'R2    ', res.rsquared
 #    print 'R2 adj', res.rsquared_adj
 
-print 'OLS on Factors'
+print('OLS on Factors')
 results = []
 xred, fact, eva, eve  = pca(x0, keepdim=0, normalize=1)
 for k in range(0, x0.shape[1]+1):
@@ -86,10 +86,10 @@
     results.append([k, res.aic, res.bic, res.rsquared_adj, prederr2])
 
 results = np.array(results)
-print results
-print 'best result for k, by AIC, BIC, R2_adj, L1O'
-print np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
-             np.argmin(results[:,-1],0))]
+print(results)
+print('best result for k, by AIC, BIC, R2_adj, L1O')
+print(np.r_[(np.argmin(results[:,1:3],0), np.argmax(results[:,3],0),
+             np.argmin(results[:,-1],0))])
 
 from statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
                         default_latex_fmt, default_html_fmt)
@@ -99,13 +99,13 @@
 txt_fmt1 = dict(data_fmts = numformat)
 tabl = SimpleTable(results, headers, None, txt_fmt=txt_fmt1)
 
-print "PCA regression on simulated data,"
-print "DGP: 2 factors and 4 explanatory variables"
-print tabl
-print "Notes: k is number of components of PCA,"
-print "       constant is added additionally"
-print "       k=0 means regression on constant only"
-print "       L1O: sum of squared prediction errors for leave-one-out"
+print("PCA regression on simulated data,")
+print("DGP: 2 factors and 4 explanatory variables")
+print(tabl)
+print("Notes: k is number of components of PCA,")
+print("       constant is added additionally")
+print("       k=0 means regression on constant only")
+print("       L1O: sum of squared prediction errors for leave-one-out")
 
 
 
diff --git a/statsmodels/sandbox/examples/example_sysreg.py b/statsmodels/sandbox/examples/example_sysreg.py
index 25e1fbd6ca6..02a9d26fea3 100644
--- a/statsmodels/sandbox/examples/example_sysreg.py
+++ b/statsmodels/sandbox/examples/example_sysreg.py
@@ -1,13 +1,12 @@
 """Example: statsmodels.sandbox.sysreg
 """
 #TODO: this is going to change significantly once we have a panel data structure
-
+from statsmodels.compat.python import lmap, asbytes
 import numpy as np
 import statsmodels.api as sm
 from statsmodels.sandbox.sysreg import *
 
 #for Python 3 compatibility
-from statsmodels.compatnp.py3k import asbytes
 
 # Seemingly Unrelated Regressions (SUR) Model
 
@@ -19,7 +18,7 @@
 firms = ['General Motors', 'Chrysler', 'General Electric', 'Westinghouse',
         'US Steel']
 #for Python 3 compatibility
-firms = map(asbytes, firms)
+firms = lmap(asbytes, firms)
 
 grun_exog = grun_data.exog
 grun_endog = grun_data.endog
@@ -45,19 +44,19 @@
 
 grun_mod = SUR(grun_sys)
 grun_res = grun_mod.fit()
-print "Results for the 2-step GLS"
-print "Compare to Greene Table 14.1, 5th edition"
-print grun_res.params
+print("Results for the 2-step GLS")
+print("Compare to Greene Table 14.1, 5th edition")
+print(grun_res.params)
 # or you can do an iterative fit
 # you have to define a new model though this will be fixed
 # TODO: note the above
-print "Results for iterative GLS (equivalent to MLE)"
-print "Compare to Greene Table 14.3"
+print("Results for iterative GLS (equivalent to MLE)")
+print("Compare to Greene Table 14.3")
 #TODO: these are slightly off, could be a convergence issue
 # or might use a different default DOF correction?
 grun_imod = SUR(grun_sys)
 grun_ires = grun_imod.fit(igls=True)
-print grun_ires.params
+print(grun_ires.params)
 
 # Two-Stage Least Squares for Simultaneous Equations
 #TODO: we are going to need *some kind* of formula framework
@@ -113,9 +112,9 @@
 macro_mod = Sem2SLS(macro_sys, indep_endog=indep_endog, instruments=instruments)
 # Right now this only returns parameters
 macro_params = macro_mod.fit()
-print "The parameters for the first equation are correct."
-print "The parameters for the second equation are not."
-print macro_params
+print("The parameters for the first equation are correct.")
+print("The parameters for the second equation are not.")
+print(macro_params)
 
 #TODO: Note that the above is incorrect, because we have no way of telling the
 # model that *part* of the y_{t} - y_{t-1} is an independent endogenous variable
@@ -126,21 +125,21 @@
 wexog = sm.add_constant(wexog, prepend=True)
 correct_params = sm.GLS(macrodata['realinv'][1:], wexog).fit().params
 
-print "If we correctly instrument everything, then these are the parameters"
-print "for the second equation"
-print correct_params
-print "Compare to output of R script statsmodels/sandbox/tests/macrodata.s"
+print("If we correctly instrument everything, then these are the parameters")
+print("for the second equation")
+print(correct_params)
+print("Compare to output of R script statsmodels/sandbox/tests/macrodata.s")
 
-print '\nUsing IV2SLS'
+print('\nUsing IV2SLS')
 from statsmodels.sandbox.regression.gmm import IV2SLS
 miv = IV2SLS(macro_sys[0], macro_sys[1], instruments)
 resiv = miv.fit()
-print "equation 1"
-print resiv.params
+print("equation 1")
+print(resiv.params)
 miv2 = IV2SLS(macro_sys[2], macro_sys[3], instruments)
 resiv2 = miv2.fit()
-print "equation 2"
-print resiv2.params
+print("equation 2")
+print(resiv2.params)
 
 ### Below is the same example using Greene's data ###
 
@@ -150,8 +149,8 @@
         data3 = np.genfromtxt('/home/skipper/school/MetricsII/Greene \
 TableF5-1.txt', names=True)
     except:
-        raise ValueError, "Based on Greene TableF5-1.  You should download it \
-from his web site and edit this script accordingly."
+        raise ValueError("Based on Greene TableF5-1.  You should download it "
+                         "from his web site and edit this script accordingly.")
 
     # Example 15.1 in Greene 5th Edition
 # c_t = constant + y_t + c_t-1
@@ -187,20 +186,20 @@
     wexog = sm.add_constant(wexog, prepend=False)
     params = sm.GLS(data3['realinvs'][1:], wexog).fit().params
 
-    print "These are the simultaneous equation estimates for Greene's \
-example 13-1 (Also application 13-1 in 6th edition."
-    print sem_params
-    print "The first set of parameters is correct.  The second set is not."
-    print "Compare to the solution manual at \
-http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm"
-    print "The reason is the restriction on (y_t - y_1)"
-    print "Compare to R script GreeneEx15_1.s"
-    print "Somehow R carries y.1 in yd to know that it needs to be \
-instrumented"
-    print "If we replace our estimate with the instrumented one"
-    print params
-    print "We get the right estimate"
-    print "Without a formula framework we have to be able to do restrictions."
+    print("These are the simultaneous equation estimates for Greene's \
+example 13-1 (Also application 13-1 in 6th edition.")
+    print(sem_params)
+    print("The first set of parameters is correct.  The second set is not.")
+    print("Compare to the solution manual at \
+http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm")
+    print("The reason is the restriction on (y_t - y_1)")
+    print("Compare to R script GreeneEx15_1.s")
+    print("Somehow R carries y.1 in yd to know that it needs to be \
+instrumented")
+    print("If we replace our estimate with the instrumented one")
+    print(params)
+    print("We get the right estimate")
+    print("Without a formula framework we have to be able to do restrictions.")
 # yep!, but how in the world does R know this when we just fed it yd??
 # must be implicit in the formula framework...
 # we are going to need to keep the two equations separate and use
diff --git a/statsmodels/sandbox/examples/run_all.py b/statsmodels/sandbox/examples/run_all.py
index ab7f46d11bc..85ebff62969 100644
--- a/statsmodels/sandbox/examples/run_all.py
+++ b/statsmodels/sandbox/examples/run_all.py
@@ -7,7 +7,7 @@
 uncomment plt.show() to show all plot windows
 
 '''
-
+from statsmodels.compat.python import input
 stop_on_error = True
 
 
@@ -15,20 +15,20 @@
 #            'example_gam.py', # exclude, currently we are not working on it
             'example_pca_regression.py']
 
-cont = raw_input("""Are you sure you want to run all of the examples?
+cont = input("""Are you sure you want to run all of the examples?
 This is done mainly to check that they are up to date.
 (y/n) >>> """)
 if 'y' in cont.lower():
     for run_all_f in filelist:
         try:
-            print "Executing example file", run_all_f
-            print "-----------------------" + "-"*len(run_all_f)
-            execfile(run_all_f)
+            print("Executing example file", run_all_f)
+            print("-----------------------" + "-"*len(run_all_f))
+            exec(open(run_all_f).read())
         except:
             #f might be overwritten in the executed file
-            print "*********************"
-            print "ERROR in example file", run_all_f
-            print "**********************" + "*"*len(run_all_f)
+            print("*********************")
+            print("ERROR in example file", run_all_f)
+            print("**********************" + "*"*len(run_all_f))
             if stop_on_error:
                 raise
 #plt.show()
diff --git a/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py b/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py
index 5e8611e6129..c6501ff5c38 100644
--- a/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py
+++ b/statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py
@@ -15,7 +15,7 @@
 import datetime as dt
 
 import pandas as pa
-import pickle
+from statsmodels.compat.python import cPickle
 
 import statsmodels.api as sm
 import statsmodels.sandbox as sb
@@ -24,9 +24,9 @@
 from statsmodels.graphics.correlation import plot_corr, plot_corr_grid
 
 try:
-    rrdm = pickle.load(file('dj30rr','rb'))
+    rrdm = cPickle.load(file('dj30rr','rb'))
 except Exception: #blanket for any unpickling error
-    print "Error with unpickling, a new pickle file can be created with findow_1"
+    print("Error with unpickling, a new pickle file can be created with findow_1")
     raise
 
 ticksym = rrdm.columns.tolist()
@@ -43,7 +43,7 @@
 
 xreda, facta, evaa, evea  = sbtools.pcasvd(rr)
 evallcs = (evaa).cumsum()
-print evallcs/evallcs[-1]
+print(evallcs/evallcs[-1])
 xred, fact, eva, eve  = sbtools.pcasvd(rr, keepdim=4)
 pcacorr = np.corrcoef(xred, rowvar=0)
 
@@ -72,8 +72,8 @@
 
 import matplotlib as mpl
 images = [c for ax in fig.axes for c in ax.get_children() if isinstance(c, mpl.image.AxesImage)]
-print images
-print ax.get_children()
+print(images)
+print(ax.get_children())
 #cax = fig.add_subplot(2,2,2)
 #[0.85, 0.1, 0.075, 0.8]
 fig. subplots_adjust(bottom=0.1, right=0.9, top=0.9)
@@ -86,7 +86,7 @@
     import sklearn
 except ImportError:
     has_sklearn = False
-    print 'sklearn not available'
+    print('sklearn not available')
 
 
 def cov2corr(cov):
@@ -131,9 +131,9 @@ def cov2corr(cov):
                             for cj in corrli])
     diffsabs = np.array([[np.max(np.abs(ci-cj)) for ci in corrli]
                             for cj in corrli])
-    print diffssq
-    print '\nmaxabs'
-    print diffsabs
+    print(diffssq)
+    print('\nmaxabs')
+    print(diffsabs)
     fig.savefig('corrmatrix_sklearn.png', dpi=120)
 
     fig2 = plot_corr_grid(corrli+[residcorr], ncols=3,
diff --git a/statsmodels/sandbox/examples/thirdparty/findow_0.py b/statsmodels/sandbox/examples/thirdparty/findow_0.py
index b1436c3c982..d644be5112e 100644
--- a/statsmodels/sandbox/examples/thirdparty/findow_0.py
+++ b/statsmodels/sandbox/examples/thirdparty/findow_0.py
@@ -17,7 +17,7 @@
 Created on Sat Jan 30 16:30:18 2010
 Author: josef-pktd
 """
-
+from statsmodels.compat.python import lzip
 import numpy as np
 import matplotlib.finance as fin
 import matplotlib.pyplot as plt
@@ -28,7 +28,7 @@
 
 def getquotes(symbol, start, end):
     quotes = fin.quotes_historical_yahoo(symbol, start, end)
-    dates, open, close, high, low, volume = zip(*quotes)
+    dates, open, close, high, low, volume = lzip(*quotes)
 
     data = {
         'open' : open,
@@ -56,7 +56,7 @@ def getquotes(symbol, start, end):
 
 # combine into WidePanel
 pawp = pa.WidePanel.fromDict(dmall)
-print pawp.values.shape
+print(pawp.values.shape)
 
 # select closing prices
 paclose = pawp.getMinorXS('close')
diff --git a/statsmodels/sandbox/examples/thirdparty/findow_1.py b/statsmodels/sandbox/examples/thirdparty/findow_1.py
index 79b073859b4..12fe9327790 100644
--- a/statsmodels/sandbox/examples/thirdparty/findow_1.py
+++ b/statsmodels/sandbox/examples/thirdparty/findow_1.py
@@ -17,7 +17,7 @@
 Created on Sat Jan 30 16:30:18 2010
 Author: josef-pktd
 """
-
+from statsmodels.compat.python import lzip
 import numpy as np
 import matplotlib.finance as fin
 import matplotlib.pyplot as plt
@@ -28,7 +28,7 @@
 
 def getquotes(symbol, start, end):
     quotes = fin.quotes_historical_yahoo(symbol, start, end)
-    dates, open, close, high, low, volume = zip(*quotes)
+    dates, open, close, high, low, volume = lzip(*quotes)
 
     data = {
         'open' : open,
@@ -60,7 +60,7 @@ def getquotes(symbol, start, end):
 
 # combine into WidePanel
 pawp = pa.WidePanel.fromDict(dmall)
-print pawp.values.shape
+print(pawp.values.shape)
 
 # select closing prices
 paclose = pawp.getMinorXS('close')
diff --git a/statsmodels/sandbox/examples/thirdparty/try_interchange.py b/statsmodels/sandbox/examples/thirdparty/try_interchange.py
index b44465661d4..8c6d1c2192a 100644
--- a/statsmodels/sandbox/examples/thirdparty/try_interchange.py
+++ b/statsmodels/sandbox/examples/thirdparty/try_interchange.py
@@ -21,7 +21,7 @@
 Created on Sat Jan 30 08:33:11 2010
 Author: josef-pktd
 """
-
+from statsmodels.compat.python import lrange, zip
 import numpy as np
 import scikits.timeseries as ts
 
@@ -29,18 +29,19 @@
             dates=ts.date_array(["2001-01","2001-01",
             "2001-02","2001-03","2001-03"],freq="M"))
 
-print '\nUsing la'
+print('\nUsing la')
 import la
-dta = la.larry(s.data, label=[range(len(s.data))])
-dat = la.larry(s.dates.tolist(), label=[range(len(s.data))])
+
+dta = la.larry(s.data, label=[lrange(len(s.data))])
+dat = la.larry(s.dates.tolist(), label=[lrange(len(s.data))])
 s2 = ts.time_series(dta.group_mean(dat).x,dates=ts.date_array(dat.x,freq="M"))
 s2u = ts.remove_duplicated_dates(s2)
-print repr(s)
-print dat
-print repr(s2)
-print repr(s2u)
+print(repr(s))
+print(dat)
+print(repr(s2))
+print(repr(s2u))
 
-print '\nUsing pandas'
+print('\nUsing pandas')
 import pandas
 pdta = pandas.DataFrame(s.data, np.arange(len(s.data)), [1])
 pa = pdta.groupby(dict(zip(np.arange(len(s.data)),
@@ -48,23 +49,23 @@
 s3 = ts.time_series(pa.values.ravel(),
             dates=ts.date_array(pa.index.tolist(),freq="M"))
 
-print pa
-print repr(s3)
+print(pa)
+print(repr(s3))
 
-print '\nUsing tabular'
+print('\nUsing tabular')
 import tabular as tb
 X = tb.tabarray(array=s.torecords(), dtype=s.torecords().dtype)
 tabx = X.aggregate(On=['_dates'], AggFuncDict={'_data':np.mean,'_mask':np.all})
 s4 = ts.time_series(tabx['_data'],dates=ts.date_array(tabx['_dates'],freq="M"))
-print tabx
-print repr(s4)
+print(tabx)
+print(repr(s4))
 
 from finance import *  #hack to make it run as standalone
 #after running pandas/examples/finance.py
 larmsft = la.larry(msft.values, [msft.index.tolist(), msft.columns.tolist()])
 laribm = la.larry(ibm.values, [ibm.index.tolist(), ibm.columns.tolist()])
 lar1 = la.larry(np.dstack((msft.values,ibm.values)), [ibm.index.tolist(), ibm.columns.tolist(), ['msft', 'ibm']])
-print lar1.mean(0)
+print(lar1.mean(0))
 
 
 y = la.larry([[1.0, 2.0], [3.0, 4.0]], [['a', 'b'], ['c', 'd']])
diff --git a/statsmodels/sandbox/examples/try_gmm_other.py b/statsmodels/sandbox/examples/try_gmm_other.py
new file mode 100644
index 00000000000..53948a22605
--- /dev/null
+++ b/statsmodels/sandbox/examples/try_gmm_other.py
@@ -0,0 +1,142 @@
+
+import numpy as np
+from scipy import stats
+
+from statsmodels.regression.linear_model import OLS
+from statsmodels.tools import tools
+from statsmodels.sandbox.regression.gmm import IV2SLS, IVGMM, DistQuantilesGMM, spec_hausman
+
+from statsmodels.sandbox.regression import gmm
+
+
+
+if __name__ == '__main__':
+    import statsmodels.api as sm
+    examples = ['ivols', 'distquant'][:]
+
+    if 'ivols' in examples:
+        exampledata = ['ols', 'iv', 'ivfake'][1]
+        nobs = nsample = 500
+        sige = 3
+        corrfactor = 0.025
+
+
+        x = np.linspace(0,10, nobs)
+        X = tools.add_constant(np.column_stack((x, x**2)), prepend=False)
+        beta = np.array([1, 0.1, 10])
+
+        def sample_ols(exog):
+            endog = np.dot(exog, beta) + sige*np.random.normal(size=nobs)
+            return endog, exog, None
+
+        def sample_iv(exog):
+            print('using iv example')
+            X = exog.copy()
+            e = sige * np.random.normal(size=nobs)
+            endog = np.dot(X, beta) + e
+            exog[:,0] = X[:,0] + corrfactor * e
+            z0 = X[:,0] + np.random.normal(size=nobs)
+            z1 = X.sum(1) + np.random.normal(size=nobs)
+            z2 = X[:,1]
+            z3 = (np.dot(X, np.array([2,1, 0])) +
+                            sige/2. * np.random.normal(size=nobs))
+            z4 = X[:,1] + np.random.normal(size=nobs)
+            instrument = np.column_stack([z0, z1, z2, z3, z4, X[:,-1]])
+            return endog, exog, instrument
+
+        def sample_ivfake(exog):
+            X = exog
+            e = sige * np.random.normal(size=nobs)
+            endog = np.dot(X, beta) + e
+            #X[:,0] += 0.01 * e
+            #z1 = X.sum(1) + np.random.normal(size=nobs)
+            #z2 = X[:,1]
+            z3 = (np.dot(X, np.array([2,1, 0])) +
+                            sige/2. * np.random.normal(size=nobs))
+            z4 = X[:,1] + np.random.normal(size=nobs)
+            instrument = np.column_stack([X[:,:2], z3, z4, X[:,-1]]) #last is constant
+            return endog, exog, instrument
+
+
+        if exampledata == 'ols':
+            endog, exog, _ = sample_ols(X)
+            instrument = exog
+        elif exampledata == 'iv':
+            endog, exog, instrument = sample_iv(X)
+        elif exampledata == 'ivfake':
+            endog, exog, instrument = sample_ivfake(X)
+
+
+        #using GMM and IV2SLS classes
+        #----------------------------
+
+        mod = gmm.IVGMM(endog, exog, instrument, nmoms=instrument.shape[1])
+        res = mod.fit()
+        modgmmols = gmm.IVGMM(endog, exog, exog, nmoms=exog.shape[1])
+        resgmmols = modgmmols.fit()
+        #the next is the same as IV2SLS, (Z'Z)^{-1} as weighting matrix
+        modgmmiv = gmm.IVGMM(endog, exog, instrument, nmoms=instrument.shape[1]) #same as mod
+        resgmmiv = modgmmiv.fitgmm(np.ones(exog.shape[1], float),
+                        weights=np.linalg.inv(np.dot(instrument.T, instrument)))
+        modls = gmm.IV2SLS(endog, exog, instrument)
+        resls = modls.fit()
+        modols = OLS(endog, exog)
+        resols = modols.fit()
+
+        print('\nIV case')
+        print('params')
+        print('IV2SLS', resls.params)
+        print('GMMIV ', resgmmiv) # .params
+        print('GMM   ', res.params)
+        print('diff  ', res.params - resls.params)
+        print('OLS   ', resols.params)
+        print('GMMOLS', resgmmols.params)
+
+        print('\nbse')
+        print('IV2SLS', resls.bse)
+        print('GMM   ', res.bse)   #bse currently only attached to model not results
+        print('diff  ', res.bse - resls.bse)
+        print('%-diff', resls.bse / res.bse * 100 - 100)
+        print('OLS   ', resols.bse)
+        print('GMMOLS', resgmmols.bse)
+        #print 'GMMiv', modgmmiv.bse
+
+        print("Hausman's specification test")
+        print(resls.spec_hausman())
+        print(spec_hausman(resols.params, res.params, resols.cov_params(),
+                           res.cov_params()))
+        print(spec_hausman(resgmmols.params, res.params, resgmmols.cov_params(),
+                           res.cov_params()))
+
+
+    if 'distquant' in examples:
+
+
+        #estimating distribution parameters from quantiles
+        #-------------------------------------------------
+
+        #example taken from distribution_estimators.py
+        gparrvs = stats.genpareto.rvs(2, size=5000)
+        x0p = [1., gparrvs.min()-5, 1]
+
+        moddist = gmm.DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto)
+        #produces non-sense because optimal weighting matrix calculations don't
+        #apply to this case
+        #resgp = moddist.fit() #now with 'cov': LinAlgError: Singular matrix
+        pit1, wit1 = moddist.fititer([1.5,0,1.5], maxiter=1)
+        print(pit1)
+        p1 = moddist.fitgmm([1.5,0,1.5])
+        print(p1)
+        moddist2 = gmm.DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto,
+                                    pquant=np.linspace(0.01,0.99,10))
+        pit1a, wit1a = moddist2.fititer([1.5,0,1.5], maxiter=1)
+        print(pit1a)
+        p1a = moddist2.fitgmm([1.5,0,1.5])
+        print(p1a)
+        #Note: pit1a and p1a are the same and almost the same (1e-5) as
+        #      fitquantilesgmm version (functions instead of class)
+        res1b = moddist2.fitonce([1.5,0,1.5])
+        print(res1b.params)
+        print(res1b.bse)  #they look much too large
+        print(np.sqrt(np.diag(res1b._cov_params)))
+
diff --git a/statsmodels/sandbox/examples/try_multiols.py b/statsmodels/sandbox/examples/try_multiols.py
index 50ba68ebb32..13d66a6a628 100644
--- a/statsmodels/sandbox/examples/try_multiols.py
+++ b/statsmodels/sandbox/examples/try_multiols.py
@@ -22,7 +22,7 @@
 
 #This select only a certain subset of the columns
 res = multiOLS('GNP + 0', df, ['GNPDEFL', 'TOTEMP', 'POP'])
-print res.to_string()
+print(res.to_string())
 
 
 url = "http://vincentarelbundock.github.com/"
@@ -41,4 +41,4 @@
 
 #do the analysis of the significance
 res3 = multigroup(pvals < 0.05, groups)
-print res3
+print(res3)
diff --git a/statsmodels/sandbox/examples/try_quantile_regression.py b/statsmodels/sandbox/examples/try_quantile_regression.py
index 198f163ac3f..19bb1c253fd 100644
--- a/statsmodels/sandbox/examples/try_quantile_regression.py
+++ b/statsmodels/sandbox/examples/try_quantile_regression.py
@@ -5,6 +5,7 @@
 '''
 
 import numpy as np
+from statsmodels.compat.python import zip
 import statsmodels.api as sm
 
 from statsmodels.regression.quantile_regression import QuantReg
@@ -39,7 +40,7 @@
 plt.figure()
 plt.plot(y[sortidx], 'o', alpha=0.75)
 for lab, beta in zip(['ols', 'qr 0.25', 'qr 0.5', 'qr 0.75'], params):
-    print '%-8s'%lab, np.round(beta, 4)
+    print('%-8s'%lab, np.round(beta, 4))
     fitted = np.dot(x_sorted, beta)
     lw = 2 if lab == 'ols' else 1
     plt.plot(fitted, lw=lw, label=lab)
diff --git a/statsmodels/sandbox/examples/try_quantile_regression1.py b/statsmodels/sandbox/examples/try_quantile_regression1.py
index 5e0f31404ca..4a162f14bbb 100644
--- a/statsmodels/sandbox/examples/try_quantile_regression1.py
+++ b/statsmodels/sandbox/examples/try_quantile_regression1.py
@@ -7,6 +7,7 @@
 '''
 
 import numpy as np
+from statsmodels.compat.python import zip
 from scipy import stats
 import statsmodels.api as sm
 
diff --git a/statsmodels/sandbox/examples/try_smoothers.py b/statsmodels/sandbox/examples/try_smoothers.py
index 7b9668fec2c..81a12c9e799 100644
--- a/statsmodels/sandbox/examples/try_smoothers.py
+++ b/statsmodels/sandbox/examples/try_smoothers.py
@@ -36,30 +36,30 @@
     start = time.time()
     KSy = KS.conf(KSx)
     KVar = KS.std(KSx)
-    print time.time() - start    # This should be significantly quicker...
+    print(time.time() - start)    # This should be significantly quicker...
     start = time.time()          #
     KS2y = KS2.conf(KSx)         #
     K2Var = KS2.std(KSx)         #
-    print time.time() - start    # ...than this.
+    print(time.time() - start)    # ...than this.
 
     KSConfIntx, KSConfInty = KS.conf(15)
 
-    print "Norm const should be 0.9375"
-    print K2.norm_const
+    print("Norm const should be 0.9375")
+    print(K2.norm_const)
 
-    print "L2 Norms Should Match:"
-    print K.L2Norm
-    print K2.L2Norm
+    print("L2 Norms Should Match:")
+    print(K.L2Norm)
+    print(K2.L2Norm)
 
-    print "Fit values should match:"
+    print("Fit values should match:")
     #print zip(KSy, KS2y)
-    print KSy[28]
-    print KS2y[28]
+    print(KSy[28])
+    print(KS2y[28])
 
-    print "Var values should match:"
+    print("Var values should match:")
     #print zip(KVar, K2Var)
-    print KVar[39]
-    print K2Var[39]
+    print(KVar[39])
+    print(K2Var[39])
 
     fig = plt.figure()
     ax = fig.add_subplot(221)
diff --git a/statsmodels/sandbox/formula.py b/statsmodels/sandbox/formula.py
index effe3c1e640..4c6365e3d3c 100644
--- a/statsmodels/sandbox/formula.py
+++ b/statsmodels/sandbox/formula.py
@@ -8,15 +8,12 @@
 
 
 """
+from statsmodels.compat.python import (iterkeys, lrange, callable, string_types,
+                                itervalues, range)
 import copy
 import types
 import numpy as np
 
-try:
-    set
-except NameError:
-    from sets import Set as set
-
 __docformat__ = 'restructuredtext'
 
 default_namespace = {}
@@ -65,7 +62,7 @@ def __pow__(self, power):
         try:
             power = float(power)
         except:
-            raise ValueError, 'expecting a float'
+            raise ValueError('expecting a float')
 
         if power == int(power):
             name = '%s^%d' % (self.name, int(power))
@@ -86,8 +83,8 @@ def __init__(self, name, func=None, termname=None):
         else:
             self.termname = termname
 
-        if type(self.termname) is not types.StringType:
-            raise ValueError, 'expecting a string for termname'
+        if not isinstance(self.termname, string_types):
+            raise ValueError('expecting a string for termname')
         if func:
             self.func = func
 
@@ -123,7 +120,7 @@ def __mul__(self, other):
         Formula(self) * Formula(other)
         """
 
-        if type(other) is Term and other.name is 'intercept':
+        if isinstance(other, Term) and other.name is 'intercept':
             f = Formula(self, namespace=self.namespace)
         elif self.name is 'intercept':
             f = Formula(other, namespace=other.namespace)
@@ -139,7 +136,7 @@ def names(self):
         Return the names of the columns in design associated to the terms,
         i.e. len(self.names()) = self().shape[0].
         """
-        if type(self.name) is types.StringType:
+        if isinstance(self.name, string_types):
             return [self.name]
         else:
             return list(self.name)
@@ -187,7 +184,7 @@ def __init__(self, termname, keys, ordinal=False):
         else:
             self.keys = keys
             if len(set(keys)) != len(list(keys)):
-                raise ValueError, 'keys for ordinal Factor should be unique, in increasing order'
+                raise ValueError('keys for ordinal Factor should be unique, in increasing order')
         self._name = termname
         self.termname = termname
         self.ordinal = ordinal
@@ -243,7 +240,7 @@ def verify(self, values):
         """
         s = set(values)
         if not s.issubset(self.keys):
-            raise ValueError, 'unknown keys in values'
+            raise ValueError('unknown keys in values')
 
     def __add__(self, other):
         """
@@ -255,7 +252,7 @@ def __add__(self, other):
 
         """
 
-        if type(other) is Term and other.name is 'intercept':
+        if isinstance(other, Term) and other.name is 'intercept':
             return Formula(self, namespace=self.namespace)
         else:
             return Term.__add__(self, other)
@@ -283,13 +280,13 @@ def main_effect(self, reference=None):
 
         def maineffect_func(value, reference=reference):
             rvalue = []
-            keep = range(value.shape[0])
+            keep = lrange(value.shape[0])
             keep.pop(reference)
             for i in range(len(keep)):
                 rvalue.append(value[keep[i]] - value[reference])
             return np.array(rvalue)
 
-        keep = range(len(self.names()))
+        keep = lrange(len(self.names()))
         keep.pop(reference)
         __names = self.names()
         _names = ['%s-%s' % (__names[keep[i]], __names[reference]) for i in range(len(keep))]
@@ -385,7 +382,7 @@ def __init__(self, termlist, namespace=default_namespace):
         self.__namespace = namespace
         if isinstance(termlist, Formula):
             self.terms = copy.copy(list(termlist.terms))
-        elif type(termlist) is types.ListType:
+        elif isinstance(termlist, list):
             self.terms = termlist
         elif isinstance(termlist, Term):
             self.terms = [termlist]
@@ -456,7 +453,7 @@ def __call__(self, *args, **kw):
                 allvals[interceptindex] = np.ones((1,n), np.float64)
                 allvals = np.concatenate(allvals)
             elif nrow <= 1:
-                raise ValueError, 'with only intercept in formula, keyword \'nrow\' argument needed'
+                raise ValueError('with only intercept in formula, keyword \'nrow\' argument needed')
             else:
                 allvals = I(nrow=nrow)
                 allvals.shape = (1,) + allvals.shape
@@ -468,7 +465,7 @@ def hasterm(self, query_term):
         """
 
         if not isinstance(query_term, Formula):
-            if type(query_term) == type("name"):
+            if isinstance(query_term, string_types):
                 try:
                     query = self[query_term]
                     return query.termname in self.termnames()
@@ -480,14 +477,14 @@ def hasterm(self, query_term):
             query_term = query_term.terms[0]
             return query_term.termname in self.termnames()
         else:
-            raise ValueError, 'more than one term passed to hasterm'
+            raise ValueError('more than one term passed to hasterm')
 
     def __getitem__(self, name):
         t = self.termnames()
         if name in t:
             return self.terms[t.index(name)]
         else:
-            raise KeyError, 'formula has no such term: %s' % repr(name)
+            raise KeyError('formula has no such term: %s' % repr(name))
 
     def termcolumns(self, query_term, dict=False):
         """
@@ -501,11 +498,11 @@ def termcolumns(self, query_term, dict=False):
             for name in names:
                 value[name] = self._names.index(name)
         else:
-            raise ValueError, 'term not in formula'
+            raise ValueError('term not in formula')
         if dict:
             return value
         else:
-            return value.values()
+            return list(itervalues(value))
 
     def names(self):
         """
@@ -558,8 +555,7 @@ def __mul__(self, other, nested=False):
         for i in range(I):
             for j in range(J):
                 termname = '%s*%s' % (str(selftermnames[i]), str(othertermnames[j]))
-                pieces = termname.split('*')
-                pieces.sort()
+                pieces = sorted(termname.split('*'))
                 termname = '*'.join(pieces)
                 termnames.append(termname)
 
@@ -581,8 +577,7 @@ def __mul__(self, other, nested=False):
                     for r in range(d1):
                         for s in range(d2):
                             name = '%s*%s' % (str(selfnames[r]), str(othernames[s]))
-                            pieces = name.split('*')
-                            pieces.sort()
+                            pieces = sorted(name.split('*'))
                             name = '*'.join(pieces)
                             names.append(name)
 
@@ -621,8 +616,7 @@ def __add__(self, other):
 
         other = Formula(other)
         terms = self.terms + other.terms
-        pieces = [(term.name, term) for term in terms]
-        pieces.sort()
+        pieces = sorted([(term.name, term) for term in terms])
         terms = [piece[1] for piece in pieces]
         f = Formula(terms)
         if _namespace_equal(self.namespace, other.namespace):
@@ -667,7 +661,7 @@ def isnested(A, B, namespace=None):
     b = B(values=True)[0]
 
     if len(a) != len(b):
-        raise ValueError, 'A() and B() should be sequences of the same length'
+        raise ValueError('A() and B() should be sequences of the same length')
 
     nA = len(set(a))
     nB = len(set(b))
@@ -723,7 +717,7 @@ def interactions(terms, order=[1,2]):
     >>> print interactions([Term(l) for l in ['a', 'b', 'c']])
     <formula: a*b + a*c + b*c + a + b + c>
     >>>
-    >>> print interactions([Term(l) for l in ['a', 'b', 'c']], order=range(5))
+    >>> print interactions([Term(l) for l in ['a', 'b', 'c']], order=list(range(5)))
     <formula: a*b + a*b*c + a*c + b*c + a + b + c>
     >>>
 
@@ -733,7 +727,7 @@ def interactions(terms, order=[1,2]):
     values = {}
 
     if np.asarray(order).shape == ():
-        order = range(1, int(order)+1)
+        order = lrange(1, int(order)+1)
 
     # First order
 
@@ -752,10 +746,10 @@ def interactions(terms, order=[1,2]):
                     v *= ll[ii+1]
                 values[tuple(I[:,m])] = v
 
-    key = values.keys()[0]
+    key = list(iterkeys(values))[0]
     value = values[key]; del(values[key])
 
-    for v in values.values():
+    for v in itervalues(values):
         value += v
     return value
 
diff --git a/statsmodels/sandbox/gam.py b/statsmodels/sandbox/gam.py
index b7ea652f395..f9d259bf1bf 100644
--- a/statsmodels/sandbox/gam.py
+++ b/statsmodels/sandbox/gam.py
@@ -46,11 +46,15 @@
 # NOTE: example script is now in examples folder
 #update: I did some of the above, see module docstring
 
+from statsmodels.compat.python import next, range
 import numpy as np
 
 from statsmodels.genmod import families
 from statsmodels.sandbox.nonparametric.smoothers import PolySmoother
 from statsmodels.genmod.generalized_linear_model import GLM
+from statsmodels.tools.sm_exceptions import IterationLimitWarning, iteration_limit_doc
+
+import warnings
 
 DEBUG = False
 
@@ -234,8 +238,8 @@ def next(self):
             #print 'next shape', (Y - alpha - mu + tmp).shape
             bad = np.isnan(Y - alpha - mu + tmp).any()
             if bad: #temporary assert while debugging
-                print Y, alpha, mu, tmp
-                raise
+                print(Y, alpha, mu, tmp)
+                raise ValueError("nan encountered")
             #self.smoothers[i].smooth(Y - alpha - mu + tmp,
             self.smoothers[i].smooth(Y - mu + tmp,
                                      weights=self.weights)
@@ -243,7 +247,7 @@ def next(self):
             self.results.offset[i] = -(tmp2*self.weights).sum() / self.weights.sum()
             #self.offset used in smoothed
             if DEBUG:
-                print self.smoothers[i].params
+                print(self.smoothers[i].params)
             mu += tmp2 - tmp
         #change setting offset here: tests still pass, offset equal to constant
         #in component ??? what's the effect of offset
@@ -267,8 +271,8 @@ def cont(self):
         '''
         self.iter += 1 #moved here to always count, not necessary
         if DEBUG:
-            print self.iter, self.results.Y.shape,
-            print self.results.predict(self.exog).shape, self.weights.shape
+            print(self.iter, self.results.Y.shape)
+            print(self.results.predict(self.exog).shape, self.weights.shape)
         curdev = (((self.results.Y - self.results.predict(self.exog))**2) * self.weights).sum()
 
         if self.iter > self.maxiter: #kill it, no max iterationoption
@@ -321,8 +325,7 @@ def fit(self, Y, rtol=1.0e-06, maxiter=30):
             self.results = self.next()
 
         if self.iter >= self.maxiter:
-            import warnings
-            warnings.warn('maximum number of iterations reached')
+            warnings.warn(iteration_limit_doc, IterationLimitWarning)
 
         return self.results
 
@@ -353,7 +356,7 @@ def next(self):
         _results = self.results
         Y = _results.Y
         if np.isnan(self.weights).all():
-            print "nanweights1"
+            print("nanweights1")
 
         _results.mu = self.family.link.inverse(_results.predict(self.exog))
         #eta = _results.predict(self.exog)
@@ -361,10 +364,10 @@ def next(self):
         weights = self.family.weights(_results.mu)
         if np.isnan(weights).all():
             self.weights = weights
-            print "nanweights2"
+            print("nanweights2")
         self.weights = weights
         if DEBUG:
-            print 'deriv isnan', np.isnan(self.family.link.deriv(_results.mu)).any()
+            print('deriv isnan', np.isnan(self.family.link.deriv(_results.mu)).any())
 
         #Z = _results.predict(self.exog) + \
         Z = _results.predict(self.exog) + \
@@ -426,6 +429,6 @@ def fit(self, Y, rtol=1.0e-06, maxiter=30):
 
         if self.iter >= self.maxiter:
             import warnings
-            warnings.warn('maximum number of iterations reached')
+            warnings.warn(iteration_limit_doc, IterationLimitWarning)
 
         return self.results
diff --git a/statsmodels/sandbox/infotheo.py b/statsmodels/sandbox/infotheo.py
index ee805b6784b..2db4727ec60 100644
--- a/statsmodels/sandbox/infotheo.py
+++ b/statsmodels/sandbox/infotheo.py
@@ -32,17 +32,15 @@
 #bias and variance. Technical Report 2003/131 School of Computer Science and Software Engineer-
 #ing, Monash University.
 
+from statsmodels.compat.python import range, lzip, lmap
 from scipy import stats
 import numpy as np
 from matplotlib import pyplot as plt
+from scipy.misc import logsumexp as sp_logsumexp
 
 #TODO: change these to use maxentutils so that over/underflow is handled
 #with the logsumexp.
 
-try:
-    from scipy.maxentropy import logsumexp as sp_logsumexp
-except:
-    from scipy.misc import logsumexp as sp_logsumexp
 
 def logsumexp(a, axis=None):
     """
@@ -118,7 +116,7 @@ def discretize(X, method="ef", nbins=None):
         binnum = 1
         base = svec[0]
         discrete[ivec[0]] = binnum
-        for i in xrange(1,nobs):
+        for i in range(1,nobs):
             if svec[i] < base + width:
                 discrete[ivec[i]] = binnum
             else:
@@ -184,7 +182,7 @@ def shannonentropy(px, logbase=2):
 #TODO: haven't defined the px,py case?
     px = np.asarray(px)
     if not np.all(px <= 1) or not np.all(px >= 0):
-        raise ValueError, "px does not define proper distribution"
+        raise ValueError("px does not define proper distribution")
     entropy = -np.sum(np.nan_to_num(px*np.log2(px)))
     if logbase != 2:
         return logbasechange(2,logbase) * entropy
@@ -208,7 +206,7 @@ def shannoninfo(px, logbase=2):
     """
     px = np.asarray(px)
     if not np.all(px <= 1) or not np.all(px >= 0):
-        raise ValueError, "px does not define proper distribution"
+        raise ValueError("px does not define proper distribution")
     if logbase != 2:
         return - logbasechange(2,logbase) * np.log2(px)
     else:
@@ -235,9 +233,9 @@ def condentropy(px, py, pxpy=None, logbase=2):
     and w_kj = X[k,j]
     """
     if not _isproperdist(px) or not _isproperdist(py):
-        raise ValueError, "px or py is not a proper probability distribution"
+        raise ValueError("px or py is not a proper probability distribution")
     if pxpy != None and not _isproperdist(pxpy):
-        raise ValueError, "pxpy is not a proper joint distribtion"
+        raise ValueError("pxpy is not a proper joint distribtion")
     if pxpy == None:
         pxpy = np.outer(py,px)
     condent = np.sum(pxpy * np.nan_to_num(np.log2(py/pxpy)))
@@ -268,9 +266,9 @@ def mutualinfo(px,py,pxpy, logbase=2):
     shannonentropy(px) - condentropy(px,py,pxpy)
     """
     if not _isproperdist(px) or not _isproperdist(py):
-        raise ValueError, "px or py is not a proper probability distribution"
+        raise ValueError("px or py is not a proper probability distribution")
     if pxpy != None and not _isproperdist(pxpy):
-        raise ValueError, "pxpy is not a proper joint distribtion"
+        raise ValueError("pxpy is not a proper joint distribtion")
     if pxpy == None:
         pxpy = np.outer(py,px)
     return shannonentropy(px, logbase=logbase) - condentropy(px,py,pxpy,
@@ -307,9 +305,9 @@ def corrent(px,py,pxpy,logbase=2):
     corrent(px,py,pxpy) = 1 - condent(px,py,pxpy)/shannonentropy(py)
     """
     if not _isproperdist(px) or not _isproperdist(py):
-        raise ValueError, "px or py is not a proper probability distribution"
+        raise ValueError("px or py is not a proper probability distribution")
     if pxpy != None and not _isproperdist(pxpy):
-        raise ValueError, "pxpy is not a proper joint distribtion"
+        raise ValueError("pxpy is not a proper joint distribtion")
     if pxpy == None:
         pxpy = np.outer(py,px)
 
@@ -348,9 +346,9 @@ def covent(px,py,pxpy,logbase=2):
     covent(px,py,pxpy) = condent(px,py,pxpy) + condent(py,px,pxpy)
     """
     if not _isproperdist(px) or not _isproperdist(py):
-        raise ValueError, "px or py is not a proper probability distribution"
+        raise ValueError("px or py is not a proper probability distribution")
     if pxpy != None and not _isproperdist(pxpy):
-        raise ValueError, "pxpy is not a proper joint distribtion"
+        raise ValueError("pxpy is not a proper joint distribtion")
     if pxpy == None:
         pxpy = np.outer(py,px)
 
@@ -390,7 +388,7 @@ def renyientropy(px,alpha=1,logbase=2,measure='R'):
 #TODO:finish returns
 #TODO:add checks for measure
     if not _isproperdist(px):
-        raise ValueError, "px is not a proper probability distribution"
+        raise ValueError("px is not a proper probability distribution")
     alpha = float(alpha)
     if alpha == 1:
         genent = shannonentropy(px)
@@ -435,20 +433,20 @@ def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'):
 
 
 if __name__ == "__main__":
-    print "From Golan (2008) \"Information and Entropy Econometrics -- A Review \
-and Synthesis"
-    print "Table 3.1"
+    print("From Golan (2008) \"Information and Entropy Econometrics -- A Review \
+and Synthesis")
+    print("Table 3.1")
     # Examples from Golan (2008)
 
     X = [.2,.2,.2,.2,.2]
     Y = [.322,.072,.511,.091,.004]
 
     for i in X:
-        print shannoninfo(i)
+        print(shannoninfo(i))
     for i in Y:
-        print shannoninfo(i)
-    print shannonentropy(X)
-    print shannonentropy(Y)
+        print(shannoninfo(i))
+    print(shannonentropy(X))
+    print(shannonentropy(Y))
 
     p = [1e-5,1e-4,.001,.01,.1,.15,.2,.25,.3,.35,.4,.45,.5]
 
@@ -463,7 +461,7 @@ def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'):
     plt.ylabel("Entropy")
     plt.xlabel("Probability")
     x = np.linspace(0,1,101)
-    plt.plot(x, map(shannonentropy, zip(x,1-x)))
+    plt.plot(x, lmap(shannonentropy, lzip(x,1-x)))
 #    plt.show()
 
     # define a joint probability distribution
@@ -481,10 +479,10 @@ def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'):
     D_YX = logbasechange(2,np.e)*stats.entropy(px, py)
     D_XY = logbasechange(2,np.e)*stats.entropy(py, px)
     I_XY = mutualinfo(px,py,w)
-    print "Table 3.3"
-    print H_X,H_Y, H_XY, H_XgivenY, H_YgivenX, D_YX, D_XY, I_XY
+    print("Table 3.3")
+    print(H_X,H_Y, H_XY, H_XgivenY, H_YgivenX, D_YX, D_XY, I_XY)
 
-    print "discretize functions"
+    print("discretize functions")
     X=np.array([21.2,44.5,31.0,19.5,40.6,38.7,11.1,15.8,31.9,25.8,20.2,14.2,
         24.0,21.0,11.3,18.0,16.3,22.2,7.8,27.8,16.3,35.1,14.9,17.1,28.2,16.4,
         16.5,46.0,9.5,18.8,32.1,26.1,16.1,7.3,21.4,20.0,29.3,14.9,8.3,22.5,
@@ -493,29 +491,29 @@ def gencrossentropy(px,py,pxpy,alpha=1,logbase=2, measure='T'):
     #CF: R's infotheo
 #TODO: compare to pyentropy quantize?
     print
-    print "Example in section 3.6 of Golan, using table 3.3"
-    print "Bounding errors using Fano's inequality"
-    print "H(P_{e}) + P_{e}log(K-1) >= H(X|Y)"
-    print "or, a weaker inequality"
-    print "P_{e} >= [H(X|Y) - 1]/log(K)"
-    print "P(x) = %s" % px
-    print "X = 3 has the highest probability, so this is the estimate Xhat"
+    print("Example in section 3.6 of Golan, using table 3.3")
+    print("Bounding errors using Fano's inequality")
+    print("H(P_{e}) + P_{e}log(K-1) >= H(X|Y)")
+    print("or, a weaker inequality")
+    print("P_{e} >= [H(X|Y) - 1]/log(K)")
+    print("P(x) = %s" % px)
+    print("X = 3 has the highest probability, so this is the estimate Xhat")
     pe = 1 - px[2]
-    print "The probability of error Pe is 1 - p(X=3) = %0.4g" % pe
+    print("The probability of error Pe is 1 - p(X=3) = %0.4g" % pe)
     H_pe = shannonentropy([pe,1-pe])
-    print "H(Pe) = %0.4g and K=3" % H_pe
-    print "H(Pe) + Pe*log(K-1) = %0.4g >= H(X|Y) = %0.4g" % \
-            (H_pe+pe*np.log2(2), H_XgivenY)
-    print "or using the weaker inequality"
-    print "Pe = %0.4g >= [H(X) - 1]/log(K) = %0.4g" % (pe, (H_X - 1)/np.log2(3))
-    print "Consider now, table 3.5, where there is additional information"
-    print "The conditional probabilities of P(X|Y=y) are "
+    print("H(Pe) = %0.4g and K=3" % H_pe)
+    print("H(Pe) + Pe*log(K-1) = %0.4g >= H(X|Y) = %0.4g" % \
+            (H_pe+pe*np.log2(2), H_XgivenY))
+    print("or using the weaker inequality")
+    print("Pe = %0.4g >= [H(X) - 1]/log(K) = %0.4g" % (pe, (H_X - 1)/np.log2(3)))
+    print("Consider now, table 3.5, where there is additional information")
+    print("The conditional probabilities of P(X|Y=y) are ")
     w2 = np.array([[0.,0.,1.],[1/3.,1/3.,1/3.],[1/6.,1/3.,1/2.]])
-    print w2
+    print(w2)
 # not a proper distribution?
-    print "The probability of error given this information is"
-    print "Pe = [H(X|Y) -1]/log(K) = %0.4g" % ((np.mean([0,shannonentropy(w2[1]),shannonentropy(w2[2])])-1)/np.log2(3))
-    print "such that more information lowers the error"
+    print("The probability of error given this information is")
+    print("Pe = [H(X|Y) -1]/log(K) = %0.4g" % ((np.mean([0,shannonentropy(w2[1]),shannonentropy(w2[2])])-1)/np.log2(3)))
+    print("such that more information lowers the error")
 
 ### Stochastic processes
     markovchain = np.array([[.553,.284,.163],[.465,.312,.223],[.420,.322,.258]])
diff --git a/statsmodels/sandbox/km_class.py b/statsmodels/sandbox/km_class.py
index 6e230e81a1b..d63478e17e9 100644
--- a/statsmodels/sandbox/km_class.py
+++ b/statsmodels/sandbox/km_class.py
@@ -1,4 +1,5 @@
 #a class for the Kaplan-Meier estimator
+from statsmodels.compat.python import range
 import numpy as np
 from math import sqrt
 import matplotlib.pyplot as plt
diff --git a/statsmodels/sandbox/mcevaluate/arma.py b/statsmodels/sandbox/mcevaluate/arma.py
index a51d945d223..0ccf60d6b5d 100644
--- a/statsmodels/sandbox/mcevaluate/arma.py
+++ b/statsmodels/sandbox/mcevaluate/arma.py
@@ -33,8 +33,8 @@ def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5):
         results.append(rhohat2a)
         err2a = arest2.geterrors(rhohat2a)
         sige2a = np.sqrt(np.dot(err2a,err2a)/nsample)
-        #print 'sige2a', sige2a,
-        #print 'cov_x2a.shape', cov_x2a.shape
+        #print('sige2a', sige2a,
+        #print('cov_x2a.shape', cov_x2a.shape
         #results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
         if not cov_x2a is None:
             results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
@@ -46,29 +46,29 @@ def mc_summary(res, rt=None):
     if rt is None:
         rt = np.zeros(res.shape[1])
     nanrows = np.isnan(res).any(1)
-    print 'fractions of iterations with nans', nanrows.mean()
+    print('fractions of iterations with nans', nanrows.mean())
     res = res[~nanrows]
-    print 'RMSE'
-    print np.sqrt(((res-rt)**2).mean(0))
-    print 'mean bias'
-    print (res-rt).mean(0)
-    print 'median bias'
-    print np.median((res-rt),0)
-    print 'median bias percent'
-    print np.median((res-rt)/rt*100,0)
-    print 'median absolute error'
-    print np.median(np.abs(res-rt),0)
-    print 'positive error fraction'
-    print (res > rt).mean(0)
+    print('RMSE')
+    print(np.sqrt(((res-rt)**2).mean(0)))
+    print('mean bias')
+    print((res-rt).mean(0))
+    print('median bias')
+    print(np.median((res-rt),0))
+    print('median bias percent')
+    print(np.median((res-rt)/rt*100,0))
+    print('median absolute error')
+    print(np.median(np.abs(res-rt),0))
+    print('positive error fraction')
+    print((res > rt).mean(0))
 
 
 if __name__ == '__main__':
 
 #short version
 #    true, est, bse = mcarma22(niter=50)
-#    print true
-#    #print est
-#    print est.mean(0)
+#    print(true
+#    #print(est
+#    print(est.mean(0)
 
     ''' niter 50, sample size=1000, 2 runs
     [-0.55 -0.1   0.3   0.2 ]
@@ -130,18 +130,18 @@ def mc_summary(res, rt=None):
             import time
             t0 = time.time()
             rt, res_rho, res_bse = mcarma22(niter=100, sig=sig)
-            print '\nResults for Monte Carlo'
-            print 'true'
-            print rt
-            print 'nsample =', nsample, 'sigma = ', sig
-            print 'elapsed time for Monte Carlo', time.time()-t0
+            print('\nResults for Monte Carlo')
+            print('true')
+            print(rt)
+            print('nsample =', nsample, 'sigma = ', sig)
+            print('elapsed time for Monte Carlo', time.time()-t0)
             # 20 seconds for ARMA(2,2), 1000 iterations with 1000 observations
             #sige2a = np.sqrt(np.dot(err2a,err2a)/nsample)
-            #print '\nbse of one sample'
-            #print sige2a * np.sqrt(np.diag(cov_x2a))
-            print '\nMC of rho versus true'
+            #print('\nbse of one sample'
+            #print(sige2a * np.sqrt(np.diag(cov_x2a))
+            print('\nMC of rho versus true')
             mc_summary(res_rho, rt)
-            print '\nMC of bse versus zero'  # this implies inf in percent
+            print('\nMC of bse versus zero')  # this implies inf in percent
             mc_summary(res_bse)
-            print '\nMC of bse versus std'
+            print('\nMC of bse versus std')
             mc_summary(res_bse, res_rho.std(0))
diff --git a/statsmodels/sandbox/mle.py b/statsmodels/sandbox/mle.py
index de2a4fb2d05..cdf43214c52 100644
--- a/statsmodels/sandbox/mle.py
+++ b/statsmodels/sandbox/mle.py
@@ -3,7 +3,7 @@
 
 changes: JP corrections to imports so it runs, comment out print
 '''
-
+from __future__ import print_function
 import numpy as np
 from numpy import dot,  outer, random, argsort
 from scipy import io, linalg, optimize
@@ -38,19 +38,19 @@ def Rpp(v):
 random.seed(1)
 v_0=random.rand(n)
 
-print "try fmin_bfgs"
+print("try fmin_bfgs")
 full_output = 1
 data=[]
 v,fopt, gopt, Hopt, func_calls, grad_calls, warnflag, allvecs = \
         optimize.fmin_bfgs(R,v_0,fprime=Rp,full_output=full_output,retall=1)
 if warnflag == 0:
    plt.semilogy(np.arange(0,len(data)),data)
-   print 'Rayleigh quotient BFGS',R(v)
+   print('Rayleigh quotient BFGS',R(v))
 
 
-print "fmin_bfgs OK"
+print("fmin_bfgs OK")
 
-print "try fmin_ncg"
+print("try fmin_ncg")
 
 #
 # WARNING: the program may hangs if fmin_ncg is used
@@ -61,5 +61,5 @@ def Rpp(v):
 if warnflag==0:
    plt.figure()
    plt.semilogy(np.arange(0,len(data)),data)
-   print 'Rayleigh quotient NCG',R(v)
+   print('Rayleigh quotient NCG',R(v))
 
diff --git a/statsmodels/sandbox/multilinear.py b/statsmodels/sandbox/multilinear.py
index 0aebbf391c6..54154157021 100644
--- a/statsmodels/sandbox/multilinear.py
+++ b/statsmodels/sandbox/multilinear.py
@@ -11,6 +11,7 @@
     more significant than outside the group.
 """
 
+from statsmodels.compat.python import iteritems, string_types
 from patsy import dmatrix
 import pandas as pd
 from statsmodels.api import OLS
@@ -147,7 +148,7 @@ def multiOLS(model, dataframe, column_list=None, method='fdr_bh',
         column_list = [name for name in dataframe.columns
                       if dataframe[name].dtype != object and name not in model]
     # if it's a single string transform it in a single element list
-    if isinstance(column_list, basestring):
+    if isinstance(column_list, string_types):
         column_list = [column_list]
     if subset is not None:
         dataframe = dataframe.ix[subset]
@@ -191,12 +192,7 @@ def _test_group(pvalues, group_name, group, exact=True):
     The test is performed on the pvalues set (ad a pandas series) over
     the group specified via a fisher exact test.
     """
-    from scipy.stats import fisher_exact
-    try:
-        from scipy.stats import chi2_contingency
-    except ImportError:
-        def chi2_contingency(*args, **kwds):
-            raise ValueError('exact=False is not available with old scipy')
+    from scipy.stats import fisher_exact, chi2_contingency
 
     totals = 1.0 * len(pvalues)
     total_significant = 1.0 * np.sum(pvalues)
@@ -312,7 +308,7 @@ def multigroup(pvals, groups, exact=True, keep_all=True, alpha=0.05):
         '_in_non': {},
         '_out_sign': {},
         '_out_non': {}}
-    for group_name, group_list in groups.iteritems():
+    for group_name, group_list in iteritems(groups):
         res = _test_group(pvals, group_name, group_list, exact)
         results['pvals'][group_name] = res[0]
         results['increase'][group_name] = res[1]
diff --git a/statsmodels/sandbox/nonparametric/densityorthopoly.py b/statsmodels/sandbox/nonparametric/densityorthopoly.py
index 14e8344c999..983251c5bec 100644
--- a/statsmodels/sandbox/nonparametric/densityorthopoly.py
+++ b/statsmodels/sandbox/nonparametric/densityorthopoly.py
@@ -37,7 +37,8 @@
 
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import zip
 from scipy import stats, integrate
 import numpy as np
 
@@ -454,10 +455,10 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
         from scipy import integrate
         fint = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid))
         #f_hat -= fint/2.
-        print 'f_hat.min()', f_hat.min()
+        print('f_hat.min()', f_hat.min())
         f_hat = (f_hat - f_hat.min()) #/ f_hat.max() - f_hat.min
         fint2 = integrate.trapz(f_hat, grid)# dx=(grid.max() - grid.min())/len(grid))
-        print 'fint2', fint, fint2
+        print('fint2', fint, fint2)
         f_hat /= fint2
 
         # note that this uses a *huge* grid by default
@@ -474,10 +475,10 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
 
         for i,p in enumerate(polys[:5]):
             for j,p2 in enumerate(polys[:5]):
-                print i,j,integrate.quad(lambda x: p(x)*p2(x), -1,1)[0]
+                print(i,j,integrate.quad(lambda x: p(x)*p2(x), -1,1)[0])
 
         for p in polys:
-            print integrate.quad(lambda x: p(x)**2, -1,1)
+            print(integrate.quad(lambda x: p(x)**2, -1,1))
 
 
     #examples using the new class
@@ -486,9 +487,9 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
         dop = DensityOrthoPoly().fit(obs_dist, ChebyTPoly, order=20)
         grid = np.linspace(obs_dist.min(), obs_dist.max())
         xf = dop(grid)
-        #print 'np.max(np.abs(xf - f_hat0))', np.max(np.abs(xf - f_hat0))
+        #print('np.max(np.abs(xf - f_hat0))', np.max(np.abs(xf - f_hat0))
         dopint = integrate.quad(dop, *dop.limits)[0]
-        print 'dop F integral', dopint
+        print('dop F integral', dopint)
         mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm],
                    kwargs=mix_kwds)
 
@@ -507,9 +508,9 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
         dop = dop.fit(obs_dist, F2Poly, order=30)
         grid = np.linspace(obs_dist.min(), obs_dist.max())
         xf = dop(grid)
-        #print np.max(np.abs(xf - f_hat0))
+        #print(np.max(np.abs(xf - f_hat0))
         dopint = integrate.quad(dop, *dop.limits)[0]
-        print 'dop F integral', dopint
+        print('dop F integral', dopint)
         mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm],
                    kwargs=mix_kwds)
 
@@ -523,7 +524,7 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
             #plt.show()
 
         #check orthonormality:
-        print np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5)))
+        print(np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5))))
 
     if "hermite" in examples:
         dop = DensityOrthoPoly()
@@ -531,9 +532,9 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
         dop = dop.fit(obs_dist, HPoly, order=20)
         grid = np.linspace(obs_dist.min(), obs_dist.max())
         xf = dop(grid)
-        #print np.max(np.abs(xf - f_hat0))
+        #print(np.max(np.abs(xf - f_hat0))
         dopint = integrate.quad(dop, *dop.limits)[0]
-        print 'dop F integral', dopint
+        print('dop F integral', dopint)
 
         mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm],
                    kwargs=mix_kwds)
@@ -548,22 +549,22 @@ def density_orthopoly(x, polybase, order=5, xeval=None):
             plt.show()
 
         #check orthonormality:
-        print np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5)))
+        print(np.max(np.abs(inner_cont(dop.polys[:5], 0, 1)[0] -np.eye(5))))
 
 
     #check orthonormality
 
     hpolys = [HPoly(i) for i in range(5)]
     inn = inner_cont(hpolys, -6, 6)[0]
-    print np.max(np.abs(inn - np.eye(5)))
-    print (inn*100000).astype(int)
+    print(np.max(np.abs(inn - np.eye(5))))
+    print((inn*100000).astype(int))
 
     from scipy.special import hermite, chebyt
     htpolys = [hermite(i) for i in range(5)]
     innt = inner_cont(htpolys, -10, 10)[0]
-    print (innt*100000).astype(int)
+    print((innt*100000).astype(int))
 
     polysc = [chebyt(i) for i in range(4)]
     r, e = inner_cont(polysc, -1, 1, weight=lambda x: (1-x*x)**(-1/2.))
-    print np.max(np.abs(r - np.diag(np.diag(r))))
+    print(np.max(np.abs(r - np.diag(np.diag(r)))))
 
diff --git a/statsmodels/sandbox/nonparametric/kde2.py b/statsmodels/sandbox/nonparametric/kde2.py
index 47f481d2df4..7dab02f7ea4 100644
--- a/statsmodels/sandbox/nonparametric/kde2.py
+++ b/statsmodels/sandbox/nonparametric/kde2.py
@@ -1,7 +1,8 @@
 # -*- coding: utf-8 -*-
-
+from __future__ import print_function
+from statsmodels.compat.python import lzip, zip
 import numpy as np
-import kernels
+from . import kernels
 
 
 #TODO: should this be a function?
@@ -63,8 +64,8 @@ def evaluate(self, x, h="silverman"):
 
     kern = kernels.Gaussian(h = h)
     kde = KDE( x, kern)
-    print kde.density(1.015469)
-    print 0.2034675
+    print(kde.density(1.015469))
+    print(0.2034675)
     Xs = np.arange(-10,10,0.1)
 
     fig = plt.figure()
@@ -75,14 +76,14 @@ def evaluate(self, x, h="silverman"):
 
 
     # 2-D case
-    x = zip(kdetest.faithfulData["eruptions"], kdetest.faithfulData["waiting"])
+    x = lzip(kdetest.faithfulData["eruptions"], kdetest.faithfulData["waiting"])
     x = np.array(x)
     x = (x - x.mean(0))/x.std(0)
     nobs = x.shape[0]
     H = kdetest.Hpi
     kern = kernels.NdKernel( 2 )
     kde = KDE( x, kern )
-    print kde.density( np.matrix( [1,2 ])) #.T )
+    print(kde.density( np.matrix( [1,2 ]))) #.T
     plt.figure()
     plt.plot(x[:,0], x[:,1], 'o')
 
diff --git a/statsmodels/sandbox/nonparametric/kdecovclass.py b/statsmodels/sandbox/nonparametric/kdecovclass.py
index 0c9ec47f4df..54611f00c0a 100644
--- a/statsmodels/sandbox/nonparametric/kdecovclass.py
+++ b/statsmodels/sandbox/nonparametric/kdecovclass.py
@@ -40,8 +40,7 @@ def covariance_factor(self):
         elif self.covfact:
             return float(self.covfact)
         else:
-            raise ValueError, \
-                'covariance factor has to be scotts, silverman or a number'
+            raise ValueError('covariance factor has to be scotts, silverman or a number')
 
     def reset_covfact(self, covfact):
         self.covfact = covfact
@@ -71,7 +70,7 @@ def test_kde_1d():
     xn = np.random.randn(n_basesample)
     xnmean = xn.mean()
     xnstd = xn.std(ddof=1)
-    print xnmean, xnstd
+    print(xnmean, xnstd)
 
     # get kde for original sample
     gkde = stats.gaussian_kde(xn)
@@ -80,16 +79,16 @@ def test_kde_1d():
     xs = np.linspace(-7,7,501)
     kdepdf = gkde.evaluate(xs)
     normpdf = stats.norm.pdf(xs, loc=xnmean, scale=xnstd)
-    print 'MSE', np.sum((kdepdf - normpdf)**2)
-    print 'axabserror', np.max(np.abs(kdepdf - normpdf))
+    print('MSE', np.sum((kdepdf - normpdf)**2))
+    print('axabserror', np.max(np.abs(kdepdf - normpdf)))
     intervall = xs[1] - xs[0]
     assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
     #assert_array_almost_equal(kdepdf, normpdf, decimal=2)
-    print gkde.integrate_gaussian(0.0, 1.0)
-    print gkde.integrate_box_1d(-np.inf, 0.0)
-    print gkde.integrate_box_1d(0.0, np.inf)
-    print gkde.integrate_box_1d(-np.inf, xnmean)
-    print gkde.integrate_box_1d(xnmean, np.inf)
+    print(gkde.integrate_gaussian(0.0, 1.0))
+    print(gkde.integrate_box_1d(-np.inf, 0.0))
+    print(gkde.integrate_box_1d(0.0, np.inf))
+    print(gkde.integrate_box_1d(-np.inf, xnmean))
+    print(gkde.integrate_box_1d(xnmean, np.inf))
 
     assert_almost_equal(gkde.integrate_box_1d(xnmean, np.inf), 0.5, decimal=1)
     assert_almost_equal(gkde.integrate_box_1d(-np.inf, xnmean), 0.5, decimal=1)
diff --git a/statsmodels/sandbox/nonparametric/kernel_extras.py b/statsmodels/sandbox/nonparametric/kernel_extras.py
index 89dcee2ac73..b161e399e52 100644
--- a/statsmodels/sandbox/nonparametric/kernel_extras.py
+++ b/statsmodels/sandbox/nonparametric/kernel_extras.py
@@ -30,7 +30,7 @@
 
 # TODO: make default behavior efficient=True above a certain n_obs
 
-
+from statsmodels.compat.python import range, next
 import numpy as np
 from scipy import optimize
 from scipy.stats.mstats import mquantiles
@@ -106,7 +106,7 @@ def _compute_sig(self):
         u2 = fct2 * resid
         r = fct2 / sqrt5
         I_dist = np.empty((self.nboot,1))
-        for j in xrange(self.nboot):
+        for j in range(self.nboot):
             u_boot = u2.copy()
 
             prob = np.random.uniform(0,1, size = (n,))
@@ -135,7 +135,7 @@ def _compute_test_stat(self, u):
         I = 0
         S2 = 0
         for i, X_not_i in enumerate(XLOO):
-            u_j = uLOO.next()
+            u_j = next(uLOO)
             u_j = np.squeeze(u_j)
             # See Bootstrapping procedure on p. 357 in [1]
             K = gpke(self.bw, data=-X_not_i, data_predict=-self.exog[i, :],
@@ -225,7 +225,7 @@ def cv_loo(self, params):
         LOO_Y = LeaveOneOut(self.endog).__iter__()
         L = 0
         for i, X_not_i in enumerate(LOO_X):
-            Y = LOO_Y.next()
+            Y = next(LOO_Y)
             #print b.shape, np.dot(self.exog[i:i+1, :], b).shape, bw,
             G = self.func(bw, endog=Y, exog=-np.dot(X_not_i, b)[:,None],
                           #data_predict=-b*self.exog[i, :])[0]
@@ -245,7 +245,7 @@ def fit(self, data_predict=None):
         N_data_predict = np.shape(data_predict)[0]
         mean = np.empty((N_data_predict,))
         mfx = np.empty((N_data_predict, self.K))
-        for i in xrange(N_data_predict):
+        for i in range(N_data_predict):
             mean_mfx = self.func(self.bw, self.endog,
                                  np.dot(self.exog, self.b)[:,None],
                                  data_predict=np.dot(data_predict[i:i+1, :],self.b))
@@ -368,8 +368,8 @@ def cv_loo(self, params):
         Xb = np.dot(self.exog, b)[:,None]
         L = 0
         for ii, X_not_i in enumerate(LOO_X):
-            Y = LOO_Y.next()
-            Z = LOO_Z.next()
+            Y = next(LOO_Y)
+            Z = next(LOO_Z)
             Xb_j = np.dot(X_not_i, b)[:,None]
             Yx = Y - Xb_j
             G = self.func(bw, endog=Yx, exog=-Z,
@@ -396,7 +396,7 @@ def fit(self, exog_predict=None, exog_nonparametric_predict=None):
         mean = np.empty((N_data_predict,))
         mfx = np.empty((N_data_predict, self.K))
         Y = self.endog - np.dot(exog_predict, self.b)[:,None]
-        for i in xrange(N_data_predict):
+        for i in range(N_data_predict):
             mean_mfx = self.func(self.bw, Y, self.exog_nonparametric,
                                  data_predict=exog_nonparametric_predict[i, :])
             mean[i] = mean_mfx[0]
diff --git a/statsmodels/sandbox/nonparametric/kernels.py b/statsmodels/sandbox/nonparametric/kernels.py
index 7cc7898e073..8aee1672bc7 100644
--- a/statsmodels/sandbox/nonparametric/kernels.py
+++ b/statsmodels/sandbox/nonparametric/kernels.py
@@ -18,9 +18,10 @@
 # pylint: disable-msg=W0142
 # pylint: disable-msg=E1101
 # pylint: disable-msg=E0611
-
+from statsmodels.compat.python import lzip, lfilter, callable, zip
 import numpy as np
 import scipy.integrate
+from scipy.misc import factorial
 from numpy import exp, multiply, square, divide, subtract, inf
 
 
@@ -48,6 +49,7 @@ def __init__(self, n, kernels = None, H = None):
             kernels = Gaussian()
 
         self._kernels = kernels
+        self.weights = None
 
         if H is None:
             H = np.matrix( np.identity(n))
@@ -66,13 +68,17 @@ def setH(self, value):
     H = property(getH, setH, doc="Kernel bandwidth matrix")
 
     def density(self, xs, x):
+
         n = len(xs)
-        #xs = self.inDomain( xs, xs, x )[0]
+        #xs = self.in_domain( xs, xs, x )[0]
 
         if len(xs)>0:  ## Need to do product of marginal distributions
             #w = np.sum([self(self._Hrootinv * (xx-x).T ) for xx in xs])/n
             #vectorized doesn't work:
-            w = np.mean(self((xs-x) * self._Hrootinv )) #transposed
+            if self.weights is not None:
+                w = np.mean(self((xs-x) * self._Hrootinv).T * self.weights)/sum(self.weights)
+            else:
+                w = np.mean(self((xs-x) * self._Hrootinv )) #transposed
             #w = np.mean([self(xd) for xd in ((xs-x) * self._Hrootinv)] ) #transposed
             return w
         else:
@@ -112,37 +118,39 @@ class CustomKernel(object):
 
     def __init__(self, shape, h = 1.0, domain = None, norm = None):
         """
-        shape should be a lambda taking and returning numeric type.
+        shape should be a function taking and returning numeric type.
 
         For sanity it should always return positive or zero but this isn't
-        enforced incase you want to do weird things.  Bear in mind that the
+        enforced in case you want to do weird things. Bear in mind that the
         statistical tests etc. may not be valid for non-positive kernels.
 
         The bandwidth of the kernel is supplied as h.
 
-        You may specify a domain as a list of 2 values [min,max], in which case
-        kernel will be treated as zero outside these values.  This will speed up
+        You may specify a domain as a list of 2 values [min, max], in which case
+        kernel will be treated as zero outside these values. This will speed up
         calculation.
 
         You may also specify the normalisation constant for the supplied Kernel.
         If you do this number will be stored and used as the normalisation
         without calculation.  It is recommended you do this if you know the
         constant, to speed up calculation.  In particular if the shape function
-        provided is already normalised you should provide
-        norm = 1.0
-        or
-        norm = True
+        provided is already normalised you should provide norm = 1.0.
+
+        Warning: I think several calculations assume that the kernel is
+        normalized. No tests for non-normalized kernel.
         """
-        if norm is True:
-            norm = 1.0
-        self._normconst = norm
+        self._normconst = norm   # a value or None, if None, then calculate
         self.domain = domain
+        self.weights = None
         if callable(shape):
             self._shape = shape
         else:
             raise TypeError("shape must be a callable object/function")
         self._h = h
         self._L2Norm = None
+        self._kernel_var = None
+        self._normal_reference_constant = None
+        self._order = None
 
     def geth(self):
         """Getter for kernel bandwidth, h"""
@@ -152,7 +160,7 @@ def seth(self, value):
         self._h = value
     h = property(geth, seth, doc="Kernel Bandwidth")
 
-    def inDomain(self, xs, ys, x):
+    def in_domain(self, xs, ys, x):
         """
         Returns the filtered (xs, ys) based on the Kernel domain centred on x
         """
@@ -167,9 +175,9 @@ def isInDomain(xy):
         if self.domain is None:
             return (xs, ys)
         else:
-            filtered = filter(isInDomain, zip(xs, ys))
+            filtered = lfilter(isInDomain, lzip(xs, ys))
             if len(filtered) > 0:
-                xs, ys = zip(*filtered)
+                xs, ys = lzip(*filtered)
                 return (xs, ys)
             else:
                 return ([], [])
@@ -179,23 +187,90 @@ def density(self, xs, x):
         xs
         """
         xs = np.asarray(xs)
-        n = len(xs) # before inDomain?
-        xs = self.inDomain( xs, xs, x )[0]
+        n = len(xs) # before in_domain?
+        if self.weights is not None:
+            xs, weights = self.in_domain( xs, self.weights, x )
+        else:
+            xs = self.in_domain( xs, xs, x )[0]
+        xs = np.asarray(xs)
+        #print 'len(xs)', len(xs), x
         if xs.ndim == 1:
             xs = xs[:,None]
         if len(xs)>0:
             h = self.h
-            w = 1/h * np.mean(self((xs-x)/h), axis=0)
+            if self.weights is not None:
+                w = 1 / h * np.sum(self((xs-x)/h).T * weights, axis=1)
+            else:
+                w = 1. / (h * n) * np.sum(self((xs-x)/h), axis=0)
             return w
         else:
             return np.nan
 
+    def density_var(self, density, nobs):
+        """approximate pointwise variance for kernel density
+
+        not verified
+
+        Parameters
+        ----------
+        density : array_lie
+            pdf of the kernel density
+        nobs : int
+            number of observations used in the KDE estimation
+
+        Returns
+        -------
+        kde_var : ndarray
+            estimated variance of the density estimate
+
+        Notes
+        -----
+        This uses the asymptotic normal approximation to the distribution of
+        the density estimate.
+        """
+        return np.asarray(density) * self.L2Norm / self.h / nobs
+
+    def density_confint(self, density, nobs, alpha=0.05):
+        """approximate pointwise confidence interval for kernel density
+
+        The confidence interval is centered at the estimated density and
+        ignores the bias of the density estimate.
+
+        not verified
+
+        Parameters
+        ----------
+        density : array_lie
+            pdf of the kernel density
+        nobs : int
+            number of observations used in the KDE estimation
+
+        Returns
+        -------
+        conf_int : ndarray
+            estimated confidence interval of the density estimate, lower bound
+            in first column and upper bound in second column
+
+        Notes
+        -----
+        This uses the asymptotic normal approximation to the distribution of
+        the density estimate. The lower bound can be negative for density
+        values close to zero.
+
+        """
+        from scipy import stats
+        crit = stats.norm.isf(alpha / 2.)
+        density = np.asarray(density)
+        half_width = crit * np.sqrt(self.density_var(density, nobs))
+        conf_int = np.column_stack((density - half_width, density + half_width))
+        return conf_int
+
     def smooth(self, xs, ys, x):
         """Returns the kernel smoothing estimate for point x based on x-values
         xs and y-values ys.
         Not expected to be called by the user.
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs)>0:
             w = np.sum(self((xs-x)/self.h))
@@ -208,7 +283,7 @@ def smooth(self, xs, ys, x):
     def smoothvar(self, xs, ys, x):
         """Returns the kernel smoothing estimate of the variance at point x.
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs) > 0:
             fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
@@ -219,23 +294,27 @@ def smoothvar(self, xs, ys, x):
         else:
             return np.nan
 
-    def smoothconf(self, xs, ys, x):
+    def smoothconf(self, xs, ys, x, alpha=0.05):
         """Returns the kernel smoothing estimate with confidence 1sigma bounds
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs) > 0:
             fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
+            #fittedvals = self.smooth(xs, ys, x) # x or xs in Haerdle
             sqresid = square(
                 subtract(ys, fittedvals)
             )
             w = np.sum(self((xs-x)/self.h))
+            #var = sqresid.sum() / (len(sqresid) - 0)  # nonlocal var ? JP just trying
             v = np.sum([rr*self((xx-x)/self.h) for xx, rr in zip(xs, sqresid)])
             var = v / w
             sd = np.sqrt(var)
             K = self.L2Norm
             yhat = self.smooth(xs, ys, x)
-            err = sd * K / np.sqrt(w * self.h * self.norm_const)
+            from scipy import stats
+            crit = stats.norm.isf(alpha / 2)
+            err = crit * sd * np.sqrt(K) / np.sqrt(w * self.h * self.norm_const)
             return (yhat - err, yhat, yhat + err)
         else:
             return (np.nan, np.nan, np.nan)
@@ -266,6 +345,58 @@ def norm_const(self):
             self._normconst = 1.0/(quadres[0])
         return self._normconst
 
+    @property
+    def kernel_var(self):
+        """Returns the second moment of the kernel"""
+        if self._kernel_var is None:
+            func = lambda x: x**2 * self.norm_const * self._shape(x)
+            if self.domain is None:
+                self._kernel_var = scipy.integrate.quad(func, -inf, inf)[0]
+            else:
+                self._kernel_var = scipy.integrate.quad(func, self.domain[0],
+                                               self.domain[1])[0]
+        return self._kernel_var
+
+    def moments(self, n):
+
+        if n > 2:
+            msg = "Only first and second moment currently implemented"
+            raise NotImplementedError(msg)
+
+        if n == 1:
+            return 0
+
+        if n == 2:
+            return self.kernel_var
+
+    @property
+    def normal_reference_constant(self):
+        """
+        Constant used for silverman normal reference asymtotic bandwidth
+        calculation.
+
+        C  = 2((pi^(1/2)*(nu!)^3 R(k))/(2nu(2nu)!kap_nu(k)^2))^(1/(2nu+1))
+        nu = kernel order
+        kap_nu = nu'th moment of kernel
+        R = kernel roughness (square of L^2 norm)
+
+        Note: L2Norm property returns square of norm.
+        """
+        nu = self._order
+
+        if not nu == 2:
+            msg = "Only implemented for second order kernels"
+            raise NotImplementedError(msg)
+
+        if self._normal_reference_constant is None:
+            C = np.pi**(.5) * factorial(nu)**3 * self.L2Norm
+            C /= (2 * nu * factorial(2 * nu) * self.moments(nu)**2)
+            C = 2*C**(1.0/(2*nu+1))
+            self._normal_reference_constant = C
+
+        return self._normal_reference_constant
+
+
     def weight(self, x):
         """This returns the normalised weight at distance x"""
         return self.norm_const*self._shape(x)
@@ -278,29 +409,41 @@ def __call__(self, x):
         """
         return self._shape(x)
 
+
 class Uniform(CustomKernel):
     def __init__(self, h=1.0):
-        CustomKernel.__init__(self, shape=lambda x: 0.5, h=h,
+        CustomKernel.__init__(self, shape=lambda x: 0.5 * np.ones(x.shape), h=h,
                               domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = 0.5
+        self._kernel_var = 1. / 3
+        self._order = 2
+
 
 class Triangular(CustomKernel):
     def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape=lambda x: 1 - abs(x), h=h,
                               domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = 2.0/3.0
+        self._kernel_var = 1. / 6
+        self._order = 2
+
 
 class Epanechnikov(CustomKernel):
     def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape=lambda x: 0.75*(1 - x*x), h=h,
                               domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = 0.6
+        self._kernel_var = 0.2
+        self._order = 2
+
 
 class Biweight(CustomKernel):
     def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape=lambda x: 0.9375*(1 - x*x)**2, h=h,
                               domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = 5.0/7.0
+        self._kernel_var = 1. / 7
+        self._order = 2
 
     def smooth(self, xs, ys, x):
         """Returns the kernel smoothing estimate for point x based on x-values
@@ -309,7 +452,7 @@ def smooth(self, xs, ys, x):
 
         Special implementation optimised for Biweight.
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs) > 0:
             w = np.sum(square(subtract(1, square(divide(subtract(xs, x),
@@ -324,7 +467,7 @@ def smoothvar(self, xs, ys, x):
         """
         Returns the kernel smoothing estimate of the variance at point x.
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs) > 0:
             fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
@@ -337,10 +480,10 @@ def smoothvar(self, xs, ys, x):
         else:
             return np.nan
 
-    def smoothconf(self, xs, ys, x):
+    def smoothconf_(self, xs, ys, x):
         """Returns the kernel smoothing estimate with confidence 1sigma bounds
         """
-        xs, ys = self.inDomain(xs, ys, x)
+        xs, ys = self.in_domain(xs, ys, x)
 
         if len(xs) > 0:
             fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
@@ -363,6 +506,9 @@ def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape=lambda x: 1.09375*(1 - x*x)**3, h=h,
                               domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = 350.0/429.0
+        self._kernel_var = 1. / 9
+        self._order = 2
+
 
 class Gaussian(CustomKernel):
     """
@@ -374,6 +520,8 @@ def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape = lambda x: 0.3989422804014327 *
                         np.exp(-x**2/2.0), h = h, domain = None, norm = 1.0)
         self._L2Norm = 1.0/(2.0*np.sqrt(np.pi))
+        self._kernel_var = 1.0
+        self._order = 2
 
     def smooth(self, xs, ys, x):
         """Returns the kernel smoothing estimate for point x based on x-values
@@ -398,3 +546,21 @@ def __init__(self, h=1.0):
         CustomKernel.__init__(self, shape=lambda x: 0.78539816339744828 *
                 np.cos(np.pi/2.0 * x), h=h, domain=[-1.0, 1.0], norm = 1.0)
         self._L2Norm = np.pi**2/16.0
+        self._kernel_var = 0.1894305308612978 # = 1 - 8 / np.pi**2
+        self._order = 2
+
+
+class Cosine2(CustomKernel):
+    """
+    Cosine2 Kernel
+
+    K(u) = 1 + cos(2 * pi * u) between -0.5 and 0.5
+
+    Note: this  is the same Cosine kernel that Stata uses
+    """
+    def __init__(self, h=1.0):
+        CustomKernel.__init__(self, shape=lambda x: 1 + np.cos(2.0 * np.pi * x)
+                , h=h, domain=[-0.5, 0.5], norm = 1.0)
+        self._L2Norm = 1.5
+        self._kernel_var = 0.03267274151216444  # = 1/12. - 0.5 / np.pi**2
+        self._order = 2
diff --git a/statsmodels/sandbox/nonparametric/smoothers.py b/statsmodels/sandbox/nonparametric/smoothers.py
index 65ab0b4b3a8..ae30157810c 100644
--- a/statsmodels/sandbox/nonparametric/smoothers.py
+++ b/statsmodels/sandbox/nonparametric/smoothers.py
@@ -7,9 +7,9 @@
 # pylint: disable-msg=W0142
 # pylint: disable-msg=E0611
 # pylint: disable-msg=E1101
-
+from __future__ import print_function
 import numpy as np
-import kernels
+from . import kernels
 #import numbers
 #from scipy.linalg import solveh_banded
 #from scipy.optimize import golden
@@ -116,7 +116,7 @@ def __init__(self, order, x=None):
         self.coef = np.zeros((order+1,), np.float64)
         if x is not None:
             if x.ndim > 1:
-                print 'Warning: 2d x detected in PolySmoother init, shape:', x.shape
+                print('Warning: 2d x detected in PolySmoother init, shape:', x.shape)
                 x=x[0,:] #check orientation
             self.X = np.array([x**i for i in range(order+1)]).T
 
@@ -158,7 +158,7 @@ def predict(self, x=None):
         if x is not None:
             #if x.ndim > 1: x=x[0,:]  #why this this should select column not row
             if x.ndim > 1:
-                print 'Warning: 2d x detected in PolySmoother predict, shape:', x.shape
+                print('Warning: 2d x detected in PolySmoother predict, shape:', x.shape)
                 x=x[:,0]  #TODO: check and clean this up
             X = np.array([(x**i) for i in range(self.order+1)])
         else: X = self.X
@@ -183,7 +183,7 @@ def fit(self, y, x=None, weights=None):
                 raise ValueError("x needed to fit PolySmoother")
         else:
             if x.ndim > 1: 
-                print 'Warning: 2d x detected in PolySmoother predict, shape:', x.shape
+                print('Warning: 2d x detected in PolySmoother predict, shape:', x.shape)
                 #x=x[0,:] #TODO: check orientation, row or col
             self.X = np.array([(x**i) for i in range(self.order+1)]).T
         #print _w.shape
diff --git a/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py b/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py
index 63bdca0302e..254e05e8e4c 100644
--- a/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py
+++ b/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py
@@ -10,7 +10,8 @@
 Author: Josef Perktold
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange, zip
 import time
 
 import numpy as np
@@ -38,7 +39,7 @@
 x2 = np.sin(2*x1)
 x = np.column_stack((x1/x1.max()*2, x2))
 exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1)
-idx = range((order+1)*2)
+idx = lrange((order+1)*2)
 del idx[order+1]
 exog_reduced = exog[:,idx]  #remove duplicate constant
 y_true = exog.sum(1) / 2.
@@ -56,10 +57,10 @@
 
 
 for ss in m.smoothers:
-    print ss.params
+    print(ss.params)
 
 res_ols = OLS(y, exog_reduced).fit()
-print res_ols.params
+print(res_ols.params)
 
 #assert_almost_equal(y_pred, res_ols.fittedvalues, 3)
 
diff --git a/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py b/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py
index 7f22a8b52e3..d1a912a6166 100644
--- a/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py
+++ b/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py
@@ -9,7 +9,8 @@
 
 Author: Josef Perktold
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange, zip
 import time
 
 import numpy as np
@@ -38,7 +39,7 @@
 x2 = np.sin(2*x1)
 x = np.column_stack((x1/x1.max()*1, 1.*x2))
 exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1)
-idx = range((order+1)*2)
+idx = lrange((order+1)*2)
 del idx[order+1]
 exog_reduced = exog[:,idx]  #remove duplicate constant
 y_true = exog.sum(1) #/ 4.
@@ -49,7 +50,7 @@
 example = 3
 
 if example == 2:
-    print "binomial"
+    print("binomial")
     f = family.Binomial()
     mu_true = f.link.inverse(z)
     #b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)])
@@ -59,14 +60,14 @@
     toc = time.time()
     m.fit(b)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
     #for plotting
     yp = f.link.inverse(y)
     p = b
 
 
 if example == 3:
-    print "Poisson"
+    print("Poisson")
     f = family.Poisson()
     #y = y/y.max() * 3
     yp = f.link.inverse(z)
@@ -77,10 +78,10 @@
     toc = time.time()
     m.fit(p)
     tic = time.time()
-    print tic-toc
+    print(tic-toc)
 
 for ss in m.smoothers:
-    print ss.params
+    print(ss.params)
 
 if example > 1:
     import matplotlib.pyplot as plt
diff --git a/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py b/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py
index e965804a0e3..5de93aa1f8d 100644
--- a/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py
+++ b/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py
@@ -4,7 +4,7 @@
 
 @author: josef
 """
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_equal
 
@@ -31,9 +31,9 @@
 y_pred = pmod.predict(x)
 error = y - y_pred
 mse = (error*error).mean()
-print mse
+print(mse)
 res_ols = OLS(y, exog[:,:3]).fit()
-print np.squeeze(pmod.coef) - res_ols.params
+print(np.squeeze(pmod.coef) - res_ols.params)
 
 
 weights = np.ones(nobs)
@@ -45,9 +45,9 @@
 y_predw = pmodw.predict(x)
 error = y - y_predw
 mse = (error*error).mean()
-print mse
+print(mse)
 res_wls = WLS(y, exog[:,:3], weights=weights).fit()
-print np.squeeze(pmodw.coef) - res_wls.params
+print(np.squeeze(pmodw.coef) - res_wls.params)
 
 
 
diff --git a/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py b/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py
index e9a159b5f96..37abc6bc648 100644
--- a/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py
+++ b/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py
@@ -58,6 +58,7 @@ def setUp(self):
 
 
 class TestSemiLinear(MyTest):
+
     def test_basic(self):
         nobs = 300
         np.random.seed(1234)
diff --git a/statsmodels/sandbox/panel/mixed.py b/statsmodels/sandbox/panel/mixed.py
index 5c2fbab7131..1b5590e4cff 100644
--- a/statsmodels/sandbox/panel/mixed.py
+++ b/statsmodels/sandbox/panel/mixed.py
@@ -17,7 +17,7 @@
 example.
 
 """
-
+from __future__ import print_function
 import numpy as np
 import numpy.linalg as L
 
@@ -481,7 +481,7 @@ def fit(self, maxiter=100, ML=False, rtol=1.0e-05, params_rtol=1e-6, params_atol
                 break
         else: #if end of loop is reached without break
             self.termination = 'maxiter'
-            print 'Warning: maximum number of iterations reached'
+            print('Warning: maximum number of iterations reached')
 
         self.iterations = i
 
@@ -520,7 +520,7 @@ def cov_random(self):
     def mean_random(self, idx='lastexog'):
         if idx == 'lastexog':
             meanr = self.params[-self.model.k_exog_re:]
-        elif type(idx) == list:
+        elif isinstance(idx, list):
             if not len(idx) == self.model.k_exog_re:
                 raise ValueError('length of idx different from k_exog_re')
             else:
diff --git a/statsmodels/sandbox/panel/panelmod.py b/statsmodels/sandbox/panel/panelmod.py
index ad98c7d5e95..5a8fb9c1486 100644
--- a/statsmodels/sandbox/panel/panelmod.py
+++ b/statsmodels/sandbox/panel/panelmod.py
@@ -6,19 +6,15 @@
 
 Baltagi, Badi H. `Econometric Analysis of Panel Data.` 4th ed. Wiley, 2008.
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import range, reduce
 from statsmodels.tools.tools import categorical
 from statsmodels.regression.linear_model import GLS, WLS
 import numpy as np
 
 __all__ = ["PanelModel"]
 
-try:
-    from pandas import LongPanel, __version__
-    __version__ >= .1
-except:
-    raise ImportError("While in the sandbox this code depends on the pandas \
-package.  http://code.google.com/p/pandas/")
+from pandas import LongPanel, __version__
 
 
 def group(X):
@@ -34,7 +30,7 @@ def group(X):
     """
     uniq_dict = {}
     group = np.zeros(len(X))
-    for i in xrange(len(X)):
+    for i in range(len(X)):
         if not X[i] in uniq_dict:
             uniq_dict.update({X[i] : len(uniq_dict)})
         group[i] = uniq_dict[X[i]]
@@ -334,11 +330,7 @@ class DynamicPanel(PanelModel):
     pass
 
 if __name__ == "__main__":
-    try:
-        import pandas
-        pandas.version >= .1
-    except:
-        raise ImportError("pandas >= .10 not installed")
+    import pandas
     from pandas import LongPanel
     import statsmodels.api as sm
     import numpy.lib.recfunctions as nprf
@@ -406,18 +398,18 @@ class DynamicPanel(PanelModel):
     # omega is the error variance-covariance matrix for the stacked
     # observations
     omega = np.dot(dummyall, dummyall.T) + sigma* np.eye(nobs)
-    print omega
-    print np.linalg.cholesky(omega)
+    print(omega)
+    print(np.linalg.cholesky(omega))
     ev, evec = np.linalg.eigh(omega)  #eig doesn't work
     omegainv = np.dot(evec, (1/ev * evec).T)
     omegainv2 = np.linalg.inv(omega)
     omegacomp = np.dot(evec, (ev * evec).T)
-    print np.max(np.abs(omegacomp - omega))
+    print(np.max(np.abs(omegacomp - omega)))
     #check
-    #print np.dot(omegainv,omega)
-    print np.max(np.abs(np.dot(omegainv,omega) - np.eye(nobs)))
+    #print(np.dot(omegainv,omega)
+    print(np.max(np.abs(np.dot(omegainv,omega) - np.eye(nobs))))
     omegainvhalf = evec/np.sqrt(ev)  #not sure whether ev shouldn't be column
-    print np.max(np.abs(np.dot(omegainvhalf,omegainvhalf.T) - omegainv))
+    print(np.max(np.abs(np.dot(omegainvhalf,omegainvhalf.T) - omegainv)))
 
     # now we can use omegainvhalf in GLS (instead of the cholesky)
 
@@ -431,9 +423,9 @@ class DynamicPanel(PanelModel):
     sigmas2 = np.array([sigmagr, sigmape, sigma])
     groups2 = np.column_stack((groups, periods))
     omega_, omegainv_, omegainvhalf_ = repanel_cov(groups2, sigmas2)
-    print np.max(np.abs(omega_ - omega))
-    print np.max(np.abs(omegainv_ - omegainv))
-    print np.max(np.abs(omegainvhalf_ - omegainvhalf))
+    print(np.max(np.abs(omega_ - omega)))
+    print(np.max(np.abs(omegainv_ - omegainv)))
+    print(np.max(np.abs(omegainvhalf_ - omegainvhalf)))
 
     # notation Baltagi (3rd) section 9.4.1 (Fixed Effects Model)
     Pgr = reduce(np.dot,[dummygr,
@@ -441,4 +433,4 @@ class DynamicPanel(PanelModel):
     Qgr = np.eye(nobs) - Pgr
     # within group effect: np.dot(Qgr, groups)
     # but this is not memory efficient, compared to groupstats
-    print np.max(np.abs(np.dot(Qgr, groups)))
+    print(np.max(np.abs(np.dot(Qgr, groups))))
diff --git a/statsmodels/sandbox/panel/random_panel.py b/statsmodels/sandbox/panel/random_panel.py
index 83711adabb5..e2101882dbe 100644
--- a/statsmodels/sandbox/panel/random_panel.py
+++ b/statsmodels/sandbox/panel/random_panel.py
@@ -16,7 +16,7 @@
 """
 
 import numpy as np
-import correlation_structures as cs
+from . import correlation_structures as cs
 
 
 class PanelSample(object):
diff --git a/statsmodels/sandbox/panel/sandwich_covariance_generic.py b/statsmodels/sandbox/panel/sandwich_covariance_generic.py
index 527e6e89b63..d3f0f70b3bd 100644
--- a/statsmodels/sandbox/panel/sandwich_covariance_generic.py
+++ b/statsmodels/sandbox/panel/sandwich_covariance_generic.py
@@ -13,7 +13,7 @@
 License: BSD-3
 
 """
-
+from statsmodels.compat.python import range
 import numpy as np
 
 def kernel(d1, d2, r=None, weights=None):
@@ -80,8 +80,8 @@ def aggregate_cov(x, d, r=None, weights=None):
     count = 0 #count non-zero pairs for cross checking, not needed
     res = 0 * np.outer(x[0], x[0])  #get output shape
 
-    for ii in xrange(nobs):
-        for jj in xrange(nobs):
+    for ii in range(nobs):
+        for jj in range(nobs):
             w = kernel(d[ii], d[jj], r=r, weights=weights)
             if w:  #true or non-zero
                 res += w * np.outer(x[0], x[0])
diff --git a/statsmodels/sandbox/pca.py b/statsmodels/sandbox/pca.py
index 57a5962603d..bfae79cb687 100644
--- a/statsmodels/sandbox/pca.py
+++ b/statsmodels/sandbox/pca.py
@@ -1,5 +1,6 @@
 #Copyright (c) 2008 Erik Tollerud (etolleru@uci.edu)
 
+from statsmodels.compat.python import zip
 import numpy as np
 from math import pi
 
@@ -24,12 +25,12 @@ def __init__(self,data,names=None):
         """
         p X N matrix input
         """
-        from warnings import warn
         A = np.array(data).T
         n,p = A.shape
         self.n,self.p = n,p
         if p > n:
-            warn('p > n - intentional?')
+            from warnings import warn
+            warn('p > n - intentional?', RuntimeWarning)
         self.A = A
         self._origA=A.copy()
 
diff --git a/statsmodels/sandbox/regression/anova_nistcertified.py b/statsmodels/sandbox/regression/anova_nistcertified.py
index 2636af2d601..8c617d16075 100644
--- a/statsmodels/sandbox/regression/anova_nistcertified.py
+++ b/statsmodels/sandbox/regression/anova_nistcertified.py
@@ -2,6 +2,8 @@
 
 compares my implementations, stats.f_oneway and anova using statsmodels.OLS
 '''
+from __future__ import print_function
+from statsmodels.compat.python import lmap
 import os
 import numpy as np
 from scipy import stats
@@ -13,8 +15,8 @@
 
 
 ##path = __file__
-##print locals().keys()
-###print path
+##print(locals().keys()
+###print(path
 
 
 def getnist(filename):
@@ -35,7 +37,7 @@ def getnist(filename):
     return y, x, np.array([f, prob, R2, resstd]), certified, caty
 
 
-from try_catdata import groupsstats_dummy, groupstatsbin
+from .try_catdata import groupsstats_dummy, groupstatsbin
 
 
 def anova_oneway(y, x, seq=0):
@@ -60,15 +62,15 @@ def anova_oneway(y, x, seq=0):
     prob = stats.f.sf(f,dfbn,dfwn)
     R2 = (ssbn/(sswn+ssbn))  #R-squared
     resstd = np.sqrt(msw) #residual standard deviation
-    #print f, prob
+    #print(f, prob
     def _fix2scalar(z): # return number
         if np.shape(z) == (1, 1): return z[0,0]
         else: return z
-    f, prob, R2, resstd = map(_fix2scalar, (f, prob, R2, resstd))
+    f, prob, R2, resstd = lmap(_fix2scalar, (f, prob, R2, resstd))
     return f, prob, R2, resstd
 
 import statsmodels.api as sm
-from try_ols_anova import data2dummy
+from .try_ols_anova import data2dummy
 
 def anova_ols(y, x):
     X = sm.add_constant(data2dummy(x), prepend=False)
@@ -78,28 +80,26 @@ def anova_ols(y, x):
 
 
 if __name__ == '__main__':
-    print '\n using new ANOVA anova_oneway'
-    print 'f, prob, R2, resstd'
+    print('\n using new ANOVA anova_oneway')
+    print('f, prob, R2, resstd')
     for fn in filenameli:
-        print fn
+        print(fn)
         y, x, cert, certified, caty = getnist(fn)
         res = anova_oneway(y, x)
-        print np.array(res) - cert
+        print(np.array(res) - cert)
 
-    print '\n using stats ANOVA f_oneway'
+    print('\n using stats ANOVA f_oneway')
     for fn in filenameli:
-        print fn
+        print(fn)
         y, x, cert, certified, caty = getnist(fn)
         xlist = [x[y==ii] for ii in caty]
         res = stats.f_oneway(*xlist)
-        print np.array(res) - cert[:2]
+        print(np.array(res) - cert[:2])
 
-    print '\n using statsmodels.OLS'
-    print 'f, prob, R2, resstd'
+    print('\n using statsmodels.OLS')
+    print('f, prob, R2, resstd')
     for fn in filenameli[:]:
-        print fn
+        print(fn)
         y, x, cert, certified, caty = getnist(fn)
         res = anova_ols(x, y)
-        print np.array(res) - cert
-
-
+        print(np.array(res) - cert)
diff --git a/statsmodels/sandbox/regression/ar_panel.py b/statsmodels/sandbox/regression/ar_panel.py
index dcd6f4fc9b0..702d03fc3f1 100644
--- a/statsmodels/sandbox/regression/ar_panel.py
+++ b/statsmodels/sandbox/regression/ar_panel.py
@@ -21,7 +21,7 @@
 '''
 
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import optimize
 
@@ -40,7 +40,7 @@ def __init__(self, endog, exog=None, groups=None):
         self.groups_valid = ~self.groups_start
 
     def ar1filter(self, xy, alpha):
-        #print alpha,
+        #print(alpha,)
         return (xy[1:] - alpha * xy[:-1])[self.groups_valid]
 
     def fit_conditional(self, alpha):
@@ -90,13 +90,13 @@ def fit(self):
     y = data + np.dot(group_dummy, np.array([10, 20, 30]))
     y0 = data0 + np.dot(group_dummy, np.array([10, 20, 30]))
 
-    print groups_valid
-    print np.diff(y)[groups_valid]
+    print(groups_valid)
+    print(np.diff(y)[groups_valid])
 
     alpha = 1  #test with 1
-    print (y0[1:] - alpha*y0[:-1])[groups_valid]
+    print((y0[1:] - alpha*y0[:-1])[groups_valid])
     alpha = 0.2  #test with 1
-    print (y0[1:] - alpha*y0[:-1] + 0.001)[groups_valid]
+    print((y0[1:] - alpha*y0[:-1] + 0.001)[groups_valid])
     #this is now AR(1) for each group separately
 
 
@@ -108,7 +108,7 @@ def fit(self):
     exog = group_dummy
     mod = PanelAR1(y, exog, groups=groups)
     #mod = PanelAR1(data, exog, groups=groups) #data doesn't contain different means
-    #print mod.ar1filter(mod.endog, 1)
+    #print(mod.ar1filter(mod.endog, 1))
     resa, reso = mod.fit()
-    print resa[0], reso.params
+    print(resa[0], reso.params)
 
diff --git a/statsmodels/sandbox/regression/example_kernridge.py b/statsmodels/sandbox/regression/example_kernridge.py
index 5dc67d37999..ab85f2a8d66 100644
--- a/statsmodels/sandbox/regression/example_kernridge.py
+++ b/statsmodels/sandbox/regression/example_kernridge.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from kernridgeregress_class import GaussProcess, kernel_euclid
+from .kernridgeregress_class import GaussProcess, kernel_euclid
 
 
 m,k = 50,4
diff --git a/statsmodels/sandbox/regression/gmm.py b/statsmodels/sandbox/regression/gmm.py
index 389aacd76e0..69f9ed11138 100644
--- a/statsmodels/sandbox/regression/gmm.py
+++ b/statsmodels/sandbox/regression/gmm.py
@@ -48,15 +48,20 @@
 '''
 
 
-
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange
 import numpy as np
 from scipy import optimize, stats
 from statsmodels.tools.numdiff import approx_fprime, approx_hess
-from statsmodels.base.model import LikelihoodModel, LikelihoodModelResults
-from statsmodels.regression.linear_model import RegressionResults, OLS
-import statsmodels.tools.tools as tools
+from statsmodels.base.model import (Model,
+                                    LikelihoodModel, LikelihoodModelResults)
+from statsmodels.regression.linear_model import (OLS, RegressionResults,
+                                                 RegressionResultsWrapper)
+import statsmodels.stats.sandwich_covariance as smcov
+from statsmodels.tools.decorators import (resettable_cache, cache_readonly)
+from statsmodels.compat.numpy import np_matrix_rank
 
+DEBUG = 0
 
 def maxabs(x):
     '''just a shortcut to np.abs(x).max()
@@ -66,7 +71,7 @@ def maxabs(x):
 
 class IV2SLS(LikelihoodModel):
     '''
-    class for instrumental variables estimation using Two-Stage Least-Squares
+    Class for instrumental variables estimation using Two-Stage Least-Squares
 
 
     Parameters
@@ -85,6 +90,10 @@ class for instrumental variables estimation using Two-Stage Least-Squares
     in exog are not supposed to be instrumented out, then these variables
     need also to be included in the instrument array.
 
+    Degrees of freedom in the calculation of the standard errors uses
+    `df_resid = (nobs - k_vars)`.
+    (This corresponds to the `small` option in Stata's ivreg2.)
+
 
     '''
 
@@ -94,7 +103,9 @@ def __init__(self, endog, exog, instrument=None):
         # where is this supposed to be handled
         #Note: Greene p.77/78 dof correction is not necessary (because only
         #       asy results), but most packages do it anyway
-        self.df_resid = exog.shape[0] - exog.shape[1] + 1
+        self.df_resid = self.exog.shape[0] - self.exog.shape[1]
+        #self.df_model = float(self.rank - self.k_constant)
+        self.df_model = float(self.exog.shape[1] - self.k_constant)
 
     def initialize(self):
         self.wendog = self.endog
@@ -123,6 +134,7 @@ def fit(self):
         #Greene 5th edt., p.78 section 5.4
         #move this maybe
         y,x,z = self.endog, self.exog, self.instrument
+        # TODO: this uses "textbook" calculation, improve linalg
         ztz = np.dot(z.T, z)
         ztx = np.dot(z.T, x)
         self.xhatparams = xhatparams = np.linalg.solve(ztz, ztx)
@@ -136,10 +148,15 @@ def fit(self):
         Ftxinv = np.linalg.inv(Ftx)
         self.normalized_cov_params = np.dot(Ftxinv.T, np.dot(FtF, Ftxinv))
 
-        lfit = RegressionResults(self, params,
+        lfit = IVRegressionResults(self, params,
                        normalized_cov_params=self.normalized_cov_params)
-        self._results = lfit
-        return lfit
+
+        lfit.exog_hat_params = xhatparams
+        lfit.exog_hat = xhat # TODO: do we want to store this, might be large
+        self._results = lfit  # TODO : remove this
+        self._results_ols2nd = OLS(y, xhat).fit()
+
+        return RegressionResultsWrapper(lfit)
 
     #copied from GLS, because I subclass currently LikelihoodModel and not GLS
     def predict(self, params, exog=None):
@@ -167,12 +184,42 @@ def predict(self, params, exog=None):
         #JP: this doesn't look correct for GLMAR
         #SS: it needs its own predict method
         if self._results is None and params is None:
-            raise ValueError, "If the model has not been fit, then you must specify the params argument."
+            raise ValueError("If the model has not been fit, then you must specify the params argument.")
         if self._results is not None:
             return np.dot(exog, self._results.params)
         else:
             return np.dot(exog, params)
 
+
+
+class IVRegressionResults(RegressionResults):
+    """
+    Results class for for an OLS model.
+
+    Most of the methods and attributes are inherited from RegressionResults.
+    The special methods that are only available for OLS are:
+
+    - get_influence
+    - outlier_test
+    - el_test
+    - conf_int_el
+
+    See Also
+    --------
+    RegressionResults
+
+    """
+
+    @cache_readonly
+    def fvalue(self):
+        k_vars = len(self.params)
+        restriction = np.eye(k_vars)
+        idx_noconstant = lrange(k_vars)
+        del idx_noconstant[self.model.data.const_idx]
+        fval = self.f_test(restriction[idx_noconstant]).fvalue # without constant
+        return fval
+
+
     def spec_hausman(self, dof=None):
         '''Hausman's specification test
 
@@ -184,18 +231,21 @@ def spec_hausman(self, dof=None):
         '''
         #use normalized cov_params for OLS
 
+        endog, exog = self.model.endog, self.model.exog
         resols = OLS(endog, exog).fit()
         normalized_cov_params_ols = resols.model.normalized_cov_params
-        se2 = resols.mse_resid
+        # Stata `ivendog` doesn't use df correction for se
+        #se2 = resols.mse_resid #* resols.df_resid * 1. / len(endog)
+        se2 = resols.ssr / len(endog)
 
-        params_diff = self._results.params - resols.params
+        params_diff = self.params - resols.params
 
-        cov_diff = np.linalg.pinv(self.xhatprod) - normalized_cov_params_ols
+        cov_diff = np.linalg.pinv(self.model.xhatprod) - normalized_cov_params_ols
         #TODO: the following is very inefficient, solves problem (svd) twice
         #use linalg.lstsq or svd directly
         #cov_diff will very often be in-definite (singular)
         if not dof:
-            dof = tools.rank(cov_diff)
+            dof = np_matrix_rank(cov_diff)
         cov_diffpinv = np.linalg.pinv(cov_diff)
         H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff))/se2
         pval = stats.chi2.sf(H, dof)
@@ -203,9 +253,177 @@ def spec_hausman(self, dof=None):
         return H, pval, dof
 
 
+# copied from regression results with small changes, no llf
+    def summary(self, yname=None, xname=None, title=None, alpha=.05):
+        """Summarize the Regression Results
+
+        Parameters
+        -----------
+        yname : string, optional
+            Default is `y`
+        xname : list of strings, optional
+            Default is `var_##` for ## in p the number of regressors
+        title : string, optional
+            Title for the top table. If not None, then this replaces the
+            default title
+        alpha : float
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary
+            results
+
+        """
+
+        #TODO: import where we need it (for now), add as cached attributes
+        from statsmodels.stats.stattools import (jarque_bera,
+                omni_normtest, durbin_watson)
+        jb, jbpv, skew, kurtosis = jarque_bera(self.wresid)
+        omni, omnipv = omni_normtest(self.wresid)
+
+        #TODO: reuse condno from somewhere else ?
+        #condno = np.linalg.cond(np.dot(self.wexog.T, self.wexog))
+        wexog = self.model.wexog
+        eigvals = np.linalg.linalg.eigvalsh(np.dot(wexog.T, wexog))
+        eigvals = np.sort(eigvals) #in increasing order
+        condno = np.sqrt(eigvals[-1]/eigvals[0])
+
+        # TODO: check what is valid.
+        # box-pierce, breush-pagan, durbin's h are not with endogenous on rhs
+        # use Cumby Huizinga 1992 instead
+        self.diagn = dict(jb=jb, jbpv=jbpv, skew=skew, kurtosis=kurtosis,
+                          omni=omni, omnipv=omnipv, condno=condno,
+                          mineigval=eigvals[0])
+
+        #TODO not used yet
+        #diagn_left_header = ['Models stats']
+        #diagn_right_header = ['Residual stats']
+
+        #TODO: requiring list/iterable is a bit annoying
+        #need more control over formatting
+        #TODO: default don't work if it's not identically spelled
+
+        top_left = [('Dep. Variable:', None),
+                    ('Model:', None),
+                    ('Method:', ['Two Stage']),
+                    ('', ['Least Squares']),
+                    ('Date:', None),
+                    ('Time:', None),
+                    ('No. Observations:', None),
+                    ('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
+                    ('Df Model:', None), #[self.df_model])
+                    ]
+
+        top_right = [('R-squared:', ["%#8.3f" % self.rsquared]),
+                     ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
+                     ('F-statistic:', ["%#8.4g" % self.fvalue] ),
+                     ('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]),
+                     #('Log-Likelihood:', None), #["%#6.4g" % self.llf]),
+                     #('AIC:', ["%#8.4g" % self.aic]),
+                     #('BIC:', ["%#8.4g" % self.bic])
+                     ]
+
+        diagn_left = [('Omnibus:', ["%#6.3f" % omni]),
+                      ('Prob(Omnibus):', ["%#6.3f" % omnipv]),
+                      ('Skew:', ["%#6.3f" % skew]),
+                      ('Kurtosis:', ["%#6.3f" % kurtosis])
+                      ]
+
+        diagn_right = [('Durbin-Watson:', ["%#8.3f" % durbin_watson(self.wresid)]),
+                       ('Jarque-Bera (JB):', ["%#8.3f" % jb]),
+                       ('Prob(JB):', ["%#8.3g" % jbpv]),
+                       ('Cond. No.', ["%#8.3g" % condno])
+                       ]
+
+
+        if title is None:
+            title = self.model.__class__.__name__ + ' ' + "Regression Results"
+
+        #create summary table instance
+        from statsmodels.iolib.summary import Summary
+        smry = Summary()
+        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
+                          yname=yname, xname=xname, title=title)
+        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
+                             use_t=True)
+
+        smry.add_table_2cols(self, gleft=diagn_left, gright=diagn_right,
+                          yname=yname, xname=xname,
+                          title="")
+
+
+
+        return smry
+
+
+
+
 ############# classes for Generalized Method of Moments GMM
 
-class GMM(object):
+_gmm_options = '''\
+
+Options for GMM
+---------------
+
+Type of GMM
+~~~~~~~~~~~
+
+ - one-step
+ - iterated
+ - CUE : not tested yet
+
+weight matrix
+~~~~~~~~~~~~~
+
+ - `weights_method` : string, defines method for robust
+   Options here are similar to :mod:`statsmodels.stats.robust_covariance`
+   default is heteroscedasticity consistent, HC0
+
+   currently available methods are
+
+   - `cov` : HC0, optionally with degrees of freedom correction
+   - `hac` :
+   - `iid` : untested, only for Z*u case, IV cases with u as error indep of Z
+   - `ac` : not available yet
+   - `cluster` : not connected yet
+   - others from robust_covariance
+
+other arguments:
+
+ - `wargs` : tuple or dict, required arguments for weights_method
+
+   - `centered` : bool,
+     indicates whether moments are centered for the calculation of the weights
+     and covariance matrix, applies to all weight_methods
+   - `ddof` : int
+     degrees of freedom correction, applies currently only to `cov`
+   - maxlag : int
+     number of lags to include in HAC calculation , applies only to `hac`
+   - others not yet, e.g. groups for cluster robust
+
+covariance matrix
+~~~~~~~~~~~~~~~~~
+
+The same options as for weight matrix also apply to the calculation of the
+estimate of the covariance matrix of the parameter estimates.
+The additional option is
+
+ - `has_optimal_weights`: If true, then the calculation of the covariance
+   matrix assumes that we have optimal GMM with :math:`W = S^{-1}`.
+   Default is True.
+   TODO: do we want to have a different default after `onestep`?
+
+
+'''
+
+class GMM(Model):
     '''
     Class for estimation by Generalized Method of Moments
 
@@ -253,34 +471,134 @@ class GMM(object):
     are still missing in several methods.
 
 
+    TODO:
+    currently onestep (maxiter=0) still produces an updated estimate of bse
+    and cov_params.
 
     '''
 
-    def __init__(self, endog, exog, instrument, nmoms=None, **kwds):
+    results_class = 'GMMResults'
+
+    def __init__(self, endog, exog, instrument, k_moms=None, k_params=None,
+                 missing='none', **kwds):
         '''
         maybe drop and use mixin instead
 
-        GMM doesn't really care about the data, just the moment conditions
+        TODO: GMM doesn't really care about the data, just the moment conditions
         '''
-        self.endog = endog
-        self.exog = exog
-        self.instrument = instrument
-        self.nmoms = nmoms or instrument.shape[1]
-        self.results = GMMResults()
+        instrument = self._check_inputs(instrument, endog) # attaches if needed
+        super(GMM, self).__init__(endog, exog, missing=missing,
+                instrument=instrument)
+#         self.endog = endog
+#         self.exog = exog
+#         self.instrument = instrument
+        self.nobs = endog.shape[0]
+        if k_moms is not None:
+            self.nmoms = k_moms
+        elif instrument is not None:
+            self.nmoms = instrument.shape[1]
+        else:
+            self.nmoms = np.nan
+
+        if k_params is not None:
+            self.k_params = k_params
+        elif instrument is not None:
+            self.k_params = exog.shape[1]
+        else:
+            self.k_params = np.nan
+
         self.__dict__.update(kwds)
         self.epsilon_iter = 1e-6
 
-    def fit(self, start=None):
+    def _check_inputs(self, instrument, endog):
+        if instrument is not None:
+            offset = np.asarray(instrument)
+            if offset.shape[0] != endog.shape[0]:
+                raise ValueError("instrument is not the same length as endog")
+        return instrument
+
+    def _fix_param_names(self, params, param_names=None):
+        # TODO: this is a temporary fix, need
+        xnames = self.data.xnames
+
+        if not param_names is None:
+            if len(params) == len(param_names):
+                self.data.xnames = param_names
+            else:
+                raise ValueError('param_names has the wrong length')
+
+        else:
+            if len(params) < len(xnames):
+                # cut in front for poisson multiplicative
+                self.data.xnames = xnames[-len(params):]
+            elif len(params) > len(xnames):
+                # cut at the end
+                self.data.xnames = xnames[:len(params)]
+
+    def fit(self, start_params=None, maxiter=10, inv_weights=None,
+                  weights_method='cov', wargs=(),
+                  has_optimal_weights=True,
+                  optim_method='bfgs', optim_args=None):
         '''
-        Estimate the parameters using default settings.
+        Estimate parameters using GMM and return GMMResults
 
-        For estimation with more options use fititer method.
+        TODO: weight and covariance arguments still need to be made consistent
+        with similar options in other models,
+        see RegressionResult.get_robustcov_results
 
         Parameters
         ----------
-        start : array (optional)
+        start_params : array (optional)
             starting value for parameters ub minimization. If None then
-            fitstart method is called for the starting values
+            fitstart method is called for the starting values.
+        maxiter : int or 'cue'
+            Number of iterations in iterated GMM. The onestep estimate can be
+            obtained with maxiter=0 or 1. If maxiter is large, then the
+            iteration will stop either at maxiter or on convergence of the
+            parameters (TODO: no options for convergence criteria yet.)
+            If `maxiter == 'cue'`, the the continuously updated GMM is
+            calculated which updates the weight matrix during the minimization
+            of the GMM objective function. The CUE estimation uses the onestep
+            parameters as starting values.
+        inv_weights : None or ndarray
+            inverse of the starting weighting matrix. If inv_weights are not
+            given then the method `start_weights` is used which depends on
+            the subclass, for IV subclasses `inv_weights = z'z` where `z` are
+            the instruments, otherwise an identity matrix is used.
+        weights_method : string, defines method for robust
+            Options here are similar to :mod:`statsmodels.stats.robust_covariance`
+            default is heteroscedasticity consistent, HC0
+
+            currently available methods are
+
+            - `cov` : HC0, optionally with degrees of freedom correction
+            - `hac` :
+            - `iid` : untested, only for Z*u case, IV cases with u as error indep of Z
+            - `ac` : not available yet
+            - `cluster` : not connected yet
+            - others from robust_covariance
+
+        wargs` : tuple or dict,
+            required and optional arguments for weights_method
+
+            - `centered` : bool,
+              indicates whether moments are centered for the calculation of the weights
+              and covariance matrix, applies to all weight_methods
+            - `ddof` : int
+              degrees of freedom correction, applies currently only to `cov`
+            - `maxlag` : int
+              number of lags to include in HAC calculation , applies only to `hac`
+            - others not yet, e.g. groups for cluster robust
+
+        has_optimal_weights: If true, then the calculation of the covariance
+              matrix assumes that we have optimal GMM with :math:`W = S^{-1}`.
+              Default is True.
+              TODO: do we want to have a different default after `onestep`?
+        optim_method : string, default is 'bfgs'
+            numerical optimization method. Currently not all optimizers that
+            are available in LikelihoodModels are connected.
+        optim_args : dict
+            keyword arguments for the numerical optimizer.
 
         Returns
         -------
@@ -289,28 +607,88 @@ def fit(self, start=None):
 
         Notes
         -----
-        This function attaches the estimated parameters, params, the
-        weighting matrix of the final iteration, weights, and the value
-        of the GMM objective function, jval to results. The results are
-        attached to this instance and also returned.
 
-        fititer is called with maxiter=10
+        Warning: One-step estimation, `maxiter` either 0 or 1, still has
+        problems (at least compared to Stata's gmm).
+        By default it uses a heteroscedasticity robust covariance matrix, but
+        uses the assumption that the weight matrix is optimal.
+        See options for cov_params in the results instance.
 
+        The same options as for weight matrix also apply to the calculation of
+        the estimate of the covariance matrix of the parameter estimates.
 
         '''
+        # TODO: add check for correct wargs keys
+        #       currently a misspelled key is not detected,
+        #       because I'm still adding options
+
+        # TODO: check repeated calls to fit with different options
+        #       arguments are dictionaries, i.e. mutable
+        #       unit test if anything  is stale or spilled over.
+
         #bug: where does start come from ???
+        start = start_params  # alias for renaming
         if start is None:
             start = self.fitstart() #TODO: temporary hack
-        params, weights = self.fititer(start, maxiter=10, start_weights=None,
-                                        weights_method='cov', wargs=())
-        self.results.params = params
-        self.results.weights = weights
-        self.results.jval = self.gmmobjective(params, weights)
 
-        return self.results
+        if inv_weights is None:
+            inv_weights
 
+        if optim_args is None:
+            optim_args = {}
+        if not 'disp' in optim_args:
+            optim_args['disp'] = 1
 
-    def fitgmm(self, start, weights=None):
+        if maxiter == 0 or maxiter == 'cue':
+            if inv_weights is not None:
+                weights = np.linalg.pinv(inv_weights)
+            else:
+                # let start_weights handle the inv=False for maxiter=0
+                weights = self.start_weights(inv=False)
+
+            params = self.fitgmm(start, weights=weights,
+                                 optim_method=optim_method, optim_args=optim_args)
+            weights_ = weights  # temporary alias used in jval
+        else:
+            params, weights = self.fititer(start,
+                                           maxiter=maxiter,
+                                           start_invweights=inv_weights,
+                                           weights_method=weights_method,
+                                           wargs=wargs,
+                                           optim_method=optim_method,
+                                           optim_args=optim_args)
+            # TODO weights returned by fititer is inv_weights - not true anymore
+            # weights_ currently not necessary and used anymore
+            weights_ = np.linalg.pinv(weights)
+
+        if maxiter == 'cue':
+            #we have params from maxiter= 0 as starting value
+            # TODO: need to give weights options to gmmobjective_cu
+            params = self.fitgmm_cu(params,
+                                     optim_method=optim_method,
+                                     optim_args=optim_args)
+            # weights is stored as attribute
+            weights = self._weights_cu
+
+        #TODO: use Bunch instead ?
+        options_other = {'weights_method':weights_method,
+                         'has_optimal_weights':has_optimal_weights,
+                         'optim_method':optim_method}
+
+        # check that we have the right number of xnames
+        self._fix_param_names(params, param_names=None)
+        results = results_class_dict[self.results_class](
+                                        model = self,
+                                        params = params,
+                                        weights = weights,
+                                        wargs = wargs,
+                                        options_other = options_other,
+                                        optim_args = optim_args)
+
+        self.results = results # FIXME: remove, still keeping it temporarily
+        return results
+
+    def fitgmm(self, start, weights=None, optim_method='bfgs', optim_args=None):
         '''estimate parameters using GMM
 
         Parameters
@@ -337,13 +715,84 @@ def fitgmm(self, start, weights=None):
 ##        if not fixed is None:  #fixed not defined in this version
 ##            raise NotImplementedError
 
-        #tmp = momcond(start, *args)  # forgott to delete this
-        #nmoms = tmp.shape[-1]
+        # TODO: should start_weights only be in `fit`
         if weights is None:
-            weights = np.eye(self.nmoms)
+            weights = self.start_weights(inv=False)
+
+        if optim_args is None:
+            optim_args = {}
+
+        if optim_method == 'nm':
+            optimizer = optimize.fmin
+        elif optim_method == 'bfgs':
+            optimizer = optimize.fmin_bfgs
+            # TODO: add score
+            optim_args['fprime'] = self.score #lambda params: self.score(params, weights)
+        elif optim_method == 'ncg':
+            optimizer = optimize.fmin_ncg
+            optim_args['fprime'] = self.score
+        elif optim_method == 'cg':
+            optimizer = optimize.fmin_cg
+            optim_args['fprime'] = self.score
+        elif optim_method == 'fmin_l_bfgs_b':
+            optimizer = optimize.fmin_l_bfgs_b
+            optim_args['fprime'] = self.score
+        elif optim_method == 'powell':
+            optimizer = optimize.fmin_powell
+        elif optim_method == 'slsqp':
+            optimizer = optimize.fmin_slsqp
+        else:
+            raise ValueError('optimizer method not available')
+
+        if DEBUG:
+            print(np.linalg.det(weights))
+
+        #TODO: add other optimization options and results
+        return optimizer(self.gmmobjective, start, args=(weights,),
+                         **optim_args)
+
+
+    def fitgmm_cu(self, start, optim_method='bfgs', optim_args=None):
+        '''estimate parameters using continuously updating GMM
+
+        Parameters
+        ----------
+        start : array_like
+            starting values for minimization
+
+        Returns
+        -------
+        paramest : array
+            estimated parameters
+
+        Notes
+        -----
+        todo: add fixed parameter option, not here ???
+
+        uses scipy.optimize.fmin
+
+        '''
+##        if not fixed is None:  #fixed not defined in this version
+##            raise NotImplementedError
+
+        if optim_args is None:
+            optim_args = {}
+
+        if optim_method == 'nm':
+            optimizer = optimize.fmin
+        elif optim_method == 'bfgs':
+            optimizer = optimize.fmin_bfgs
+            optim_args['fprime'] = self.score_cu
+        elif optim_method == 'ncg':
+            optimizer = optimize.fmin_ncg
+        else:
+            raise ValueError('optimizer method not available')
 
         #TODO: add other optimization options and results
-        return optimize.fmin(self.gmmobjective, start, (weights,), disp=0)
+        return optimizer(self.gmmobjective_cu, start, args=(), **optim_args)
+
+    def start_weights(self, inv=True):
+        return np.eye(self.nmoms)
 
     def gmmobjective(self, params, weights):
         '''
@@ -361,13 +810,40 @@ def gmmobjective(self, params, weights):
         jval : float
             value of objective function
 
+        '''
+        moms = self.momcond_mean(params)
+        return np.dot(np.dot(moms, weights), moms)
+        #moms = self.momcond(params)
+        #return np.dot(np.dot(moms.mean(0),weights), moms.mean(0))
+
+
+    def gmmobjective_cu(self, params, weights_method='cov',
+                        wargs=()):
+        '''
+        objective function for continuously updating  GMM minimization
+
+        Parameters
+        ----------
+        params : array
+            parameter values at which objective is evaluated
+
+        Returns
+        -------
+        jval : float
+            value of objective function
+
         '''
         moms = self.momcond(params)
-        return np.dot(np.dot(moms.mean(0),weights), moms.mean(0))
+        inv_weights = self.calc_weightmatrix(moms, weights_method=weights_method,
+                                             wargs=wargs)
+        weights = np.linalg.pinv(inv_weights)
+        self._weights_cu = weights  # store if we need it later
+        return np.dot(np.dot(moms.mean(0), weights), moms.mean(0))
 
 
-    def fititer(self, start, maxiter=2, start_weights=None,
-                    weights_method='cov', wargs=()):
+    def fititer(self, start, maxiter=2, start_invweights=None,
+                    weights_method='cov', wargs=(), optim_method='bfgs',
+                    optim_args=None):
         '''iterative estimation with updating of optimal weighting matrix
 
         stopping criteria are maxiter or change in parameter estimate less
@@ -401,47 +877,58 @@ def fititer(self, start, maxiter=2, start_weights=None,
 
 
         '''
+        self.history = []
         momcond = self.momcond
 
-        if start_weights is None:
-            w = np.eye(self.nmoms)
+        if start_invweights is None:
+            w = self.start_weights(inv=True)
         else:
-            w = start_weights
+            w = start_invweights
 
         #call fitgmm function
         #args = (self.endog, self.exog, self.instrument)
         #args is not used in the method version
+        winv_new = w
         for it in range(maxiter):
-            winv = np.linalg.inv(w)
+            winv = winv_new
+            w = np.linalg.pinv(winv)
             #this is still calling function not method
 ##            resgmm = fitgmm(momcond, (), start, weights=winv, fixed=None,
 ##                            weightsoptimal=False)
-            resgmm = self.fitgmm(start, weights=winv)
+            resgmm = self.fitgmm(start, weights=w, optim_method=optim_method,
+                                 optim_args=optim_args)
 
             moms = momcond(resgmm)
-            w = self.calc_weightmatrix(moms, method='momcov', wargs=())
+            # the following is S = cov_moments
+            winv_new = self.calc_weightmatrix(moms,
+                                              weights_method=weights_method,
+                                              wargs=wargs, params=resgmm)
 
             if it > 2 and maxabs(resgmm - start) < self.epsilon_iter:
                 #check rule for early stopping
+                # TODO: set has_optimal_weights = True
                 break
+
             start = resgmm
         return resgmm, w
 
 
-    def calc_weightmatrix(self, moms, method='momcov', wargs=()):
-        '''calculate omega or the weighting matrix
+    def calc_weightmatrix(self, moms, weights_method='cov', wargs=(),
+                          params=None):
+        '''
+        calculate omega or the weighting matrix
 
         Parameters
         ----------
-
         moms : array, (nobs, nmoms)
             moment conditions for all observations evaluated at a parameter
             value
-        method : 'momcov', anything else
-            If method='momcov' is cov then the matrix is calculated as simple
-            covariance of the moment conditions. For anything else, a
-            constant cutoff window of length 5 is used.
-        wargs : tuple
+        weights_method : string 'cov'
+            If method='cov' is cov then the matrix is calculated as simple
+            covariance of the moment conditions.
+            see fit method for available aoptions for the weight and covariance
+            matrix
+        wargs : tuple or dict
             parameters that are required by some kernel methods to
             estimate the long-run covariance. Not used yet.
 
@@ -468,20 +955,90 @@ def calc_weightmatrix(self, moms, method='momcov', wargs=()):
         Hansen, Bruce
 
         '''
-        nobs = moms.shape[0]
-        if method == 'momcov':
-            w = np.cov(moms, rowvar=0)
-        elif method == 'fakekernel':
+        nobs, k_moms = moms.shape
+        # TODO: wargs are tuple or dict ?
+        if DEBUG:
+            print(' momcov wargs', wargs)
+
+        centered = not ('centered' in wargs and not wargs['centered'])
+        if not centered:
+            # caller doesn't want centered moment conditions
+            moms_ = moms
+        else:
+            moms_ = moms - moms.mean()
+
+        # TODO: store this outside to avoid doing this inside optimization loop
+        # TODO: subclasses need to be able to add weights_methods, and remove
+        #       IVGMM can have homoscedastic (OLS),
+        #       some options won't make sense in some cases
+        #       possible add all here and allow subclasses to define a list
+        # TODO: should other weights_methods also have `ddof`
+        if weights_method == 'cov':
+            w = np.dot(moms_.T, moms_)
+            if 'ddof' in wargs:
+                # caller requests degrees of freedom correction
+                if wargs['ddof'] == 'k_params':
+                    w /= (nobs - self.k_params)
+                else:
+                    if DEBUG:
+                        print(' momcov ddof', wargs['ddof'])
+                    w /= (nobs - wargs['ddof'])
+            else:
+                # default: divide by nobs
+                w /= nobs
+
+        elif weights_method == 'flatkernel':
             #uniform cut-off window
-            moms_centered = moms - moms.mean()
-            maxlag = 5
-            h = np.ones(maxlag)
-            w = np.dot(moms.T, moms)/nobs
+            # This was a trial version, can use HAC with flatkernel
+            if not 'maxlag' in wargs:
+                raise ValueError('flatkernel requires maxlag')
+
+            maxlag = wargs['maxlag']
+            h = np.ones(maxlag + 1)
+            w = np.dot(moms_.T, moms_)/nobs
             for i in range(1,maxlag+1):
-                w += (h * np.dot(moms_centered[i:].T, moms_centered[:-i]) /
-                                                                  (nobs-i))
+                w += (h[i] * np.dot(moms_[i:].T, moms_[:-i]) / (nobs-i))
+
+        elif weights_method == 'hac':
+            maxlag = wargs['maxlag']
+            if 'kernel' in wargs:
+                weights_func = wargs['kernel']
+            else:
+                weights_func = smcov.weights_bartlett
+                wargs['kernel'] = weights_func
+
+            w = smcov.S_hac_simple(moms_, nlags=maxlag,
+                                   weights_func=weights_func)
+            w /= nobs #(nobs - self.k_params)
+
+        elif weights_method == 'iid':
+            # only when we have instruments and residual mom = Z * u
+            # TODO: problem we don't have params in argument
+            #       I cannot keep everything in here w/o params as argument
+            u = self.get_error(params)
+
+            if centered:
+                # Note: I'm not centering instruments,
+                #    shouldn't we always center u? Ok, with centered as default
+                u -= u.mean(0)  #demean inplace, we don't need original u
+
+            instrument = self.instrument
+            w = np.dot(instrument.T, instrument).dot(np.dot(u.T, u)) / nobs
+            if 'ddof' in wargs:
+                # caller requests degrees of freedom correction
+                if wargs['ddof'] == 'k_params':
+                    w /= (nobs - self.k_params)
+                else:
+                    # assume ddof is a number
+                    if DEBUG:
+                        print(' momcov ddof', wargs['ddof'])
+                    w /= (nobs - wargs['ddof'])
+            else:
+                # default: divide by nobs
+                w /= nobs
+
         else:
-            w = np.dot(moms.T, moms)/nobs
+            raise ValueError('weight method not available')
 
         return w
 
@@ -492,13 +1049,34 @@ def momcond_mean(self, params):
 
         '''
 
-        #endog, exog = args
-        return self.momcond(params).mean(0)
+        momcond = self.momcond(params)
+        self.nobs_moms, self.k_moms = momcond.shape
+        return momcond.mean(0)
+
 
-    def gradient_momcond(self, params, epsilon=1e-4, method='centered'):
+    def gradient_momcond(self, params, epsilon=1e-4, centered=True):
+        '''gradient of moment conditions
+
+        Parameters
+        ----------
+        params : ndarray
+            parameter at which the moment conditions are evaluated
+        epsilon : float
+            stepsize for finite difference calculation
+        centered : bool
+            This refers to the finite difference calculation. If `centered`
+            is true, then the centered finite difference calculation is
+            used. Otherwise the one-sided forward differences are used.
+
+        TODO: looks like not used yet
+              missing argument `weights`
+
+        '''
 
         momcond = self.momcond_mean
-        if method == 'centered':
+
+        # TODO: approx_fprime has centered keyword
+        if centered:
             gradmoms = (approx_fprime(params, momcond, epsilon=epsilon) +
                     approx_fprime(params, momcond, epsilon=-epsilon))/2
         else:
@@ -506,27 +1084,72 @@ def gradient_momcond(self, params, epsilon=1e-4, method='centered'):
 
         return gradmoms
 
+    def score(self, params, weights, epsilon=None, centered=True):
+
+        deriv = approx_fprime(params, self.gmmobjective, args=(weights,),
+                              centered=centered, epsilon=epsilon)
 
-    def cov_params(self, **kwds):  #TODO add options ???
-        if not hasattr(self.results, 'params'):
-            raise ValueError('the model has to be fit first')
+        return deriv
 
-        if hasattr(self.results, '_cov_params'):
-            #replace with decorator later
-            return self.results._cov_params
+    def score_cu(self, params, epsilon=None, centered=True):
 
-        gradmoms = self.gradient_momcond(self.results.params)
-        moms = self.momcond(self.results.params)
+        deriv = approx_fprime(params, self.gmmobjective_cu, args=(),
+                              centered=centered, epsilon=epsilon)
+
+        return deriv
+
+
+# TODO: wrong superclass, I want tvalues, ... right now
+class GMMResults(LikelihoodModelResults):
+    '''just a storage class right now'''
+
+    def __init__(self, *args, **kwds):
+        self.__dict__.update(kwds)
+
+        self.nobs = self.model.nobs
+
+
+    @cache_readonly
+    def q(self):
+        return self.model.gmmobjective(self.params, self.weights)
+
+
+    @cache_readonly
+    def jval(self):
+        # nobs_moms attached by momcond_mean
+        return self.q * self.model.nobs_moms
+
+
+    def cov_params(self, **kwds):
+        #TODO add options ???)
+        # this should use by default whatever options have been specified in
+        # fit
+
+        # TODO: don't do this when we want to change options
+#         if hasattr(self, '_cov_params'):
+#             #replace with decorator later
+#             return self._cov_params
+
+        # set defaults based on fit arguments
+        if not 'wargs' in kwds:
+            # Note: we don't check the keys in wargs, use either all or nothing
+            kwds['wargs'] = self.wargs
+        if not 'weights_method' in kwds:
+            kwds['weights_method'] = self.options_other['weights_method']
+        if not 'has_optimal_weights' in kwds:
+            kwds['has_optimal_weights'] = self.options_other['has_optimal_weights']
+
+        gradmoms = self.model.gradient_momcond(self.params)
+        moms = self.model.momcond(self.params)
         covparams = self.calc_cov_params(moms, gradmoms, **kwds)
-        self.results._cov_params = covparams
-        return self.results._cov_params
 
+        self._cov_params = covparams
+        return self._cov_params
 
 
-    #still needs to be fully converted to method
-    def calc_cov_params(self, moms, gradmoms, weights=None,
+    def calc_cov_params(self, moms, gradmoms, weights=None, use_weights=False,
                                               has_optimal_weights=True,
-                                              method='momcov', wargs=()):
+                                              weights_method='cov', wargs=()):
         '''calculate covariance of parameter estimates
 
         not all options tried out yet
@@ -542,22 +1165,37 @@ def calc_cov_params(self, moms, gradmoms, weights=None,
         '''
 
         nobs = moms.shape[0]
+
         if weights is None:
-            omegahat = self.calc_weightmatrix(moms, method=method, wargs=wargs)
-            has_optimal_weights = True
+            #omegahat = self.model.calc_weightmatrix(moms, method=method, wargs=wargs)
+            #has_optimal_weights = True
             #add other options, Barzen, ...  longrun var estimators
+            # TODO: this might still be inv_weights after fititer
+            weights = self.weights
         else:
-            omegahat = weights   #2 different names used,
+            pass
+            #omegahat = weights   #2 different names used,
             #TODO: this is wrong, I need an estimate for omega
 
+        if use_weights:
+            omegahat = weights
+        else:
+            omegahat = self.model.calc_weightmatrix(
+                                                moms,
+                                                weights_method=weights_method,
+                                                wargs=wargs,
+                                                params=self.params)
+
+
         if has_optimal_weights: #has_optimal_weights:
+            # TOD0 make has_optimal_weights depend on convergence or iter >2
             cov = np.linalg.inv(np.dot(gradmoms.T,
-                                       np.dot(np.linalg.inv(omegahat), gradmoms)))
+                                    np.dot(np.linalg.inv(omegahat), gradmoms)))
         else:
             gw = np.dot(gradmoms.T, weights)
             gwginv = np.linalg.inv(np.dot(gw, gradmoms))
             cov = np.dot(np.dot(gwginv, np.dot(np.dot(gw, omegahat), gw.T)), gwginv)
-            cov = np.linalg.inv(cov)
+            #cov /= nobs
 
         return cov/nobs
 
@@ -567,12 +1205,21 @@ def bse(self):
         '''
         return self.get_bse()
 
-    def get_bse(self, method=None):
-        '''
+    def get_bse(self, **kwds):
+        '''standard error of the parameter estimates with options
+
+        Parameters
+        ----------
+        kwds : optional keywords
+            options for calculating cov_params
+
+        Returns
+        -------
+        bse : ndarray
+            estimated standard error of parameter estimates
 
-        method option not defined yet
         '''
-        return np.sqrt(np.diag(self.cov_params()))
+        return np.sqrt(np.diag(self.cov_params(**kwds)))
 
     def jtest(self):
         '''overidentification test
@@ -581,50 +1228,385 @@ def jtest(self):
         what's the normalization in jval ?
         '''
 
-        jstat = self.results.jval
-        nparams = self.results.params.size #self.nparams
-        return jstat, stats.chi2.sf(jstat, self.nmoms - nparams)
+        jstat = self.jval
+        nparams = self.params.size #self.nparams
+        df = self.model.nmoms - nparams
+        return jstat, stats.chi2.sf(jstat, df), df
+
+
+    def compare_j(self, other):
+        '''overidentification test for comparing two nested gmm estimates
+
+        This assumes that some moment restrictions have been dropped in one
+        of the GMM estimates relative to the other.
+
+        Not tested yet
+
+        We are comparing two separately estimated models, that use different
+        weighting matrices. It is not guaranteed that the resulting
+        difference is positive.
+
+        TODO: Check in which cases Stata programs use the same weigths
+
+        '''
+        jstat1 = self.jval
+        k_moms1 = self.model.nmoms
+        jstat2 = other.jval
+        k_moms2 = other.model.nmoms
+        jdiff = jstat1 - jstat2
+        df = k_moms1 - k_moms2
+        if df < 0:
+            # possible nested in other way, TODO allow this or not
+            # flip sign instead of absolute
+            df = - df
+            jdiff = - jdiff
+        return jdiff, stats.chi2.sf(jdiff, df), df
+
+
+    def summary(self, yname=None, xname=None, title=None, alpha=.05):
+        """Summarize the Regression Results
+
+        Parameters
+        -----------
+        yname : string, optional
+            Default is `y`
+        xname : list of strings, optional
+            Default is `var_##` for ## in p the number of regressors
+        title : string, optional
+            Title for the top table. If not None, then this replaces the
+            default title
+        alpha : float
+            significance level for the confidence intervals
+
+        Returns
+        -------
+        smry : Summary instance
+            this holds the summary tables and text, which can be printed or
+            converted to various output formats.
+
+        See Also
+        --------
+        statsmodels.iolib.summary.Summary : class to hold summary
+            results
+
+        """
+        #TODO: add a summary text for options that have been used
+
+        jvalue, jpvalue, jdf = self.jtest()
+
+        top_left = [('Dep. Variable:', None),
+                    ('Model:', None),
+                    ('Method:', ['GMM']),
+                    ('Date:', None),
+                    ('Time:', None),
+                    ('No. Observations:', None),
+                    #('Df Residuals:', None), #[self.df_resid]), #TODO: spelling
+                    #('Df Model:', None), #[self.df_model])
+                    ]
+
+        top_right = [#('R-squared:', ["%#8.3f" % self.rsquared]),
+                     #('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
+                     ('Hansen J:', ["%#8.4g" % jvalue] ),
+                     ('Prob (Hansen J):', ["%#6.3g" % jpvalue]),
+                     #('F-statistic:', ["%#8.4g" % self.fvalue] ),
+                     #('Prob (F-statistic):', ["%#6.3g" % self.f_pvalue]),
+                     #('Log-Likelihood:', None), #["%#6.4g" % self.llf]),
+                     #('AIC:', ["%#8.4g" % self.aic]),
+                     #('BIC:', ["%#8.4g" % self.bic])
+                     ]
+
+        if title is None:
+            title = self.model.__class__.__name__ + ' ' + "Results"
+
+        #create summary table instance
+        from statsmodels.iolib.summary import Summary
+        smry = Summary()
+        smry.add_table_2cols(self, gleft=top_left, gright=top_right,
+                          yname=yname, xname=xname, title=title)
+        smry.add_table_params(self, yname=yname, xname=xname, alpha=alpha,
+                             use_t=False)
+
+        return smry
+
 
-class GMMResults(object):
-    '''just a storage class right now'''
-    pass
 
 class IVGMM(GMM):
     '''
-    Class for linear instrumental variables estimation with homoscedastic
-    errors
+    Basic class for instrumental variables estimation using GMM
+
+    A linear function for the conditional mean is defined as default but the
+    methods should be overwritten by subclasses, currently `LinearIVGMM` and
+    `NonlinearIVGMM` are implemented as subclasses.
 
-    currently mainly a test case, doesn't exploit linear structure
+    See Also
+    --------
+    LinearIVGMM
+    NonlinearIVGMM
 
     '''
 
+    results_class = 'IVGMMResults'
+
     def fitstart(self):
         return np.zeros(self.exog.shape[1])
 
+
+    def start_weights(self, inv=True):
+        zz = np.dot(self.instrument.T, self.instrument)
+        nobs = self.instrument.shape[0]
+        if inv:
+            return zz / nobs
+        else:
+            return np.linalg.pinv(zz / nobs)
+
+
+    def get_error(self, params):
+        return self.endog - self.predict(params)
+
+
+    def predict(self, params, exog=None):
+
+        if exog is None:
+            exog = self.exog
+
+        return np.dot(exog, params)
+
     def momcond(self, params):
-        endog, exog, instrum = self.endog, self.exog, self.instrument
-        return instrum * (endog - np.dot(exog, params))[:,None]
+        instrument = self.instrument
+        return instrument * self.get_error(params)[:,None]
 
-#not tried out yet
-class NonlinearIVGMM(GMM):
-    '''
-    Class for linear instrumental variables estimation with homoscedastic
-    errors
 
-    currently mainly a test case, not checked yet
+class LinearIVGMM(IVGMM):
+    """class for linear instrumental variables models estimated with GMM
+
+    Uses closed form expression instead of nonlinear optimizers for each step
+    of the iterative GMM.
+
+    The model is assumed to have the following moment condition
+
+        E( z * (y - x beta)) = 0
+
+    Where `y` is the dependent endogenous variable, `x` are the explanatory
+    variables and `z` are the instruments. Variables in `x` that are exogenous
+    need also be included in `z`.
+
+    Notation Warning: our name `exog` stands for the explanatory variables,
+    and includes both exogenous and explanatory variables that are endogenous,
+    i.e. included endogenous variables
+
+    Parameters
+    ----------
+    endog : array_like
+        dependent endogenous variable
+    exog : array_like
+        explanatory, right hand side variables, including explanatory variables
+        that are endogenous
+    instruments : array_like
+        Instrumental variables, variables that are exogenous to the error
+        in the linear model containing both included and excluded exogenous
+        variables
+
+
+    """
+
+    def fitgmm(self, start, weights=None, optim_method=None, **kwds):
+        '''estimate parameters using GMM for linear model
+
+        Uses closed form expression instead of nonlinear optimizers
+
+        Parameters
+        ----------
+        start : not used
+            starting values for minimization, not used, only for consistency
+            of method signature
+        weights : array
+            weighting matrix for moment conditions. If weights is None, then
+            the identity matrix is used
+        optim_method : not used,
+            optimization method, not used, only for consistency of method
+            signature
+        **kwds : keyword arguments
+            not used, will be silently ignored (for compatibility with generic)
+
+
+        Returns
+        -------
+        paramest : array
+            estimated parameters
+
+        '''
+##        if not fixed is None:  #fixed not defined in this version
+##            raise NotImplementedError
+
+        # TODO: should start_weights only be in `fit`
+        if weights is None:
+            weights = self.start_weights(inv=False)
+
+        y, x, z = self.endog, self.exog, self.instrument
+
+        zTx = np.dot(z.T, x)
+        zTy = np.dot(z.T, y)
+        # normal equation, solved with pinv
+        part0 = zTx.T.dot(weights)
+        part1 = part0.dot(zTx)
+        part2 = part0.dot(zTy)
+        params = np.linalg.pinv(part1).dot(part2)
+
+        return params
+
+
+    def predict(self, params, exog=None):
+        if exog is None:
+            exog = self.exog
+
+        return np.dot(exog, params)
+
+
+    def gradient_momcond(self, params, **kwds):
+        # **kwds for compatibility not used
+
+        x, z = self.exog, self.instrument
+        gradmoms = -np.dot(z.T, x) / self.nobs
+
+        return gradmoms
+
+    def score(self, params, weights, **kwds):
+        # **kwds for compatibility, not used
+        # Note: I coud use general formula with gradient_momcond instead
+
+        x, z = self.exog, self.instrument
+        nobs = z.shape[0]
+
+        u = self.get_errors(params)
+        score = -2 * np.dot(x.T, z).dot(weights.dot(np.dot(z.T, u)))
+        score /= nobs * nobs
+
+        return score
+
+
+
+class NonlinearIVGMM(IVGMM):
+    """
+    Class for non-linear instrumental variables estimation wusing GMM
+
+    The model is assumed to have the following moment condition
+
+        E[ z * (y - f(X, beta)] = 0
+
+    Where `y` is the dependent endogenous variable, `x` are the explanatory
+    variables and `z` are the instruments. Variables in `x` that are exogenous
+    need also be included in z. `f` is a nonlinear function.
+
+    Notation Warning: our name `exog` stands for the explanatory variables,
+    and includes both exogenous and explanatory variables that are endogenous,
+    i.e. included endogenous variables
+
+    Parameters
+    ----------
+    endog : array_like
+        dependent endogenous variable
+    exog : array_like
+        explanatory, right hand side variables, including explanatory variables
+        that are endogenous.
+    instruments : array_like
+        Instrumental variables, variables that are exogenous to the error
+        in the linear model containing both included and excluded exogenous
+        variables
+    func : callable
+        function for the mean or conditional expectation of the endogenous
+        variable. The function will be called with parameters and the array of
+        explanatory, right hand side variables, `func(params, exog)`
+
+    Notes
+    -----
+    This class uses numerical differences to obtain the derivative of the
+    objective function. If the jacobian of the conditional mean function, `func`
+    is available, then it can be used by subclassing this class and defining
+    a method `jac_func`.
+
+    TODO: check required signature of jac_error and jac_func
+
+    """
+    # This should be reversed:
+    # NonlinearIVGMM is IVGMM and need LinearIVGMM as special case (fit, predict)
 
-    '''
 
     def fitstart(self):
         #might not make sense for more general functions
         return np.zeros(self.exog.shape[1])
 
-    def __init__(self, endog, exog, instrument, **kwds):
+
+    def __init__(self, endog, exog, instrument, func, **kwds):
         self.func = func
+        super(NonlinearIVGMM, self).__init__(endog, exog, instrument, **kwds)
+
+
+    def predict(self, params, exog=None):
+        if exog is None:
+            exog = self.exog
+
+        return self.func(params, exog)
+
+    #----------  the following a semi-general versions,
+    # TODO: move to higher class after testing
+
+    def jac_func(self, params, weights, args=None, centered=True, epsilon=None):
+
+        # TODO: Why are ther weights in the signature - copy-paste error?
+        deriv = approx_fprime(params, self.func, args=(self.exog,),
+                              centered=centered, epsilon=epsilon)
+
+        return deriv
+
+
+    def jac_error(self, params, weights, args=None, centered=True,
+                   epsilon=None):
+
+        jac_func = self.jac_func(params, weights, args=None, centered=True,
+                                 epsilon=None)
+
+        return -jac_func
+
+
+    def score(self, params, weights, **kwds):
+        # **kwds for compatibility not used
+        # Note: I coud use general formula with gradient_momcond instead
+
+        z = self.instrument
+        nobs = z.shape[0]
+
+        jac_u = self.jac_error(params, weights, args=None, epsilon=None,
+                               centered=True)
+        x = -jac_u  # alias, plays the same role as X in linear model
+
+        u = self.get_error(params)
+
+        score = -2 * np.dot(np.dot(x.T, z), weights).dot(np.dot(z.T, u))
+        score /= nobs * nobs
+
+        return score
+
+
+class IVGMMResults(GMMResults):
+
+
+    # this assumes that we have an additive error model `(y - f(x, params))`
+
+    @cache_readonly
+    def fittedvalues(self):
+        return self.model.predict(self.params)
+
+
+    @cache_readonly
+    def resid(self):
+        return self.model.endog - self.fittedvalues
+
+
+    @cache_readonly
+    def ssr(self):
+        return (self.resid * self.resid).sum(0)
+
 
-    def momcond(self, params):
-        endog, exog, instrum = self.endog, self.exog, self.instrument
-        return instrum * (endog - self.func(params, exog))[:,None]
 
 
 def spec_hausman(params_e, params_i, cov_params_e, cov_params_i, dof=None):
@@ -666,7 +1648,7 @@ def spec_hausman(params_e, params_i, cov_params_e, cov_params_i, dof=None):
     #use linalg.lstsq or svd directly
     #cov_diff will very often be in-definite (singular)
     if not dof:
-        dof = tools.rank(cov_diff)
+        dof = np_matrix_rank(cov_diff)
     cov_diffpinv = np.linalg.pinv(cov_diff)
     H = np.dot(params_diff, np.dot(cov_diffpinv, params_diff))
     pval = stats.chi2.sf(H, dof)
@@ -691,7 +1673,7 @@ class DistQuantilesGMM(GMM):
 
     def __init__(self, endog, exog, instrument, **kwds):
         #TODO: something wrong with super
-        #super(self.__class__).__init__(endog, exog, instrument) #, **kwds)
+        super(DistQuantilesGMM, self).__init__(endog, exog, instrument)
         #self.func = func
         self.epsilon_iter = 1e-5
 
@@ -715,7 +1697,7 @@ def __init__(self, endog, exog, instrument, **kwds):
         self.endog = endog
         self.exog = exog
         self.instrument = instrument
-        self.results = GMMResults()
+        self.results = GMMResults(model=self)
         #self.__dict__.update(kwds)
         self.epsilon_iter = 1e-6
 
@@ -724,11 +1706,11 @@ def fitstart(self):
         #      added but not used during testing, avoid Travis
         distfn = self.distfn
         if hasattr(distfn, '_fitstart'):
-            start = distfn._fitstart(x)
+            start = distfn._fitstart(self.endog)
         else:
             start = [1]*distfn.numargs + [0.,1.]
 
-        return np.array([1]*self.distfn.numargs + [0,1])
+        return np.asarray(start)
 
     def momcond(self, params): #drop distfn as argument
         #, mom2, quantile=None, shape=None
@@ -792,148 +1774,22 @@ def fitonce(self, start=None, weights=None, has_optimal_weights=False):
         if weights is None:
             weights = np.eye(self.nmoms)
         params = self.fitgmm(start=start)
+        # TODO: rewrite this old hack, should use fitgmm or fit maxiter=0
         self.results.params = params  #required before call to self.cov_params
-        _cov_params = self.cov_params(weights=weights,
+        self.results.wargs = {} #required before call to self.cov_params
+        self.results.options_other = {'weights_method':'cov'}
+        # TODO: which weights_method?  There shouldn't be any needed ?
+        _cov_params = self.results.cov_params(weights=weights,
                                       has_optimal_weights=has_optimal_weights)
 
-
         self.results.weights = weights
         self.results.jval = self.gmmobjective(params, weights)
-        return self.results
-
+        self.results.options_other.update({'has_optimal_weights':has_optimal_weights})
 
+        return self.results
 
 
-
-if __name__ == '__main__':
-    import statsmodels.api as sm
-    examples = ['ivols', 'distquant'][:]
-
-    if 'ivols' in examples:
-        exampledata = ['ols', 'iv', 'ivfake'][1]
-        nobs = nsample = 500
-        sige = 3
-        corrfactor = 0.025
-
-
-        x = np.linspace(0,10, nobs)
-        X = tools.add_constant(np.column_stack((x, x**2)), prepend=False)
-        beta = np.array([1, 0.1, 10])
-
-        def sample_ols(exog):
-            endog = np.dot(exog, beta) + sige*np.random.normal(size=nobs)
-            return endog, exog, None
-
-        def sample_iv(exog):
-            print 'using iv example'
-            X = exog.copy()
-            e = sige * np.random.normal(size=nobs)
-            endog = np.dot(X, beta) + e
-            exog[:,0] = X[:,0] + corrfactor * e
-            z0 = X[:,0] + np.random.normal(size=nobs)
-            z1 = X.sum(1) + np.random.normal(size=nobs)
-            z2 = X[:,1]
-            z3 = (np.dot(X, np.array([2,1, 0])) +
-                            sige/2. * np.random.normal(size=nobs))
-            z4 = X[:,1] + np.random.normal(size=nobs)
-            instrument = np.column_stack([z0, z1, z2, z3, z4, X[:,-1]])
-            return endog, exog, instrument
-
-        def sample_ivfake(exog):
-            X = exog
-            e = sige * np.random.normal(size=nobs)
-            endog = np.dot(X, beta) + e
-            #X[:,0] += 0.01 * e
-            #z1 = X.sum(1) + np.random.normal(size=nobs)
-            #z2 = X[:,1]
-            z3 = (np.dot(X, np.array([2,1, 0])) +
-                            sige/2. * np.random.normal(size=nobs))
-            z4 = X[:,1] + np.random.normal(size=nobs)
-            instrument = np.column_stack([X[:,:2], z3, z4, X[:,-1]]) #last is constant
-            return endog, exog, instrument
-
-
-        if exampledata == 'ols':
-            endog, exog, _ = sample_ols(X)
-            instrument = exog
-        elif exampledata == 'iv':
-            endog, exog, instrument = sample_iv(X)
-        elif exampledata == 'ivfake':
-            endog, exog, instrument = sample_ivfake(X)
-
-
-        #using GMM and IV2SLS classes
-        #----------------------------
-
-        mod = IVGMM(endog, exog, instrument, nmoms=instrument.shape[1])
-        res = mod.fit()
-        modgmmols = IVGMM(endog, exog, exog, nmoms=exog.shape[1])
-        resgmmols = modgmmols.fit()
-        #the next is the same as IV2SLS, (Z'Z)^{-1} as weighting matrix
-        modgmmiv = IVGMM(endog, exog, instrument, nmoms=instrument.shape[1]) #same as mod
-        resgmmiv = modgmmiv.fitgmm(np.ones(exog.shape[1], float),
-                        weights=np.linalg.inv(np.dot(instrument.T, instrument)))
-        modls = IV2SLS(endog, exog, instrument)
-        resls = modls.fit()
-        modols = OLS(endog, exog)
-        resols = modols.fit()
-
-        print '\nIV case'
-        print 'params'
-        print 'IV2SLS', resls.params
-        print 'GMMIV ', resgmmiv # .params
-        print 'GMM   ', res.params
-        print 'diff  ', res.params - resls.params
-        print 'OLS   ', resols.params
-        print 'GMMOLS', resgmmols.params
-
-        print '\nbse'
-        print 'IV2SLS', resls.bse
-        print 'GMM   ', mod.bse   #bse currently only attached to model not results
-        print 'diff  ', mod.bse - resls.bse
-        print '%-diff', resls.bse / mod.bse * 100 - 100
-        print 'OLS   ', resols.bse
-        print 'GMMOLS', modgmmols.bse
-        #print 'GMMiv', modgmmiv.bse
-
-        print "Hausman's specification test"
-        print modls.spec_hausman()
-        print spec_hausman(resols.params, res.params, resols.cov_params(),
-                           mod.cov_params())
-        print spec_hausman(resgmmols.params, res.params, modgmmols.cov_params(),
-                           mod.cov_params())
-
-
-    if 'distquant' in examples:
-
-
-        #estimating distribution parameters from quantiles
-        #-------------------------------------------------
-
-        #example taken from distribution_estimators.py
-        gparrvs = stats.genpareto.rvs(2, size=5000)
-        x0p = [1., gparrvs.min()-5, 1]
-
-        moddist = DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto)
-        #produces non-sense because optimal weighting matrix calculations don't
-        #apply to this case
-        #resgp = moddist.fit() #now with 'cov': LinAlgError: Singular matrix
-        pit1, wit1 = moddist.fititer([1.5,0,1.5], maxiter=1)
-        print pit1
-        p1 = moddist.fitgmm([1.5,0,1.5])
-        print p1
-        moddist2 = DistQuantilesGMM(gparrvs, None, None, distfn=stats.genpareto,
-                                    pquant=np.linspace(0.01,0.99,10))
-        pit1a, wit1a = moddist2.fititer([1.5,0,1.5], maxiter=1)
-        print pit1a
-        p1a = moddist2.fitgmm([1.5,0,1.5])
-        print p1a
-        #Note: pit1a and p1a are the same and almost the same (1e-5) as
-        #      fitquantilesgmm version (functions instead of class)
-        res1b = moddist2.fitonce([1.5,0,1.5])
-        print res1b.params
-        print moddist2.bse  #they look much too large
-        print np.sqrt(np.diag(res1b._cov_params))
-
-
+results_class_dict = {'GMMResults': GMMResults,
+                      'IVGMMResults': IVGMMResults,
+                      'DistQuantilesGMM': GMMResults}  #TODO: should be a default
 
diff --git a/statsmodels/sandbox/regression/ols_anova_original.py b/statsmodels/sandbox/regression/ols_anova_original.py
index 427fc66aed5..ce32a8e56e6 100644
--- a/statsmodels/sandbox/regression/ols_anova_original.py
+++ b/statsmodels/sandbox/regression/ols_anova_original.py
@@ -6,9 +6,10 @@
 
 '''
 
-
+from __future__ import print_function
 import numpy as np
 #from scipy import stats
+from statsmodels.compat.python import lmap
 import statsmodels.api as sm
 
 
@@ -23,9 +24,9 @@
 
 
 dta = np.genfromtxt('dftest3.data')
-print dta.shape
+print(dta.shape)
 mask = np.isnan(dta)
-print "rows with missing values", mask.any(1).sum()
+print("rows with missing values", mask.any(1).sum())
 vars = dict((v[0], (idx, v[1])) for idx, v in enumerate((
                 ('breed', int), ('sex', int), ('litter', int),
                ('pen', int), ('pig', int), ('age', float),
@@ -35,7 +36,7 @@
 #possible to avoid temporary array ?
 dta_use = dta[:, [vars[col][0] for col in datavarnames]]
 keeprows = ~np.isnan(dta_use).any(1)
-print 'number of complete observations', keeprows.sum()
+print('number of complete observations', keeprows.sum())
 dta_used = dta_use[keeprows,:]
 
 varsused = dict((k, [dta_used[:,idx], idx, vars[k][1]]) for idx, k in enumerate(datavarnames))
@@ -66,7 +67,7 @@ def data2proddummy(x):
     '''
     #brute force, assumes x is 2d
     #replace with encoding if possible
-    groups = np.unique(map(tuple, x.tolist()))
+    groups = np.unique(lmap(tuple, x.tolist()))
     #includes singularity with additive factors
     return (x==groups[:,None,:]).all(-1).T.astype(int)[:,:-1]
 
@@ -104,8 +105,8 @@ def data2groupcont(x1,x2):
 X_b0 = np.c_[sexdummy, dta_used[:,2], np.ones((dta_used.shape[0],1))]
 y_b0 = dta_used[:,0]
 res_b0 = sm.OLS(y_b0, X_b0).results
-print res_b0.params
-print res_b0.ssr
+print(res_b0.params)
+print(res_b0.ssr)
 
 anova_str0 = '''
 ANOVA statistics (model sum of squares excludes constant)
@@ -127,7 +128,7 @@ def data2groupcont(x1,x2):
 R squared  %(rsquared)f
 '''
 
-#print anova_str % dict([('df_model', res.df_model)])
+#print(anova_str % dict([('df_model', res.df_model)])
 #anovares = ['df_model' , 'df_resid'
 
 def anovadict(res):
@@ -147,24 +148,24 @@ def anovadict(res):
     return ad
 
 
-print anova_str0 % anovadict(res_b0)
+print(anova_str0 % anovadict(res_b0))
 #the following leaves the constant in, not with NIST regression
 #but something fishy with res.ess negative in examples
-print anova_str % anovadict(res_b0)
+print(anova_str % anovadict(res_b0))
 
-print 'using sex only'
+print('using sex only')
 X2 = np.c_[sexdummy, np.ones((dta_used.shape[0],1))]
 res2 = sm.OLS(y_b0, X2).results
-print res2.params
-print res2.ssr
-print anova_str % anovadict(res2)
+print(res2.params)
+print(res2.ssr)
+print(anova_str % anovadict(res2))
 
-print 'using age only'
+print('using age only')
 X3 = np.c_[ dta_used[:,2], np.ones((dta_used.shape[0],1))]
 res3 = sm.OLS(y_b0, X3).results
-print res3.params
-print res3.ssr
-print anova_str % anovadict(res3)
+print(res3.params)
+print(res3.ssr)
+print(anova_str % anovadict(res3))
 
 
 def form2design(ss, data):
@@ -225,7 +226,7 @@ def form2design(ss, data):
             vars[''.join(v)] = data2groupcont(data[v[0]], data[v[1]])
             names.append(''.join(v))
         else:
-            raise ValueError, 'unknown expression in formula'
+            raise ValueError('unknown expression in formula')
     return vars, names
 
 nobs = 1000
@@ -241,13 +242,13 @@ def form2design(ss, data):
     testdata[name] = testdatacont[name]
 
 
-#print form2design('a',testdata)
+#print(form2design('a',testdata))
 
 if 0:
     xx, n = form2design('F:a',testdata)
-    print xx
-    print form2design('P:a*b',testdata)
-    print data2proddummy((np.c_[testdata['a'],testdata['b']]))
+    print(xx)
+    print(form2design('P:a*b',testdata))
+    print(data2proddummy((np.c_[testdata['a'],testdata['b']])))
 
     xx, names = form2design('a F:b P:c*d',testdata)
 
@@ -260,8 +261,8 @@ def form2design(ss, data):
 # simple test version: all coefficients equal to one
 y = X.sum(1) + 0.01*np.random.normal(size=(nobs))
 rest1 = sm.OLS(y,X).results
-print rest1.params
-print anova_str % anovadict(rest1)
+print(rest1.params)
+print(anova_str % anovadict(rest1))
 
 def dropname(ss, li):
     '''drop names from a list of strings,
@@ -277,8 +278,8 @@ def dropname(ss, li):
 # simple test version: all coefficients equal to one
 y = X.sum(1) + 0.01*np.random.normal(size=(nobs))
 rest1 = sm.OLS(y,X).results
-print rest1.params
-print anova_str % anovadict(rest1)
+print(rest1.params)
+print(anova_str % anovadict(rest1))
 
 
 # Example: from Bruce
@@ -286,14 +287,14 @@ def dropname(ss, li):
 
 # read data set and drop rows with missing data
 dta = np.genfromtxt('dftest3.data', dt_b,missing='.', usemask=True)
-print 'missing', [dta.mask[k].sum() for k in dta.dtype.names]
+print('missing', [dta.mask[k].sum() for k in dta.dtype.names])
 m = dta.mask.view(bool)
 droprows = m.reshape(-1,len(dta.dtype.names)).any(1)
 # get complete data as plain structured array
 # maybe doesn't work with masked arrays
 dta_use_b1 = dta[~droprows,:].data
-print dta_use_b1.shape
-print dta_use_b1.dtype
+print(dta_use_b1.shape)
+print(dta_use_b1.dtype)
 
 #Example b1: variables from Bruce's glm
 
@@ -304,11 +305,11 @@ def dropname(ss, li):
 y_b1 = dta_use_b1['y']
 # estimate using OLS
 rest_b1 = sm.OLS(y_b1, X_b1).results
-# print results
-print rest_b1.params
-print anova_str % anovadict(rest_b1)
+# print(results)
+print(rest_b1.params)
+print(anova_str % anovadict(rest_b1))
 #compare with original version only in original version
-print anova_str % anovadict(res_b0)
+print(anova_str % anovadict(res_b0))
 
 # Example: use all variables except pig identifier
 
@@ -319,15 +320,15 @@ def dropname(ss, li):
 X_b1a = np.column_stack([xx_b1a[nn] for nn in dropname('', names_b1a)])
 y_b1a = dta_use_b1['y']
 rest_b1a = sm.OLS(y_b1a, X_b1a).results
-print rest_b1a.params
-print anova_str % anovadict(rest_b1a)
+print(rest_b1a.params)
+print(anova_str % anovadict(rest_b1a))
 
 for dropn in names_b1a:
-    print '\nResults dropping', dropn
+    print('\nResults dropping', dropn)
     X_b1a_ = np.column_stack([xx_b1a[nn] for nn in dropname(dropn, names_b1a)])
     y_b1a_ = dta_use_b1['y']
     rest_b1a_ = sm.OLS(y_b1a_, X_b1a_).results
-    #print rest_b1a_.params
-    print anova_str % anovadict(rest_b1a_)
+    #print(rest_b1a_.params
+    print(anova_str % anovadict(rest_b1a_))
 
 
diff --git a/statsmodels/sandbox/regression/onewaygls.py b/statsmodels/sandbox/regression/onewaygls.py
index 99d590210ef..7db0921f6ba 100644
--- a/statsmodels/sandbox/regression/onewaygls.py
+++ b/statsmodels/sandbox/regression/onewaygls.py
@@ -52,6 +52,7 @@
 Author: josef-pktd
 """
 import numpy as np
+from statsmodels.compat.python import zip
 from scipy import stats
 from statsmodels.regression.linear_model import OLS, WLS
 
diff --git a/statsmodels/sandbox/regression/penalized.py b/statsmodels/sandbox/regression/penalized.py
index b18f48f8940..5c13224633e 100644
--- a/statsmodels/sandbox/regression/penalized.py
+++ b/statsmodels/sandbox/regression/penalized.py
@@ -33,7 +33,8 @@
 
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange
 import numpy as np
 import statsmodels.base.model as base
 from statsmodels.regression.linear_model import OLS, GLS, RegressionResults
@@ -81,7 +82,7 @@ def fit(self, lambd=1.):
         y = self.wendog[:,None]
         #why are sigma2_e * lambd multiplied, not ratio?
         #larger lambd -> stronger prior  (it's not the variance)
-        #print 'lambd inside fit', lambd
+        #print('lambd inside fit', lambd
         xpx = np.dot(x.T, x) + \
               sigma2_e * lambd * np.dot(r_matrix.T, np.dot(sigma_prior_inv, r_matrix))
         xpy = np.dot(x.T, y) + \
@@ -144,7 +145,7 @@ def hatmatrix_diag(self):
         '''
         xpxi = self.model.normalized_cov_params
         #something fishy with self.normalized_cov_params in result, doesn't update
-        #print self.model.wexog.shape, np.dot(xpxi, self.model.wexog.T).shape
+        #print(self.model.wexog.shape, np.dot(xpxi, self.model.wexog.T).shape
         return (self.model.wexog * np.dot(xpxi, self.model.wexog.T).T).sum(1)
 
     def hatmatrix_trace(self):
@@ -186,7 +187,7 @@ def coef_restriction_diffbase(n_coeffs, n_vars=None, position=0, base_idx=0):
     reduced = -np.eye(n_coeffs)  #make all rows, drop one row later
     reduced[:, base_idx] = 1
 
-    keep = range(n_coeffs)
+    keep = lrange(n_coeffs)
     del keep[base_idx]
     reduced = np.take(reduced, keep, axis=0)
 
@@ -254,8 +255,8 @@ def coef_restriction_diffseq(n_coeffs, degree=1, n_vars=None, position=0, base_i
     lambd = 1 #1e-4
     mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd)
     res = mod.fit()
-    print res_ols.params
-    print res.params
+    print(res_ols.params)
+    print(res.params)
 
     #example 2
     #I need more flexible penalization in example, the penalization should
@@ -288,17 +289,17 @@ def coef_restriction_diffseq(n_coeffs, degree=1, n_vars=None, position=0, base_i
     lambd = 2 #1e-4
     mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd)
     res = mod.fit()
-    print res_ols.params
-    print res.params
+    print(res_ols.params)
+    print(res.params)
 
     res_bic = mod.fit_minic()   #this will just return zero
     res = mod.fit(res_bic)
 
-    print res_bic
+    print(res_bic)
     for lambd in np.linspace(0, 80, 21):
         res_l = mod.fit(lambd)
-        #print lambd, res_l.params[-2:], res_l.bic, res_l.bic + 1./lambd, res.df_model
-        print lambd, res_l.params[-2:], res_l.bic, res.df_model, np.trace(res.normalized_cov_params)
+        #print(lambd, res_l.params[-2:], res_l.bic, res_l.bic + 1./lambd, res.df_model
+        print((lambd, res_l.params[-2:], res_l.bic, res.df_model, np.trace(res.normalized_cov_params)))
 
 
     import matplotlib.pyplot as plt
@@ -328,7 +329,7 @@ def coef_restriction_diffseq(n_coeffs, degree=1, n_vars=None, position=0, base_i
         from statsmodels.sandbox.panel.random_panel import PanelSample
         dgp = PanelSample(nobs, k_vars, n_groups)
         dgp.group_means = 2 + np.random.randn(n_groups) #add random intercept
-        print 'seed', dgp.seed
+        print('seed', dgp.seed)
         y = dgp.generate_panel()
         X = np.column_stack((dgp.exog[:,1:],
                                dgp.groups[:,None] == np.arange(n_groups)))
@@ -343,7 +344,7 @@ def coef_restriction_diffseq(n_coeffs, degree=1, n_vars=None, position=0, base_i
         lambd = 1 #1e-4
         mod = TheilGLS(y, X, r_matrix=R, q_matrix=r, sigma_prior=lambd)
         res = mod.fit()
-        print res.params
+        print(res.params)
 
         params_l = []
         for lambd in np.linspace(0, 20, 21):
diff --git a/statsmodels/sandbox/regression/predstd.py b/statsmodels/sandbox/regression/predstd.py
index 311bc568f65..31ef7f97588 100644
--- a/statsmodels/sandbox/regression/predstd.py
+++ b/statsmodels/sandbox/regression/predstd.py
@@ -75,14 +75,19 @@ def wls_prediction_std(res, exog=None, weights=None, alpha=0.05):
     if exog is None:
         exog = res.model.exog
         predicted = res.fittedvalues
+        if weights is None:
+            weights = res.model.weights
     else:
         exog = np.atleast_2d(exog)
         if covb.shape[1] != exog.shape[1]:
             raise ValueError('wrong shape of exog')
         predicted = res.model.predict(res.params, exog)
-
-    if weights is None:
-        weights = res.model.weights
+        if weights is None:
+            weights = 1.
+        else:
+            weights = np.asarray(weights)
+            if weights.size > 1 and len(weights) != exog.shape[0]:
+                raise ValueError('weights and exog do not have matching shape')
 
 
     # full covariance:
@@ -94,43 +99,3 @@ def wls_prediction_std(res, exog=None, weights=None, alpha=0.05):
     interval_u = predicted + tppf * predstd
     interval_l = predicted - tppf * predstd
     return predstd, interval_l, interval_u
-
-
-if __name__ == '__main__':
-    import statsmodels.api as sm
-
-    # generate dataset
-    nsample = 50
-    x1 = np.linspace(0, 20, nsample)
-    X = np.c_[x1, (x1-5)**2, np.ones(nsample)]
-    np.random.seed(0)#9876789) #9876543)
-    beta = [0.5, -0.01, 5.]
-    y_true2 = np.dot(X, beta)
-    w = np.ones(nsample)
-    w[nsample*6/10:] = 3
-    sig = 0.5
-    y2 = y_true2 + sig*w* np.random.normal(size=nsample)
-    X2 = X[:,[0,2]]
-
-    # estimate OLS, WLS, (OLS not used in these tests)
-    res2 = sm.OLS(y2, X2).fit()
-    res3 = sm.WLS(y2, X2, 1./w).fit()
-
-    #direct calculation
-    covb = res3.cov_params()
-    predvar = res3.mse_resid*w + (X2 * np.dot(covb,X2.T).T).sum(1)
-    predstd = np.sqrt(predvar)
-
-
-    prstd, iv_l, iv_u = wls_prediction_std(res3)
-    np.testing.assert_almost_equal(predstd, prstd, 15)
-
-    # testing shapes of exog
-    prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1:,:], weights=3.)
-    np.testing.assert_equal( prstd[-1], prstd)
-    prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1,:], weights=3.)
-    np.testing.assert_equal( prstd[-1], prstd)
-    #use wrong size for exog
-    #prstd, iv_l, iv_u = wls_prediction_std(res3, X2[-1,0], weights=3.)
-    np.testing.assert_raises(ValueError, wls_prediction_std, res3, X2[-1,0], weights=3.)
-
diff --git a/statsmodels/sandbox/regression/runmnl.py b/statsmodels/sandbox/regression/runmnl.py
index 48f6e23cf95..28476e32162 100644
--- a/statsmodels/sandbox/regression/runmnl.py
+++ b/statsmodels/sandbox/regression/runmnl.py
@@ -23,7 +23,8 @@
 License: BSD (simplified)
 '''
 
-
+from __future__ import print_function
+from statsmodels.compat.python import zip
 import numpy as np
 import numpy.lib.recfunctions as recf
 
@@ -200,20 +201,20 @@ def calc_prob(self, tree, keys=None):
         branchsum = self.branchsum
 
 
-        if type(tree) == tuple:   #assumes leaves are int for choice index
+        if isinstance(tree, tuple):   #assumes leaves are int for choice index
             name, subtree = tree
-            print name, datadict[name]
-            print 'subtree', subtree
+            print(name, datadict[name])
+            print('subtree', subtree)
             keys = []
             if testxb:
                 branchsum = datadict[name]
             else:
                 branchsum = name  #0
             for b in subtree:
-                print b
+                print(b)
                 #branchsum += branch2(b)
                 branchsum = branchsum + self.calc_prob(b, keys)
-            print 'branchsum', branchsum, keys
+            print('branchsum', branchsum, keys)
             for k in keys:
                 self.probs[k] = self.probs[k] + ['*' + name + '-prob']
 
@@ -223,12 +224,12 @@ def calc_prob(self, tree, keys=None):
                                 '(%s)' % ', '.join(self.paramsind[tree])]
             if testxb:
                 leavessum = sum((datadict[bi] for bi in tree))
-                print 'final branch with', tree, ''.join(tree), leavessum #sum(tree)
+                print('final branch with', tree, ''.join(tree), leavessum) #sum(tree)
                 return leavessum  #sum(xb[tree])
             else:
                 return ''.join(tree) #sum(tree)
 
-        print 'working on branch', tree, branchsum
+        print('working on branch', tree, branchsum)
         return branchsum
 
 
@@ -245,11 +246,11 @@ def calc_prob(self, tree, keys=None):
 datafloat = dta.view(float).reshape(-1,7)
 exog = datafloat[:,1:].reshape(-1,6*nchoices).copy() #I don't want a view
 
-print endog.sum(0)
+print(endog.sum(0))
 varnames = dta.dtype.names
-print varnames[1:]
+print(varnames[1:])
 modes = ['Air', 'Train', 'Bus', 'Car']
-print exog.mean(0).reshape(nchoices, -1) # Greene Table 23.23
+print(exog.mean(0).reshape(nchoices, -1)) # Greene Table 23.23
 
 
 
@@ -335,10 +336,10 @@ def calc_prob(self, tree, keys=None):
 '''
 res3corr = res3[[1, 0, 2, 3, 4, 5]]
 res3corr[0] *= 10
-print res3corr - tab2324  # diff 1e-5 to 1e-6
-#199.128369 - 199.1284  #llf same up to print precision of Greene
+print(res3corr - tab2324)  # diff 1e-5 to 1e-6
+#199.128369 - 199.1284  #llf same up to print(precision of Greene
 
-print clogit.fit()
+print(clogit.fit())
 
 
 tree0 = ('top',
@@ -368,6 +369,6 @@ def calc_prob(self, tree, keys=None):
              }
 
 modru = RU2NMNL(endog, datadict, tree0, paramsind)
-print modru.calc_prob(modru.tree)
-print '\nmodru.probs'
-print modru.probs
+print(modru.calc_prob(modru.tree))
+print('\nmodru.probs')
+print(modru.probs)
diff --git a/statsmodels/sandbox/regression/sympy_diff.py b/statsmodels/sandbox/regression/sympy_diff.py
index 9eb00edca2b..ad0686d7027 100644
--- a/statsmodels/sandbox/regression/sympy_diff.py
+++ b/statsmodels/sandbox/regression/sympy_diff.py
@@ -4,7 +4,7 @@
 
 Author: josef-pktd
 """
-
+from __future__ import print_function
 import sympy as sy
 
 
@@ -28,34 +28,34 @@ def cdf(x, mu, sigma):
 s = sy.Symbol('s')
 
 dldxnorm = sy.log(pdf(x, mu,sigma)).diff(x)
-print sy.simplify(dldxnorm)
-print sy.diff(sy.log(sy.gamma((s+1)/2)),s)
+print(sy.simplify(dldxnorm))
+print(sy.diff(sy.log(sy.gamma((s+1)/2)),s))
 
-print sy.diff((df+1)/2. * sy.log(1+df/(df-2)), df)
+print(sy.diff((df+1)/2. * sy.log(1+df/(df-2)), df))
 
 #standard t distribution, not verified
 tllf1 = sy.log(sy.gamma((df+1)/2.)) - sy.log(sy.gamma(df/2.)) - 0.5*sy.log((df)*sy.pi)
 tllf2 = (df+1.)/2. * sy.log(1. + (y-mu)**2/(df)/sigma2) + 0.5 * sy.log(sigma2)
 tllf2std = (df+1.)/2. * sy.log(1. + y**2/df) + 0.5
 tllf = tllf1 - tllf2
-print tllf1.diff(df)
-print tllf2.diff(y)
+print(tllf1.diff(df))
+print(tllf2.diff(y))
 dlddf = (tllf1-tllf2).diff(df)
-print dlddf
-print sy.cse(dlddf)
-print '\n derivative of loglike of t distribution wrt df'
-for k,v in sy.cse(dlddf)[0]: print k,'=',v
-print sy.cse(dlddf)[1][0]
+print(dlddf)
+print(sy.cse(dlddf))
+print('\n derivative of loglike of t distribution wrt df')
+for k,v in sy.cse(dlddf)[0]: print(k,'=',v)
+print(sy.cse(dlddf)[1][0])
 
-print '\nstandard t distribution, dll_df, dll_dy'
+print('\nstandard t distribution, dll_df, dll_dy')
 tllfstd = tllf1 - tllf2std
-print tllfstd.diff(df)
-print tllfstd.diff(y)
+print(tllfstd.diff(df))
+print(tllfstd.diff(y))
 
-print '\n'
+print('\n')
 
-print dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001))
-print dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001)).evalf()
+print(dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001)))
+print(dlddf.subs(dict(y=1,mu=1,sigma2=1.5,df=10.0001)).evalf())
 # Note: derivatives of nested function doesn't work in sympy
 #       at least not higher order derivatives (second or larger)
-#       looks like print failure
+#       looks like print(failure
diff --git a/statsmodels/sandbox/regression/tests/__init__.py b/statsmodels/sandbox/regression/tests/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/statsmodels/sandbox/regression/tests/griliches76.dta b/statsmodels/sandbox/regression/tests/griliches76.dta
new file mode 100644
index 00000000000..bd58a9375f1
Binary files /dev/null and b/statsmodels/sandbox/regression/tests/griliches76.dta differ
diff --git a/statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv b/statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv
new file mode 100644
index 00000000000..7c021c532f5
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv
@@ -0,0 +1,3630 @@
+offer,ssiratio,age,educyr,physician,nonphysician,medicaid,private,female,phylim,actlim,income,totchr,insured,age2,linc,bh,docvis,ldocvis,ldocvisa,docbin,aget,aget2,incomet
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diff --git a/statsmodels/sandbox/regression/tests/results_gmm_griliches.py b/statsmodels/sandbox/regression/tests/results_gmm_griliches.py
new file mode 100644
index 00000000000..9ee6eebff02
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/results_gmm_griliches.py
@@ -0,0 +1,280 @@
+'''Results for GMM estimation with Stata gmm
+
+autogenerated, but edited
+'''
+
+import numpy as np
+
+# gmm twostep
+
+est = dict(
+           rank = 13,
+           N = 758,
+           Q = .0153053843867424,
+           J = 11.60148136515076,
+           J_df = 2,
+           k_1 = 13,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 13,
+           n_moments = 15,
+           k_aux = 13,
+           k_eq_model = 0,
+           k_eq = 13,
+           cmdline = "gmm (lw - {xb:s iq expr tenure rns smsa dyear*} - {b0}), instruments(expr tenure rns smsa dyear* med kww age mrt)",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "twostep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           inst_1 = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 med kww age mrt _cons",
+           params_1 = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           sexp_1 = "lw - ({xb_s} *s + {xb_iq} *iq + {xb_expr} *expr + {xb_tenure} *tenure + {xb_rns} *rns + {xb_smsa} *smsa + {xb_dyear_67} *dyear_67 + {xb_dyear_68} *dyear_68 + {xb_dyear_69} *dyear_69 + {xb_dyear_70} *dyear_70 + {xb_dyear_71} *dyear_71 + {xb_dyear_73} *dyear_73) - {b0}",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17579576802018,  .02085135579518,  8.4309034744314,  3.430065999e-17,
+      .1349278616328,  .21666367440756, np.nan,  1.9599639845401,
+                   0, -.00928615655842,  .00491818692744, -1.8881259893969,
+     .05900903858228, -.01892562580544,  .00035331268861, np.nan,
+     1.9599639845401,                0,  .05028275907275,  .00810402250669,
+     6.2046667603925,  5.481292387e-10,  .03439916682973,  .06616635131577,
+    np.nan,  1.9599639845401,                0,  .04252138311466,
+     .00956014552889,  4.4477757149388,  8.676405373e-06,  .02378384219108,
+     .06125892403824, np.nan,  1.9599639845401,                0,
+    -.10409306762507,  .03372997947655, -3.0860696994324,  .00202821272955,
+    -.17020261259839, -.03798352265175, np.nan,  1.9599639845401,
+                   0,  .12475123236049,  .03098732229429,  4.0258797186699,
+     .00005676270037,  .06401719668634,  .18548526803464, np.nan,
+     1.9599639845401,                0,  -.0530431735239,  .05178756424595,
+    -1.0242453819993,  .30571938841786, -.15454493429301,  .04845858724521,
+    np.nan,  1.9599639845401,                0,    .045954590377,
+      .0500069437958,  .91896418554698,  .35811430537838, -.05205721843969,
+     .14396639919369, np.nan,  1.9599639845401,                0,
+     .15548006235586,  .04801256009054,   3.238320599082,  .00120235613037,
+     .06137717377284,  .24958295093889, np.nan,  1.9599639845401,
+                   0,  .16698745541498,  .06133412154231,  2.7225865670836,
+     .00647730609718,  .04677478616864,  .28720012466132, np.nan,
+     1.9599639845401,                0,  .08464846645187,  .05581696231774,
+      1.516536603515,  .12938372202927, -.02475076941733,  .19404770232108,
+    np.nan,  1.9599639845401,                0,   .0996068440051,
+     .06123938652803,  1.6265160324477,  .10383992558646, -.02042014802516,
+     .21963383603536, np.nan,  1.9599639845401,                0,
+     4.0039243732137,  .33647541282379,  11.899604608883,  1.189091957e-32,
+     3.3444446823958,  4.6634040640315, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+      .0004347790385, -.00007935266682,  .00002810133321,  .00001482865838,
+    -.00017806902959, -6.657494262e-06, -.00011587283596, -.00018806600631,
+    -.00012201035826, -.00008278800394, -.00031494508432, -.00063549805233,
+     .00264135978484, -.00007935266682,  .00002418856265,  4.931157962e-06,
+    -.00001114070074,  .00006618982562,  -.0000220304377,  4.705098991e-07,
+     .00003206383766, -.00002261417242, -.00006024150154, -.00001412826237,
+     .00001471698858, -.00144299517314,  .00002810133321,  4.931157962e-06,
+     .00006567518079, -.00002036677634,  .00005210753053, -.00003295500811,
+     .00003592276303,  .00008754309097,  .00003052996731,  .00001691178156,
+    -.00008575591893, -.00013143657018, -.00094318538399,  .00001482865838,
+    -.00001114070074, -.00002036677634,  .00009139638253, -.00003771101246,
+     7.851603106e-06,  .00008478298315,  .00006722294295,  .00011231163007,
+      .0001007216652,  .00011202284802,  .00009437878507,  .00075643538833,
+    -.00017806902959,  .00006618982562,  .00005210753053, -.00003771101246,
+     .00113771151549,   .0001300530702,  .00018006693931,  .00018772842105,
+    -9.500874246e-06,  .00001633701903, -.00005338908155,  .00008260866257,
+    -.00499436928105, -6.657494262e-06,  -.0000220304377, -.00003295500811,
+     7.851603106e-06,   .0001300530702,  .00096021414297,  .00005702363753,
+     .00011167528598,  .00025281311283,  .00010653704891,  .00030212216421,
+       .000307795004,  .00157107924026, -.00011587283596,  4.705098991e-07,
+     .00003592276303,  .00008478298315,  .00018006693931,  .00005702363753,
+     .00268195181053,  .00085892679447,  .00091106709634,  .00096277498668,
+     .00090313286214,  .00102719488714,  .00034624154943, -.00018806600631,
+     .00003206383766,  .00008754309097,  .00006722294295,  .00018772842105,
+     .00011167528598,  .00085892679447,   .0025006944278,  .00092531815147,
+     .00088200162521,  .00082339570405,  .00095012566921,  -.0020631120951,
+    -.00012201035826, -.00002261417242,  .00003052996731,  .00011231163007,
+    -9.500874246e-06,  .00025281311283,  .00091106709634,  .00092531815147,
+     .00230520592645,  .00118209509709,  .00111002620771,  .00129242901685,
+     .00256100032752, -.00008278800394, -.00006024150154,  .00001691178156,
+      .0001007216652,  .00001633701903,  .00010653704891,  .00096277498668,
+     .00088200162521,  .00118209509709,  .00376187446537,  .00124524263644,
+     .00155856745623,  .00599140534858, -.00031494508432, -.00001412826237,
+    -.00008575591893,  .00011202284802, -.00005338908155,  .00030212216421,
+     .00090313286214,  .00082339570405,  .00111002620771,  .00124524263644,
+     .00311553328238,  .00184297728198,  .00431291320555, -.00063549805233,
+     .00001471698858, -.00013143657018,  .00009437878507,  .00008260866257,
+       .000307795004,  .00102719488714,  .00095012566921,  .00129242901685,
+     .00155856745623,  .00184297728198,  .00375026246233,  .00538820067052,
+     .00264135978484, -.00144299517314, -.00094318538399,  .00075643538833,
+    -.00499436928105,  .00157107924026,  .00034624154943,  -.0020631120951,
+     .00256100032752,  .00599140534858,  .00431291320555,  .00538820067052,
+     .11321570343494]).reshape(13,13)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_twostep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+# begin gmm onestep
+
+est = dict(
+           rank = 13,
+           N = 758,
+           Q = .0175043949471787,
+           J = 13.26833136996146,
+           J_df = 2,
+           k_1 = 13,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 13,
+           n_moments = 15,
+           k_aux = 13,
+           k_eq_model = 0,
+           k_eq = 13,
+           cmdline = "gmm (lw - {xb:s iq expr tenure rns smsa dyear*} - {b0}), instruments(expr tenure rns smsa dyear* med kww age mrt) onestep",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "onestep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           inst_1 = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 med kww age mrt _cons",
+           params_1 = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           sexp_1 = "lw - ({xb_s} *s + {xb_iq} *iq + {xb_expr} *expr + {xb_tenure} *tenure + {xb_rns} *rns + {xb_smsa} *smsa + {xb_dyear_67} *dyear_67 + {xb_dyear_68} *dyear_68 + {xb_dyear_69} *dyear_69 + {xb_dyear_70} *dyear_70 + {xb_dyear_71} *dyear_71 + {xb_dyear_73} *dyear_73) - {b0}",
+           properties = "b V",
+          )
+
+params_table = np.array([
+      .1724253119448,  .02073946970972,  8.3138727440057,  9.262847838e-17,
+     .13177669825528,  .21307392563431, np.nan,  1.9599639845401,
+                   0, -.00909883104783,  .00488623921543, -1.8621337692816,
+     .06258423710802, -.01867568392991,  .00047802183425, np.nan,
+     1.9599639845401,                0,  .04928948974112,  .00804979771953,
+     6.1230718408647,  9.178828333e-10,    .033512176128,  .06506680335423,
+    np.nan,  1.9599639845401,                0,  .04221709210829,
+     .00946363451925,  4.4609808232133,  8.158539147e-06,   .0236687092877,
+     .06076547492887, np.nan,  1.9599639845401,                0,
+    -.10179345005432,   .0337105276595, -3.0196338390938,  .00253080446805,
+    -.16786487016678, -.03572202994187, np.nan,  1.9599639845401,
+                   0,  .12611094948071,   .0308113805617,  4.0929989887401,
+     .00004258295784,  .06572175326583,  .18650014569559, np.nan,
+     1.9599639845401,                0,  -.0596171062088,  .05171372339438,
+    -1.1528295062831,  .24898037089783, -.16097414156825,  .04173992915064,
+    np.nan,  1.9599639845401,                0,  .04867955998567,
+     .04981322392381,  .97724170714436,  .32844950450919, -.04895256485883,
+     .14631168483017, np.nan,  1.9599639845401,                0,
+     .15281763323761,  .04792849748935,   3.188450321681,  .00143037585682,
+     .05887950432536,  .24675576214986, np.nan,  1.9599639845401,
+                   0,  .17443605153365,  .06112514589814,  2.8537527227227,
+     .00432061472141,  .05463296702353,  .29423913604377, np.nan,
+     1.9599639845401,                0,   .0916659665856,   .0554618025144,
+     1.6527765494425,  .09837634840443, -.01703716886029,  .20036910203149,
+    np.nan,  1.9599639845401,                0,  .09323976498299,
+     .06084900556261,  1.5323137021041,    .125445041459, -.02602209441479,
+     .21250162438078, np.nan,  1.9599639845401,                0,
+     4.0335098954259,  .33503289261392,   12.03914595954,  2.212341104e-33,
+     3.3768574922664,  4.6901622985855, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .00043012560384, -.00007821948168,  .00002814664044,  .00001470659469,
+    -.00018137297337, -8.404214163e-06,   -.000116495836, -.00019098604401,
+    -.00012670601919, -.00008672920733, -.00031350033095, -.00062509206531,
+     .00258704275396, -.00007821948168,  .00002387533367,  4.911669728e-06,
+    -.00001098678322,  .00006618473561, -.00002158670034,  8.107545213e-07,
+     .00003255315461, -.00002143051924,  -.0000597535309, -.00001402380853,
+     .00001385883996, -.00142630446035,  .00002814664044,  4.911669728e-06,
+     .00006479924333, -.00001977796199,  .00005110284341, -.00003232809926,
+     .00003557970376,  .00008581782553,  .00002961847494,  .00001478700432,
+    -.00008727552546, -.00012994173168, -.00094120116335,  .00001470659469,
+    -.00001098678322, -.00001977796199,  .00008956037831, -.00003784800308,
+     7.059546860e-06,  .00008151950631,  .00006348047144,  .00010852497856,
+     .00009624187488,  .00010823787214,  .00009132957164,  .00074787094553,
+    -.00018137297337,  .00006618473561,  .00005110284341, -.00003784800308,
+     .00113639967508,  .00013313518183,  .00019039509438,   .0002000965573,
+     7.191780465e-06,  .00002329093697, -.00005087978271,  .00009086571425,
+    -.00495748724374, -8.404214163e-06, -.00002158670034, -.00003232809926,
+     7.059546860e-06,  .00013313518183,  .00094934117212,  .00006195450052,
+     .00011810217311,  .00025505395817,  .00011081126685,  .00030134673539,
+     .00030676742472,  .00155300401754,   -.000116495836,  8.107545213e-07,
+     .00003557970376,  .00008151950631,  .00019039509438,  .00006195450052,
+     .00267430918731,  .00086135304709,  .00092017339035,  .00095567351479,
+       .000887006474,  .00102883960359,   .0003167617596, -.00019098604401,
+     .00003255315461,  .00008581782553,  .00006348047144,   .0002000965573,
+     .00011810217311,  .00086135304709,  .00248135727768,   .0009302682109,
+      .0008777378644,  .00081079994623,  .00094288525746, -.00207087031796,
+    -.00012670601919, -.00002143051924,  .00002961847494,  .00010852497856,
+     7.191780465e-06,  .00025505395817,  .00092017339035,   .0009302682109,
+     .00229714087159,  .00117701812554,  .00109484405919,  .00129252524238,
+     .00250083573173, -.00008672920733,  -.0000597535309,  .00001478700432,
+     .00009624187488,  .00002329093697,  .00011081126685,  .00095567351479,
+      .0008777378644,  .00117701812554,  .00373628346107,  .00123495172035,
+     .00154490399953,  .00600809353679, -.00031350033095, -.00001402380853,
+    -.00008727552546,  .00010823787214, -.00005087978271,  .00030134673539,
+       .000887006474,  .00081079994623,  .00109484405919,  .00123495172035,
+     .00307601153815,   .0018118788444,  .00430884303498, -.00062509206531,
+     .00001385883996, -.00012994173168,  .00009132957164,  .00009086571425,
+     .00030676742472,  .00102883960359,  .00094288525746,  .00129252524238,
+     .00154490399953,   .0018118788444,  .00370260147796,  .00534911865442,
+     .00258704275396, -.00142630446035, -.00094120116335,  .00074787094553,
+    -.00495748724374,  .00155300401754,   .0003167617596, -.00207087031796,
+     .00250083573173,  .00600809353679,  .00430884303498,  .00534911865442,
+     .11224703913325]).reshape(13,13)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+
+results_onestep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py b/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py
new file mode 100644
index 00000000000..9a4652df9dc
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py
@@ -0,0 +1,141 @@
+import numpy as np
+
+est = dict(
+           rank = 13,
+           N = 758,
+           Q = .0150568875809373,
+           J = 11.41312078635046,
+           J_df = 2,
+           k_1 = 13,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 13,
+           n_moments = 15,
+           k_aux = 13,
+           k_eq_model = 0,
+           ic = 6,
+           k_eq = 13,
+           cmdline = "gmm (lw - {xb:s iq expr tenure rns smsa dyear*} - {b0}) , instruments(expr tenure rns smsa dyear* med kww age mrt) igmm",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "igmm",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           inst_1 = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 med kww age mrt _cons",
+           params_1 = "xb_s xb_iq xb_expr xb_tenure xb_rns xb_smsa xb_dyear_67 xb_dyear_68 xb_dyear_69 xb_dyear_70 xb_dyear_71 xb_dyear_73 b0",
+           sexp_1 = "lw - ({xb_s} *s + {xb_iq} *iq + {xb_expr} *expr + {xb_tenure} *tenure + {xb_rns} *rns + {xb_smsa} *smsa + {xb_dyear_67} *dyear_67 + {xb_dyear_68} *dyear_68 + {xb_dyear_69} *dyear_69 + {xb_dyear_70} *dyear_70 + {xb_dyear_71} *dyear_71 + {xb_dyear_73} *dyear_73) - {b0}",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17587739850768,  .02085563162829,  8.4330890400415,  3.366583555e-17,
+      .1350011116414,  .21675368537396, np.nan,  1.9599639845401,
+                   0, -.00928586712743,  .00491894287617,   -1.88777697997,
+     .05905589683705, -.01892681800673,  .00035508375188, np.nan,
+     1.9599639845401,                0,  .05031651549731,  .00810558790493,
+     6.2076330659127,  5.378855978e-10,  .03442985513012,   .0662031758645,
+    np.nan,  1.9599639845401,                0,  .04246235782951,
+     .00956418082077,  4.4397276280375,  9.007280073e-06,  .02371690787918,
+     .06120780777985, np.nan,  1.9599639845401,                0,
+     -.1039476753865,  .03373281188749, -3.0815004611293,  .00205960157647,
+    -.17006277178325, -.03783257898975, np.nan,  1.9599639845401,
+                   0,  .12477256813508,  .03099244898605,  4.0259021864082,
+      .0000567572801,  .06402848432973,  .18551665194043, np.nan,
+     1.9599639845401,                0, -.05297127223127,   .0517946935923,
+    -1.0227162003936,  .30644204936546, -.15448700626247,  .04854446179993,
+    np.nan,  1.9599639845401,                0,  .04564516152971,
+     .05001865637643,  .91256272831865,  .36147256434055, -.05238960352318,
+      .1436799265826, np.nan,  1.9599639845401,                0,
+     .15574543741982,  .04802004585645,  3.2433421218593,  .00118136262363,
+     .06162787700523,  .24986299783442, np.nan,  1.9599639845401,
+                   0,  .16681173496168,  .06134387289984,  2.7192892635594,
+     .00654223677971,   .0465799534058,  .28704351651757, np.nan,
+     1.9599639845401,                0,  .08417610675323,  .05582688740597,
+      1.507805838092,  .13160422753823, -.02524258193145,  .19359479543791,
+    np.nan,  1.9599639845401,                0,  .09964580476612,
+     .06124947866865,  1.6268841291727,  .10376170930541, -.02040096749628,
+     .21969257702853, np.nan,  1.9599639845401,                0,
+     4.0027753075622,  .33649589464938,  11.895465505554,  1.249543428e-32,
+     3.3432554731038,  4.6622951420205, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .00043495737061, -.00007938790704,  .00002809207919,  .00001486824321,
+    -.00017806650894, -6.696078938e-06, -.00011595347261, -.00018816769626,
+    -.00012205118386, -.00008281236274, -.00031504876539, -.00063574245306,
+     .00264272738846, -.00007938790704,  .00002419599902,  4.932871670e-06,
+    -.00001114848619,  .00006618803917, -.00002202930782,  4.808220835e-07,
+     .00003206765662, -.00002261059773, -.00006024105579, -.00001412126593,
+     .00001474591556, -.00144330101198,  .00002809207919,  4.932871670e-06,
+     .00006570055528,  -.0000203894891,  .00005213529923, -.00003297805448,
+     .00003595284891,  .00008758906787,  .00003058926358,  .00001696423798,
+    -.00008568569767, -.00013140753648, -.00094326672008,  .00001486824321,
+    -.00001114848619,  -.0000203894891,  .00009147355477, -.00003774547245,
+     7.828122784e-06,  .00008484461309,  .00006729820252,  .00011236802193,
+     .00010082715772,  .00011217081931,  .00009440153548,  .00075659901252,
+    -.00017806650894,  .00006618803917,  .00005213529923, -.00003774547245,
+     .00113790259784,  .00013005865302,  .00018021354375,  .00018779266096,
+    -9.435310865e-06,   .0000165483542, -.00005323328914,  .00008265052168,
+    -.00499436873124, -6.696078938e-06, -.00002202930782, -.00003297805448,
+     7.828122784e-06,  .00013005865302,  .00096053189415,  .00005704546746,
+     .00011160225767,  .00025285680201,  .00010656723202,  .00030213005331,
+     .00030792696913,  .00157128168902, -.00011595347261,  4.808220835e-07,
+     .00003595284891,  .00008484461309,  .00018021354375,  .00005704546746,
+     .00268269028432,  .00085942321667,  .00091151417222,  .00096327250114,
+     .00090372304081,  .00102768195348,  .00034563629591, -.00018816769626,
+     .00003206765662,  .00008758906787,  .00006729820252,  .00018779266096,
+     .00011160225767,  .00085942321667,   .0025018659857,  .00092591134763,
+     .00088266305412,   .0008241186538,  .00095084381197, -.00206285154639,
+    -.00012205118386, -.00002261059773,  .00003058926358,  .00011236802193,
+    -9.435310865e-06,  .00025285680201,  .00091151417222,  .00092591134763,
+     .00230592480406,  .00118265696692,   .0011106470199,  .00129290662149,
+     .00256049741814, -.00008281236274, -.00006024105579,  .00001696423798,
+     .00010082715772,   .0000165483542,  .00010656723202,  .00096327250114,
+     .00088266305412,  .00118265696692,  .00376307074235,  .00124584145426,
+     .00155915431219,  .00599086304364, -.00031504876539, -.00001412126593,
+    -.00008568569767,  .00011217081931, -.00005323328914,  .00030213005331,
+     .00090372304081,   .0008241186538,   .0011106470199,  .00124584145426,
+     .00311664135744,   .0018437604357,  .00431259131307, -.00063574245306,
+     .00001474591556, -.00013140753648,  .00009440153548,  .00008265052168,
+     .00030792696913,  .00102768195348,  .00095084381197,  .00129290662149,
+     .00155915431219,   .0018437604357,  .00375149863718,  .00538769349865,
+     .00264272738846, -.00144330101198, -.00094326672008,  .00075659901252,
+    -.00499436873124,  .00157128168902,  .00034563629591, -.00206285154639,
+     .00256049741814,  .00599086304364,  .00431259131307,  .00538769349865,
+     .11322948711589]).reshape(13,13)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/sandbox/regression/tests/results_gmm_poisson.py b/statsmodels/sandbox/regression/tests/results_gmm_poisson.py
new file mode 100644
index 00000000000..ef553505bd4
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/results_gmm_poisson.py
@@ -0,0 +1,582 @@
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = 4.59536484786e-20,
+           J = 1.66765790329e-16,
+           J_df = 0,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 8,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( docvis - exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})), instruments(incomet ssiratio aget aget2 educyr actlim totchr) onestep vce(robust)",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "onestep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "incomet ssiratio aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "docvis - exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .62093805844748,  .35860052573857,   1.731559252928,  .08335206643438,
+    -.08190605683724,  1.3237821737322, np.nan,  1.9599639845401,
+                   0,  .68895699568302,  .43817618784254,  1.5723286997298,
+     .11587434043505,  -.1698525513714,  1.5477665427374, np.nan,
+     1.9599639845401,                0,  .25750627258076,  .05009451793791,
+     5.1404082358855,  2.741421857e-07,  .15932282159956,  .35568972356197,
+    np.nan,  1.9599639845401,                0, -.05352997420414,
+     .01103202674353, -4.8522339048464,  1.220785200e-06, -.07515234929795,
+    -.03190759911034, np.nan,  1.9599639845401,                0,
+     .03106248018916,  .01032090201131,  3.0096671933432,  .00261534090329,
+     .01083388395902,  .05129107641931, np.nan,  1.9599639845401,
+                   0,  .14175365608301,   .0494498280382,  2.8666157539212,
+     .00414886404159,  .04483377408643,  .23867353807958, np.nan,
+     1.9599639845401,                0,  .23128095221422,  .01565221628818,
+     14.776243054406,  2.084750820e-49,  .20060317201116,  .26195873241727,
+    np.nan,  1.9599639845401,                0,  .34763567088735,
+     .31615794015526,  1.0995633091379,  .27152243570261, -.27202250524333,
+     .96729384701803, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .12859433705998,  .13265896898444,  .00910916927048, -.00144786113189,
+    -.00037337560793, -.00152379041042, -.00336772308907, -.09899309651531,
+     .13265896898444,  .19199837159222,  .00979636564963, -.00135323134276,
+     .00180599814286, -.00930935415071, -.00460031335865, -.13429156867927,
+     .00910916927048,  .00979636564963,  .00250946072743, -.00052373946978,
+     5.155389870e-07, -.00016461502154, -.00025816911604, -.00869892550441,
+    -.00144786113189, -.00135323134276, -.00052373946978,  .00012170561407,
+     8.334416260e-06, -.00002526568199,  .00003797456789,  .00131001446811,
+    -.00037337560793,  .00180599814286,  5.155389870e-07,  8.334416260e-06,
+     .00010652101833, -.00026856403693, -.00003344387872, -.00122933496346,
+    -.00152379041042, -.00930935415071, -.00016461502154, -.00002526568199,
+    -.00026856403693,  .00244528549301,  .00003610001892,  .00527355381855,
+    -.00336772308907, -.00460031335865, -.00025816911604,  .00003797456789,
+    -.00003344387872,  .00003610001892,  .00024499187473,  .00300075896709,
+    -.09899309651531, -.13429156867927, -.00869892550441,  .00131001446811,
+    -.00122933496346,  .00527355381855,  .00300075896709,  .09995584312322
+    ]).reshape(8,8)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_addonestep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = 6.09567389485e-33,
+           J = 2.21212005644e-29,
+           J_df = 0,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 8,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( docvis - exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})), instruments(incomet ssiratio aget aget2 educyr actlim totchr) twostep vce(robust)",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "twostep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "incomet ssiratio aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "docvis - exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )",
+           properties = "b V",
+          )
+
+params_table = np.array([
+      .6209380584426,  .35860052570457,  1.7315592530786,  .08335206640755,
+    -.08190605677548,  1.3237821736607, np.nan,  1.9599639845401,
+                   0,  .68895699501744,  .43817618789764,  1.5723286980131,
+     .11587434083298, -.16985255214498,  1.5477665421799, np.nan,
+     1.9599639845401,                0,  .25750627271754,  .05009451794125,
+     5.1404082382732,  2.741421823e-07,  .15932282172979,  .35568972370529,
+    np.nan,  1.9599639845401,                0, -.05352997423123,
+     .01103202674378, -4.8522339071944,  1.220785186e-06, -.07515234932551,
+    -.03190759913694, np.nan,  1.9599639845401,                0,
+     .03106248018903,  .01032090201422,  3.0096671924822,   .0026153409107,
+     .01083388395319,  .05129107642488, np.nan,  1.9599639845401,
+                   0,  .14175365616691,  .04944982804302,  2.8666157553386,
+     .00414886402301,  .04483377416089,  .23867353817294, np.nan,
+     1.9599639845401,                0,  .23128095224221,  .01565221628892,
+     14.776243055497,  2.084750786e-49,  .20060317203771,  .26195873244672,
+    np.nan,  1.9599639845401,                0,  .34763567064032,
+     .31615794015859,   1.099563308345,  .27152243604826, -.27202250549689,
+     .96729384677754, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .12859433703559,   .1326589689683,  .00910916927021, -.00144786113188,
+    -.00037337560766, -.00152379040753, -.00336772308885, -.09899309649807,
+      .1326589689683,   .1919983716405,  .00979636565235, -.00135323134324,
+     .00180599814488, -.00930935415256, -.00460031335946, -.13429156869395,
+     .00910916927021,  .00979636565235,  .00250946072777, -.00052373946983,
+     5.155391569e-07, -.00016461502162, -.00025816911611, -.00869892550672,
+    -.00144786113188, -.00135323134324, -.00052373946983,  .00012170561408,
+     8.334416227e-06, -.00002526568198,   .0000379745679,  .00131001446858,
+    -.00037337560766,  .00180599814488,  5.155391569e-07,  8.334416227e-06,
+     .00010652101839, -.00026856403706, -.00003344387875, -.00122933496459,
+    -.00152379040753, -.00930935415256, -.00016461502162, -.00002526568198,
+    -.00026856403706,  .00244528549348,  .00003610001887,  .00527355381795,
+    -.00336772308885, -.00460031335946, -.00025816911611,   .0000379745679,
+    -.00003344387875,  .00003610001887,  .00024499187475,  .00300075896724,
+    -.09899309649807, -.13429156869395, -.00869892550672,  .00131001446858,
+    -.00122933496459,  .00527355381795,  .00300075896724,  .09995584312533
+    ]).reshape(8,8)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+results_addtwostep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = .0002538911897719,
+           J = .9213711276820714,
+           J_df = 1,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 9,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) onestep vce(robust)",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "onestep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .67045580921478,  .25039046077656,  2.6776411814389,  .00741425985435,
+     .17969952402034,  1.1612120944092, np.nan,  1.9599639845401,
+                   0,  .28551241628798,  .10358919281318,  2.7561988710819,
+     .00584774303307,  .08248132918657,   .4885435033894, np.nan,
+     1.9599639845401,                0,   .2672004738793,  .05203985579809,
+     5.1345352476769,  2.828420839e-07,  .16520423075439,  .36919671700421,
+    np.nan,  1.9599639845401,                0,  -.0560702624564,
+     .01191485946838, -4.7059105149509,  2.527353692e-06, -.07942295789528,
+    -.03271756701753, np.nan,  1.9599639845401,                0,
+     .01448379701656,  .00782559934942,  1.8508227127214,  .06419506241955,
+    -.00085409586574,  .02982168989887, np.nan,  1.9599639845401,
+                   0,  .18130374188096,   .0382173439987,  4.7440173206998,
+     2.095209222e-06,  .10639912405874,  .25620835970318, np.nan,
+     1.9599639845401,                0,  .28146161235562,  .01380395117777,
+     20.389931022715,  2.054354003e-92,  .25440636520284,  .30851685950839,
+    np.nan,  1.9599639845401,                0,  .51399857133918,
+     .10262653035745,  5.0084375799215,  5.487366567e-07,  .31285426798028,
+     .71514287469808, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+      .0626953828479,  .02323594786658,  .00535172023578, -.00103050587759,
+    -.00154311442856,  .00154515839603, -.00043159973572, -.01570852578318,
+     .02323594786658,  .01073072086769,  .00207768328305, -.00039713375955,
+    -.00049396171685,  .00027652302157, -.00020408147523, -.00701276303887,
+     .00535172023578,  .00207768328305,  .00270814659149, -.00059652725999,
+    -.00012298559534,  .00021079055266, -.00004341699196,  -.0031278522429,
+    -.00103050587759, -.00039713375955, -.00059652725999,  .00014196387615,
+     .00002481291175, -.00006035908648,  .00001093157006,  .00059187926133,
+    -.00154311442856, -.00049396171685, -.00012298559534,  .00002481291175,
+     .00006124000518, -.00001857594061,  .00001436652009,  .00008106194688,
+     .00154515839603,  .00027652302157,  .00021079055266, -.00006035908648,
+    -.00001857594061,  .00146056538231, -.00016708887634, -.00074321753343,
+    -.00043159973572, -.00020408147523, -.00004341699196,  .00001093157006,
+     .00001436652009, -.00016708887634,  .00019054906812, -.00028024031412,
+    -.01570852578318, -.00701276303887,  -.0031278522429,  .00059187926133,
+     .00008106194688, -.00074321753343, -.00028024031412,  .01053220473321
+    ]).reshape(8,8)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+
+results_multonestep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = .0002589826272982,
+           J = .9398479544653281,
+           J_df = 1,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 9,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep vce(robust)",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "twostep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .67815288158883,  .25053953449054,  2.7067699433856,  .00679413212727,
+     .18710441728393,  1.1692013458937, np.nan,  1.9599639845401,
+                   0,  .28872837589732,   .1032733938985,  2.7957672833051,
+     .00517766683505,  .08631624329503,  .49114050849961, np.nan,
+     1.9599639845401,                0,  .27067071818542,  .05199695467114,
+     5.2055109745809,  1.934635127e-07,  .16875855972422,  .37258287664662,
+    np.nan,  1.9599639845401,                0, -.05690856524563,
+     .01189861686254, -4.7827882772482,  1.728801925e-06, -.08022942576205,
+    -.03358770472921, np.nan,  1.9599639845401,                0,
+     .01438118999252,  .00783219080428,  1.8361644081315,  .06633334485657,
+    -.00096962190392,  .02973200188896, np.nan,  1.9599639845401,
+                   0,  .18038262255626,  .03826653224544,  4.7138481584715,
+     2.430818311e-06,  .10538159754195,  .25538364757056, np.nan,
+     1.9599639845401,                0,  .28251027986119,  .01378475918788,
+     20.494393555287,  2.415775858e-93,  .25549264831739,  .30952791140498,
+    np.nan,  1.9599639845401,                0,   .5077134442587,
+     .10235830367214,  4.9601588346456,  7.043556343e-07,  .30709485554269,
+      .7083320329747, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .06277005834274,  .02315710174743,  .00533574120292, -.00102544979294,
+    -.00154463417995,   .0015508406274, -.00043796451278, -.01559999387335,
+     .02315710174743,  .01066539388732,  .00206217803508, -.00039331197813,
+    -.00049172930967,  .00027603135609, -.00020644763374, -.00694810289238,
+     .00533574120292,  .00206217803508,  .00270368329507,  -.0005950942106,
+    -.00012276584915,  .00021462173623, -.00004681980342, -.00310767551047,
+    -.00102544979294, -.00039331197813,  -.0005950942106,  .00014157708324,
+     .00002474211336, -.00006134660609,  .00001178280314,  .00058658157366,
+    -.00154463417995, -.00049172930967, -.00012276584915,  .00002474211336,
+     .00006134321279, -.00001855941375,  .00001443470174,   .0000776612477,
+      .0015508406274,  .00027603135609,  .00021462173623, -.00006134660609,
+    -.00001855941375,  .00146432749009, -.00016643326394, -.00074847803836,
+    -.00043796451278, -.00020644763374, -.00004681980342,  .00001178280314,
+     .00001443470174, -.00016643326394,  .00019001958587, -.00027573517109,
+    -.01559999387335, -.00694810289238, -.00310767551047,  .00058658157366,
+      .0000776612477, -.00074847803836, -.00027573517109,  .01047722233064
+    ]).reshape(8,8)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+results_multtwostep = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = .0002590497181628,
+           J = .940091427212973,
+           J_df = 1,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 9,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep wmatrix(robust) vce(unadjusted) center",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "twostep",
+           wmatrix = "robust",
+           vce = "unadjusted",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .67815486150911,  .25018082946574,  2.7106587781218,  .00671496899138,
+      .1878094461339,  1.1685002768843, np.nan,  1.9599639845401,
+                   0,  .28872920226215,  .10311429027815,  2.8000891193967,
+     .00510884999633,  .08662890702558,  .49082949749873, np.nan,
+     1.9599639845401,                0,  .27067161407481,   .0518802415232,
+     5.2172388972735,  1.816099638e-07,  .16898820918009,  .37235501896953,
+    np.nan,  1.9599639845401,                0, -.05690878166227,
+      .0118728670827, -4.7931793783164,  1.641587211e-06, -.08017917353758,
+    -.03363838978695, np.nan,  1.9599639845401,                0,
+     .01438116368432,  .00781887593806,  1.8392878718448,   .0658728559523,
+    -.00094355155385,   .0297058789225, np.nan,  1.9599639845401,
+                   0,  .18038238197017,  .03819661477822,  4.7224703816696,
+     2.329970297e-06,  .10551839267351,  .25524637126682, np.nan,
+     1.9599639845401,                0,  .28251055147828,  .01376659609161,
+     20.521452768591,  1.385109204e-93,  .25552851894901,  .30949258400755,
+    np.nan,  1.9599639845401,                0,  .50771182444237,
+     .10208891085993,  4.9732318639284,  6.584582712e-07,  .30762123593598,
+     .70780241294876, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 'xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0'.split()
+
+cov = np.array([
+     .06259044743217,  .02308524749042,  .00531802921719,  -.0010223122446,
+    -.00154027662468,  .00154945994717, -.00043816683551, -.01554486097815,
+     .02308524749042,  .01063255685957,  .00205438168765, -.00039193802388,
+    -.00049039628782,   .0002760841411,  -.0002064504141, -.00691934867666,
+     .00531802921719,  .00205438168765,  .00269155946051, -.00059250696972,
+    -.00012247118567,  .00021403084056, -.00004749600121, -.00308951213731,
+     -.0010223122446, -.00039193802388, -.00059250696972,  .00014096497276,
+     .00002468288871, -.00006115240604,  .00001190303672,  .00058327928125,
+    -.00154027662468, -.00049039628782, -.00012247118567,  .00002468288871,
+     .00006113482093, -.00001854325518,  .00001439868646,  .00007784185009,
+     .00154945994717,   .0002760841411,  .00021403084056, -.00006115240604,
+    -.00001854325518,  .00145898138052, -.00016596475072, -.00074697007542,
+    -.00043816683551,  -.0002064504141, -.00004749600121,  .00001190303672,
+     .00001439868646, -.00016596475072,  .00018951916795, -.00027350320218,
+    -.01554486097815, -.00691934867666, -.00308951213731,  .00058327928125,
+     .00007784185009, -.00074697007542, -.00027350320218,  .01042214572057
+    ]).reshape(8,8)
+
+cov_colnames = 'xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0'.split()
+
+cov_rownames = 'xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0'.split()
+
+
+results_multtwostepdefault = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+
+import numpy as np
+
+est = dict(
+           rank = 8,
+           N = 3629,
+           Q = .0002590497181628,
+           J = .940091427212973,
+           J_df = 1,
+           k_1 = 8,
+           converged = 1,
+           has_xtinst = 0,
+           type = 1,
+           n_eq = 1,
+           k = 8,
+           n_moments = 9,
+           k_aux = 8,
+           k_eq_model = 0,
+           k_eq = 8,
+           cmdline = "gmm ( (docvis / exp({xb:private medicaid aget aget2 educyr actlim totchr}+{b0})) - 1), instruments(income ssiratio medicaid aget aget2 educyr actlim totchr) twostep wmatrix(robust) center",
+           cmd = "gmm",
+           estat_cmd = "gmm_estat",
+           predict = "gmm_p",
+           marginsnotok = "_ALL",
+           eqnames = "1",
+           technique = "gn",
+           winit = "Unadjusted",
+           estimator = "twostep",
+           wmatrix = "robust",
+           vce = "robust",
+           vcetype = "Robust",
+           params = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           inst_1 = "income ssiratio medicaid aget aget2 educyr actlim totchr _cons",
+           params_1 = "xb_private xb_medicaid xb_aget xb_aget2 xb_educyr xb_actlim xb_totchr b0",
+           sexp_1 = "(docvis / exp( ({xb_private} *private + {xb_medicaid} *medicaid + {xb_aget} *aget + {xb_aget2} *aget2 + {xb_educyr} *educyr + {xb_actlim} *actlim + {xb_totchr} *totchr) + {b0} )) - 1",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .67815486150911,  .25053960844836,  2.7067770469869,  .00679398676131,
+     .18710625224955,  1.1692034707687, np.nan,  1.9599639845401,
+                   0,  .28872920226215,  .10327332768441,  2.7957770775479,
+     .00517750993835,  .08631719943712,  .49114120508719, np.nan,
+     1.9599639845401,                0,  .27067161407481,  .05199697557915,
+     5.2055261110869,  1.934477426e-07,  .16875941463467,  .37258381351495,
+    np.nan,  1.9599639845401,                0, -.05690878166227,
+     .01189862079945, -4.7828048831437,  1.728659059e-06, -.08022964989488,
+    -.03358791342965, np.nan,  1.9599639845401,                0,
+     .01438116368432,  .00783219272776,  1.8361605982125,  .06633390816397,
+    -.00096965198207,  .02973197935072, np.nan,  1.9599639845401,
+                   0,  .18038238197017,  .03826654814775,    4.71383991244,
+     2.430916736e-06,  .10538132578791,  .25538343815243, np.nan,
+     1.9599639845401,                0,  .28251055147828,  .01378476509846,
+     20.494404471929,  2.415234157e-93,  .25549290834996,   .3095281946066,
+    np.nan,  1.9599639845401,                0,  .50771182444237,
+     .10235828870929,   4.960143734762,  7.044103886e-07,    .307093265053,
+     .70833038383174, np.nan,  1.9599639845401,                0
+    ]).reshape(8,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov = np.array([
+     .06277009540146,  .02315708886727,  .00533574465012,  -.0010254503134,
+    -.00154463481696,  .00155084007911, -.00043796389511, -.01559997980204,
+     .02315708886727,  .01066538021101,  .00206217721135, -.00039331175814,
+    -.00049172883672,  .00027603038575, -.00020644729789, -.00694809209467,
+     .00533574465012,  .00206217721135,  .00270368546938, -.00059509464294,
+      -.000122765895,  .00021462183651, -.00004681968717,   -.003107676362,
+     -.0010254503134, -.00039331175814, -.00059509464294,  .00014157717693,
+     .00002474211983, -.00006134664668,  .00001178278294,  .00058658166731,
+    -.00154463481696, -.00049172883672,   -.000122765895,  .00002474211983,
+     .00006134324292, -.00001855938213,  .00001443468876,  .00007766055925,
+     .00155084007911,  .00027603038575,  .00021462183651, -.00006134664668,
+    -.00001855938213,  .00146432870714, -.00016643336248, -.00074847778305,
+    -.00043796389511, -.00020644729789, -.00004681968717,  .00001178278294,
+     .00001443468876, -.00016643336248,  .00019001974882, -.00027573582025,
+    -.01559997980204, -.00694809209467,   -.003107676362,  .00058658166731,
+     .00007766055925, -.00074847778305, -.00027573582025,   .0104772192675
+    ]).reshape(8,8)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+
+results_multtwostepcenter = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py b/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py
new file mode 100644
index 00000000000..9fbabc01eaf
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py
@@ -0,0 +1,828 @@
+import numpy as np
+
+est = dict(
+           N = 758,
+           inexog_ct = 10,
+           exexog_ct = 4,
+           endog_ct = 2,
+           partial_ct = 0,
+           df_m = 12,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .2279825291623523,
+           rmse = .3766456260250817,
+           rss = 107.5313411236999,
+           mss = 31.75480871825532,
+           r2_a = .2155473484240278,
+           F = 37.63903370585436,
+           Fp = 1.04881083780e-68,
+           Fdf1 = 12,
+           Fdf2 = 745,
+           yy = 24652.2466174172,
+           yyc = 139.2861498419552,
+           r2u = .9956380713371686,
+           partialcons = 0,
+           cons = 1,
+           cdf = 12.55161416131593,
+           widstat = 12.55161416131593,
+           cd = .0675726199801665,
+           idp = 2.15251425210e-10,
+           iddf = 3,
+           idstat = 47.97804382236573,
+           sarganp = .0013146751383334,
+           sargandf = 2,
+           sargan = 13.26833137393004,
+           jp = .0013146751383334,
+           jdf = 2,
+           j = 13.26833137393004,
+           ll = -335.4059158173529,
+           rankV = 13,
+           rankxx = 13,
+           rankzz = 15,
+           r2c = .2279825291623523,
+           hacsubtitleV = "Statistics consistent for homoskedasticity only",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "IV (2SLS) estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 lw expr tenure rns smsa dyear* (s iq = med kww age mrt)",
+           cmd = "ivreg2",
+           model = "iv",
+           depvar = "lw",
+           partialsmall = "small",
+           exexog = "med kww age mrt",
+           inexog = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           insts = "med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           instd = "s iq",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17242531190423,  .02073807855911,  8.3144304527925,  9.219394861e-17,
+     .13177942481982,  .21307119898864, np.nan,  1.9599639845401,
+                   0, -.00909883103476,  .00470440157185, -1.9341101935687,
+     .05309958088226, -.01831928868441,  .00012162661488, np.nan,
+     1.9599639845401,                0,  .04928948974574,    .008154589406,
+     6.0443864542646,  1.499796319e-09,  .03330678820126,  .06527219129022,
+    np.nan,  1.9599639845401,                0,  .04221709210309,
+     .00884287446052,   4.774136768712,  1.804797032e-06,  .02488537664065,
+     .05954880756552, np.nan,  1.9599639845401,                0,
+    -.10179345001799,  .03417647128271, -2.9784657747708,  .00289695381119,
+    -.16877810285078, -.03480879718521, np.nan,  1.9599639845401,
+                   0,  .12611094946923,  .03092747922238,  4.0776342799379,
+     .00004549625503,  .06549420406076,  .18672769487769, np.nan,
+     1.9599639845401,                0, -.05961710621535,  .05529546018534,
+    -1.0781555305902,  .28096435347028, -.16799421668718,  .04876000425648,
+    np.nan,  1.9599639845401,                0,  .04867955999401,
+     .05201609347935,  .93585574651683,  .34934746462842, -.05327010984199,
+        .15062922983, np.nan,  1.9599639845401,                0,
+     .15281763322545,   .0515629903814,  2.9637077309732,   .0030395682829,
+     .05175602914272,  .25387923730817, np.nan,  1.9599639845401,
+                   0,  .17443605148569,  .05975759031645,  2.9190610023255,
+     .00351087511908,  .05731332666255,  .29155877630882, np.nan,
+     1.9599639845401,                0,  .09166596656323,  .05414400395495,
+     1.6930031003894,  .09045487706511, -.01445433116727,  .19778626429372,
+    np.nan,  1.9599639845401,                0,  .09323976497853,
+      .0571819085978,  1.6305815469428,  .10297864547348, -.01883471644041,
+     .20531424639747, np.nan,  1.9599639845401,                0,
+     4.0335098946211,  .31542152364325,  12.787681221092,  1.921209213e-37,
+     3.4152950683316,  4.6517247209107, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov = np.array([
+     .00043006790232, -.00007669911732,  .00003390383154,  .00001947510761,
+    -.00014211117725,  .00001984430949,  .00001313809086, -.00010726217801,
+     -.0000934373525,  .00003291577926, -.00026067342405, -.00058025280789,
+     .00230075400852, -.00007669911732,  .00002213139415,  4.388754481e-06,
+    -8.774272042e-06,  .00005855699916, -.00001627155824, -.00001175114527,
+     .00001447038262, -.00001630144788, -.00007254738147,  -.0000221423658,
+     .00001715527313, -.00125964216852,  .00003390383154,  4.388754481e-06,
+     .00006649732838, -.00001561989737,  .00003340985346, -6.286271714e-06,
+     .00004284715305,  .00006535720207,  .00002474647562,  2.348670591e-06,
+    -.00012774309302, -.00013889592824, -.00097053265371,  .00001947510761,
+    -8.774272042e-06, -.00001561989737,  .00007819642872, -.00001656102532,
+    -1.454183906e-06,  .00006415542548,   .0000658729123,  .00007759572764,
+     .00006755177717,  .00006035533972,  .00007925563165,  .00048915076309,
+    -.00014211117725,  .00005855699916,  .00003340985346, -.00001656102532,
+     .00116803118934,  .00009910080232,  .00010687488928,   .0000916567869,
+    -.00004794231232, -.00018139826234, -.00013904727086, -.00016941373862,
+    -.00453388036786,  .00001984430949, -.00001627155824, -6.286271714e-06,
+    -1.454183906e-06,  .00009910080232,  .00095650897105,  2.293607157e-06,
+    -4.175180521e-06,  .00008023363682,   .0000277191399,  .00011455631826,
+     7.627834100e-06,  .00071035658346,  .00001313809086, -.00001175114527,
+     .00004284715305,  .00006415542548,  .00010687488928,  2.293607157e-06,
+     .00305758791711,  .00082806878751,  .00084056268139,  .00086879959596,
+     .00072687243235,  .00079702544855,  .00005969922435, -.00010726217801,
+     .00001447038262,  .00006535720207,   .0000658729123,   .0000916567869,
+    -4.175180521e-06,  .00082806878751,  .00270567398085,  .00090000327564,
+      .0008652672979,  .00075215815711,  .00093055832029,  -.0011308128858,
+     -.0000934373525, -.00001630144788,  .00002474647562,  .00007759572764,
+    -.00004794231232,  .00008023363682,  .00084056268139,  .00090000327564,
+     .00265874197707,  .00106768253686,  .00097403133741,  .00118960267354,
+     .00179874271988,  .00003291577926, -.00007254738147,  2.348670591e-06,
+     .00006755177717, -.00018139826234,   .0000277191399,  .00086879959596,
+      .0008652672979,  .00106768253686,  .00357096960043,  .00115404290469,
+     .00134956217212,  .00598978065251, -.00026067342405,  -.0000221423658,
+    -.00012774309302,  .00006035533972, -.00013904727086,  .00011455631826,
+     .00072687243235,  .00075215815711,  .00097403133741,  .00115404290469,
+     .00293157316427,  .00171538928992,   .0048030008792, -.00058025280789,
+     .00001715527313, -.00013889592824,  .00007925563165, -.00016941373862,
+     7.627834100e-06,  .00079702544855,  .00093055832029,  .00118960267354,
+     .00134956217212,  .00171538928992,  .00326977067089,  .00483241607215,
+     .00230075400852, -.00125964216852, -.00097053265371,  .00048915076309,
+    -.00453388036786,  .00071035658346,  .00005969922435,  -.0011308128858,
+     .00179874271988,  .00598978065251,   .0048030008792,  .00483241607215,
+     .09949073757743]).reshape(13,13)
+
+cov_colnames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           N = 758,
+           inexog_ct = 10,
+           exexog_ct = 4,
+           endog_ct = 2,
+           partial_ct = 0,
+           df_m = 12,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .2279825291623523,
+           rmse = .3766456260250817,
+           rss = 107.5313411236999,
+           mss = 31.75480871825532,
+           r2_a = .2155473484240278,
+           F = 40.08955571761713,
+           Fp = 1.50331141073e-72,
+           Fdf1 = 12,
+           Fdf2 = 745,
+           yy = 24652.2466174172,
+           yyc = 139.2861498419552,
+           r2u = .9956380713371686,
+           partialcons = 0,
+           cons = 1,
+           cdf = 12.55161416131593,
+           widstat = 11.46142788662503,
+           cd = .0675726199801665,
+           idp = 6.77658650925e-09,
+           iddf = 3,
+           idstat = 40.92698219921901,
+           jp = .0030253131145893,
+           jdf = 2,
+           j = 11.60148136780177,
+           ll = -335.4059158173529,
+           rankV = 13,
+           rankS = 15,
+           rankxx = 13,
+           rankzz = 15,
+           r2c = .2279825291623523,
+           hacsubtitleV = "Statistics robust to heteroskedasticity",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "IV (2SLS) estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 lw expr tenure rns smsa dyear* (s iq = med kww age mrt), robust",
+           cmd = "ivreg2",
+           model = "iv",
+           depvar = "lw",
+           vcetype = "Robust",
+           partialsmall = "small",
+           exexog = "med kww age mrt",
+           inexog = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           insts = "med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           instd = "s iq",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17242531190423,  .02073946970741,  8.3138727429773,  9.262847918e-17,
+     .13177669821925,  .21307392558922, np.nan,  1.9599639845401,
+                   0, -.00909883103476,  .00488623921475,  -1.862133766863,
+     .06258423744885, -.01867568391553,    .000478021846, np.nan,
+     1.9599639845401,                0,  .04928948974574,  .00804979771814,
+     6.1230718424932,  9.178828239e-10,  .03351217613534,  .06506680335614,
+    np.nan,  1.9599639845401,                0,  .04221709210309,
+     .00946363451747,  4.4609808235038,  8.158539136e-06,  .02366870928599,
+     .06076547492018, np.nan,  1.9599639845401,                0,
+    -.10179345001799,  .03371052765435, -3.0196338384772,   .0025308044732,
+    -.16786487012036, -.03572202991563, np.nan,  1.9599639845401,
+                   0,  .12611094946923,  .03081138055695,  4.0929989889975,
+      .0000425829578,  .06572175326364,  .18650014567481, np.nan,
+     1.9599639845401,                0, -.05961710621535,  .05171372338658,
+    -1.1528295065836,  .24898037077447, -.16097414155951,  .04173992912881,
+    np.nan,  1.9599639845401,                0,  .04867955999401,
+     .04981322391886,  .97724170740882,  .32844950437829, -.04895256484079,
+     .14631168482881, np.nan,  1.9599639845401,                0,
+     .15281763322545,  .04792849748217,  3.1884503219051,  .00143037585571,
+     .05887950432728,  .24675576212361, np.nan,  1.9599639845401,
+                   0,  .17443605148569,  .06112514588945,  2.8537527223437,
+     .00432061472656,   .0546329669926,  .29423913597877, np.nan,
+     1.9599639845401,                0,  .09166596656323,  .05546180250571,
+     1.6527765492979,  .09837634843385, -.01703716886563,  .20036910199209,
+    np.nan,  1.9599639845401,                0,  .09323976497853,
+     .06084900555321,  1.5323137022675,  .12544504141872, -.02602209440084,
+     .21250162435791, np.nan,  1.9599639845401,                0,
+     4.0335098946211,  .33503289255951,  12.039145959093,  2.212341116e-33,
+     3.3768574915682,  4.6901622976741, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov = np.array([
+     .00043012560374, -.00007821948167,  .00002814664043,  .00001470659468,
+     -.0001813729733, -8.404214148e-06, -.00011649583596,   -.000190986044,
+     -.0001267060192, -.00008672920729, -.00031350033086, -.00062509206513,
+     .00258704275336, -.00007821948167,  .00002387533366,  4.911669726e-06,
+    -.00001098678321,  .00006618473559, -.00002158670033,  8.107545182e-07,
+     .00003255315461, -.00002143051923, -.00005975353088, -.00001402380853,
+     .00001385883995, -.00142630445991,  .00002814664043,  4.911669726e-06,
+      .0000647992433, -.00001977796198,  .00005110284339, -.00003232809925,
+     .00003557970375,  .00008581782551,  .00002961847493,  .00001478700432,
+    -.00008727552542, -.00012994173162,   -.000941201163,  .00001470659468,
+    -.00001098678321, -.00001977796198,  .00008956037828, -.00003784800305,
+     7.059546852e-06,   .0000815195063,  .00006348047141,  .00010852497853,
+     .00009624187487,   .0001082378721,   .0000913295716,  .00074787094515,
+     -.0001813729733,  .00006618473559,  .00005110284339, -.00003784800305,
+     .00113639967473,   .0001331351818,  .00019039509428,  .00020009655722,
+     7.191780470e-06,  .00002329093699, -.00005087978262,  .00009086571417,
+     -.0049574872418, -8.404214148e-06, -.00002158670033, -.00003232809925,
+     7.059546852e-06,   .0001331351818,  .00094934117183,  .00006195450043,
+     .00011810217306,  .00025505395801,  .00011081126682,   .0003013467353,
+     .00030676742453,  .00155300401661, -.00011649583596,  8.107545182e-07,
+     .00003557970375,   .0000815195063,  .00019039509428,  .00006195450043,
+      .0026743091865,  .00086135304687,  .00092017339013,  .00095567351458,
+     .00088700647379,  .00102883960334,  .00031676175972,   -.000190986044,
+     .00003255315461,  .00008581782551,  .00006348047141,  .00020009655722,
+     .00011810217306,  .00086135304687,  .00248135727719,  .00093026821071,
+     .00087773786421,  .00081079994607,   .0009428852573, -.00207087031744,
+     -.0001267060192, -.00002143051923,  .00002961847493,  .00010852497853,
+     7.191780470e-06,  .00025505395801,  .00092017339013,  .00093026821071,
+      .0022971408709,  .00117701812528,  .00109484405896,  .00129252524214,
+     .00250083573092, -.00008672920729, -.00005975353088,  .00001478700432,
+     .00009624187487,  .00002329093699,  .00011081126682,  .00095567351458,
+     .00087773786421,  .00117701812528,  .00373628346001,  .00123495172003,
+     .00154490399913,  .00600809353497, -.00031350033086, -.00001402380853,
+    -.00008727552542,   .0001082378721, -.00005087978262,   .0003013467353,
+     .00088700647379,  .00081079994607,  .00109484405896,  .00123495172003,
+     .00307601153718,  .00181187884386,  .00430884303329, -.00062509206513,
+     .00001385883995, -.00012994173162,   .0000913295716,  .00009086571417,
+     .00030676742453,  .00102883960334,   .0009428852573,  .00129252524214,
+     .00154490399913,  .00181187884386,  .00370260147681,  .00534911865268,
+     .00258704275336, -.00142630445991,   -.000941201163,  .00074787094515,
+     -.0049574872418,  .00155300401661,  .00031676175972, -.00207087031744,
+     .00250083573092,  .00600809353497,  .00430884303329,  .00534911865268,
+     .11224703909679]).reshape(13,13)
+
+cov_colnames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           N = 758,
+           inexog_ct = 10,
+           exexog_ct = 4,
+           endog_ct = 2,
+           partial_ct = 0,
+           df_r = 745,
+           df_m = 12,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .2279825291623523,
+           rmse = .3799175840045295,
+           rss = 107.5313411236999,
+           mss = 31.75480871825532,
+           r2_a = .2155473484240278,
+           F = 37.63903370585438,
+           Fp = 1.04881083780e-68,
+           Fdf1 = 12,
+           Fdf2 = 745,
+           yy = 24652.2466174172,
+           yyc = 139.2861498419552,
+           partialcons = 0,
+           cons = 1,
+           cdf = 12.55161416131593,
+           widstat = 12.55161416131593,
+           cd = .0675726199801665,
+           idp = 2.15251425210e-10,
+           iddf = 3,
+           idstat = 47.97804382236573,
+           sarganp = .0013146751383334,
+           sargandf = 2,
+           sargan = 13.26833137393004,
+           jp = .0013146751383334,
+           jdf = 2,
+           j = 13.26833137393004,
+           ll = -335.4059158173529,
+           rankV = 13,
+           rankxx = 13,
+           rankzz = 15,
+           r2c = .2279825291623523,
+           r2u = .9956380713371686,
+           hacsubtitleV = "Statistics consistent for homoskedasticity only",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "IV (2SLS) estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 lw expr tenure rns smsa dyear* (s iq = med kww age mrt), small",
+           cmd = "ivreg2",
+           model = "iv",
+           depvar = "lw",
+           partialsmall = "small",
+           small = "small",
+           exexog = "med kww age mrt",
+           inexog = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           insts = "med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           instd = "s iq",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17242531190423,  .02091823230823,  8.2428242197305,  7.570254109e-16,
+     .13135961443277,   .2134910093757,              745,  1.9631533327653,
+                   0, -.00909883103476,  .00474526917577,  -1.917453088061,
+       .055562500369, -.01841452203205,  .00021685996252,              745,
+     1.9631533327653,                0,  .04928948974574,  .00822542913447,
+     5.9923304839106,  3.219967401e-09,  .03314171112698,   .0654372683645,
+                 745,  1.9631533327653,                0,  .04221709210309,
+     .00891969338965,  4.7330205488974,  2.647391010e-06,  .02470636629795,
+     .05972781790823,              745,  1.9631533327653,                0,
+    -.10179345001799,  .03447336568476, -2.9528143828148,  .00324794122176,
+    -.16946995275367, -.03411694728232,              745,  1.9631533327653,
+                   0,  .12611094946923,  .03119614930755,  4.0425165370872,
+     .00005838098525,  .06486812498666,  .18735377395179,              745,
+     1.9631533327653,                0, -.05961710621535,  .05577581734252,
+    -1.0688701493919,  .28547438462206, -.16911358791902,  .04987937548832,
+                 745,  1.9631533327653,                0,  .04867955999401,
+     .05246796245209,  .92779589141575,  .35381393703187, -.05432309535722,
+     .15168221534524,              745,  1.9631533327653,                0,
+     .15281763322545,   .0520109232025,  2.9381834394759,  .00340336072573,
+     .05071221600025,  .25492305045064,              745,  1.9631533327653,
+                   0,  .17443605148569,  .06027671044146,  2.8939212211172,
+     .00391575116799,   .0561036264944,  .29276847647697,              745,
+     1.9631533327653,                0,  .09166596656323,  .05461435829744,
+      1.678422477547,  .09368402236573, -.01555039294523,  .19888232607168,
+                 745,  1.9631533327653,                0,  .09323976497853,
+     .05767865351978,  1.6165385162217,  .10640126701552, -.01999227590824,
+     .20647180586531,              745,  1.9631533327653,                0,
+     4.0335098946211,  .31816162176165,  12.677550083784,  1.723599587e-33,
+     3.4089098465017,  4.6581099427405,              745,  1.9631533327653,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov = np.array([
+      .0004375724429, -.00007803749118,  .00003449544203,   .0000198149417,
+    -.00014459096961,  .00002019058603,  .00001336734613, -.00010913386703,
+    -.00009506780295,  .00003349014856, -.00026522208782, -.00059037802467,
+      .0023409013939, -.00007803749118,  .00002251757955,  4.465336774e-06,
+    -8.927380145e-06,  .00005957879915, -.00001655549147, -.00001195619881,
+     .00001472288594, -.00001658590268, -.00007381330893, -.00002252874265,
+     .00001745462689, -.00128162250166,  .00003449544203,  4.465336774e-06,
+     .00006765768445, -.00001589245933,  .00003399284419, -6.395965046e-06,
+      .0000435948215,  .00006649766331,  .00002517829331,  2.389654105e-06,
+    -.00012997216713, -.00014131961558, -.00098746812283,   .0000198149417,
+    -8.927380145e-06, -.00001589245933,  .00007956093017, -.00001685000965,
+    -1.479558927e-06,  .00006527491612,  .00006702237252,  .00007894974705,
+     .00006873053302,  .00006140852014,  .00008063861583,  .00049768627977,
+    -.00014459096961,  .00005957879915,  .00003399284419, -.00001685000965,
+     .00118841294164,  .00010083007806,  .00010873982024,  .00009325616707,
+    -.00004877888958, -.00018456360114, -.00014147359908, -.00017236995151,
+    -.00461299505884,  .00002019058603, -.00001655549147, -6.395965046e-06,
+    -1.479558927e-06,  .00010083007806,  .00097319973162,  2.333629833e-06,
+    -4.248036020e-06,  .00008163368686,  .00002820282959,  .00011655528757,
+     7.760937245e-06,  .00072275206747,  .00001336734613, -.00001195619881,
+      .0000435948215,  .00006527491612,  .00010873982024,  2.333629833e-06,
+     .00311094180023,  .00084251830998,  .00085523021811,  .00088395985737,
+     .00073955611238,  .00081093327517,  .00006074095578, -.00010913386703,
+     .00001472288594,  .00006649766331,  .00006702237252,  .00009325616707,
+    -4.248036020e-06,  .00084251830998,  .00275288708387,  .00091570803079,
+     .00088036592189,  .00076528306455,  .00094679625072, -.00115054519119,
+    -.00009506780295, -.00001658590268,  .00002517829331,  .00007894974705,
+    -.00004877888958,  .00008163368686,  .00085523021811,  .00091570803079,
+     .00270513613238,  .00108631323884,  .00099102785739,    .001210360841,
+     .00183013017674,  .00003349014856, -.00007381330893,  2.389654105e-06,
+     .00006873053302, -.00018456360114,  .00002820282959,  .00088395985737,
+     .00088036592189,  .00108631323884,  .00363328182164,  .00117418056612,
+     .00137311157915,   .0060943003149, -.00026522208782, -.00002252874265,
+    -.00012997216713,  .00006140852014, -.00014147359908,  .00011655528757,
+     .00073955611238,  .00076528306455,  .00099102785739,  .00117418056612,
+     .00298272813224,  .00174532225739,   .0048868116328, -.00059037802467,
+     .00001745462689, -.00014131961558,  .00008063861583, -.00017236995151,
+     7.760937245e-06,  .00081093327517,  .00094679625072,    .001210360841,
+     .00137311157915,  .00174532225739,  .00332682707186,  .00491674011099,
+      .0023409013939, -.00128162250166, -.00098746812283,  .00049768627977,
+    -.00461299505884,  .00072275206747,  .00006074095578, -.00115054519119,
+     .00183013017674,   .0060943003149,   .0048868116328,  .00491674011099,
+       .101226817562]).reshape(13,13)
+
+cov_colnames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+# not autogenerated
+# calculated with `ivendog` after ivreg2
+hausman = dict(
+                df_r =  743,
+                df =  2,
+                WHFp =  1.47099195224e-16,
+                WHF =  38.30408858936179,
+                DWHp =  4.12270104038e-16,
+                DWH =  70.84970589405181
+                )
+
+results_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                hausman = hausman,
+                **est
+                )
+
+
+
+import numpy as np
+
+est = dict(
+           N = 758,
+           inexog_ct = 10,
+           exexog_ct = 4,
+           endog_ct = 2,
+           partial_ct = 0,
+           df_r = 745,
+           df_m = 12,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .2279825291623523,
+           rmse = .3799175840045295,
+           rss = 107.5313411236999,
+           mss = 31.75480871825532,
+           r2_a = .2155473484240278,
+           F = 40.08955571761724,
+           Fp = 1.50331141073e-72,
+           Fdf1 = 12,
+           Fdf2 = 745,
+           yy = 24652.2466174172,
+           yyc = 139.2861498419552,
+           partialcons = 0,
+           cons = 1,
+           cdf = 12.55161416131593,
+           widstat = 11.46142788662503,
+           cd = .0675726199801665,
+           idp = 6.77658650925e-09,
+           iddf = 3,
+           idstat = 40.92698219921901,
+           jp = .0030253131145893,
+           jdf = 2,
+           j = 11.60148136780177,
+           ll = -335.4059158173529,
+           rankV = 13,
+           rankS = 15,
+           rankxx = 13,
+           rankzz = 15,
+           r2c = .2279825291623523,
+           r2u = .9956380713371686,
+           hacsubtitleV = "Statistics robust to heteroskedasticity",
+           hacsubtitleB = "Estimates efficient for homoskedasticity only",
+           title = "IV (2SLS) estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 lw expr tenure rns smsa dyear* (s iq = med kww age mrt), small robust",
+           cmd = "ivreg2",
+           model = "iv",
+           depvar = "lw",
+           vcetype = "Robust",
+           partialsmall = "small",
+           small = "small",
+           exexog = "med kww age mrt",
+           inexog = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           insts = "med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           instd = "s iq",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17242531190423,  .02091963554158,  8.2422713130704,  7.602390656e-16,
+     .13135685967055,  .21349376413792,              745,  1.9631533327653,
+                   0, -.00909883103476,  .00492868646034, -1.8460965427549,
+     .06527471189517, -.01877459828554,  .00057693621602,              745,
+     1.9631533327653,                0,  .04928948974574,  .00811972711081,
+     6.0703382112603,  2.032123371e-09,  .03334922040701,  .06522975908447,
+                 745,  1.9631533327653,                0,  .04221709210309,
+      .0095458460509,  4.4225615915015,  .00001120706314,   .0234771326142,
+     .06095705159197,              745,  1.9631533327653,                0,
+    -.10179345001799,   .0340033743578, -2.9936278954812,  .00284801771574,
+    -.16854728771377, -.03503961232222,              745,  1.9631533327653,
+                   0,  .12611094946923,  .03107904208149,   4.057748920915,
+     .00005477891606,   .0650980244278,  .18712387451065,              745,
+     1.9631533327653,                0, -.05961710621535,  .05216296564028,
+    -1.1429010119263,  .25344683532742, -.16202100605898,  .04278679362828,
+                 745,  1.9631533327653,                0,  .04867955999401,
+     .05024595634484,  .96882542467533,  .33294671206223, -.04996095666234,
+     .14732007665036,              745,  1.9631533327653,                0,
+     .15281763322545,  .04834485710231,  3.1609904834768,  .00163599246861,
+     .05790926588299,  .24772600056791,              745,  1.9631533327653,
+                   0,  .17443605148569,  .06165614610562,  2.8291753945643,
+     .00479228267652,  .05339558277297,   .2954765201984,              745,
+     1.9631533327653,                0,  .09166596656323,  .05594360469515,
+      1.638542368922,  .10173082236229, -.01815990744096,  .20149184056742,
+                 745,  1.9631533327653,                0,  .09323976497853,
+     .06137760691084,  1.5191169820938,  .12915730016675, -.02725388858564,
+     .21373341854271,              745,  1.9631533327653,                0,
+     4.0335098946211,  .33794335658841,  11.935461419748,  3.577808889e-30,
+     3.3700752678487,  4.6969445213936,              745,  1.9631533327653,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov = np.array([
+     .00043763115119, -.00007958438537,  .00002863778986,  .00001496321982,
+    -.00018453787082, -8.550864865e-06,  -.0001185286492, -.00019431868638,
+    -.00012891699672, -.00008824260285, -.00031897080643,  -.0006359997119,
+     .00263218578127, -.00007958438537,  .00002429195022,  4.997376715e-06,
+    -.00001117849889,  .00006733963701, -.00002196338101,  8.249019125e-07,
+     .00003312119623,  -.0000218044746, -.00006079620995, -.00001426851928,
+     .00001410067206, -.00145119299411,  .00002863778986,  4.997376715e-06,
+     .00006592996835, -.00002012308078,  .00005199457086, -.00003289221373,
+     .00003620055764,  .00008731531776,  .00003013530738,  .00001504503258,
+    -.00008879845405, -.00013220917123, -.00095762480745,  .00001496321982,
+    -.00001117849889, -.00002012308078,  .00009112317683,   -.000038508438,
+     7.182733576e-06,  .00008294199433,  .00006458818434,  .00011041870299,
+     .00009792126329,  .00011012658665,  .00009292324197,  .00076092104218,
+    -.00018453787082,  .00006733963701,  .00005199457086,   -.000038508438,
+     .00115622946772,  .00013545834604,  .00019371742478,    .000203588175,
+     7.317274626e-06,  .00002369735603, -.00005176761775,  .00009245129039,
+    -.00504399373059, -8.550864865e-06, -.00002196338101, -.00003289221373,
+     7.182733576e-06,  .00013545834604,   .0009659068567,  .00006303558567,
+     .00012016301635,  .00025950456399,  .00011274488624,  .00030660513471,
+     .00031212041314,  .00158010341555,  -.0001185286492,  8.249019125e-07,
+     .00003620055764,  .00008294199433,  .00019371742478,  .00006303558567,
+     .00272097498439,   .0008763833685,    .000936230107,  .00097234969671,
+      .0009024844391,  .00104679250917,  .00032228914613, -.00019431868638,
+     .00003312119623,  .00008731531776,  .00006458818434,    .000203588175,
+     .00012016301635,   .0008763833685,  .00252465612901,  .00094650107881,
+      .0008930540954,  .00082494813305,  .00095933828863, -.00210700630956,
+    -.00012891699672,  -.0000218044746,  .00003013530738,  .00011041870299,
+     7.317274626e-06,  .00025950456399,    .000936230107,  .00094650107881,
+     .00233722520824,   .0011975566966,  .00111394872039,  .00131507937389,
+     .00254447447521, -.00008824260285, -.00006079620995,  .00001504503258,
+     .00009792126329,  .00002369735603,  .00011274488624,  .00097234969671,
+      .0008930540954,   .0011975566966,   .0038014803526,  .00125650121313,
+     .00157186205549,  .00611293275102, -.00031897080643, -.00001426851928,
+    -.00008879845405,  .00011012658665, -.00005176761775,  .00030660513471,
+      .0009024844391,  .00082494813305,  .00111394872039,  .00125650121313,
+     .00312968690629,  .00184349552167,   .0043840308983,  -.0006359997119,
+     .00001410067206, -.00013220917123,  .00009292324197,  .00009245129039,
+     .00031212041314,  .00104679250917,  .00095933828863,  .00131507937389,
+     .00157186205549,  .00184349552167,   .0037672106301,  .00544245897816,
+     .00263218578127, -.00145119299411, -.00095762480745,  .00076092104218,
+    -.00504399373059,  .00158010341555,  .00032228914613, -.00210700630956,
+     .00254447447521,  .00611293275102,   .0043840308983,  .00544245897816,
+     .11420571226224]).reshape(13,13)
+
+cov_colnames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_small_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
+
+import numpy as np
+
+est = dict(
+           N = 758,
+           inexog_ct = 10,
+           exexog_ct = 4,
+           endog_ct = 2,
+           partial_ct = 0,
+           df_m = 12,
+           sdofminus = 0,
+           dofminus = 0,
+           r2 = .2168305947462866,
+           rmse = .3793562300139549,
+           rss = 109.0846511318037,
+           mss = 30.20149871015154,
+           r2_a = .2042157855341463,
+           F = 41.97598961240392,
+           Fp = 1.89290505854e-75,
+           Fdf1 = 12,
+           Fdf2 = 745,
+           yy = 24652.2466174172,
+           yyc = 139.2861498419552,
+           r2u = .995575062475047,
+           partialcons = 0,
+           cons = 1,
+           cdf = 12.55161416131593,
+           widstat = 11.46142788662503,
+           cd = .0675726199801665,
+           idp = 6.77658650925e-09,
+           iddf = 3,
+           idstat = 40.92698219921901,
+           jp = .0030253131145893,
+           jdf = 2,
+           j = 11.60148136780177,
+           ll = -340.8414755627023,
+           rankV = 13,
+           rankS = 15,
+           rankxx = 13,
+           rankzz = 15,
+           r2c = .2168305947462866,
+           hacsubtitleV = "Statistics robust to heteroskedasticity",
+           hacsubtitleB = "Estimates efficient for arbitrary heteroskedasticity",
+           title = "2-Step GMM estimation",
+           predict = "ivreg2_p",
+           version = "02.2.08",
+           cmdline = "ivreg2 lw expr tenure rns smsa dyear* (s iq = med kww age mrt), gmm2s robust",
+           cmd = "ivreg2",
+           model = "gmm2s",
+           depvar = "lw",
+           vcetype = "Robust",
+           partialsmall = "small",
+           exexog = "med kww age mrt",
+           inexog = "expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           insts = "med kww age mrt expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73",
+           instd = "s iq",
+           properties = "b V",
+          )
+
+params_table = np.array([
+     .17579576800916,  .02067662557145,  8.5021498020418,  1.861114268e-17,
+     .13527032656731,  .21632120945101, np.nan,  1.9599639845401,
+                   0, -.00928615655484,  .00488241935929, -1.9019579989933,
+     .05717664576009, -.01885552265647,  .00028320954679, np.nan,
+     1.9599639845401,                0,   .0502827590727,  .00804384217519,
+      6.251087226427,  4.076051363e-10,  .03451711811201,  .06604840003339,
+    np.nan,  1.9599639845401,                0,  .04252138311207,
+     .00945488880069,  4.4972906618407,  6.882486994e-06,   .0239901415849,
+     .06105262463925, np.nan,  1.9599639845401,                0,
+    -.10409306761385,  .03352385821237,  -3.105044382255,  .00190250475151,
+    -.16979862233293, -.03838751289477, np.nan,  1.9599639845401,
+                   0,  .12475123235604,  .03077474836143,  4.0536881371349,
+     .00005041641801,  .06443383393436,  .18506863077773, np.nan,
+     1.9599639845401,                0, -.05304317352459,  .05146091261443,
+    -1.0307468490116,  .30265954893659, -.15390470886044,  .04781836181126,
+    np.nan,  1.9599639845401,                0,  .04595459037414,
+     .04957352345681,  .92699867126001,  .35392722417214, -.05120773018796,
+     .14311691093625, np.nan,  1.9599639845401,                0,
+     .15548006234452,     .04763105506,   3.264258206094,  .00109751095685,
+     .06212490988128,  .24883521480777, np.nan,  1.9599639845401,
+                   0,  .16698745539298,  .06100058345996,  2.7374730850337,
+     .00619131861175,  .04742850877554,  .28654640201043, np.nan,
+     1.9599639845401,                0,  .08464846645323,  .05540348923564,
+     1.5278544297672,  .12654868468826, -.02394037706648,  .19323730997294,
+    np.nan,  1.9599639845401,                0,  .09960684400937,
+     .06070338085689,  1.6408780302402,  .10082273637942, -.01936959620995,
+      .2185832842287, np.nan,  1.9599639845401,                0,
+     4.0039243729942,  .33484233500232,  11.957640819123,  5.922056626e-33,
+     3.3476454558904,  4.6602032900981, np.nan,  1.9599639845401,
+                   0]).reshape(13,9)
+
+params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()
+
+params_table_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov = np.array([
+     .00042752284502, -.00007792910934,  .00002799053689,  .00001411255161,
+    -.00017627354771, -6.164167361e-06, -.00011238067476,  -.0001840592946,
+    -.00012075971225, -.00008045016526, -.00030956302647, -.00062213915849,
+      .0025864317125, -.00007792910934,   .0000238380188,  4.908457545e-06,
+     -.0000109083863,  .00006550360735, -.00002187678826,  4.129956698e-08,
+     .00003161755605, -.00002236410253, -.00006047183643,  -.0000144130349,
+     .00001326368633, -.00142544425011,  .00002799053689,  4.908457545e-06,
+     .00006470339694, -.00001976276105,  .00005093819277,  -.0000323622135,
+     .00003452531659,  .00008555059005,   .0000288422901,  .00001508898973,
+    -.00008682961428, -.00013087868262, -.00093791708189,  .00001411255161,
+     -.0000109083863, -.00001976276105,  .00008939492223, -.00003640760589,
+     7.669817608e-06,  .00008322349071,  .00006546263505,  .00011054793547,
+     .00009771882719,  .00010901321979,   .0000926582973,  .00074577978354,
+    -.00017627354771,  .00006550360735,  .00005093819277, -.00003640760589,
+     .00112384906944,  .00012782990916,   .0001752536517,  .00018281146997,
+    -.00001062129812,   .0000105829103, -.00005745271672,  .00007901951626,
+    -.00493822443862, -6.164167361e-06, -.00002187678826,  -.0000323622135,
+     7.669817608e-06,  .00012782990916,  .00094708513671,  .00005587818268,
+      .0001108109152,  .00024772966236,  .00010526192189,  .00029836672951,
+      .0003020559345,  .00155999842113, -.00011238067476,  4.129956698e-08,
+     .00003452531659,  .00008322349071,   .0001752536517,  .00005587818268,
+     .00264822552711,  .00084013008538,  .00089372787896,  .00094462335409,
+     .00088393735147,  .00100740924129,  .00036769011237,  -.0001840592946,
+     .00003161755605,  .00008555059005,  .00006546263505,  .00018281146997,
+      .0001108109152,  .00084013008538,  .00245753422792,  .00090550111616,
+     .00086043845508,  .00080197738555,  .00092623266791, -.00204303139567,
+    -.00012075971225, -.00002236410253,   .0000288422901,  .00011054793547,
+    -.00001062129812,  .00024772966236,  .00089372787896,  .00090550111616,
+     .00226871740613,  .00116169566043,  .00108859648529,  .00127118636737,
+     .00254572878148, -.00008045016526, -.00006047183643,  .00001508898973,
+     .00009771882719,   .0000105829103,  .00010526192189,  .00094462335409,
+     .00086043845508,  .00116169566043,  .00372107118246,  .00122563410541,
+     .00153681917159,  .00601243235865, -.00030956302647,  -.0000144130349,
+    -.00008682961428,  .00010901321979, -.00005745271672,  .00029836672951,
+     .00088393735147,  .00080197738555,  .00108859648529,  .00122563410541,
+     .00306954661948,  .00181011399651,  .00430171295221, -.00062213915849,
+     .00001326368633, -.00013087868262,   .0000926582973,  .00007901951626,
+      .0003020559345,  .00100740924129,  .00092623266791,  .00127118636737,
+     .00153681917159,  .00181011399651,  .00368490044746,  .00539258837124,
+      .0025864317125, -.00142544425011, -.00093791708189,  .00074577978354,
+    -.00493822443862,  .00155999842113,  .00036769011237, -.00204303139567,
+     .00254572878148,  .00601243235865,  .00430171295221,  .00539258837124,
+     .11211938930981]).reshape(13,13)
+
+cov_colnames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+cov_rownames = 's iq expr tenure rns smsa dyear_67 dyear_68 dyear_69 dyear_70 dyear_71 dyear_73 _cons'.split()
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+results_gmm2s_robust = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                **est
+                )
diff --git a/statsmodels/sandbox/regression/tests/test_gmm.py b/statsmodels/sandbox/regression/tests/test_gmm.py
new file mode 100644
index 00000000000..9086ff9fa84
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/test_gmm.py
@@ -0,0 +1,670 @@
+# -*- coding: utf-8 -*-
+"""
+
+Created on Fri Oct 04 13:19:01 2013
+
+Author: Josef Perktold
+"""
+from __future__ import print_function
+from statsmodels.compat.python import lrange, lmap
+import numpy as np
+from numpy.testing import assert_allclose, assert_almost_equal, assert_equal
+
+from statsmodels import iolib
+from statsmodels.tools.tools import add_constant
+from statsmodels.regression.linear_model import OLS
+import statsmodels.sandbox.regression.gmm as gmm
+
+
+def get_griliches76_data():
+    import os
+    curdir = os.path.split(__file__)[0]
+    path = os.path.join(curdir, 'griliches76.dta')
+    griliches76_data = iolib.genfromdta(path, missing_flt=np.NaN, pandas=True)
+
+    # create year dummies
+    years = griliches76_data['year'].unique()
+    N = griliches76_data.shape[0]
+
+    for yr in years:
+        griliches76_data['D_%i' %yr] = np.zeros(N)
+        for i in range(N):
+            if griliches76_data['year'][i] == yr:
+                griliches76_data['D_%i' %yr][i] = 1
+            else:
+                pass
+
+    griliches76_data['const'] = 1
+
+    X = add_constant(griliches76_data[['s', 'iq', 'expr', 'tenure', 'rns',
+                                       'smsa', 'D_67', 'D_68', 'D_69', 'D_70',
+                                       'D_71', 'D_73']],
+                                       prepend=True)  # for R comparison
+                                       #prepend=False)  # for Stata comparison
+
+    Z = add_constant(griliches76_data[['expr', 'tenure', 'rns', 'smsa', \
+                                       'D_67', 'D_68', 'D_69', 'D_70', 'D_71',
+                                       'D_73', 'med', 'kww', 'age', 'mrt']])
+    Y = griliches76_data['lw']
+
+    return Y, X, Z
+
+# use module global to load only once
+yg_df, xg_df, zg_df = get_griliches76_data()
+
+endog = np.asarray(yg_df, dtype=float)  # TODO: why is yg_df float32
+exog, instrument = lmap(np.asarray, [xg_df, zg_df])
+
+assert exog.dtype == np.float64
+assert instrument.dtype == np.float64
+
+
+# from R
+#-----------------
+varnames = np.array(["(Intercept)", "s", "iq", "expr", "tenure", "rns", "smsa", "D_67", "D_68", "D_69", "D_70",
+       "D_71", "D_73"])
+params = np.array([ 4.03350989,  0.17242531, -0.00909883,  0.04928949,  0.04221709,
+       -0.10179345,  0.12611095, -0.05961711,  0.04867956,  0.15281763,
+        0.17443605,  0.09166597,  0.09323977])
+bse = np.array([ 0.31816162,  0.02091823,  0.00474527,  0.00822543,  0.00891969,
+        0.03447337,  0.03119615,  0.05577582,  0.05246796,  0.05201092,
+        0.06027671,  0.05461436,  0.05767865])
+tvalues = np.array([ 12.6775501,   8.2428242,  -1.9174531,   5.9923305,   4.7330205,
+        -2.9528144,   4.0425165,  -1.0688701,   0.9277959,   2.9381834,
+         2.8939212,   1.6784225,   1.6165385])
+pvalues = np.array([  1.72360000e-33,   7.57025400e-16,   5.55625000e-02,
+         3.21996700e-09,   2.64739100e-06,   3.24794100e-03,
+         5.83809900e-05,   2.85474400e-01,   3.53813900e-01,
+         3.40336100e-03,   3.91575100e-03,   9.36840200e-02,
+         1.06401300e-01])
+    #-----------------
+
+def test_iv2sls_r():
+
+    mod = gmm.IV2SLS(endog, exog, instrument)
+    res = mod.fit()
+
+    # print(res.params)
+    # print(res.params - params)
+
+    n, k = exog.shape
+
+    assert_allclose(res.params, params, rtol=1e-7, atol=1e-9)
+    # TODO: check df correction
+    #assert_allclose(res.bse * np.sqrt((n - k) / (n - k - 1.)), bse,
+    assert_allclose(res.bse, bse, rtol=0, atol=3e-7)
+
+
+
+def test_ivgmm0_r():
+    n, k = exog.shape
+    nobs, k_instr = instrument.shape
+
+    w0inv = np.dot(instrument.T, instrument) / nobs
+    w0 = np.linalg.inv(w0inv)
+
+    mod = gmm.IVGMM(endog, exog, instrument)
+    res = mod.fit(np.ones(exog.shape[1], float), maxiter=0, inv_weights=w0inv,
+                  optim_method='bfgs',
+                  optim_args={'gtol':1e-8, 'disp': 0})
+
+
+    assert_allclose(res.params, params, rtol=1e-4, atol=1e-4)
+    # TODO : res.bse and bse are not the same, rtol=0.09 is large in this case
+    #res.bse is still robust?, bse is not a sandwich ?
+    assert_allclose(res.bse, bse, rtol=0.09, atol=0)
+
+    score = res.model.score(res.params, w0)
+    assert_allclose(score, np.zeros(score.shape), rtol=0, atol=5e-6) # atol=1e-8) ??
+
+
+def test_ivgmm1_stata():
+
+    # copied constant to the beginning
+    params_stata = np.array(
+          [ 4.0335099 ,  0.17242531, -0.00909883,  0.04928949,  0.04221709,
+           -0.10179345,  0.12611095, -0.05961711,  0.04867956,  0.15281763,
+            0.17443605,  0.09166597,  0.09323976])
+
+    # robust bse with gmm onestep
+    bse_stata = np.array(
+          [ 0.33503289,  0.02073947,  0.00488624,  0.0080498 ,  0.00946363,
+            0.03371053,  0.03081138,  0.05171372,  0.04981322,  0.0479285 ,
+            0.06112515,  0.0554618 ,  0.06084901])
+
+    n, k = exog.shape
+    nobs, k_instr = instrument.shape
+
+    w0inv = np.dot(instrument.T, instrument) / nobs
+    w0 = np.linalg.inv(w0inv)
+    start = OLS(endog, exog).fit().params
+
+    mod = gmm.IVGMM(endog, exog, instrument)
+    res = mod.fit(start, maxiter=1, inv_weights=w0inv, optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
+
+
+# move constant to end for Stata
+idx = lrange(len(params))
+idx = idx[1:] + idx[:1]
+exog_st = exog[:, idx]
+
+
+class TestGMMOLS(object):
+
+    @classmethod
+    def setup_class(self):
+        exog = exog_st  # with const at end
+        res_ols = OLS(endog, exog).fit()
+
+        #  use exog as instrument
+        nobs, k_instr = exog.shape
+        w0inv = np.dot(exog.T, exog) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, exog)
+        res = mod.fit(np.ones(exog.shape[1], float), maxiter=0, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
+
+        self.res1 = res
+        self.res2 = res_ols
+
+
+    def test_basic(self):
+        res1, res2 = self.res1, self.res2
+        # test both absolute and relative difference
+        assert_allclose(res1.params, res2.params, rtol=5e-4, atol=0)
+        assert_allclose(res1.params, res2.params, rtol=0, atol=1e-5)
+
+        n = res1.model.exog.shape[0]
+        dffac = 1#np.sqrt((n - 1.) / n)   # currently different df in cov calculation
+        assert_allclose(res1.bse * dffac, res2.HC0_se, rtol=5e-6, atol=0)
+        assert_allclose(res1.bse * dffac, res2.HC0_se, rtol=0, atol=1e-7)
+
+
+    def test_other(self):
+        res1, res2 = self.res1, self.res2
+
+
+
+
+class CheckGMM(object):
+
+    params_tol = [5e-6, 5e-6]
+    bse_tol = [5e-7, 5e-7]
+
+    def test_basic(self):
+        res1, res2 = self.res1, self.res2
+        # test both absolute and relative difference
+        rtol,  atol = self.params_tol
+        assert_allclose(res1.params, res2.params, rtol=rtol, atol=0)
+        assert_allclose(res1.params, res2.params, rtol=0, atol=atol)
+
+        n = res1.model.exog.shape[0]
+        dffac = 1 #np.sqrt((n - 1.) / n)   # currently different df in cov calculation
+        rtol,  atol = self.bse_tol
+        assert_allclose(res1.bse * dffac, res2.bse, rtol=rtol, atol=0)
+        assert_allclose(res1.bse * dffac, res2.bse, rtol=0, atol=atol)
+
+    #skip temporarily
+    def _est_other(self):
+        res1, res2 = self.res1, self.res2
+        assert_allclose(res1.q, res2.Q, rtol=5e-6, atol=0)
+        assert_allclose(res1.jval, res2.J, rtol=5e-5, atol=0)
+
+
+
+class TestGMMSt1(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        #self.bse_tol = [5e-7, 5e-7]
+        # compare to Stata default options, iterative GMM
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res10 = mod.fit(start, maxiter=10, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False})
+        self.res1 = res10
+
+        from .results_gmm_griliches_iter import results
+        self.res2 = results
+
+class TestGMMStTwostep(CheckGMM):
+    #compares has_optimal_weights=True with Stata's has_optimal_weights=False
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        self.params_tol = [5e-5, 5e-6]
+        self.bse_tol = [5e-6, 5e-7]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res10 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False})
+        self.res1 = res10
+
+        from .results_gmm_griliches import results_twostep as results
+        self.res2 = results
+
+
+class TestGMMStTwostepNO(CheckGMM):
+    #with Stata default `has_optimal_weights=False`
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        self.params_tol = [5e-5, 5e-6]
+        self.bse_tol = [1e-6, 5e-5]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res10 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res10
+
+        from .results_gmm_griliches import results_twostep as results
+        self.res2 = results
+
+
+class TestGMMStOnestep(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        self.params_tol = [5e-4, 5e-5]
+        self.bse_tol = [7e-3, 5e-4]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=0, inv_weights=w0inv,
+                        optim_method='bfgs',
+                        optim_args={'gtol':1e-6, 'disp': 0})
+        self.res1 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+    def test_bse_other(self):
+        res1, res2 = self.res1, self.res2
+        # try other versions for bse,
+        # TODO: next two produce the same as before (looks like)
+        bse = np.sqrt(np.diag((res1.cov_params(has_optimal_weights=False))))
+                                            #weights=res1.weights))))
+        # TODO: doesn't look different
+        #assert_allclose(res1.bse, res2.bse, rtol=5e-06, atol=0)
+        #nobs = instrument.shape[0]
+        #w0inv = np.dot(instrument.T, instrument) / nobs
+        q = self.res1.model.gmmobjective(self.res1.params, np.linalg.inv(self.res1.weights))
+        #assert_allclose(q, res2.Q, rtol=5e-6, atol=0)
+
+class TestGMMStOnestepNO(CheckGMM):
+    # matches Stats's defaults wargs={'centered':False}, has_optimal_weights=False
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        self.params_tol = [1e-5, 1e-6]
+        self.bse_tol = [5e-6, 5e-7]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=0, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+class TestGMMStOneiter(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        # this uses maxiter=1, one iteration in loop
+        self.params_tol = [5e-4, 5e-5]
+        self.bse_tol = [7e-3, 5e-4]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
+        self.res1 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+
+    def test_bse_other(self):
+        res1, res2 = self.res1, self.res2
+
+        moms = res1.model.momcond(res1.params)
+        w = res1.model.calc_weightmatrix(moms)
+        # try other versions for bse,
+        # TODO: next two produce the same as before (looks like)
+        bse = np.sqrt(np.diag((res1.cov_params(has_optimal_weights=False,
+                                            weights=res1.weights))))
+        # TODO: doesn't look different
+        #assert_allclose(res1.bse, res2.bse, rtol=5e-06, atol=0)
+        bse = np.sqrt(np.diag((res1.cov_params(has_optimal_weights=False,
+                                               #use_weights=True #weights=w
+                                                         ))))
+        #assert_allclose(res1.bse, res2.bse, rtol=5e-06, atol=0)
+
+        #This doesn't replicate Stata oneway either
+        nobs = instrument.shape[0]
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        q = self.res1.model.gmmobjective(self.res1.params, w)#self.res1.weights)
+        #assert_allclose(q, res2.Q, rtol=5e-6, atol=0)
+
+
+class TestGMMStOneiterNO(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        # this uses maxiter=1, one iteration in loop
+        self.params_tol = [1e-5, 1e-6]
+        self.bse_tol = [5e-6, 5e-7]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+
+#------------ Crosscheck subclasses
+
+class TestGMMStOneiterNO_Linear(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        # this uses maxiter=1, one iteration in loop
+        self.params_tol = [5e-9, 1e-9]
+        self.bse_tol = [5e-10, 1e-10]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.LinearIVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res3 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+
+class TestGMMStOneiterNO_Nonlinear(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, onestep GMM
+        # this uses maxiter=1, one iteration in loop
+        self.params_tol = [5e-5, 5e-6]
+        self.bse_tol = [5e-6, 1e-1]
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        def func(params, exog):
+            return np.dot(exog, params)
+
+        mod = gmm.NonlinearIVGMM(endog, exog, instrument, func)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res3 = res
+
+        from .results_gmm_griliches import results_onestep as results
+        self.res2 = results
+
+
+    def test_score(self):
+        params = self.res1.params * 1.1
+        weights = self.res1.weights
+        sc1 = self.res1.model.score(params, weights)
+        sc2 = super(self.res1.model.__class__, self.res1.model).score(params,
+                                                                      weights)
+        assert_allclose(sc1, sc2, rtol=1e-6, atol=0)
+        assert_allclose(sc1, sc2, rtol=0, atol=1e-7)
+
+        # score at optimum
+        sc1 = self.res1.model.score(self.res1.params, weights)
+        assert_allclose(sc1, np.zeros(len(params)), rtol=0, atol=1e-8)
+
+
+
+class TestGMMStOneiterOLS_Linear(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        # replicating OLS by GMM - high agreement
+        self.params_tol = [1e-11, 1e-12]
+        self.bse_tol = [1e-13, 1e-13]
+        exog = exog_st  # with const at end
+        res_ols = OLS(endog, exog).fit()
+        #Note: start is irrelevant but required
+        start = np.ones(len(res_ols.params))
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(exog.T, exog) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.LinearIVGMM(endog, exog, exog)
+        res = mod.fit(start, maxiter=0, inv_weights=w0inv,
+                        #optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0},
+                        optim_args={'disp': 0},
+                        weights_method='iid',
+                        wargs={'centered':False, 'ddof':'k_params'},
+                        has_optimal_weights=True)
+        self.res1 = res
+
+        #from .results_gmm_griliches import results_onestep as results
+        #self.res2 = results
+        self.res2 = res_ols
+
+
+#------------------
+
+class TestGMMSt2(object):
+    # this looks like an old version, trying out different comparisons
+    # of options with Stats
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, iterative GMM
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+        #w0 = np.linalg.inv(w0inv)
+
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                      wargs={'ddof':0, 'centered':False},
+                      optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
+        self.res1 = res
+
+        from .results_ivreg2_griliches import results_gmm2s_robust as results
+        self.res2 = results
+
+        # TODO: remove after testing, compare bse from 1 iteration
+        # see test_basic
+        mod = gmm.IVGMM(endog, exog, instrument)
+        res = mod.fit(start, maxiter=1, inv_weights=w0inv,
+                      wargs={'ddof':0, 'centered':False},
+                      optim_method='bfgs', optim_args={'gtol':1e-6, 'disp': 0})
+        self.res3 = res
+
+
+    def test_basic(self):
+        res1, res2 = self.res1, self.res2
+        # test both absolute and relative difference
+        assert_allclose(res1.params, res2.params, rtol=5e-05, atol=0)
+        assert_allclose(res1.params, res2.params, rtol=0, atol=5e-06)
+
+        n = res1.model.exog.shape[0]
+        # TODO: check df correction np.sqrt(745./758 )*res1.bse matches better
+        dffact = np.sqrt(745. / 758 )
+        assert_allclose(res1.bse * dffact, res2.bse, rtol=5e-03, atol=0)
+        assert_allclose(res1.bse * dffact, res2.bse, rtol=0, atol=5e-03)
+
+        # try other versions for bse,
+        # TODO: next two produce the same as before (looks like)
+        bse = np.sqrt(np.diag((res1.cov_params(has_optimal_weights=True,
+                                            weights=res1.weights))))
+        assert_allclose(res1.bse, res2.bse, rtol=5e-01, atol=0)
+
+        bse = np.sqrt(np.diag((res1.cov_params(has_optimal_weights=True,
+                                               weights=res1.weights,
+                                               use_weights=True))))
+        assert_allclose(res1.bse, res2.bse, rtol=5e-02, atol=0)
+
+        # TODO: resolve this
+        # try bse from previous step, is closer to Stata
+        # guess: Stata ivreg2 doesn't calc for bse update after final iteration
+        # need better test case, bse difference is close to numerical optimization precision
+        assert_allclose(self.res3.bse, res2.bse, rtol=5e-05, atol=0)
+        assert_allclose(self.res3.bse, res2.bse, rtol=0, atol=5e-06)
+
+
+
+        # TODO; tvalues are not available yet, no inheritance
+        #assert_allclose(res1.tvalues, res2.tvalues, rtol=5e-10, atol=0)
+
+
+
+
+class CheckIV2SLS(object):
+
+    def test_basic(self):
+        res1, res2 = self.res1, self.res2
+        # test both absolute and relative difference
+        assert_allclose(res1.params, res2.params, rtol=1e-9, atol=0)
+        assert_allclose(res1.params, res2.params, rtol=0, atol=1e-10)
+
+        n = res1.model.exog.shape[0]
+        assert_allclose(res1.bse, res2.bse, rtol=1e-10, atol=0)
+        assert_allclose(res1.bse, res2.bse, rtol=0, atol=1e-11)
+
+        assert_allclose(res1.tvalues, res2.tvalues, rtol=5e-10, atol=0)
+
+
+    def test_other(self):
+        res1, res2 = self.res1, self.res2
+        assert_allclose(res1.rsquared, res2.r2, rtol=1e-7, atol=0)
+        assert_allclose(res1.rsquared_adj, res2.r2_a, rtol=1e-7, atol=0)
+
+        # TODO: why is fvalue different, IV2SLS uses inherited linear
+        assert_allclose(res1.fvalue, res2.F, rtol=1e-10, atol=0)
+        assert_allclose(res1.f_pvalue, res2.Fp, rtol=1e-8, atol=0)
+        assert_allclose(np.sqrt(res1.mse_resid), res2.rmse, rtol=1e-10, atol=0)
+        assert_allclose(res1.ssr, res2.rss, rtol=1e-10, atol=0)
+        assert_allclose(res1.uncentered_tss, res2.yy, rtol=1e-10, atol=0)
+        assert_allclose(res1.centered_tss, res2.yyc, rtol=1e-10, atol=0)
+        assert_allclose(res1.ess, res2.mss, rtol=1e-9, atol=0)
+
+        assert_equal(res1.df_model, res2.df_m)
+        assert_equal(res1.df_resid, res2.df_r)
+
+        # TODO: llf raise NotImplementedError
+        #assert_allclose(res1.llf, res2.ll, rtol=1e-10, atol=0)
+
+
+    def test_hypothesis(self):
+        res1, res2 = self.res1, self.res2
+        restriction = np.eye(len(res1.params))
+        res_t = res1.t_test(restriction)
+        assert_allclose(res_t.tvalue, res1.tvalues, rtol=1e-12, atol=0)
+        assert_allclose(res_t.pvalue, res1.pvalues, rtol=1e-12, atol=0)
+        res_f = res1.f_test(restriction[:-1]) # without constant
+        # TODO res1.fvalue problem, see issue #1104
+        assert_allclose(res_f.fvalue, res1.fvalue, rtol=1e-12, atol=0)
+        assert_allclose(res_f.pvalue, res1.f_pvalue, rtol=1e-12, atol=0)
+        assert_allclose(res_f.fvalue, res2.F, rtol=1e-10, atol=0)
+        assert_allclose(res_f.pvalue, res2.Fp, rtol=1e-08, atol=0)
+
+
+    def test_hausman(self):
+        res1, res2 = self.res1, self.res2
+        hausm = res1.spec_hausman()
+        # hausman uses se2 = ssr / nobs, no df correction
+        assert_allclose(hausm[0], res2.hausman['DWH'], rtol=1e-11, atol=0)
+        assert_allclose(hausm[1], res2.hausman['DWHp'], rtol=1e-10, atol=1e-25)
+
+
+    def test_smoke(self):
+        res1 = self.res1
+        res1.summary()
+
+
+
+class TestIV2SLSSt1(CheckIV2SLS):
+
+    @classmethod
+    def setup_class(self):
+        exog = exog_st  # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+
+        mod = gmm.IV2SLS(endog, exog, instrument)
+        res = mod.fit()
+        self.res1 = res
+
+        from .results_ivreg2_griliches import results_small as results
+        self.res2 = results
diff --git a/statsmodels/sandbox/regression/tests/test_gmm_poisson.py b/statsmodels/sandbox/regression/tests/test_gmm_poisson.py
new file mode 100644
index 00000000000..637abc89520
--- /dev/null
+++ b/statsmodels/sandbox/regression/tests/test_gmm_poisson.py
@@ -0,0 +1,409 @@
+'''
+
+TestGMMMultTwostepDefault() has lower precision
+
+'''
+
+from statsmodels.compat.python import lmap
+import numpy as np
+from numpy.testing.decorators import skipif
+import pandas
+import scipy
+from scipy import stats
+
+from statsmodels.regression.linear_model import OLS
+from statsmodels.sandbox.regression import gmm
+
+from numpy.testing import assert_allclose, assert_equal
+from statsmodels.compat.scipy import NumpyVersion
+
+
+def get_data():
+    import os
+    curdir = os.path.split(__file__)[0]
+    dt = pandas.read_csv(os.path.join(curdir, 'racd10data_with_transformed.csv'))
+
+    # Transformations compared to original data
+    ##dt3['income'] /= 10.
+    ##dt3['aget'] = (dt3['age'] - dt3['age'].min()) / 5.
+    ##dt3['aget2'] = dt3['aget']**2
+
+    # How do we do this with pandas
+    mask = ~((np.asarray(dt['private']) == 1) & (dt['medicaid'] == 1))
+    mask = mask & (dt['docvis'] <= 70)
+    dt3 = dt[mask]
+    dt3['const'] = 1   # add constant
+    return dt3
+
+DATA = get_data()
+
+#------------- moment conditions for example
+
+def moment_exponential_add(params, exog, exp=True):
+
+    if not np.isfinite(params).all():
+        print("invalid params", params)
+
+    # moment condition without instrument
+    if exp:
+        predicted = np.exp(np.dot(exog, params))
+        #if not np.isfinite(predicted).all():
+            #print "invalid predicted", predicted
+            #raise RuntimeError('invalid predicted')
+        predicted = np.clip(predicted, 0, 1e100)  # try to avoid inf
+    else:
+        predicted = np.dot(exog, params)
+
+    return predicted
+
+
+def moment_exponential_mult(params, data, exp=True):
+    # multiplicative error model
+
+    endog = data[:,0]
+    exog = data[:,1:]
+
+    if not np.isfinite(params).all():
+        print("invalid params", params)
+
+    # moment condition without instrument
+    if exp:
+        predicted = np.exp(np.dot(exog, params))
+        predicted = np.clip(predicted, 0, 1e100)  # avoid inf
+        resid = endog / predicted - 1
+        if not np.isfinite(resid).all():
+            print("invalid resid", resid)
+
+    else:
+        resid = endog - np.dot(exog, params)
+
+    return resid
+
+#------------------- test classes
+
+# copied from test_gmm.py, with changes
+class CheckGMM(object):
+
+    # default tolerance, overwritten by subclasses
+    params_tol = [5e-6, 5e-6]
+    bse_tol = [5e-7, 5e-7]
+    q_tol = [5e-6, 1e-9]
+    j_tol = [5e-5, 1e-9]
+
+    def test_basic(self):
+        res1, res2 = self.res1, self.res2
+        # test both absolute and relative difference
+        rtol,  atol = self.params_tol
+        assert_allclose(res1.params, res2.params, rtol=rtol, atol=0)
+        assert_allclose(res1.params, res2.params, rtol=0, atol=atol)
+
+        rtol,  atol = self.bse_tol
+        assert_allclose(res1.bse, res2.bse, rtol=rtol, atol=0)
+        assert_allclose(res1.bse, res2.bse, rtol=0, atol=atol)
+
+    def test_other(self):
+        res1, res2 = self.res1, self.res2
+        rtol,  atol = self.q_tol
+        assert_allclose(res1.q, res2.Q, rtol=atol, atol=rtol)
+        rtol,  atol = self.j_tol
+        assert_allclose(res1.jval, res2.J, rtol=atol, atol=rtol)
+
+        j, jpval, jdf = res1.jtest()
+        # j and jval should be the same
+        assert_allclose(res1.jval, res2.J, rtol=13, atol=13)
+        #pvalue is not saved in Stata results
+        pval = stats.chi2.sf(res2.J, res2.J_df)
+        #assert_allclose(jpval, pval, rtol=1e-4, atol=1e-6)
+        assert_allclose(jpval, pval, rtol=rtol, atol=atol)
+        assert_equal(jdf, res2.J_df)
+
+
+    def test_smoke(self):
+        res1 = self.res1
+        res1.summary()
+
+
+class TestGMMAddOnestep(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+
+        self.bse_tol = [5e-6, 5e-7]
+        q_tol = [0.04, 0]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(np.log(endog+1), exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog, exog, instrument, moment_exponential_add)
+        res0 = mod.fit(start, maxiter=0, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False})
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_addonestep as results
+        self.res2 = results
+
+
+class TestGMMAddTwostep(CheckGMM):
+
+    @classmethod
+    def setup_class(self):
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+
+        self.bse_tol = [5e-6, 5e-7]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(np.log(endog+1), exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog, exog, instrument, moment_exponential_add)
+        res0 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_addtwostep as results
+        self.res2 = results
+
+
+class TestGMMMultOnestep(CheckGMM):
+    #compares has_optimal_weights=True with Stata's has_optimal_weights=False
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income medicaid ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+        # Need to add all data into exog
+        endog_ = np.zeros(len(endog))
+        exog_ = np.column_stack((endog, exog))
+
+
+        self.bse_tol = [5e-6, 5e-7]
+        self.q_tol = [0.04, 0]
+        self.j_tol = [0.04, 0]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog_, exog_, instrument, moment_exponential_mult)
+        res0 = mod.fit(start, maxiter=0, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_multonestep as results
+        self.res2 = results
+
+class TestGMMMultTwostep(CheckGMM):
+    #compares has_optimal_weights=True with Stata's has_optimal_weights=False
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income medicaid ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+        # Need to add all data into exog
+        endog_ = np.zeros(len(endog))
+        exog_ = np.column_stack((endog, exog))
+
+
+        self.bse_tol = [5e-6, 5e-7]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog_, exog_, instrument, moment_exponential_mult)
+        res0 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':False}, has_optimal_weights=False)
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_multtwostep as results
+        self.res2 = results
+
+
+class TestGMMMultTwostepDefault(CheckGMM):
+    # compares my defaults with the same options in Stata
+    # agreement is not very high, maybe vce(unadjusted) is different after all
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income medicaid ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+        # Need to add all data into exog
+        endog_ = np.zeros(len(endog))
+        exog_ = np.column_stack((endog, exog))
+
+
+        self.bse_tol = [0.004, 5e-4]
+        self.params_tol = [5e-5, 5e-5]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog_, exog_, instrument, moment_exponential_mult)
+        res0 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        #wargs={'centered':True}, has_optimal_weights=True
+                       )
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_multtwostepdefault as results
+        self.res2 = results
+
+
+class TestGMMMultTwostepCenter(CheckGMM):
+    #compares my defaults with the same options in Stata
+
+    @classmethod
+    def setup_class(self):
+        # compare to Stata default options, twostep GMM
+        XLISTEXOG2 = 'aget aget2 educyr actlim totchr'.split()
+
+        endog_name = 'docvis'
+        exog_names = 'private medicaid'.split() + XLISTEXOG2 + ['const']
+        instrument_names = 'income medicaid ssiratio'.split() + XLISTEXOG2 + ['const']
+
+        endog = DATA[endog_name]
+        exog = DATA[exog_names]
+        instrument = DATA[instrument_names]
+
+        asarray = lambda x: np.asarray(x, float)
+        endog, exog, instrument = lmap(asarray, [endog, exog, instrument])
+
+        # Need to add all data into exog
+        endog_ = np.zeros(len(endog))
+        exog_ = np.column_stack((endog, exog))
+
+
+        self.bse_tol = [5e-4, 5e-5]
+        self.params_tol = [5e-5, 5e-5]
+        q_tol = [5e-5, 1e-8]
+        # compare to Stata default options, iterative GMM
+        # with const at end
+        start = OLS(endog, exog).fit().params
+        nobs, k_instr = instrument.shape
+        w0inv = np.dot(instrument.T, instrument) / nobs
+
+        mod = gmm.NonlinearIVGMM(endog_, exog_, instrument, moment_exponential_mult)
+        res0 = mod.fit(start, maxiter=2, inv_weights=w0inv,
+                        optim_method='bfgs', optim_args={'gtol':1e-8, 'disp': 0},
+                        wargs={'centered':True}, has_optimal_weights=False
+                       )
+        self.res1 = res0
+
+        from .results_gmm_poisson import results_multtwostepcenter as results
+        self.res2 = results
+
+    def test_more(self):
+
+        # from Stata `overid`
+        J_df = 1
+        J_p = 0.332254330027383
+        J = 0.940091427212973
+
+        j, jpval, jdf = self.res1.jtest()
+
+        assert_allclose(jpval, J_p, rtol=5e-5, atol=0)
+
+
+
+if __name__ == '__main__':
+    tt = TestGMMAddOnestep()
+    tt.setup_class()
+    tt.test_basic()
+    tt.test_other()
+
+    tt = TestGMMAddTwostep()
+    tt.setup_class()
+    tt.test_basic()
+    tt.test_other()
+
+    tt = TestGMMMultOnestep()
+    tt.setup_class()
+    tt.test_basic()
+    #tt.test_other()
+
+    tt = TestGMMMultTwostep()
+    tt.setup_class()
+    tt.test_basic()
+    tt.test_other()
+
+    tt = TestGMMMultTwostepDefault()
+    tt.setup_class()
+    tt.test_basic()
+    tt.test_other()
+
+    tt = TestGMMMultTwostepCenter()
+    tt.setup_class()
+    tt.test_basic()
+    tt.test_other()
diff --git a/statsmodels/sandbox/regression/tools.py b/statsmodels/sandbox/regression/tools.py
index 992b837c2bc..7efb28d7d79 100644
--- a/statsmodels/sandbox/regression/tools.py
+++ b/statsmodels/sandbox/regression/tools.py
@@ -19,7 +19,7 @@
 
 '''
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import special
 from scipy.special import gammaln
@@ -106,7 +106,7 @@ def normgrad(y, x, params):
     sigma2 = params[-1]*np.ones((len(y),1))
     dmudbeta = mean_grad(x, beta)
     mu = np.dot(x, beta)
-    #print beta, sigma2
+    #print(beta, sigma2)
     params2 = np.column_stack((mu,sigma2))
     dllsdms = norm_lls_grad(y,params2)
     grad = np.column_stack((dllsdms[:,:1]*dmudbeta, dllsdms[:,:1]))
@@ -200,7 +200,7 @@ def ts_lls(y, params, df):
     >>> stats.t.stats(df, loc=0., scale=sigma*np.sqrt((df-2.)/df))
     (array(0.0), array(2.0))
     '''
-    print y, params, df
+    print(y, params, df)
     mu, sigma2 = params.T
     df = df*1.0
     #lls = gammaln((df+1)/2.) - gammaln(df/2.) - 0.5*np.log((df-2)*np.pi)
@@ -304,13 +304,13 @@ def locscale_grad(y, loc, scale, dlldy, *args):
         y = np.dot(x,beta) + sig*rvs[:,0]
 
         params = [1,1,1]
-        print normgrad(y, x, params)
+        print(normgrad(y, x, params))
 
         dllfdbeta = (y-np.dot(x, beta))[:,None]*x   #for sigma = 1
-        print dllfdbeta
+        print(dllfdbeta)
 
-        print locscale_grad(y, np.dot(x, beta), 1, norm_dlldy)
-        print (y-np.dot(x, beta))
+        print(locscale_grad(y, np.dot(x, beta), 1, norm_dlldy))
+        print(y-np.dot(x, beta))
 
     from scipy import stats, misc
 
@@ -329,44 +329,44 @@ def llnormscale(scale,y,loc):
         return np.log(stats.norm.pdf(y, loc=loc, scale=scale))
 
     if verbose:
-        print '\ngradient of t'
-        print misc.derivative(llt, 1, dx=1e-6, n=1, args=(0,1,10), order=3)
-        print 't ', locscale_grad(1, 0, 1, tstd_dlldy, 10)
-        print 'ts', locscale_grad(1, 0, 1, ts_dlldy, 10)
-        print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,1,20), order=3),
-        print 'ts', locscale_grad(1.5, 0, 1, ts_dlldy, 20)
-        print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,2,20), order=3),
-        print 'ts', locscale_grad(1.5, 0, 2, ts_dlldy, 20)
-        print misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(1,2,20), order=3),
-        print 'ts', locscale_grad(1.5, 1, 2, ts_dlldy, 20)
-        print misc.derivative(lltloc, 1, dx=1e-10, n=1, args=(1.5,2,20), order=3),
-        print misc.derivative(lltscale, 2, dx=1e-10, n=1, args=(1.5,1,20), order=3)
+        print('\ngradient of t')
+        print(misc.derivative(llt, 1, dx=1e-6, n=1, args=(0,1,10), order=3))
+        print('t ', locscale_grad(1, 0, 1, tstd_dlldy, 10))
+        print('ts', locscale_grad(1, 0, 1, ts_dlldy, 10))
+        print(misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,1,20), order=3),)
+        print('ts', locscale_grad(1.5, 0, 1, ts_dlldy, 20))
+        print(misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(0,2,20), order=3),)
+        print('ts', locscale_grad(1.5, 0, 2, ts_dlldy, 20))
+        print(misc.derivative(llt, 1.5, dx=1e-10, n=1, args=(1,2,20), order=3),)
+        print('ts', locscale_grad(1.5, 1, 2, ts_dlldy, 20))
+        print(misc.derivative(lltloc, 1, dx=1e-10, n=1, args=(1.5,2,20), order=3),)
+        print(misc.derivative(lltscale, 2, dx=1e-10, n=1, args=(1.5,1,20), order=3))
         y,loc,scale,df = 1.5, 1, 2, 20
-        print 'ts', locscale_grad(y,loc,scale, ts_dlldy, 20)
-        print misc.derivative(lltloc, loc, dx=1e-10, n=1, args=(y,scale,df), order=3),
-        print misc.derivative(lltscale, scale, dx=1e-10, n=1, args=(y,loc,df), order=3)
+        print('ts', locscale_grad(y,loc,scale, ts_dlldy, 20))
+        print(misc.derivative(lltloc, loc, dx=1e-10, n=1, args=(y,scale,df), order=3),)
+        print(misc.derivative(lltscale, scale, dx=1e-10, n=1, args=(y,loc,df), order=3))
 
-        print '\ngradient of norm'
-        print misc.derivative(llnorm, 1, dx=1e-6, n=1, args=(0,1), order=3)
-        print locscale_grad(1, 0, 1, norm_dlldy)
+        print('\ngradient of norm')
+        print(misc.derivative(llnorm, 1, dx=1e-6, n=1, args=(0,1), order=3))
+        print(locscale_grad(1, 0, 1, norm_dlldy))
         y,loc,scale = 1.5, 1, 2
-        print 'ts', locscale_grad(y,loc,scale, norm_dlldy)
-        print misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3),
-        print misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3)
+        print('ts', locscale_grad(y,loc,scale, norm_dlldy))
+        print(misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3),)
+        print(misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3))
         y,loc,scale = 1.5, 0, 1
-        print 'ts', locscale_grad(y,loc,scale, norm_dlldy)
-        print misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3),
-        print misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3)
-        #print 'still something wrong with handling of scale and variance'
+        print('ts', locscale_grad(y,loc,scale, norm_dlldy))
+        print(misc.derivative(llnormloc, loc, dx=1e-10, n=1, args=(y,scale), order=3),)
+        print(misc.derivative(llnormscale, scale, dx=1e-10, n=1, args=(y,loc), order=3))
+        #print('still something wrong with handling of scale and variance'
         #looks ok now
-        print '\nloglike of t'
-        print tstd_lls(1, np.array([0,1]), 100), llt(1,0,1,100), 'differently standardized'
-        print tstd_lls(1, np.array([0,1]), 10), llt(1,0,1,10), 'differently standardized'
-        print ts_lls(1, np.array([0,1]), 10), llt(1,0,1,10)
-        print tstd_lls(1, np.array([0,1.*10./8.]), 10), llt(1.,0,1.,10)
-        print ts_lls(1, np.array([0,1]), 100), llt(1,0,1,100)
-
-        print tstd_lls(1, np.array([0,1]), 10), llt(1,0,1.*np.sqrt(8/10.),10)
+        print('\nloglike of t')
+        print(tstd_lls(1, np.array([0,1]), 100), llt(1,0,1,100), 'differently standardized')
+        print(tstd_lls(1, np.array([0,1]), 10), llt(1,0,1,10), 'differently standardized')
+        print(ts_lls(1, np.array([0,1]), 10), llt(1,0,1,10))
+        print(tstd_lls(1, np.array([0,1.*10./8.]), 10), llt(1.,0,1.,10))
+        print(ts_lls(1, np.array([0,1]), 100), llt(1,0,1,100))
+
+        print(tstd_lls(1, np.array([0,1]), 10), llt(1,0,1.*np.sqrt(8/10.),10))
 
 
     from numpy.testing import assert_almost_equal
diff --git a/statsmodels/sandbox/regression/treewalkerclass.py b/statsmodels/sandbox/regression/treewalkerclass.py
index 17b7610a642..106c8716a60 100644
--- a/statsmodels/sandbox/regression/treewalkerclass.py
+++ b/statsmodels/sandbox/regression/treewalkerclass.py
@@ -101,7 +101,8 @@
 Author: Josef Perktold
 License : BSD (3-clause)
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import lzip, iteritems, itervalues, lrange, zip
 import numpy as np
 from pprint import pprint
 
@@ -156,7 +157,7 @@ def getbranches(tree):
         list of all branch names
 
     '''
-    if type(tree) == tuple:
+    if isinstance(tree, tuple):
         name, subtree = tree
         a = [name]
         for st in subtree:
@@ -181,7 +182,7 @@ def getnodes(tree):
         list of all leaves names
 
     '''
-    if type(tree) == tuple:
+    if isinstance(tree, tuple):
         name, subtree = tree
         ab = [name]
         al = []
@@ -265,7 +266,8 @@ def __init__(self, endog, exog, tree, paramsind):
         #copied over but not quite sure yet
         #unique, parameter array names,
         #sorted alphabetically, order is/should be only internal
-        self.paramsnames = (sorted(set([i for j in paramsind.values()
+
+        self.paramsnames = (sorted(set([i for j in itervalues(paramsind)
                                        for i in j])) +
                             ['tau_%s' % bname for bname in self.branches])
 
@@ -277,7 +279,7 @@ def __init__(self, endog, exog, tree, paramsind):
 
         #mapping branch and leaf names to index in parameter array
         self.parinddict = dict((k, [self.paramsidx[j] for j in v])
-                               for k,v in self.paramsind.items())
+                               for k,v in iteritems(self.paramsind))
 
         self.recursionparams = 1. + np.arange(len(self.paramsnames))
         #for testing that individual parameters are used in the right place
@@ -331,16 +333,16 @@ def calc_prob(self, tree, parent=None):
         branchsum = self.branchsum
 
 
-        if type(tree) == tuple:   #assumes leaves are int for choice index
+        if isinstance(tree, tuple):   #assumes leaves are int for choice index
 
             name, subtree = tree
             self.branchleaves[name] = []  #register branch in dictionary
 
             tau = self.recursionparams[self.paramsidx['tau_'+name]]
             if DEBUG:
-                print '----------- starting next branch-----------'
-                print name, datadict[name], 'tau=', tau
-                print 'subtree', subtree
+                print('----------- starting next branch-----------')
+                print(name, datadict[name], 'tau=', tau)
+                print('subtree', subtree)
             branchvalue = []
             if testxb == 2:
                 branchsum = 0
@@ -350,7 +352,7 @@ def calc_prob(self, tree, parent=None):
                 branchsum = name
             for b in subtree:
                 if DEBUG:
-                    print b
+                    print(b)
                 bv = self.calc_prob(b, name)
                 bv = np.exp(bv/tau)  #this shouldn't be here, when adding branch data
                 branchvalue.append(bv)
@@ -358,13 +360,13 @@ def calc_prob(self, tree, parent=None):
             self.branchvalues[name] = branchvalue #keep track what was returned
 
             if DEBUG:
-                print '----------- returning to branch-----------',
-                print name
-                print 'branchsum in branch', name, branchsum
+                print('----------- returning to branch-----------')
+                print(name)
+                print('branchsum in branch', name, branchsum)
 
             if parent:
                 if DEBUG:
-                    print 'parent', parent
+                    print('parent', parent)
                 self.branchleaves[parent].extend(self.branchleaves[name])
             if 0:  #not name == 'top':  # not used anymore !!! ???
             #if not name == 'top':
@@ -388,8 +390,8 @@ def calc_prob(self, tree, parent=None):
 ##                            np.exp(-self.datadict[name] *
 ##                             np.sum(self.recursionparams[self.parinddict[name]]))
                         if DEBUG:
-                            print 'self.datadict[name], self.probs[k]',
-                            print self.datadict[name], self.probs[k]
+                            print('self.datadict[name], self.probs[k]')
+                            print(self.datadict[name], self.probs[k])
                     #if not name == 'top':
                     #    self.probs[k] = self.probs[k] * np.exp( iv)
 
@@ -397,9 +399,9 @@ def calc_prob(self, tree, parent=None):
             self.bprobs[name] = []
             for bidx, b in enumerate(subtree):
                 if DEBUG:
-                    print 'repr(b)', repr(b), bidx
+                    print('repr(b)', repr(b), bidx)
                 #if len(b) == 1: #TODO: skip leaves, check this
-                if not type(b) == tuple: # isinstance(b, str):
+                if not isinstance(b,  tuple): # isinstance(b, str):
                     #TODO: replace this with a check for branch (tuple) instead
                     #this implies name is a bottom branch,
                     #possible to add special things here
@@ -407,7 +409,7 @@ def calc_prob(self, tree, parent=None):
                     #TODO: need tau possibly here
                     self.probs[b] = self.probs[b] / branchsum
                     if DEBUG:
-                        print '*********** branchsum at bottom branch', branchsum
+                        print('*********** branchsum at bottom branch', branchsum)
                     #self.bprobs[name].append(self.probs[b])
                 else:
                     bname = b[0]
@@ -419,13 +421,13 @@ def calc_prob(self, tree, parent=None):
                     for k in self.branchleaves[bname]:
 
                         if DEBUG:
-                            print 'branchprob', bname, k, bprob, branchsum
+                            print('branchprob', bname, k, bprob, branchsum)
                         #temporary hack with maximum to avoid zeros
                         self.probs[k] = self.probs[k] * np.maximum(bprob, 1e-4)
 
 
             if DEBUG:
-                print 'working on branch', tree, branchsum
+                print('working on branch', tree, branchsum)
             if testxb<2:
                 return branchsum
             else: #this is the relevant part
@@ -446,7 +448,7 @@ def calc_prob(self, tree, parent=None):
         else:
             tau = self.recursionparams[self.paramsidx['tau_'+parent]]
             if DEBUG:
-                print 'parent', parent
+                print('parent', parent)
             self.branchleaves[parent].append(tree) # register leave with parent
             self.probstxt[tree] = [tree + '-prob' +
                                 '(%s)' % ', '.join(self.paramsind[tree])]
@@ -463,7 +465,7 @@ def calc_prob(self, tree, parent=None):
             elif testxb == 1:
                 leavessum = np.array(datadict[tree]) # sum((datadict[bi] for bi in datadict[tree]))
                 if DEBUG:
-                    print 'final branch with', tree, ''.join(tree), leavessum #sum(tree)
+                    print('final branch with', tree, ''.join(tree), leavessum) #sum(tree)
                 return leavessum  #sum(xb[tree])
             elif testxb == 0:
                 return ''.join(tree) #sum(tree)
@@ -479,7 +481,7 @@ def calc_prob(self, tree, parent=None):
     ##############  Example similar to Greene
 
     #get pickled data
-    #endog3, xifloat3 = pickle.load(open('xifloat2.pickle','rb'))
+    #endog3, xifloat3 = cPickle.load(open('xifloat2.pickle','rb'))
 
 
     tree0 = ('top',
@@ -518,13 +520,13 @@ def calc_prob(self, tree, parent=None):
     modru.recursionparams[-1] = 2
     modru.recursionparams[1] = 1
 
-    print 'Example 1'
-    print '---------\n'
-    print modru.calc_prob(modru.tree)
+    print('Example 1')
+    print('---------\n')
+    print(modru.calc_prob(modru.tree))
 
-    print 'Tree'
+    print('Tree')
     pprint(modru.tree)
-    print '\nmodru.probs'
+    print('\nmodru.probs')
     pprint(modru.probs)
 
 
@@ -559,7 +561,8 @@ def calc_prob(self, tree, parent=None):
      'h': ['consth', 'p', 'h'],
      'top': []}
 
-    datadict2 = dict([i for i in zip('abcdefgh',range(8))])
+
+    datadict2 = dict([i for i in zip('abcdefgh',lrange(8))])
     datadict2.update({'top':1000, 'B1':100, 'B2':200, 'B21':21,'B22':22, 'B3':300})
     '''
     >>> pprint(datadict2)
@@ -583,24 +586,25 @@ def calc_prob(self, tree, parent=None):
     modru2 = RU2NMNL(endog, datadict2, tree2, paramsind2)
     modru2.recursionparams[-3] = 2
     modru2.recursionparams[3] = 1
-    print '\n\nExample 2'
-    print '---------\n'
-    print modru2.calc_prob(modru2.tree)
-    print 'Tree'
+    print('\n\nExample 2')
+    print('---------\n')
+    print(modru2.calc_prob(modru2.tree))
+    print('Tree')
     pprint(modru2.tree)
-    print '\nmodru.probs'
+    print('\nmodru.probs')
     pprint(modru2.probs)
 
-    print 'sum of probs', sum(modru2.probs.values())
-    print 'branchvalues'
-    print modru2.branchvalues
-    print modru.branchvalues
 
-    print 'branch probabilities'
-    print modru.bprobs
+    print('sum of probs', sum(list(itervalues(modru2.probs))))
+    print('branchvalues')
+    print(modru2.branchvalues)
+    print(modru.branchvalues)
+
+    print('branch probabilities')
+    print(modru.bprobs)
 
-    print 'degenerate branches'
-    print modru.branches_degenerate
+    print('degenerate branches')
+    print(modru.branches_degenerate)
 
     '''
     >>> modru.bprobs
@@ -610,7 +614,7 @@ def calc_prob(self, tree, parent=None):
     '''
 
     params1 = np.array([ 0.,  1.,  0.,  0.,  0.,  0.,  1.,  1.,  2.])
-    print modru.get_probs(params1)
+    print(modru.get_probs(params1))
     params2 = np.array([ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
                          0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  2.,  1.,  1.])
-    print modru2.get_probs(params2) #raises IndexError
+    print(modru2.get_probs(params2)) #raises IndexError
diff --git a/statsmodels/sandbox/regression/try_catdata.py b/statsmodels/sandbox/regression/try_catdata.py
index dfc7e55f5ca..5156f1c57ab 100644
--- a/statsmodels/sandbox/regression/try_catdata.py
+++ b/statsmodels/sandbox/regression/try_catdata.py
@@ -1,4 +1,4 @@
-
+from statsmodels.compat.python import lrange
 import numpy as np
 #from numpy import linalg as npla
 from scipy import stats, optimize
@@ -104,7 +104,7 @@ def groupsstats_1d(y, x, labelsunique):
 
 def cat2dummy(y, nonseq=0):
     if nonseq or (y.ndim == 2 and y.shape[1] > 1):
-        ycat, uniques, unitransl =  convertlabels(y, range(y.shape[1]))
+        ycat, uniques, unitransl =  convertlabels(y, lrange(y.shape[1]))
     else:
         ycat = y.copy()
         ymin = y.min()
diff --git a/statsmodels/sandbox/regression/try_ols_anova.py b/statsmodels/sandbox/regression/try_ols_anova.py
index 790e0c70567..905c1a0031a 100644
--- a/statsmodels/sandbox/regression/try_ols_anova.py
+++ b/statsmodels/sandbox/regression/try_ols_anova.py
@@ -12,7 +12,8 @@
 
 '''
 
-
+from __future__ import print_function
+from statsmodels.compat.python import lmap
 import numpy as np
 #from scipy import stats
 import statsmodels.api as sm
@@ -39,7 +40,7 @@ def data2proddummy(x):
     '''
     #brute force, assumes x is 2d
     #replace with encoding if possible
-    groups = np.unique(map(tuple, x.tolist()))
+    groups = np.unique(lmap(tuple, x.tolist()))
     #includes singularity with additive factors
     return (x==groups[:,None,:]).all(-1).T.astype(int)[:,:-1]
 
@@ -194,13 +195,13 @@ def dropname(ss, li):
         testdata[name] = testdatacont[name]
 
 
-    #print form2design('a',testdata)
+    #print(form2design('a',testdata)
 
-    if 0: # print only when nobs is small, e.g. nobs=10
+    if 0: # print(only when nobs is small, e.g. nobs=10
         xx, n = form2design('F:a',testdata)
-        print xx
-        print form2design('P:a*b',testdata)
-        print data2proddummy((np.c_[testdata['a'],testdata['b']]))
+        print(xx)
+        print(form2design('P:a*b',testdata))
+        print(data2proddummy((np.c_[testdata['a'],testdata['b']])))
 
         xx, names = form2design('a F:b P:c*d',testdata)
 
@@ -213,16 +214,16 @@ def dropname(ss, li):
     # simple test version: all coefficients equal to one
     y = X.sum(1) + 0.01*np.random.normal(size=(nobs))
     rest1 = sm.OLS(y,X).fit() #results
-    print rest1.params
-    print anova_str % anovadict(rest1)
+    print(rest1.params)
+    print(anova_str % anovadict(rest1))
 
 
     X = np.column_stack([xx[nn] for nn in dropname('ae f', names)])
     # simple test version: all coefficients equal to one
     y = X.sum(1) + 0.01*np.random.normal(size=(nobs))
     rest1 = sm.OLS(y,X).fit()
-    print rest1.params
-    print anova_str % anovadict(rest1)
+    print(rest1.params)
+    print(anova_str % anovadict(rest1))
 
 
     # Example: from Bruce
@@ -238,14 +239,14 @@ def dropname(ss, li):
                    ('pen', int), ('pig', int), ('age', float),
                    ('bage', float), ('y', float)])
     dta = np.genfromtxt('dftest3.data', dt_b,missing='.', usemask=True)
-    print 'missing', [dta.mask[k].sum() for k in dta.dtype.names]
+    print('missing', [dta.mask[k].sum() for k in dta.dtype.names])
     m = dta.mask.view(bool)
     droprows = m.reshape(-1,len(dta.dtype.names)).any(1)
     # get complete data as plain structured array
     # maybe doesn't work with masked arrays
     dta_use_b1 = dta[~droprows,:].data
-    print dta_use_b1.shape
-    print dta_use_b1.dtype
+    print(dta_use_b1.shape)
+    print(dta_use_b1.dtype)
 
     #Example b1: variables from Bruce's glm
     #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -257,11 +258,11 @@ def dropname(ss, li):
     y_b1 = dta_use_b1['y']
     # estimate using OLS
     rest_b1 = sm.OLS(y_b1, X_b1).fit()
-    # print results
-    print rest_b1.params
-    print anova_str % anovadict(rest_b1)
+    # print(results)
+    print(rest_b1.params)
+    print(anova_str % anovadict(rest_b1))
     #compare with original version only in original version
-    #print anova_str % anovadict(res_b0)
+    #print(anova_str % anovadict(res_b0))
 
     # Example: use all variables except pig identifier
     #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -273,15 +274,15 @@ def dropname(ss, li):
     X_b1a = np.column_stack([xx_b1a[nn] for nn in dropname('', names_b1a)])
     y_b1a = dta_use_b1['y']
     rest_b1a = sm.OLS(y_b1a, X_b1a).fit()
-    print rest_b1a.params
-    print anova_str % anovadict(rest_b1a)
+    print(rest_b1a.params)
+    print(anova_str % anovadict(rest_b1a))
 
     for dropn in names_b1a:
-        print '\nResults dropping', dropn
+        print(('\nResults dropping', dropn))
         X_b1a_ = np.column_stack([xx_b1a[nn] for nn in dropname(dropn, names_b1a)])
         y_b1a_ = dta_use_b1['y']
         rest_b1a_ = sm.OLS(y_b1a_, X_b1a_).fit()
-        #print rest_b1a_.params
-        print anova_str % anovadict(rest_b1a_)
+        #print(rest_b1a_.params)
+        print(anova_str % anovadict(rest_b1a_))
 
 
diff --git a/statsmodels/sandbox/regression/try_treewalker.py b/statsmodels/sandbox/regression/try_treewalker.py
index 5137f699399..9f3845de5b5 100644
--- a/statsmodels/sandbox/regression/try_treewalker.py
+++ b/statsmodels/sandbox/regression/try_treewalker.py
@@ -5,7 +5,8 @@
 should collect and aggregate likelihood contributions bottom up
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import iteritems, itervalues, lrange, zip
 import numpy as np
 
 tree = [[0,1],[[2,3],[4,5,6]],[7]]
@@ -18,22 +19,22 @@ def branch(tree):
     '''walking a tree bottom-up
     '''
 
-    if not type(tree[0]) == int:   #assumes leaves are int for choice index
+    if not isinstance(tree[0], int):   #assumes leaves are int for choice index
         branchsum = 0
         for b in tree:
             branchsum += branch(b)
     else:
-        print tree
-        print 'final branch with', tree, sum(tree)
+        print(tree)
+        print('final branch with', tree, sum(tree))
         if testxb:
             return sum(xb[tree])
         else:
             return sum(tree)
 
-    print 'working on branch', tree, branchsum
+    print('working on branch', tree, branchsum)
     return branchsum
 
-print branch(tree)
+print(branch(tree))
 
 
 
@@ -48,10 +49,10 @@ def branch2(tree):
     '''
 
 
-    if type(tree) == tuple:   #assumes leaves are int for choice index
+    if isinstance(tree,  tuple):   #assumes leaves are int for choice index
         name, subtree = tree
-        print name, data2[name]
-        print 'subtree', subtree
+        print(name, data2[name])
+        print('subtree', subtree)
         if testxb:
             branchsum = data2[name]
         else:
@@ -61,13 +62,13 @@ def branch2(tree):
             branchsum = branchsum + branch2(b)
     else:
         leavessum = sum((data2[bi] for bi in tree))
-        print 'final branch with', tree, ''.join(tree), leavessum #sum(tree)
+        print('final branch with', tree, ''.join(tree), leavessum) #sum(tree)
         if testxb:
             return leavessum  #sum(xb[tree])
         else:
             return ''.join(tree) #sum(tree)
 
-    print 'working on branch', tree, branchsum
+    print('working on branch', tree, branchsum)
     return branchsum
 
 tree = [[0,1],[[2,3],[4,5,6]],[7]]
@@ -82,7 +83,7 @@ def branch2(tree):
             ]
          )
 
-data2 = dict([i for i in zip('abcdefgh',range(8))])
+data2 = dict([i for i in zip('abcdefgh',lrange(8))])
 #data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':300,'B22':400, 'B3':400})
 data2.update({'top':1000, 'B1':100, 'B2':200, 'B21':21,'B22':22, 'B3':300})
 
@@ -90,8 +91,8 @@ def branch2(tree):
 #{'a': 0, 'c': 2, 'b': 1, 'e': 4, 'd': 3, 'g': 6, 'f': 5, 'h': 7,
 #'top': 1000, 'B22': 22, 'B21': 21, 'B1': 100, 'B2': 200, 'B3': 300}
 
-print '\n tree with dictionary data'
-print branch2(tree2)  # results look correct for testxb=0 and 1
+print('\n tree with dictionary data')
+print(branch2(tree2))  # results look correct for testxb=0 and 1
 
 
 #parameters/coefficients map coefficient names to indices, list of indices into
@@ -116,13 +117,14 @@ def branch2(tree):
 
 #unique, parameter array names,
 #sorted alphabetically, order is/should be only internal
-paramsnames = sorted(set([i for j in paramsind.values() for i in j]))
+
+paramsnames = sorted(set([i for j in itervalues(paramsind) for i in j]))
 
 #mapping coefficient names to indices to unique/parameter array
 paramsidx = dict((name, idx) for (idx,name) in enumerate(paramsnames))
 
 #mapping branch and leaf names to index in parameter array
-inddict = dict((k,[paramsidx[j] for j in v]) for k,v in paramsind.items())
+inddict = dict((k,[paramsidx[j] for j in v]) for k,v in iteritems(paramsind))
 
 '''
 >>> paramsnames
diff --git a/statsmodels/sandbox/rls.py b/statsmodels/sandbox/rls.py
index 1f955827332..dcb23fd03b6 100644
--- a/statsmodels/sandbox/rls.py
+++ b/statsmodels/sandbox/rls.py
@@ -3,7 +3,7 @@
 from pandas
 License: Simplified BSD
 """
-
+from __future__ import print_function
 import numpy as np
 from statsmodels.regression.linear_model import WLS, GLS, RegressionResults
 
@@ -148,5 +148,5 @@ def fit(self):
     design = sm.add_constant(design, prepend=True)
     rls_mod = RLS(dta['G'],design, constr=[0,0,0,1,1,1,1])
     rls_fit = rls_mod.fit()
-    print rls_fit.params
+    print(rls_fit.params)
 
diff --git a/statsmodels/sandbox/stats/contrast_tools.py b/statsmodels/sandbox/stats/contrast_tools.py
index f2ab89c2d15..67d74407982 100644
--- a/statsmodels/sandbox/stats/contrast_tools.py
+++ b/statsmodels/sandbox/stats/contrast_tools.py
@@ -22,7 +22,8 @@
 
 
 
-
+from __future__ import print_function
+from statsmodels.compat.python import zip
 import numpy as np
 
 #next 3 functions copied from multicomp.py
@@ -291,7 +292,7 @@ def dummy_limits(d):
     start1, col1 = np.nonzero(np.diff(d,axis=0)==1)
     end1, col1_ = np.nonzero(np.diff(d,axis=0)==-1)
     cc = np.arange(nvars)
-    #print cc, np.r_[[0], col1], np.r_[col1_, [nvars-1]]
+    #print(cc, np.r_[[0], col1], np.r_[col1_, [nvars-1]]
     if ((not (np.r_[[0], col1] == cc).all())
             or (not (np.r_[col1_, [nvars-1]] == cc).all())):
         raise ValueError('dummy variable is not sorted')
@@ -634,7 +635,7 @@ def test_dummy_limits(self):
     v1name = ['a0', 'a1', 'a2']
     v2name = ['b0', 'b1']
     res_cp = contrast_product(v1name, v2name)
-    print res_cp
+    print(res_cp)
 
     y = np.arange(12)
     x1 = np.arange(12)//4
@@ -659,7 +660,7 @@ def test_dummy_limits(self):
 
     #Note: full parameterization of dummies is orthogonal
     #np.eye(6)*10 in "large" example
-    print (np.dot(dd_full.T, dd_full) == np.diag(dd_full.sum(0))).all()
+    print((np.dot(dd_full.T, dd_full) == np.diag(dd_full.sum(0))).all())
 
     #check that transforms work
     #generate 3 data sets with the 3 different parameterizations
@@ -685,28 +686,28 @@ def test_dummy_limits(self):
 
     tr_of = np.linalg.lstsq(dd_dropf, dd_full)[0]
     tr_fo = np.linalg.lstsq(dd_full, dd_dropf)[0]
-    print np.dot(tr_fo, params_df_df) - params_df_f
-    print np.dot(tr_of, params_f_f) - params_f_df
+    print(np.dot(tr_fo, params_df_df) - params_df_f)
+    print(np.dot(tr_of, params_f_f) - params_f_df)
 
     transf_f_df = DummyTransform(dd_full, dd_dropf)
-    print np.max(np.abs((dd_full - transf_f_df.inv_dot_right(dd_dropf))))
-    print np.max(np.abs((dd_dropf - transf_f_df.dot_right(dd_full))))
-    print np.max(np.abs((params_df_df
-                         - transf_f_df.inv_dot_left(params_df_f))))
+    print(np.max(np.abs((dd_full - transf_f_df.inv_dot_right(dd_dropf)))))
+    print(np.max(np.abs((dd_dropf - transf_f_df.dot_right(dd_full)))))
+    print(np.max(np.abs((params_df_df
+                         - transf_f_df.inv_dot_left(params_df_f)))))
     np.max(np.abs((params_f_df
                          - transf_f_df.inv_dot_left(params_f_f))))
 
     prodlab, C1, C1lab, C2, C2lab,_ = contrast_product(v1name, v2name)
 
-    print '\ntvalues for no effect of factor 1'
-    print 'each test is conditional on a level of factor 2'
-    print C1lab
-    print resols_dropf_full.t_test(C1).tvalue
+    print('\ntvalues for no effect of factor 1')
+    print('each test is conditional on a level of factor 2')
+    print(C1lab)
+    print(resols_dropf_full.t_test(C1).tvalue)
 
-    print '\ntvalues for no effect of factor 2'
-    print 'each test is conditional on a level of factor 1'
-    print C2lab
-    print resols_dropf_full.t_test(C2).tvalue
+    print('\ntvalues for no effect of factor 2')
+    print('each test is conditional on a level of factor 1')
+    print(C2lab)
+    print(resols_dropf_full.t_test(C2).tvalue)
 
     #covariance matrix of restrictions C2, note: orthogonal
     resols_dropf_full.cov_params(C2)
@@ -715,17 +716,17 @@ def test_dummy_limits(self):
     R_noint = np.hstack((np.zeros((2,4)), np.eye(2)))
     inter_direct = resols_full_dropf.tvalues[-2:]
     inter_transf = resols_full_full.t_test(transf_f_df.inv_dot_right(R_noint)).tvalue
-    print np.max(np.abs((inter_direct - inter_transf)))
+    print(np.max(np.abs((inter_direct - inter_transf))))
 
     #now with class version
     tw = TwoWay(ydata_dropf, x1, x2)
-    print tw.ttest_interaction().tvalue
-    print tw.ttest_interaction().pvalue
-    print tw.ftest_interaction().fvalue
-    print tw.ftest_interaction().pvalue
-    print tw.ttest_conditional_effect(1)[0].tvalue
-    print tw.ttest_conditional_effect(2)[0].tvalue
-    print tw.summary_coeff()
+    print(tw.ttest_interaction().tvalue)
+    print(tw.ttest_interaction().pvalue)
+    print(tw.ftest_interaction().fvalue)
+    print(tw.ftest_interaction().pvalue)
+    print(tw.ttest_conditional_effect(1)[0].tvalue)
+    print(tw.ttest_conditional_effect(2)[0].tvalue)
+    print(tw.summary_coeff())
 
 
 
diff --git a/statsmodels/sandbox/stats/diagnostic.py b/statsmodels/sandbox/stats/diagnostic.py
index c23ed7a9c8b..81fc40436ec 100644
--- a/statsmodels/sandbox/stats/diagnostic.py
+++ b/statsmodels/sandbox/stats/diagnostic.py
@@ -29,13 +29,15 @@
 
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import iteritems, lrange, map
 import numpy as np
 from scipy import stats
 from statsmodels.regression.linear_model import OLS
 from statsmodels.tools.tools import add_constant
 from statsmodels.tsa.stattools import acf, adfuller
 from statsmodels.tsa.tsatools import lagmat
+from statsmodels.compat.numpy import np_matrix_rank
 
 #get the old signature back so the examples work
 def unitroot_adf(x, maxlag=None, trendorder=0, autolag='AIC', store=False):
@@ -210,7 +212,7 @@ def run(self, results_x, results_z, attach=True):
         pval = res_zx.pvalues[0]
         if attach:
             self.res_zx = res_zx
-            self.dist = stats.t(res_zx.model.df_resid)
+            self.dist = stats.t(res_zx.df_resid)
             self.teststat = tstat
             self.pvalue = pval
 
@@ -281,9 +283,9 @@ def acorr_ljungbox(x, lags=None, boxpierce=False):
     x = np.asarray(x)
     nobs = x.shape[0]
     if lags is None:
-        lags = range(1,41)  #TODO: check default; SS: changed to 40
+        lags = lrange(1,41)  #TODO: check default; SS: changed to 40
     elif isinstance(lags, int):
-        lags = range(1,lags+1)
+        lags = lrange(1,lags+1)
     maxlag = max(lags)
     lags = np.asarray(lags)
 
@@ -370,9 +372,9 @@ def acorr_lm(x, maxlag=None, autolag='AIC', store=False, regresults=False):
             results[mlag] = OLS(xshort, xdall[:,:mlag+1]).fit()
 
         if autolag.lower() == 'aic':
-            bestic, icbestlag = min((v.aic,k) for k,v in results.iteritems())
+            bestic, icbestlag = min((v.aic,k) for k,v in iteritems(results))
         elif autolag.lower() == 'bic':
-            icbest, icbestlag = min((v.bic,k) for k,v in results.iteritems())
+            icbest, icbestlag = min((v.bic,k) for k,v in iteritems(results))
         else:
             raise ValueError("autolag can only be None, 'AIC' or 'BIC'")
 
@@ -653,9 +655,8 @@ def het_white(resid, exog, retres=False):
     # Note: degrees of freedom for LM test is nvars minus constant
     #degrees of freedom take possible reduced rank in exog into account
     #df_model checks the rank to determine df
-    from statsmodels.tools.tools import rank
     #extra calculation that can be removed:
-    assert resols.df_model == rank(exog) - 1
+    assert resols.df_model == np_matrix_rank(exog) - 1
     lmpval = stats.chi2.sf(lm, resols.df_model)
     return lm, lmpval, fval, fpval
 
@@ -1053,9 +1054,9 @@ def _recursive_olsresiduals2(olsresults, skip):
         yi = y[i]
         xxT = np.dot(xi.T, xi)  #xi is 2d 1 row
         xy = (xi*yi).ravel() # XTY is 1d  #np.dot(xi, yi)   #np.dot(xi, y)
-        print xy.shape, XTY.shape
-        print XTX
-        print XTY
+        print(xy.shape, XTY.shape)
+        print(XTX)
+        print(XTY)
         beta = np.linalg.solve(XTX, XTY)
         rparams[i-1] = beta  #this is beta based on info up to t-1
         yipred = np.dot(xi, beta)
@@ -1162,7 +1163,7 @@ def recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95):
     XTY = np.dot(x0.T, y0) #xi * y   #np.dot(xi, y)
     #beta = np.linalg.solve(XTX, XTY)
     beta = np.dot(XTXi, XTY)
-    #print 'beta', beta
+    #print('beta', beta
     rparams[skip-1] = beta
     yipred = np.dot(x[skip-1], beta)
     rypred[skip-1] = yipred
@@ -1173,9 +1174,9 @@ def recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95):
         yi = y[i]
         #xxT = np.dot(xi.T, xi)  #xi is 2d 1 row
         xy = (xi*yi).ravel() # XTY is 1d  #np.dot(xi, yi)   #np.dot(xi, y)
-        #print xy.shape, XTY.shape
-        #print XTX
-        #print XTY
+        #print(xy.shape, XTY.shape
+        #print(XTX
+        #print(XTY
 
         # get prediction error with previous beta
         yipred = np.dot(xi, beta)
@@ -1189,13 +1190,13 @@ def recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95):
 
         XTXi = XTXi - np.dot(tmp,tmp.T) / ft  #BigJudge equ 5.5.15
 
-        #print 'beta', beta
+        #print('beta', beta
         beta = beta + (tmp*residi / ft).ravel()  #BigJudge equ 5.5.14
 #        #version for testing
 #        XTY += xy
 #        beta = np.dot(XTXi, XTY)
-#        print (tmp*yipred / ft).shape
-#        print 'tmp.shape, ft.shape, beta.shape', tmp.shape, ft.shape, beta.shape
+#        print((tmp*yipred / ft).shape
+#        print('tmp.shape, ft.shape, beta.shape', tmp.shape, ft.shape, beta.shape
         rparams[i] = beta
         rvarraw[i] = ft
 
@@ -1329,7 +1330,7 @@ def breaks_cusumolsresid(olsresidual, ddof=0):
     nobs = len(resid)
     nobssigma2 = (resid**2).sum()
     if ddof > 0:
-        #print 'ddof', ddof, 1. / (nobs - ddof) * nobs
+        #print('ddof', ddof, 1. / (nobs - ddof) * nobs
         nobssigma2 = nobssigma2 / (nobs - ddof) * nobs
     #B is asymptotically a Brownian Bridge
     B = resid.cumsum()/np.sqrt(nobssigma2) # use T*sigma directly
@@ -1403,7 +1404,7 @@ class StatTestMC(object):
     """class to run Monte Carlo study on a statistical test'''
 
     TODO
-    print summary, for quantiles and for histogram
+    print(summary, for quantiles and for histogram
     draft in trying out script log
 
 
@@ -1497,8 +1498,8 @@ def quantiles(self, idx=None, frac=[0.01, 0.025, 0.05, 0.1, 0.975]):
     if 'adf' in examples:
 
         x = np.random.randn(20)
-        print acorr_ljungbox(x,4)
-        print unitroot_adf(x)
+        print(acorr_ljungbox(x,4))
+        print(unitroot_adf(x))
 
         nrepl = 100
         nobs = 100
@@ -1507,18 +1508,18 @@ def quantiles(self, idx=None, frac=[0.01, 0.025, 0.05, 0.1, 0.975]):
             x = (1e-4+np.random.randn(nobs)).cumsum()
             mcres[ii] = unitroot_adf(x, 2,trendorder=0, autolag=None)[0]
 
-        print (mcres<-2.57).sum()
-        print np.histogram(mcres)
+        print((mcres<-2.57).sum())
+        print(np.histogram(mcres))
         mcressort = np.sort(mcres)
         for ratio in [0.01, 0.025, 0.05, 0.1]:
-            print ratio, mcressort[int(nrepl*ratio)]
+            print(ratio, mcressort[int(nrepl*ratio)])
 
-        print 'critical values in Green table 20.5'
-        print 'sample size = 100'
-        print 'with constant'
-        print '0.01: -19.8,  0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47'
+        print('critical values in Green table 20.5')
+        print('sample size = 100')
+        print('with constant')
+        print('0.01: -19.8,  0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47')
 
-        print '0.01: -3.50,  0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26'
+        print('0.01: -3.50,  0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26')
         crvdg = dict([map(float,s.split(':')) for s in ('0.01: -19.8,  0.025: -16.3, 0.05: -13.7, 0.01: -11.0, 0.975: 0.47'.split(','))])
         crvd = dict([map(float,s.split(':')) for s in ('0.01: -3.50,  0.025: -3.17, 0.05: -2.90, 0.01: -2.58, 0.975: 0.26'.split(','))])
         '''
@@ -1530,19 +1531,19 @@ def quantiles(self, idx=None, frac=[0.01, 0.025, 0.05, 0.1, 0.975]):
 
         #for trend = 0
         crit_5lags0p05 =-4.41519 + (-14.0406)/nobs + (-12.575)/nobs**2
-        print crit_5lags0p05
+        print(crit_5lags0p05)
 
 
         adfstat, _,_,resstore = unitroot_adf(x, 2,trendorder=0, autolag=None, store=1)
 
-        print (mcres>crit_5lags0p05).sum()
+        print((mcres>crit_5lags0p05).sum())
 
-        print resstore.resols.model.exog[-5:]
-        print x[-5:]
+        print(resstore.resols.model.exog[-5:])
+        print(x[-5:])
 
-        print np.histogram(mcres, bins=[-np.inf, -3.5, -3.17, -2.9 , -2.58,  0.26, np.inf])
+        print(np.histogram(mcres, bins=[-np.inf, -3.5, -3.17, -2.9 , -2.58,  0.26, np.inf]))
 
-        print mcressort[(nrepl*(np.array([0.01, 0.025, 0.05, 0.1, 0.975]))).astype(int)]
+        print(mcressort[(nrepl*(np.array([0.01, 0.025, 0.05, 0.1, 0.975]))).astype(int)])
 
 
         def randwalksim(nobs=100, drift=0.0):
@@ -1554,13 +1555,13 @@ def normalnoisesim(nobs=500, loc=0.0):
         def adf20(x):
             return unitroot_adf(x, 2,trendorder=0, autolag=None)[:2]
 
-        print '\nResults with MC class'
+        print('\nResults with MC class')
         mc1 = StatTestMC(randwalksim, adf20)
         mc1.run(1000, statindices=[0,1])
-        print mc1.histogram(0, critval=[-3.5, -3.17, -2.9 , -2.58,  0.26])
-        print mc1.quantiles(0)
+        print(mc1.histogram(0, critval=[-3.5, -3.17, -2.9 , -2.58,  0.26]))
+        print(mc1.quantiles(0))
 
-        print '\nLjung Box'
+        print('\nLjung Box')
 
         def lb4(x):
             s,p = acorr_ljungbox(x, lags=4)
@@ -1570,44 +1571,44 @@ def lb4(x):
             s,p = acorr_ljungbox(x, lags=1)
             return s[0], p[0]
 
-        print 'Results with MC class'
+        print('Results with MC class')
         mc1 = StatTestMC(normalnoisesim, lb4)
         mc1.run(1000, statindices=[0,1])
-        print mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975])
-        print mc1.quantiles(1)
-        print mc1.quantiles(0)
-        print mc1.histogram(0)
+        print(mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975]))
+        print(mc1.quantiles(1))
+        print(mc1.quantiles(0))
+        print(mc1.histogram(0))
 
     nobs = 100
     x = np.ones((nobs,2))
     x[:,1] = np.arange(nobs)/20.
     y = x.sum(1) + 1.01*(1+1.5*(x[:,1]>10))*np.random.rand(nobs)
-    print het_goldfeldquandt(y,x, 1)
+    print(het_goldfeldquandt(y,x, 1))
 
     y = x.sum(1) + 1.01*(1+0.5*(x[:,1]>10))*np.random.rand(nobs)
-    print het_goldfeldquandt(y,x, 1)
+    print(het_goldfeldquandt(y,x, 1))
 
     y = x.sum(1) + 1.01*(1-0.5*(x[:,1]>10))*np.random.rand(nobs)
-    print het_goldfeldquandt(y,x, 1)
+    print(het_goldfeldquandt(y,x, 1))
 
-    print het_breushpagan(y,x)
-    print het_white(y,x)
+    print(het_breushpagan(y,x))
+    print(het_white(y,x))
 
     f, fp, fo = het_goldfeldquandt(y,x, 1)
-    print f, fp
+    print(f, fp)
     resgq = het_goldfeldquandt(y,x, 1, retres=True)
-    print resgq
+    print(resgq)
 
     #this is just a syntax check:
-    print _neweywestcov(y, x)
+    print(_neweywestcov(y, x))
 
     resols1 = OLS(y, x).fit()
-    print _neweywestcov(resols1.resid, x)
-    print resols1.cov_params()
-    print resols1.HC0_se
-    print resols1.cov_HC0
+    print(_neweywestcov(resols1.resid, x))
+    print(resols1.cov_params())
+    print(resols1.HC0_se)
+    print(resols1.cov_HC0)
 
     y = x.sum(1) + 10.*(1-0.5*(x[:,1]>10))*np.random.rand(nobs)
-    print HetGoldfeldQuandt().run(y,x, 1, alternative='dec')
+    print(HetGoldfeldQuandt().run(y,x, 1, alternative='dec'))
 
 
diff --git a/statsmodels/sandbox/stats/ex_newtests.py b/statsmodels/sandbox/stats/ex_newtests.py
index 8d0015d5543..79a7742e14e 100644
--- a/statsmodels/sandbox/stats/ex_newtests.py
+++ b/statsmodels/sandbox/stats/ex_newtests.py
@@ -1,14 +1,14 @@
 
-
-from diagnostic import unitroot_adf
+from __future__ import print_function
+from .diagnostic import unitroot_adf
 
 import statsmodels.datasets.macrodata.data as macro
 
 macrod = macro.load().data
 
-print macro.NOTE
+print(macro.NOTE)
 
-print macrod.dtype.names
+print(macrod.dtype.names)
 
 datatrendli = [
                ('realgdp', 1),
@@ -25,9 +25,9 @@
                ('realint', 0)
                ]
 
-print '%-10s %5s %-8s' % ('variable', 'trend', '  adf')
+print('%-10s %5s %-8s' % ('variable', 'trend', '  adf'))
 for name, torder in datatrendli:
     adf_, pval = unitroot_adf(macrod[name], trendorder=torder)[:2]
-    print '%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval)
+    print('%-10s %5d %8.4f %8.4f' % (name, torder, adf_, pval))
 
 
diff --git a/statsmodels/sandbox/stats/multicomp.py b/statsmodels/sandbox/stats/multicomp.py
index 738cc37b8ef..23c878bd5dd 100644
--- a/statsmodels/sandbox/stats/multicomp.py
+++ b/statsmodels/sandbox/stats/multicomp.py
@@ -63,7 +63,7 @@
 
 Traceback (most recent call last):
   File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\multicomp.py", line 711, in <module>
-    print 'sh', multipletests(tpval, alpha=0.05, method='sh')
+    print('sh', multipletests(tpval, alpha=0.05, method='sh')
   File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\stats\multicomp.py", line 241, in multipletests
     rejectmax = np.max(np.nonzero(reject))
   File "C:\Programs\Python25\lib\site-packages\numpy\core\fromnumeric.py", line 1765, in amax
@@ -80,6 +80,8 @@
 
 #import xlrd
 #import xlwt
+from __future__ import print_function
+from statsmodels.compat.python import lzip, range, lrange, zip
 import scipy.stats
 import numpy
 import numpy as np
@@ -215,10 +217,10 @@ def Tukeythreegene(first,second,third):
 
    conclusion = []
 
-##    print qcrit
-   print qtest3to1
-   print qtest3to2
-   print qtest2to1
+##    print(qcrit
+   print(qtest3to1)
+   print(qtest3to2)
+   print(qtest2to1)
 
    if(qtest3to1>qcrit): #testing all q test statistic values to q critical values
        conclusion.append('3to1null')
@@ -416,7 +418,7 @@ def mcfdr(nrepl=100, nobs=50, ntests=10, ntrue=6, mu=0.5, alpha=0.05, rho=0.):
     nfalse = ntests - ntrue
     locs = np.array([0.]*ntrue + [mu]*(ntests - ntrue))
     results = []
-    for i in xrange(nrepl):
+    for i in range(nrepl):
         #rvs = locs + stats.norm.rvs(size=(nobs, ntests))
         rvs = locs + randmvn(rho, size=(nobs, ntests))
         tt, tpval = stats.ttest_1samp(rvs, 0)
@@ -547,7 +549,7 @@ def runbasic_old(self, useranks=False):
         else:
             self.xx = x[:,0]
         self.groupsum = groupranksum = np.bincount(self.intlab, weights=self.xx)
-        #print 'groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape
+        #print('groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape
         # start at 1 for stats.rankdata :
         self.groupmean = grouprankmean = groupranksum * 1.0 / self.groupnobs # + 1
         self.groupmeanfilter = grouprankmean[self.intlab]
@@ -567,7 +569,7 @@ def runbasic(self, useranks=False):
         else:
             self.xx = x[:,0]
         self.groupsum = groupranksum = np.bincount(self.intlab, weights=self.xx)
-        #print 'groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape
+        #print('groupranksum', groupranksum, groupranksum.shape, self.groupnobs.shape
         # start at 1 for stats.rankdata :
         self.groupmean = grouprankmean = groupranksum * 1.0 / self.groupnobs # + 1
         self.groupmeanfilter = grouprankmean[self.intlab]
@@ -721,11 +723,11 @@ def plot_simultaneous(self, comparison_name=None, ax=None, figsize=(10,6),
         maxrange = [means[i] + self.halfwidths[i] for i in range(len(means))]
 
         if comparison_name is None:
-            ax1.errorbar(means, range(len(means)), xerr=self.halfwidths,
+            ax1.errorbar(means, lrange(len(means)), xerr=self.halfwidths,
                          marker='o', linestyle='None', color='k', ecolor='k')
         else:
             if comparison_name not in self.groupsunique:
-                raise ValueError, 'comparison_name not found in group names.'
+                raise ValueError('comparison_name not found in group names.')
             midx = np.where(self.groupsunique==comparison_name)[0]
             for i in range(len(means)):
                 if self.groupsunique[i] == comparison_name:
@@ -774,7 +776,8 @@ class MultiComparison(object):
     groups : array
         group labels corresponding to each data point
     group_order : list of strings, optional
-        the desired order for the group mean results to be reported in.
+        the desired order for the group mean results to be reported in. If
+        not specified, results are reported in increasing order
 
     '''
 
@@ -835,16 +838,16 @@ def kruskal(self, pairs=None, multimethod='T'):
 
         # simultaneous/separate treatment of multiple tests
         f=(tot * (tot + 1.) / 12.) / stats.tiecorrect(self.rankdata) #(xranks)
-        print 'MultiComparison.kruskal'
+        print('MultiComparison.kruskal')
         for i,j in zip(*self.pairindices):
             #pdiff = np.abs(mrs[i] - mrs[j])
             pdiff = np.abs(meanranks[i] - meanranks[j])
             se = np.sqrt(f * np.sum(1. / groupnobs[[i,j]] )) #np.array([8,8]))) #Fixme groupnobs[[i,j]] ))
             Q = pdiff / se
 
-            # TODO : print statments, fix
-            print i,j, pdiff, se, pdiff / se, pdiff / se > 2.6310,
-            print stats.norm.sf(Q) * 2
+            # TODO : print(statments, fix
+            print(i,j, pdiff, se, pdiff / se, pdiff / se > 2.6310)
+            print(stats.norm.sf(Q) * 2)
             return stats.norm.sf(Q) * 2
 
 
@@ -884,27 +887,27 @@ def allpairtest(self, testfunc, alpha=0.05, method='bonf', pvalidx=1):
         res = np.array(res)
         reject, pvals_corrected, alphacSidak, alphacBonf = \
                 multipletests(res[:, pvalidx], alpha=0.05, method=method)
-        #print np.column_stack([res[:,0],res[:,1], reject, pvals_corrected])
+        #print(np.column_stack([res[:,0],res[:,1], reject, pvals_corrected])
 
         i1, i2 = self.pairindices
         if pvals_corrected is None:
-            resarr = np.array(zip(i1, i2,
+            resarr = np.array(lzip(self.groupsunique[i1], self.groupsunique[i2],
                                   np.round(res[:,0],4),
                                   np.round(res[:,1],4),
                                   reject),
-                       dtype=[('group1', int),
-                              ('group2', int),
+                       dtype=[('group1', object),
+                              ('group2', object),
                               ('stat',float),
                               ('pval',float),
                               ('reject', np.bool8)])
         else:
-            resarr = np.array(zip(i1, i2,
+            resarr = np.array(lzip(self.groupsunique[i1], self.groupsunique[i2],
                                   np.round(res[:,0],4),
                                   np.round(res[:,1],4),
                                   np.round(pvals_corrected,4),
                                   reject),
-                       dtype=[('group1', int),
-                              ('group2', int),
+                       dtype=[('group1', object),
+                              ('group2', object),
                               ('stat',float),
                               ('pval',float),
                               ('pval_corr',float),
@@ -946,13 +949,13 @@ def tukeyhsd(self, alpha=0.05):
         #6:df_total, 7:reject2
         res = tukeyhsd(gmeans, gnobs, var_, df=None, alpha=alpha, q_crit=None)
 
-        resarr = np.array(zip(res[0][0], res[0][1],
+        resarr = np.array(lzip(self.groupsunique[res[0][0]], self.groupsunique[res[0][1]],
                                   np.round(res[2],4),
                                   np.round(res[4][:, 0],4),
                                   np.round(res[4][:, 1],4),
                                   res[1]),
-                       dtype=[('group1', int),
-                              ('group2', int),
+                       dtype=[('group1', object),
+                              ('group2', object),
                               ('meandiff',float),
                               ('lower',float),
                               ('upper',float),
@@ -1006,7 +1009,7 @@ def compare_ordered(vals, alpha):
     #v1,v2 have wrong sequence
     for i in range(4):
         for j in range(4,i, -1):
-            print i,j
+            print(i,j)
 
 
 
@@ -1555,7 +1558,7 @@ def check_set(self, indices):
             return res
 
     def stepdown(self, indices):
-        print indices
+        print(indices)
         if self.check_set(indices): # larger than critical distance
             if (len(indices) > 2):  # step down into subsets if more than 2 elements
                 for subs in self.iter_subsets(indices):
@@ -1578,7 +1581,7 @@ def run(self, alpha):
         self.accepted = []  #store accepted sets, not unique
         self.rejected = []
         self.get_distance_matrix()
-        self.stepdown(range(self.n_vals))
+        self.stepdown(lrange(self.n_vals))
 
         return list(set(self.accepted)), list(set(sd.rejected))
 
@@ -1631,7 +1634,7 @@ def homogeneous_subsets(vals, dcrit):
     '''
 
     nvals = len(vals)
-    indices_ = range(nvals)
+    indices_ = lrange(nvals)
     rejected = []
     subsetsli = []
     if np.size(dcrit) == 1:
@@ -1683,12 +1686,12 @@ def set_partition(ssli):
     '''
     part = []
     for s in sorted(list(set(ssli)), key=len)[::-1]:
-        #print s,
+        #print(s,
         s_ = set(s).copy()
         if not any(set(s_).intersection(set(t)) for t in part):
-            #print 'inside:', s
+            #print('inside:', s
             part.append(s)
-        #else: print part
+        #else: print(part
 
     missing = list(set(i for ll in ssli for i in ll)
                    - set(i for ll in part for i in ll))
@@ -1720,12 +1723,12 @@ def set_remove_subs(ssli):
     #TODO: maybe convert all tuples to sets immediately, but I don't need the extra efficiency
     part = []
     for s in sorted(list(set(ssli)), key=lambda x: len(set(x)))[::-1]:
-        #print s,
+        #print(s,
         #s_ = set(s).copy()
         if not any(set(s).issubset(set(t)) for t in part):
-            #print 'inside:', s
+            #print('inside:', s
             part.append(s)
-        #else: print part
+        #else: print(part
 
 ##    missing = list(set(i for ll in ssli for i in ll)
 ##                   - set(i for ll in part for i in ll))
@@ -1742,19 +1745,19 @@ def set_remove_subs(ssli):
     if 'tukey' in examples:
         #Example Tukey
         x = np.array([[0,0,1]]).T + np.random.randn(3, 20)
-        print Tukeythreegene(*x)
+        print(Tukeythreegene(*x))
 
         #Example FDR
         #------------
 
     if ('fdr' in examples) or ('bonf' in examples):
         x1 = [1,1,1,0,-1,-1,-1,0,1,1,-1,1]
-        print zip(np.arange(len(x1)), x1)
-        print maxzero(x1)
+        print(lzip(np.arange(len(x1)), x1))
+        print(maxzero(x1))
         #[(0, 1), (1, 1), (2, 1), (3, 0), (4, -1), (5, -1), (6, -1), (7, 0), (8, 1), (9, 1), (10, -1), (11, 1)]
         #(11, array([ 3,  7, 11]))
 
-        print maxzerodown(-np.array(x1))
+        print(maxzerodown(-np.array(x1)))
 
         locs = np.linspace(0,1,10)
         locs = np.array([0.]*6 + [0.75]*4)
@@ -1765,46 +1768,47 @@ def set_remove_subs(ssli):
 
         reject = tpval_sorted < ecdf(tpval_sorted)*0.05
         reject2 = max(np.nonzero(reject))
-        print reject
+        print(reject)
 
-        res = np.array(zip(np.round(rvs.mean(0),4),np.round(tpval,4),
+        res = np.array(lzip(np.round(rvs.mean(0),4),np.round(tpval,4),
                            reject[tpval_sortind.argsort()]),
                        dtype=[('mean',float),
                               ('pval',float),
                               ('reject', np.bool8)])
         #from statsmodels.iolib import SimpleTable
-        print SimpleTable(res, headers=res.dtype.names)
-        print fdrcorrection_bak(tpval, alpha=0.05)
-        print reject
-
-        print '\nrandom example'
-        print 'bonf', multipletests(tpval, alpha=0.05, method='bonf')
-        print 'sidak', multipletests(tpval, alpha=0.05, method='sidak')
-        print 'hs', multipletests(tpval, alpha=0.05, method='hs')
-        print 'sh', multipletests(tpval, alpha=0.05, method='sh')
+        print(SimpleTable(res, headers=res.dtype.names))
+        print(fdrcorrection_bak(tpval, alpha=0.05))
+        print(reject)
+
+        print('\nrandom example')
+        print('bonf', multipletests(tpval, alpha=0.05, method='bonf'))
+        print('sidak', multipletests(tpval, alpha=0.05, method='sidak'))
+        print('hs', multipletests(tpval, alpha=0.05, method='hs'))
+        print('sh', multipletests(tpval, alpha=0.05, method='sh'))
         pvals = np.array('0.0020 0.0045 0.0060 0.0080 0.0085 0.0090 0.0175 0.0250 '
                  '0.1055 0.5350'.split(), float)
-        print '\nexample from lecturnotes'
+        print('\nexample from lecturnotes')
         for meth in ['bonf', 'sidak', 'hs', 'sh']:
-            print meth, multipletests(pvals, alpha=0.05, method=meth)
+            print(meth)
+            print(multipletests(pvals, alpha=0.05, method=meth))
 
     if 'fdrmc' in examples:
         mcres = mcfdr(nobs=100, nrepl=1000, ntests=30, ntrue=30, mu=0.1, alpha=0.05, rho=0.3)
         mcmeans = np.array(mcres).mean(0)
-        print mcmeans
-        print mcmeans[0]/6., 1-mcmeans[1]/4.
-        print mcmeans[:4], mcmeans[-4:]
+        print(mcmeans)
+        print(mcmeans[0]/6., 1-mcmeans[1]/4.)
+        print(mcmeans[:4], mcmeans[-4:])
 
 
     if 'randmvn' in examples:
         rvsmvn = randmvn(0.8, (5000,5))
-        print np.corrcoef(rvsmvn, rowvar=0)
-        print rvsmvn.var(0)
+        print(np.corrcoef(rvsmvn, rowvar=0))
+        print(rvsmvn.var(0))
 
 
     if 'tukeycrit' in examples:
-        print get_tukeyQcrit(8, 8, alpha=0.05), 5.60
-        print get_tukeyQcrit(8, 8, alpha=0.01), 7.47
+        print(get_tukeyQcrit(8, 8, alpha=0.05), 5.60)
+        print(get_tukeyQcrit(8, 8, alpha=0.01), 7.47)
 
 
     if 'multicompdev' in examples:
@@ -1832,16 +1836,16 @@ def set_remove_subs(ssli):
 
         mrs = np.sort(meanranks)
         v1, v2 = np.triu_indices(4,1)
-        print '\nsorted rank differences'
-        print mrs[v2] - mrs[v1]
+        print('\nsorted rank differences')
+        print(mrs[v2] - mrs[v1])
         diffidx = np.argsort(mrs[v2] - mrs[v1])[::-1]
         mrs[v2[diffidx]] - mrs[v1[diffidx]]
 
-        print '\nkruskal for all pairs'
+        print('\nkruskal for all pairs')
         for i,j in zip(v2[diffidx], v1[diffidx]):
-            print i,j, stats.kruskal(xli[i], xli[j]),
+            print(i,j, stats.kruskal(xli[i], xli[j]))
             mwu, mwupval = stats.mannwhitneyu(xli[i], xli[j], use_continuity=False)
-            print mwu, mwupval*2, mwupval*2<0.05/6., mwupval*2<0.1/6.
+            print(mwu, mwupval*2, mwupval*2<0.05/6., mwupval*2<0.1/6.)
 
 
 
@@ -1858,9 +1862,9 @@ def set_remove_subs(ssli):
         grouprankmean = groupranksum * 1.0 / groupnobs + 1
         assert_almost_equal(grouprankmean[intlab], stats.rankdata(X[:,0]), 15)
         gs = GroupsStats(X, useranks=True)
-        print '\ngroupmeanfilter and grouprankmeans'
-        print gs.groupmeanfilter
-        print grouprankmean[intlab]
+        print('\ngroupmeanfilter and grouprankmeans')
+        print(gs.groupmeanfilter)
+        print(grouprankmean[intlab])
         #the following has changed
         #assert_almost_equal(gs.groupmeanfilter, stats.rankdata(X[:,0]), 15)
 
@@ -1873,42 +1877,42 @@ def set_remove_subs(ssli):
         ntot = float(len(xranks));
         tiecorrection = 1 - (nties**3 - nties).sum()/(ntot**3 - ntot)
         assert_almost_equal(tiecorrection, stats.tiecorrect(xranks),15)
-        print '\ntiecorrection for data and ranks'
-        print tiecorrection
-        print tiecorrect(xranks)
+        print('\ntiecorrection for data and ranks')
+        print(tiecorrection)
+        print(tiecorrect(xranks))
 
         tot = X.shape[0]
         t=500 #168
         f=(tot*(tot+1.)/12.)-(t/(6.*(tot-1.)))
         f=(tot*(tot+1.)/12.)/stats.tiecorrect(xranks)
-        print '\npairs of mean rank differences'
+        print('\npairs of mean rank differences')
         for i,j in zip(v2[diffidx], v1[diffidx]):
             #pdiff = np.abs(mrs[i] - mrs[j])
             pdiff = np.abs(meanranks[i] - meanranks[j])
             se = np.sqrt(f * np.sum(1./xnobs[[i,j]] )) #np.array([8,8]))) #Fixme groupnobs[[i,j]] ))
-            print i,j, pdiff, se, pdiff/se, pdiff/se>2.6310
+            print(i,j, pdiff, se, pdiff/se, pdiff/se>2.6310)
 
         multicomp = MultiComparison(*X.T)
         multicomp.kruskal()
         gsr = GroupsStats(X, useranks=True)
 
-        print '\nexamples for kruskal multicomparison'
+        print('\nexamples for kruskal multicomparison')
         for i in range(10):
             x1, x2 = (np.random.randn(30,2) + np.array([0, 0.5])).T
             skw = stats.kruskal(x1, x2)
             mc2=MultiComparison(np.r_[x1, x2], np.r_[np.zeros(len(x1)), np.ones(len(x2))])
             newskw = mc2.kruskal()
-            print skw, np.sqrt(skw[0]), skw[1]-newskw, (newskw/skw[1]-1)*100
+            print(skw, np.sqrt(skw[0]), skw[1]-newskw, (newskw/skw[1]-1)*100)
 
         tablett, restt, arrtt = multicomp.allpairtest(stats.ttest_ind)
         tablemw, resmw, arrmw = multicomp.allpairtest(stats.mannwhitneyu)
-        print
-        print tablett
-        print
-        print tablemw
+        print('')
+        print(tablett)
+        print('')
+        print(tablemw)
         tablemwhs, resmw, arrmw = multicomp.allpairtest(stats.mannwhitneyu, method='hs')
-        print
-        print tablemwhs
+        print('')
+        print(tablemwhs)
 
     if 'last' in examples:
         xli = (np.random.randn(60,4) + np.array([0, 0, 0.5, 0.5])).T
@@ -1916,13 +1920,13 @@ def set_remove_subs(ssli):
         xrvs, xrvsgr = catstack(xli)
         multicompr = MultiComparison(xrvs, xrvsgr)
         tablett, restt, arrtt = multicompr.allpairtest(stats.ttest_ind)
-        print tablett
+        print(tablett)
 
 
         xli=[[8,10,9,10,9],[7,8,5,8,5],[4,8,7,5,7]]
         x,l = catstack(xli)
         gs4 = GroupsStats(np.column_stack([x,l]))
-        print gs4.groupvarwithin()
+        print(gs4.groupvarwithin())
 
 
     #test_tukeyhsd() #moved to test_multi.py
@@ -1938,13 +1942,13 @@ def set_remove_subs(ssli):
     #same number of rejection as in BKY paper:
     #single step-up:4, two-stage:8, iterated two-step:9
     #also alpha_star is the same as theirs for TST
-    print fdrcorrection0(pvals, alpha=0.05, method='indep')
-    print fdrcorrection_twostage(pvals, alpha=0.05, iter=False)
+    print(fdrcorrection0(pvals, alpha=0.05, method='indep'))
+    print(fdrcorrection_twostage(pvals, alpha=0.05, iter=False))
     res_tst = fdrcorrection_twostage(pvals, alpha=0.05, iter=False)
     assert_almost_equal([0.047619, 0.0649], res_tst[-1][:2],3) #alpha_star for stage 2
     assert_equal(8, res_tst[0].sum())
-    print fdrcorrection_twostage(pvals, alpha=0.05, iter=True)
-    print 'fdr_gbs', multipletests(pvals, alpha=0.05, method='fdr_gbs')
+    print(fdrcorrection_twostage(pvals, alpha=0.05, iter=True))
+    print('fdr_gbs', multipletests(pvals, alpha=0.05, method='fdr_gbs'))
     #multicontrast_pvalues(tstat, tcorr, df)
     test_tukey_pvalues()
     tukey_pvalues(3.649, 3, 16)
diff --git a/statsmodels/sandbox/stats/runs.py b/statsmodels/sandbox/stats/runs.py
index 8bafbe5d6b1..016f0517f65 100644
--- a/statsmodels/sandbox/stats/runs.py
+++ b/statsmodels/sandbox/stats/runs.py
@@ -19,8 +19,11 @@
 
 '''
 
+from __future__ import print_function
 import numpy as np
 from scipy import stats
+from scipy.misc import comb
+
 
 class Runs(object):
     '''class for runs in a binary sequence
@@ -220,7 +223,7 @@ def runstest_2samp(x, y=None, groups=None, correction=True):
     x_sorted = x[xargsort]
     x_diff = np.diff(x_sorted)  # used for detecting and handling ties
     if x_diff.min() == 0:
-        print 'ties detected'   #replace with warning
+        print('ties detected')   #replace with warning
         x_mindiff = x_diff[x_diff > 0].min()
         eps = x_mindiff/2.
         xx = x.copy()  #don't change original, just in case
@@ -243,10 +246,6 @@ def runstest_2samp(x, y=None, groups=None, correction=True):
         xindicator = groups[xargsort]
         return Runs(xindicator).runs_test(correction=correction)
 
-try:
-    from scipy import comb  # pylint: disable=E0611
-except ImportError:
-    from scipy.misc import comb
 
 class TotalRunsProb(object):
     '''class for the probability distribution of total runs
@@ -618,10 +617,10 @@ def symmetry_bowker(table):
 
     x1 = np.array([1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1])
 
-    print Runs(x1).runs_test()
-    print runstest_1samp(x1, cutoff='mean')
-    print runstest_2samp(np.arange(16,0,-1), groups=x1)
-    print TotalRunsProb(7,9).cdf(11)
-    print median_test_ksample(np.random.randn(100), np.random.randint(0,2,100))
-    print cochrans_q(np.random.randint(0,2,(100,8)))
+    print(Runs(x1).runs_test())
+    print(runstest_1samp(x1, cutoff='mean'))
+    print(runstest_2samp(np.arange(16,0,-1), groups=x1))
+    print(TotalRunsProb(7,9).cdf(11))
+    print(median_test_ksample(np.random.randn(100), np.random.randint(0,2,100)))
+    print(cochrans_q(np.random.randint(0,2,(100,8))))
 
diff --git a/statsmodels/sandbox/stats/stats_dhuard.py b/statsmodels/sandbox/stats/stats_dhuard.py
index 78570e5689e..af1b88929ae 100644
--- a/statsmodels/sandbox/stats/stats_dhuard.py
+++ b/statsmodels/sandbox/stats/stats_dhuard.py
@@ -80,7 +80,7 @@
 License: BSD
 
 '''
-
+from __future__ import print_function
 import scipy.interpolate as interpolate
 import numpy as np
 
@@ -261,8 +261,8 @@ def optimize_binning(self, method='Freedman'):
     examples = [2]
     if 1 in examples:
         empiricalcdf(x)
-        print percentileofscore(x, 0.5)
-        print scoreatpercentile(x, 50)
+        print(percentileofscore(x, 0.5))
+        print(scoreatpercentile(x, 50))
         import matplotlib.pyplot as plt
         xsupp = np.linspace(x.min(), x.max())
         pos = percentileofscore(x, xsupp)
@@ -299,10 +299,10 @@ def optimize_binning(self, method='Freedman'):
 
 
     histd = HistDist(x)
-    print histd.optimize_binning()
-    print histd.cdf_emp(histd.binlimit)
-    print histd.ppf_emp([0.25, 0.5, 0.75])
-    print histd.cdf_emp([-0.5, -0.25, 0, 0.25, 0.5])
+    print(histd.optimize_binning())
+    print(histd.cdf_emp(histd.binlimit))
+    print(histd.ppf_emp([0.25, 0.5, 0.75]))
+    print(histd.cdf_emp([-0.5, -0.25, 0, 0.25, 0.5]))
 
 
     xsupp = np.linspace(x.min(), x.max(), 500)
@@ -324,9 +324,9 @@ def optimize_binning(self, method='Freedman'):
     ppfe = ppfemp(cdf_ongrid)
     plt.plot(ppfe, cdf_ongrid)
 
-    print 'negative density'
-    print '(np.diff(ppfs)).min()', (np.diff(ppfs)).min()
-    print '(np.diff(cdf_ongrid)).min()', (np.diff(cdf_ongrid)).min()
+    print('negative density')
+    print('(np.diff(ppfs)).min()', (np.diff(ppfs)).min())
+    print('(np.diff(cdf_ongrid)).min()', (np.diff(cdf_ongrid)).min())
     #plt.show()
 
 
diff --git a/statsmodels/sandbox/stats/stats_mstats_short.py b/statsmodels/sandbox/stats/stats_mstats_short.py
index 1440c74538a..fc00ecf4181 100644
--- a/statsmodels/sandbox/stats/stats_mstats_short.py
+++ b/statsmodels/sandbox/stats/stats_mstats_short.py
@@ -21,7 +21,7 @@
 
 
 '''
-
+from __future__ import print_function
 import numpy as np
 from numpy import ma
 from scipy import stats
@@ -322,44 +322,44 @@ def edf_normal_inverse_transformed(x, alpha=3./8, beta=3./8, axis=0):
 if __name__ == '__main__':
 
     x = np.arange(5)
-    print plotting_positions(x)
+    print(plotting_positions(x))
     x = np.arange(10).reshape(-1,2)
-    print plotting_positions(x)
-    print quantiles(x, axis=0)
-    print quantiles(x, axis=None)
-    print quantiles(x, axis=1)
+    print(plotting_positions(x))
+    print(quantiles(x, axis=0))
+    print(quantiles(x, axis=None))
+    print(quantiles(x, axis=1))
     xm = ma.array(x)
     x2 = x.astype(float)
     x2[1,0] = np.nan
-    print plotting_positions(xm, axis=0)
+    print(plotting_positions(xm, axis=0))
 
     # test 0d, 1d
     for sl1 in [slice(None), 0]:
-        print (plotting_positions(xm[sl1,0]) == plotting_positions(x[sl1,0])).all(),
-        print (quantiles(xm[sl1,0]) == quantiles(x[sl1,0])).all(),
-        print (stats.mstats.mquantiles(ma.fix_invalid(x2[sl1,0])) == quantiles(x2[sl1,0], masknan=1)).all(),
+        print((plotting_positions(xm[sl1,0]) == plotting_positions(x[sl1,0])).all())
+        print((quantiles(xm[sl1,0]) == quantiles(x[sl1,0])).all())
+        print((stats.mstats.mquantiles(ma.fix_invalid(x2[sl1,0])) == quantiles(x2[sl1,0], masknan=1)).all())
 
     #test 2d
     for ax in [0, 1, None, -1]:
-        print (plotting_positions(xm, axis=ax) == plotting_positions(x, axis=ax)).all(),
-        print (quantiles(xm, axis=ax) == quantiles(x, axis=ax)).all(),
-        print (stats.mstats.mquantiles(ma.fix_invalid(x2), axis=ax) == quantiles(x2, axis=ax, masknan=1)).all(),
+        print((plotting_positions(xm, axis=ax) == plotting_positions(x, axis=ax)).all())
+        print((quantiles(xm, axis=ax) == quantiles(x, axis=ax)).all())
+        print((stats.mstats.mquantiles(ma.fix_invalid(x2), axis=ax) == quantiles(x2, axis=ax, masknan=1)).all())
 
     #stats version doesn't have axis
-    print (stats.mstats.plotting_positions(ma.fix_invalid(x2)) == plotting_positions(x2, axis=None, masknan=1)).all(),
+    print((stats.mstats.plotting_positions(ma.fix_invalid(x2)) == plotting_positions(x2, axis=None, masknan=1)).all())
 
     #test 3d
     x3 = np.dstack((x,x)).T
     for ax in [1,2]:
-        print (plotting_positions(x3, axis=ax)[0] == plotting_positions(x.T, axis=ax-1)).all(),
+        print((plotting_positions(x3, axis=ax)[0] == plotting_positions(x.T, axis=ax-1)).all())
 
     np.testing.assert_equal(plotting_positions(np.arange(10), alpha=0.35, beta=1-0.35), (1+np.arange(10)-0.35)/10)
     np.testing.assert_equal(plotting_positions(np.arange(10), alpha=0.4, beta=0.4), (1+np.arange(10)-0.4)/(10+0.2))
     np.testing.assert_equal(plotting_positions(np.arange(10)), (1+np.arange(10)-0.4)/(10+0.2))
-    print
-    print scoreatpercentile(x, [10,90])
-    print plotting_positions_w1d(x[:,0])
-    print (plotting_positions_w1d(x[:,0]) == plotting_positions(x[:,0])).all()
+    print('')
+    print(scoreatpercentile(x, [10,90]))
+    print(plotting_positions_w1d(x[:,0]))
+    print((plotting_positions_w1d(x[:,0]) == plotting_positions(x[:,0])).all())
 
 
     #weights versus replicating multiple occurencies of same x value
diff --git a/statsmodels/sandbox/sysreg.py b/statsmodels/sandbox/sysreg.py
index 01b1f3de646..b8f6cd6ee2f 100644
--- a/statsmodels/sandbox/sysreg.py
+++ b/statsmodels/sandbox/sysreg.py
@@ -1,8 +1,9 @@
+from statsmodels.compat.python import iterkeys
 from statsmodels.regression.linear_model import GLS
 import numpy as np
-import statsmodels.tools.tools as tools
 from statsmodels.base.model import LikelihoodModelResults
 from scipy import sparse
+from statsmodels.compat.numpy import np_matrix_rank
 
 #http://www.irisa.fr/aladin/wg-statlin/WORKSHOPS/RENNES02/SLIDES/Foschi.pdf
 
@@ -114,9 +115,8 @@ def __init__(self, sys, sigma=None, dfk=None):
 # Degrees of Freedom
         df_resid = []
         df_model = []
-        [df_resid.append(self.nobs - tools.rank(_)) \
-                for _ in sys[1::2]]
-        [df_model.append(tools.rank(_) - 1) for _ in sys[1::2]]
+        [df_resid.append(self.nobs - np_matrix_rank(_)) for _ in sys[1::2]]
+        [df_model.append(np_matrix_rank(_) - 1) for _ in sys[1::2]]
         self.df_resid = np.asarray(df_resid)
         self.df_model = np.asarray(df_model)
 
@@ -290,14 +290,14 @@ def __init__(self, sys, indep_endog=None, instruments=None):
 # The lists are probably a bad idea
         self.endog = sys[::2]   # these are just list containers
         self.exog = sys[1::2]
-        self._K = [tools.rank(_) for _ in sys[1::2]]
+        self._K = [np_matrix_rank(_) for _ in sys[1::2]]
 #        fullexog = np.column_stack((_ for _ in self.exog))
 
         self.instruments = instruments
 
         # Keep the Y_j's in a container to get IVs
         instr_endog = {}
-        [instr_endog.setdefault(_,[]) for _ in indep_endog.keys()]
+        [instr_endog.setdefault(_,[]) for _ in iterkeys(indep_endog)]
 
         for eq_key in indep_endog:
             for varcol in indep_endog[eq_key]:
diff --git a/statsmodels/sandbox/tests/savervs.py b/statsmodels/sandbox/tests/savervs.py
index 383b44709d2..0f0975fdc75 100644
--- a/statsmodels/sandbox/tests/savervs.py
+++ b/statsmodels/sandbox/tests/savervs.py
@@ -5,7 +5,7 @@
 import numpy as np
 from statsmodels.sandbox import tsa
 from statsmodels.tsa.arima_process import arma_generate_sample
-from maketests_mlabwrap import HoldIt
+from .maketests_mlabwrap import HoldIt
 
 if __name__ == '__main__':
     filen = 'savedrvs_tmp.py'
diff --git a/statsmodels/sandbox/tests/test_gam.py b/statsmodels/sandbox/tests/test_gam.py
index be89e17a118..166a45992ad 100644
--- a/statsmodels/sandbox/tests/test_gam.py
+++ b/statsmodels/sandbox/tests/test_gam.py
@@ -74,7 +74,7 @@
 
 
 """
-
+from statsmodels.compat.python import get_class, lrange
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_equal
 
@@ -143,7 +143,7 @@ def __init__(self):
         x2 = np.sin(2*x1)
         x = np.column_stack((x1/x1.max()*1, 1.*x2))
         exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1)
-        idx = range((order+1)*2)
+        idx = lrange((order+1)*2)
         del idx[order+1]
         exog_reduced = exog[:,idx]  #remove duplicate constant
         y_true = exog.sum(1) #/ 4.
@@ -205,7 +205,7 @@ def init(self):
 
         np.random.seed(8765993)
         #y_obs = np.asarray([stats.poisson.rvs(p) for p in mu], float)
-        if issubclass(self.rvs.im_class, stats.rv_discrete):
+        if issubclass(get_class(self.rvs), stats.rv_discrete):
             # Discrete distributions don't take `scale`.
             y_obs = self.rvs(mu_true, size=nobs)
         else:
diff --git a/statsmodels/sandbox/tests/test_pca.py b/statsmodels/sandbox/tests/test_pca.py
index f26c64d773a..5ac7af85a37 100644
--- a/statsmodels/sandbox/tests/test_pca.py
+++ b/statsmodels/sandbox/tests/test_pca.py
@@ -13,7 +13,7 @@
 from statsmodels.sandbox.tools import pca, pcasvd
 from statsmodels.tsa.arima_process import arma_impulse_response
 
-from datamlw import *
+from .datamlw import *
 
 
 def check_pca_princomp(pcares, princomp):
@@ -29,7 +29,7 @@ def check_pca_svd(pcares, pcasvdres):
     xred_svd, factors_svd, evals_svd, evecs_svd = pcasvdres
     assert_array_almost_equal(evals_svd, evals, 14)
     msign = (evecs/evecs_svd)[0]
-    assert_array_almost_equal(msign*evecs_svd, evecs, 14)
+    assert_array_almost_equal(msign*evecs_svd, evecs, 13)
     assert_array_almost_equal(msign*factors_svd, factors, 13)
     assert_array_almost_equal(xred_svd, xreduced, 13)
 
diff --git a/statsmodels/sandbox/tools/__init__.py b/statsmodels/sandbox/tools/__init__.py
index 82cf1439fd2..dbc23709c7c 100644
--- a/statsmodels/sandbox/tools/__init__.py
+++ b/statsmodels/sandbox/tools/__init__.py
@@ -7,4 +7,4 @@
 pcasvd : tested against matlab
 '''
 
-from tools_pca import * #pca, pcasvd
+from .tools_pca import * #pca, pcasvd
diff --git a/statsmodels/sandbox/tools/cross_val.py b/statsmodels/sandbox/tools/cross_val.py
index 9177d148988..aae945d1457 100644
--- a/statsmodels/sandbox/tools/cross_val.py
+++ b/statsmodels/sandbox/tools/cross_val.py
@@ -13,26 +13,9 @@
 
 """
 
-
-
+from statsmodels.compat.python import range, lrange
 import numpy as np
-
-try:
-    from itertools import combinations
-except: # Using Python < 2.6
-    def combinations(seq, r=None):
-        """Generator returning combinations of items from sequence <seq>
-        taken <r> at a time. Order is not significant. If <r> is not given,
-        the entire sequence is returned.
-        """
-        if r == None:
-            r = len(seq)
-        if r <= 0:
-            yield []
-        else:
-            for i in xrange(len(seq)):
-                for cc in combinations(seq[i+1:], r-1):
-                    yield [seq[i]]+cc
+from itertools import combinations
 
 
 ################################################################################
@@ -72,7 +55,7 @@ def __init__(self, n):
 
     def __iter__(self):
         n = self.n
-        for i in xrange(n):
+        for i in range(n):
             test_index  = np.zeros(n, dtype=np.bool)
             test_index[i] = True
             train_index = np.logical_not(test_index)
@@ -130,7 +113,7 @@ def __init__(self, n, p):
     def __iter__(self):
         n = self.n
         p = self.p
-        comb = combinations(range(n), p)
+        comb = combinations(lrange(n), p)
         for idx in comb:
             test_index = np.zeros(n, dtype=np.bool)
             test_index[np.array(idx)] = True
@@ -193,7 +176,7 @@ def __iter__(self):
         k = self.k
         j = int(np.ceil(n/k))
 
-        for i in xrange(k):
+        for i in range(k):
             test_index  = np.zeros(n, dtype=np.bool)
             if i<k-1:
                 test_index[i*j:(i+1)*j] = True
@@ -357,7 +340,7 @@ def __iter__(self):
         k = self.k
         start = self.start
         if self.return_slice:
-            for i in xrange(start, n-k):
+            for i in range(start, n-k):
                 train_slice = slice(None, i, None)
                 if self.kall:
                     test_slice = slice(i, i+k)
@@ -366,7 +349,7 @@ def __iter__(self):
                 yield train_slice, test_slice
 
         else: #for compatibility with other iterators
-            for i in xrange(start, n-k):
+            for i in range(start, n-k):
                 train_index  = np.zeros(n, dtype=np.bool)
                 train_index[:i] = True
                 test_index  = np.zeros(n, dtype=np.bool)
diff --git a/statsmodels/sandbox/tools/mctools.py b/statsmodels/sandbox/tools/mctools.py
index 4c11cc9acbe..a25fc9b1690 100644
--- a/statsmodels/sandbox/tools/mctools.py
+++ b/statsmodels/sandbox/tools/mctools.py
@@ -23,8 +23,8 @@
 two-sided tests basend on t or normal distribution use the absolute value.
 
 '''
-
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange
 import numpy as np
 
 from statsmodels.iolib.table import SimpleTable
@@ -34,7 +34,7 @@ class StatTestMC(object):
     """class to run Monte Carlo study on a statistical test'''
 
     TODO
-    print summary, for quantiles and for histogram
+    print(summary, for quantiles and for histogram
     draft in trying out script log
 
     Parameters
@@ -85,17 +85,17 @@ def lb(x):
     def normalnoisesim(nobs=500, loc=0.0):
         return (loc+np.random.randn(nobs))
 
-    Create instance and run Monte Carlo. Using statindices=range(4) means that
+    Create instance and run Monte Carlo. Using statindices=list(range(4)) means that
     only the first for values of the return of the statistic (lb) are stored
     in the Monte Carlo results.
 
     mc1 = StatTestMC(normalnoisesim, lb)
-    mc1.run(5000, statindices=range(4))
+    mc1.run(5000, statindices=list(range(4)))
 
     Most of the other methods take an idx which indicates for which columns
     the results should be presented, e.g.
 
-    print mc1.cdf(crit, [1,2,3])[1]
+    print(mc1.cdf(crit, [1,2,3])[1]
 
 
     """
@@ -361,7 +361,7 @@ def summary_quantiles(self, idx, distppf, frac=[0.01, 0.025, 0.05, 0.1, 0.975],
         Returns
         -------
         table : instance of SimpleTable
-            use `print table` to see results
+            use `print(table` to see results
 
         '''
         idx = np.atleast_1d(idx)  #assure iterable, use list ?
@@ -375,7 +375,7 @@ def summary_quantiles(self, idx, distppf, frac=[0.01, 0.025, 0.05, 0.1, 0.975],
             mml.extend([mcq[:,i], crit[:,i]])
         #mmlar = np.column_stack(mml)
         mmlar = np.column_stack([quant] + mml)
-        #print mmlar.shape
+        #print(mmlar.shape
         if title:
             title = title +' Quantiles (critical values)'
         else:
@@ -408,7 +408,7 @@ def summary_cdf(self, idx, frac, crit, varnames=None, title=None):
         Returns
         -------
         table : instance of SimpleTable
-            use `print table` to see results
+            use `print(table` to see results
 
 
         '''
@@ -418,13 +418,13 @@ def summary_cdf(self, idx, frac, crit, varnames=None, title=None):
         mml=[]
         #TODO:need broadcasting in cdf
         for i in range(len(idx)):
-            #print i, mc1.cdf(crit[:,i], [idx[i]])[1].ravel()
+            #print(i, mc1.cdf(crit[:,i], [idx[i]])[1].ravel()
             mml.append(self.cdf(crit[:,i], [idx[i]])[1].ravel())
         #mml = self.cdf(crit, idx)[1]
         #mmlar = np.column_stack(mml)
-        #print mml[0].shape, np.shape(frac)
+        #print(mml[0].shape, np.shape(frac)
         mmlar = np.column_stack([frac] + mml)
-        #print mmlar.shape
+        #print(mmlar.shape
         if title:
             title = title +' Probabilites'
         else:
@@ -465,13 +465,13 @@ def normalnoisesim(nobs=500, loc=0.0):
     def adf20(x):
         return unitroot_adf(x, 2,trendorder=0, autolag=None)
 
-#    print '\nResults with MC class'
+#    print('\nResults with MC class'
 #    mc1 = StatTestMC(randwalksim, adf20)
 #    mc1.run(1000)
-#    print mc1.histogram(critval=[-3.5, -3.17, -2.9 , -2.58,  0.26])
-#    print mc1.quantiles()
+#    print(mc1.histogram(critval=[-3.5, -3.17, -2.9 , -2.58,  0.26])
+#    print(mc1.quantiles()
 
-    print '\nLjung Box'
+    print('\nLjung Box')
     from statsmodels.sandbox.stats.diagnostic import acorr_ljungbox
 
     def lb4(x):
@@ -486,31 +486,31 @@ def lb(x):
         s,p = acorr_ljungbox(x, lags=4)
         return np.r_[s, p]
 
-    print 'Results with MC class'
+    print('Results with MC class')
     mc1 = StatTestMC(normalnoisesim, lb)
-    mc1.run(10000, statindices=range(8))
-    print mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975])
-    print mc1.quantiles(1)
-    print mc1.quantiles(0)
-    print mc1.histogram(0)
-
-    #print mc1.summary_quantiles([1], stats.chi2([2]).ppf, title='acorr_ljungbox')
-    print mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf,
+    mc1.run(10000, statindices=lrange(8))
+    print(mc1.histogram(1, critval=[0.01, 0.025, 0.05, 0.1, 0.975]))
+    print(mc1.quantiles(1))
+    print(mc1.quantiles(0))
+    print(mc1.histogram(0))
+
+    #print(mc1.summary_quantiles([1], stats.chi2([2]).ppf, title='acorr_ljungbox')
+    print(mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf,
                                 varnames=['lag 1', 'lag 2', 'lag 3'],
-                                title='acorr_ljungbox')
-    print mc1.cdf(0.1026, 1)
-    print mc1.cdf(0.7278, 3)
+                                title='acorr_ljungbox'))
+    print(mc1.cdf(0.1026, 1))
+    print(mc1.cdf(0.7278, 3))
 
-    print mc1.cdf(0.7278, [1,2,3])
+    print(mc1.cdf(0.7278, [1,2,3]))
     frac = [0.01, 0.025, 0.05, 0.1, 0.975]
     crit = stats.chi2([2,4]).ppf(np.atleast_2d(frac).T)
-    print mc1.summary_cdf([1,3], frac, crit, title='acorr_ljungbox')
+    print(mc1.summary_cdf([1,3], frac, crit, title='acorr_ljungbox'))
     crit = stats.chi2([2,3,4]).ppf(np.atleast_2d(frac).T)
-    print mc1.summary_cdf([1,2,3], frac, crit,
+    print(mc1.summary_cdf([1,2,3], frac, crit,
                           varnames=['lag 1', 'lag 2', 'lag 3'],
-                          title='acorr_ljungbox')
+                          title='acorr_ljungbox'))
 
-    print mc1.cdf(crit, [1,2,3])[1].shape
+    print(mc1.cdf(crit, [1,2,3])[1].shape)
 
     #fixed broadcasting in cdf  Done 2d only
     '''
diff --git a/statsmodels/sandbox/tools/try_mctools.py b/statsmodels/sandbox/tools/try_mctools.py
index 86797279af4..ef9e85cdaa9 100644
--- a/statsmodels/sandbox/tools/try_mctools.py
+++ b/statsmodels/sandbox/tools/try_mctools.py
@@ -4,6 +4,7 @@
 
 @author: josef
 """
+from statsmodels.compat.python import lrange
 import numpy as np
 from scipy import stats
 from statsmodels.sandbox.tools.mctools import StatTestMC
@@ -20,19 +21,19 @@ def lb(x):
 
 
 mc1 = StatTestMC(normalnoisesim, lb)
-mc1.run(5000, statindices=range(4))
+mc1.run(5000, statindices=lrange(4))
 
-print mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf,
+print(mc1.summary_quantiles([1,2,3], stats.chi2([2,3,4]).ppf,
                             varnames=['lag 1', 'lag 2', 'lag 3'],
-                            title='acorr_ljungbox')
-print '\n\n'
+                            title='acorr_ljungbox'))
+print('\n\n')
 
 frac = [0.01, 0.025, 0.05, 0.1, 0.975]
 crit = stats.chi2([2,3,4]).ppf(np.atleast_2d(frac).T)
-print mc1.summary_cdf([1,2,3], frac, crit,
+print(mc1.summary_cdf([1,2,3], frac, crit,
                       varnames=['lag 1', 'lag 2', 'lag 3'],
-                      title='acorr_ljungbox')
-print mc1.cdf(crit, [1,2,3])[1]
+                      title='acorr_ljungbox'))
+print(mc1.cdf(crit, [1,2,3])[1])
 
 #----------------------
 
@@ -42,24 +43,24 @@ def randwalksim(nobs=500, drift=0.0):
 def adf20(x):
     return unitroot_adf(x, 2, trendorder=0, autolag=None)
 
-print adf20(np.random.randn(100))
+print(adf20(np.random.randn(100)))
 
 mc2 = StatTestMC(randwalksim, adf20)
 mc2.run(10000, statindices=[0,1])
 frac = [0.01, 0.05, 0.1]
 #bug
 crit = np.array([-3.4996365338407074, -2.8918307730370025, -2.5829283377617176])[:,None]
-print mc2.summary_cdf([0], frac, crit,
+print(mc2.summary_cdf([0], frac, crit,
                       varnames=['adf'],
-                      title='adf')
+                      title='adf'))
 #bug
 #crit2 = np.column_stack((crit, frac))
 #print mc2.summary_cdf([0, 1], frac, crit,
 #                      varnames=['adf'],
 #                      title='adf')
 
-print mc2.quantiles([0])
-print mc2.cdf(crit, [0])
+print(mc2.quantiles([0]))
+print(mc2.cdf(crit, [0]))
 
 doplot=1
 if doplot:
diff --git a/statsmodels/sandbox/tsa/__init__.py b/statsmodels/sandbox/tsa/__init__.py
index c675b0645ec..5bf6eee12ff 100644
--- a/statsmodels/sandbox/tsa/__init__.py
+++ b/statsmodels/sandbox/tsa/__init__.py
@@ -48,5 +48,5 @@
 
 
 #from arima import *
-from movstat import *
+from .movstat import *
 #from stattools import *
diff --git a/statsmodels/sandbox/tsa/diffusion.py b/statsmodels/sandbox/tsa/diffusion.py
index 978495fc31f..a2138cb0112 100644
--- a/statsmodels/sandbox/tsa/diffusion.py
+++ b/statsmodels/sandbox/tsa/diffusion.py
@@ -48,7 +48,7 @@
 
 
 '''
-
+from __future__ import print_function
 import numpy as np
 from scipy import stats, signal
 import matplotlib.pyplot as plt
@@ -432,7 +432,7 @@ def simulate(self, nobs, nrepl=1):
             #print nrepl,nobs,nc
             #xio = randfnc(size=(nrepl,nobs,np.max(nc))).cumsum(-1)[np.arange(nrepl)[:,None],np.arange(nobs),nc-1]
             rvs = randfnc(size=(nrepl,nobs,np.max(nc)))
-            print 'rvs.sum()', rvs.sum(), rvs.shape
+            print('rvs.sum()', rvs.sum(), rvs.shape)
             xio = rvs.cumsum(-1)[np.arange(nrepl)[:,None],np.arange(nobs),nc-1]
             #print xio.shape
             x[:,:,io] = xio
@@ -492,7 +492,7 @@ def simulate(self, nobs, nrepl=1):
             plt.title('Brownian Motion - exp')
         #plt.show()
         averr = np.linalg.norm(us[1] - np.exp(9*us[2]/8.), np.inf)
-        print averr
+        print(averr)
         #print us[1][:10]
         #print np.exp(9.*us[2][:10]/8.)
 
@@ -527,7 +527,7 @@ def simulate(self, nobs, nrepl=1):
         oue = ou.exact(1, 1, np.random.normal(size=(5,10)))
         ou.exact(0, np.linspace(0,10,10/0.1), 0)
         ou.exactprocess(0,10)
-        print ou.exactprocess(0,10, ddt=0.1,nrepl=10).mean(0)
+        print(ou.exactprocess(0,10, ddt=0.1,nrepl=10).mean(0))
         #the following looks good, approaches mu
         oues = ou.exactprocess(0,100, ddt=0.1,nrepl=100)
         if doplot:
@@ -541,19 +541,19 @@ def simulate(self, nobs, nrepl=1):
 
         so = SchwartzOne(xzero=0, mu=1, kappa=0.5, sigma=0.1)
         sos = so.exactprocess(0,50, ddt=0.1,nrepl=100)
-        print sos.mean(0)
-        print np.log(sos.mean(0))
+        print(sos.mean(0))
+        print(np.log(sos.mean(0)))
         doplot = 1
         if doplot:
             plt.figure()
             tmp = plt.plot(sos.T)
             tmp = plt.plot(sos.mean(0), linewidth=2)
             plt.title('Schwartz One')
-        print so.fitls(sos[0,:],dt=0.1)
+        print(so.fitls(sos[0,:],dt=0.1))
         sos2 = so.exactprocess(0,500, ddt=0.1,nrepl=5)
-        print 'true: mu=1, kappa=0.5, sigma=0.1'
+        print('true: mu=1, kappa=0.5, sigma=0.1')
         for i in range(5):
-            print so.fitls(sos2[i],dt=0.1)
+            print(so.fitls(sos2[i],dt=0.1))
 
 
 
@@ -578,12 +578,12 @@ def simulate(self, nobs, nrepl=1):
     # ^^^^^^^^^^^^^^^^
     cp = CompoundPoisson([1,1], [np.random.normal,np.random.normal])
     cps = cp.simulate(nobs=20000,nrepl=3)
-    print cps[0].sum(-1).sum(-1)
-    print cps[0].sum()
-    print cps[0].mean(-1).mean(-1)
-    print cps[0].mean()
-    print cps[1].size
-    print cps[1].sum()
+    print(cps[0].sum(-1).sum(-1))
+    print(cps[0].sum())
+    print(cps[0].mean(-1).mean(-1))
+    print(cps[0].mean())
+    print(cps[1].size)
+    print(cps[1].sum())
     #Note Y = sum^{N} X is compound poisson of iid x, then
     #E(Y) = E(N)*E(X)   eg. eq. (6.37) page 385 in http://ee.stanford.edu/~gray/sp.html
 
diff --git a/statsmodels/sandbox/tsa/example_arma.py b/statsmodels/sandbox/tsa/example_arma.py
index ed23dd94316..d2375c5c976 100644
--- a/statsmodels/sandbox/tsa/example_arma.py
+++ b/statsmodels/sandbox/tsa/example_arma.py
@@ -4,7 +4,8 @@
 plus 3 functions from nitime.utils
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import range
 import numpy as np
 from numpy.testing import assert_array_almost_equal
 
@@ -143,16 +144,16 @@ def acovf_ma1(ma):
 for c, args in cases:
 
     ar, ma = args
-    print
-    print c, ar, ma
+    print('')
+    print(c, ar, ma)
     myacovf = arma_acovf(ar, ma, nobs=10)
     myacf = arma_acf(ar, ma, nobs=10)
     if c[:2]=='ma':
         othacovf = comparefn[c](ma)
     else:
         othacovf = comparefn[c](ar, ma)
-    print myacovf[:5]
-    print othacovf[:5]
+    print(myacovf[:5])
+    print(othacovf[:5])
     #something broke again,
     #for high persistence case eg ar=0.99, nobs of IR has to be large
     #made changes to arma_acovf
@@ -170,9 +171,9 @@ def ar_generator(N=512, sigma=1.):
     v = np.random.normal(size=N, scale=sigma**0.5)
     u = np.zeros(N)
     P = len(taps)
-    for l in xrange(P):
+    for l in range(P):
         u[l] = v[l] + np.dot(u[:l][::-1], taps[:l])
-    for l in xrange(P,N):
+    for l in range(P,N):
         u[l] = v[l] + np.dot(u[l-P:l][::-1], taps)
     return u, v, taps
 
@@ -370,22 +371,22 @@ def xcorr(self, x, y, normed=True, detrend=detrend_none,
 arma = ARIMA(arrvs[0])
 res = arma.fit((4,0, 0))
 
-print res[0]
+print(res[0])
 
 acf1 = acf(arrvs[0])
 acovf1b = acovf(arrvs[0], unbiased=False)
 acf2 = autocorr(arrvs[0])
 acf2m = autocorr(arrvs[0]-arrvs[0].mean())
-print acf1[:10]
-print acovf1b[:10]
-print acf2[:10]
-print acf2m[:10]
+print(acf1[:10])
+print(acovf1b[:10])
+print(acf2[:10])
+print(acf2m[:10])
 
 
 x = arma_generate_sample([1.0, -0.8], [1.0], 500)
-print acf(x)[:20]
+print(acf(x)[:20])
 import statsmodels.api as sm
-print sm.regression.yule_walker(x, 10)
+print(sm.regression.yule_walker(x, 10))
 
 import matplotlib.pyplot as plt
 #ax = plt.axes()
diff --git a/statsmodels/sandbox/tsa/examples/ex_mle_arma.py b/statsmodels/sandbox/tsa/examples/ex_mle_arma.py
index 7e1646117bd..0ebbc507dab 100644
--- a/statsmodels/sandbox/tsa/examples/ex_mle_arma.py
+++ b/statsmodels/sandbox/tsa/examples/ex_mle_arma.py
@@ -7,7 +7,7 @@
 Author: josef-pktd
 copyright: Simplified BSD see license.txt
 """
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -23,8 +23,8 @@
 examples = ['arma']
 if 'arma' in examples:
 
-    print "\nExample 1"
-    print '----------'
+    print("\nExample 1")
+    print('----------')
     ar = [1.0, -0.8]
     ma = [1.0,  0.5]
     y1 = arma_generate_sample(ar,ma,1000,0.1)
@@ -34,112 +34,112 @@
     arma1.nar = 1
     arma1.nma = 1
     arma1res = arma1.fit_mle(order=(1,1), method='fmin')
-    print arma1res.params
+    print(arma1res.params)
 
     #Warning need new instance otherwise results carry over
     arma2 = Arma(y1)
     arma2.nar = 1
     arma2.nma = 1
     res2 = arma2.fit(method='bfgs')
-    print res2.params
-    print res2.model.hessian(res2.params)
-    print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)
+    print(res2.params)
+    print(res2.model.hessian(res2.params))
+    print(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))
     arest = tsa.arima.ARIMA(y1)
     resls = arest.fit((1,0,1))
-    print resls[0]
-    print resls[1]
+    print(resls[0])
+    print(resls[1])
 
 
 
-    print '\nparameter estimate - comparing methods'
-    print '---------------------------------------'
-    print 'parameter of DGP ar(1), ma(1), sigma_error'
-    print [-0.8, 0.5, 0.1]
-    print 'mle with fmin'
-    print arma1res.params
-    print 'mle with bfgs'
-    print res2.params
-    print 'cond. least squares uses optim.leastsq ?'
+    print('\nparameter estimate - comparing methods')
+    print('---------------------------------------')
+    print('parameter of DGP ar(1), ma(1), sigma_error')
+    print([-0.8, 0.5, 0.1])
+    print('mle with fmin')
+    print(arma1res.params)
+    print('mle with bfgs')
+    print(res2.params)
+    print('cond. least squares uses optim.leastsq ?')
     errls = arest.error_estimate
-    print resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0])
+    print(resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0]))
 
     err = arma1.geterrors(res2.params)
-    print 'cond least squares parameter cov'
-    #print np.dot(err,err)/err.shape[0] * resls[1]
+    print('cond least squares parameter cov')
+    #print(np.dot(err,err)/err.shape[0] * resls[1])
     #errls = arest.error_estimate
-    print np.dot(errls,errls)/errls.shape[0] * resls[1]
-#    print 'fmin hessian'
-#    print arma1res.model.optimresults['Hopt'][:2,:2]
-    print 'bfgs hessian'
-    print res2.model.optimresults['Hopt'][:2,:2]
-    print 'numdifftools inverse hessian'
-    print -np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2]
-
-    print '\nFitting Arma(1,1) to squared data'
+    print(np.dot(errls,errls)/errls.shape[0] * resls[1])
+#    print('fmin hessian')
+#    print(arma1res.model.optimresults['Hopt'][:2,:2])
+    print('bfgs hessian')
+    print(res2.model.optimresults['Hopt'][:2,:2])
+    print('numdifftools inverse hessian')
+    print(-np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2])
+
+    print('\nFitting Arma(1,1) to squared data')
     arma3 = Arma(y1**2)
     res3 = arma3.fit(method='bfgs')
-    print res3.params
+    print(res3.params)
 
-    print '\nFitting Arma(3,3) to data from DGP Arma(1,1)'
+    print('\nFitting Arma(3,3) to data from DGP Arma(1,1)')
     arma4 = Arma(y1)
     arma4.nar = 3
     arma4.nma = 3
     #res4 = arma4.fit(method='bfgs')
     res4 = arma4.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5])
-    print res4.params
-    print 'numdifftools inverse hessian'
+    print(res4.params)
+    print('numdifftools inverse hessian')
     pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params))
-    #print pcov
-    print 'standard error of parameter estimate from Hessian'
+    #print(pcov)
+    print('standard error of parameter estimate from Hessian')
     pstd = np.sqrt(np.diag(pcov))
-    print pstd
-    print 't-values'
-    print res4.params/pstd
-    print 'eigenvalues of pcov:'
-    print np.linalg.eigh(pcov)[0]
-    print 'sometimes they are negative'
+    print(pstd)
+    print('t-values')
+    print(res4.params/pstd)
+    print('eigenvalues of pcov:')
+    print(np.linalg.eigh(pcov)[0])
+    print('sometimes they are negative')
 
 
-    print "\nExample 2 - DGP is Arma(3,3)"
-    print '-----------------------------'
+    print("\nExample 2 - DGP is Arma(3,3)")
+    print('-----------------------------')
     ar = [1.0, -0.6, -0.2, -0.1]
     ma = [1.0,  0.5, 0.1, 0.1]
     y2 = arest.generate_sample(ar,ma,1000,0.1)
     y2 -= y2.mean() #no mean correction/constant in estimation so far
 
 
-    print '\nFitting Arma(3,3) to data from DGP Arma(3,3)'
+    print('\nFitting Arma(3,3) to data from DGP Arma(3,3)')
     arma4 = Arma(y2)
     arma4.nar = 3
     arma4.nma = 3
     #res4 = arma4.fit(method='bfgs')
-    print '\ntrue parameters'
-    print 'ar', ar[1:]
-    print 'ma', ma[1:]
+    print('\ntrue parameters')
+    print('ar', ar[1:])
+    print('ma', ma[1:])
     res4 = arma4.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5])
-    print res4.params
-    print 'numdifftools inverse hessian'
+    print(res4.params)
+    print('numdifftools inverse hessian')
     pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params))
-    #print pcov
-    print 'standard error of parameter estimate from Hessian'
+    #print(pcov)
+    print('standard error of parameter estimate from Hessian')
     pstd = np.sqrt(np.diag(pcov))
-    print pstd
-    print 't-values'
-    print res4.params/pstd
-    print 'eigenvalues of pcov:'
-    print np.linalg.eigh(pcov)[0]
-    print 'sometimes they are negative'
+    print(pstd)
+    print('t-values')
+    print(res4.params/pstd)
+    print('eigenvalues of pcov:')
+    print(np.linalg.eigh(pcov)[0])
+    print('sometimes they are negative')
 
     arma6 = Arma(y2)
     arma6.nar = 3
     arma6.nma = 3
     res6 = arma6.fit(start_params=[-0.5, -0.1,-0.1,0.2,0.1,0.1,0.5],
                       method='bfgs')
-    print '\nmle with bfgs'
-    print res6.params
-    print 'pstd with bfgs hessian'
+    print('\nmle with bfgs')
+    print(res6.params)
+    print('pstd with bfgs hessian')
     hopt = res6.model.optimresults['Hopt']
-    print np.sqrt(np.diag(hopt))
+    print(np.sqrt(np.diag(hopt)))
 
     #fmin estimates for coefficients in ARMA(3,3) look good
     #but not inverse Hessian, sometimes negative values for variance
diff --git a/statsmodels/sandbox/tsa/examples/ex_mle_garch.py b/statsmodels/sandbox/tsa/examples/ex_mle_garch.py
index 30b4eeda8f4..ed453df3af2 100644
--- a/statsmodels/sandbox/tsa/examples/ex_mle_garch.py
+++ b/statsmodels/sandbox/tsa/examples/ex_mle_garch.py
@@ -61,7 +61,7 @@
 
 """
 
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -86,7 +86,7 @@
 
     seed = 3842774 #91234  #8837708
     seed = np.random.randint(9999999)
-    print 'seed', seed
+    print('seed', seed)
     np.random.seed(seed)
     ar1 = -0.9
     err,h = generate_garch(nobs, [1.0, ar1], [1.0,  0.50], mu=0.0,scale=0.1)
@@ -141,7 +141,7 @@
 
 #plt.show()
 seed = np.random.randint(9999999)  # 9188410
-print 'seed', seed
+print('seed', seed)
 
 x = np.arange(20).reshape(10,2)
 x3 = np.column_stack((np.ones((x.shape[0],1)),x))
@@ -166,7 +166,7 @@
 ggmod.nma = 1
 ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0])
 ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000)
-print 'ggres.params', ggres.params
+print('ggres.params', ggres.params)
 garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated')
 
 ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
@@ -175,7 +175,7 @@
 start_params = np.array([-0.6, 0.2, 0.1])
 ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
 ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)
-print 'ggres0.params', ggres0.params
+print('ggres0.params', ggres0.params)
 
 ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
 ggmod0.nar = 1
@@ -183,13 +183,13 @@
 start_params = np.array([-0.6, 0.2, 0.1])
 ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
 ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000)
-print 'ggres0.params', ggres0.params
+print('ggres0.params', ggres0.params)
 
 
 g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2])
-print g11res
+print(g11res)
 llf = loglike_GARCH11(g11res, errgjr4-errgjr4.mean())
-print llf[0]
+print(llf[0])
 
 if 'rpyfit' in examples:
     from rpy import r
@@ -202,13 +202,13 @@
     f = r.formula('~garch(1, 1)')
     #fit = r.garchFit(f, data = errgjr4)
     x = r.garchSim( n = 500)
-    print 'R acf', tsa.acf(np.power(x,2))[:15]
+    print('R acf', tsa.acf(np.power(x,2))[:15])
     arma3 = Arma(np.power(x,2))
     arma3res = arma3.fit(start_params=[-0.2,0.1,0.5],maxiter=5000)
-    print arma3res.params
+    print(arma3res.params)
     arma3b = Arma(np.power(x,2))
     arma3bres = arma3b.fit(start_params=[-0.2,0.1,0.5],maxiter=5000, method='bfgs')
-    print arma3bres.params
+    print(arma3bres.params)
 
     xr = r.garchSim( n = 100)
 
@@ -218,16 +218,16 @@
     ggmod.nma = 1
     ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0])
     ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000)
-    print 'ggres.params', ggres.params
+    print('ggres.params', ggres.params)
 
     g11res = optimize.fmin(lambda params: -loglike_GARCH11(params, x-x.mean())[0], [0.6, 0.6, 0.2])
-    print g11res
+    print(g11res)
     llf = loglike_GARCH11(g11res, x-x.mean())
-    print llf[0]
+    print(llf[0])
 
     garchplot(ggmod.errorsest, ggmod.h, title='Garch estimated')
     fit = r.garchFit(f, data = x-x.mean(), include_mean=False, trace=False)
-    print r.summary(fit)
+    print(r.summary(fit))
 
 '''based on R default simulation
 model = list(omega = 1e-06, alpha = 0.1, beta = 0.8)
@@ -239,15 +239,15 @@
          Current function value: -448.861335
          Iterations: 385
          Function evaluations: 690
->>> print 'ggres.params', ggres.params
+>>> print('ggres.params', ggres.params
 ggres.params [ -7.75090330e-01   1.57714749e-01  -9.60223930e-02   8.76021411e-07]
 rearranged
 8.76021411e-07 1.57714749e-01(-9.60223930e-02) 7.75090330e-01
 
->>> print g11res
+>>> print(g11res
 [  2.97459808e-06   7.83128600e-01   2.41110860e-01]
 >>> llf = loglike_GARCH11(g11res, x-x.mean())
->>> print llf[0]
+>>> print(llf[0]
 442.603541936
 
 Log Likelihood:
diff --git a/statsmodels/sandbox/tsa/examples/try_ld_nitime.py b/statsmodels/sandbox/tsa/examples/try_ld_nitime.py
index 2642dd159c7..01ba9933bde 100644
--- a/statsmodels/sandbox/tsa/examples/try_ld_nitime.py
+++ b/statsmodels/sandbox/tsa/examples/try_ld_nitime.py
@@ -2,6 +2,7 @@
 
 '''
 
+from statsmodels.compat.python import range
 import numpy as np
 
 from statsmodels.tsa.stattools import acovf
@@ -26,9 +27,9 @@ def levinson_durbin_nitime(s, order=10, isacov=False):
     # initial points for the recursion
     phi[1,1] = sxx_m[1]/sxx_m[0]
     sig[1] = sxx_m[0] - phi[1,1]*sxx_m[1]
-    for k in xrange(2,order+1):
+    for k in range(2,order+1):
         phi[k,k] = (sxx_m[k]-np.dot(phi[1:k,k-1], sxx_m[1:k][::-1]))/sig[k-1]
-        for j in xrange(1,k):
+        for j in range(1,k):
             phi[j,k] = phi[j,k-1] - phi[k,k]*phi[k-j,k-1]
         sig[k] = sig[k-1]*(1 - phi[k,k]**2)
 
diff --git a/statsmodels/sandbox/tsa/fftarma.py b/statsmodels/sandbox/tsa/fftarma.py
index c7d41d508e6..f1d6ff413e5 100644
--- a/statsmodels/sandbox/tsa/fftarma.py
+++ b/statsmodels/sandbox/tsa/fftarma.py
@@ -29,6 +29,7 @@
 
 """
 
+from __future__ import print_function
 import numpy as np
 import numpy.fft as fft
 #import scipy.fftpack as fft
@@ -431,23 +432,23 @@ def maxabs(x,y):
     # same faster:
     arcomb = np.convolve(ar, ar2, mode='same')
     marep = signal.lfilter(ma,arcomb, uni) #[len(ma):]
-    print marep[:10]
+    print(marep[:10])
     mafr = fft.fft(marep)
 
     rvs = np.random.normal(size=nobs)
     datafr = fft.fft(rvs)
     y = fft.ifft(mafr*datafr)
-    print np.corrcoef(np.c_[y[2:], y[1:-1], y[:-2]],rowvar=0)
+    print(np.corrcoef(np.c_[y[2:], y[1:-1], y[:-2]],rowvar=0))
 
     arrep = signal.lfilter([1],marep, uni)
-    print arrep[:20]  # roundtrip to ar
+    print(arrep[:20])  # roundtrip to ar
     arfr = fft.fft(arrep)
     yfr = fft.fft(y)
     x = fft.ifft(arfr*yfr).real  #imag part is e-15
     # the next two are equal, roundtrip works
-    print x[:5]
-    print rvs[:5]
-    print np.corrcoef(np.c_[x[2:], x[1:-1], x[:-2]],rowvar=0)
+    print(x[:5])
+    print(rvs[:5])
+    print(np.corrcoef(np.c_[x[2:], x[1:-1], x[:-2]],rowvar=0))
 
 
     # ARMA filter using fft with ratio of fft of ma/ar lag polynomial
@@ -462,9 +463,9 @@ def maxabs(x,y):
     ma0fr = fft.fft(map_)
     y2 = fft.ifft(ma0fr/ar0fr*datafr)
     #the next two are (almost) equal in real part, almost zero but different in imag
-    print y2[:10]
-    print y[:10]
-    print maxabs(y, y2)  # from chfdiscrete
+    print(y2[:10])
+    print(y[:10])
+    print(maxabs(y, y2))  # from chfdiscrete
     #1.1282071239631782e-014
 
     ar = [1, -0.4]
@@ -481,44 +482,44 @@ def maxabs(x,y):
 
     plt.figure()
     spd1, w1 = arma1.spd(2**10)
-    print spd1.shape
+    print(spd1.shape)
     _ = plt.plot(spd1)
     plt.title('spd fft complex')
 
     plt.figure()
     spd2, w2 = arma1.spdshift(2**10)
-    print spd2.shape
+    print(spd2.shape)
     _ = plt.plot(w2, spd2)
     plt.title('spd fft shift')
 
     plt.figure()
     spd3, w3 = arma1.spddirect(2**10)
-    print spd3.shape
+    print(spd3.shape)
     _ = plt.plot(w3, spd3)
     plt.title('spd fft direct')
 
     plt.figure()
     spd3b = arma1._spddirect2(2**10)
-    print spd3b.shape
+    print(spd3b.shape)
     _ = plt.plot(spd3b)
     plt.title('spd fft direct mirrored')
 
     plt.figure()
     spdr, wr = arma1.spdroots(w)
-    print spdr.shape
+    print(spdr.shape)
     plt.plot(w, spdr)
     plt.title('spd from roots')
 
     plt.figure()
     spdar1_ = spdar1(arma1.ar, w)
-    print spdar1_.shape
+    print(spdar1_.shape)
     _ = plt.plot(w, spdar1_)
     plt.title('spd ar1')
 
 
     plt.figure()
     wper, spdper = arma1.periodogram(nfreq)
-    print spdper.shape
+    print(spdper.shape)
     _ = plt.plot(w, spdper)
     plt.title('periodogram')
 
@@ -527,7 +528,7 @@ def maxabs(x,y):
     import matplotlib.mlab as mlb
     plt.figure()
     sdm, wm = mlb.psd(x)
-    print 'sdm.shape', sdm.shape
+    print('sdm.shape', sdm.shape)
     sdm = sdm.ravel()
     plt.plot(wm, sdm)
     plt.title('matplotlib')
@@ -536,13 +537,12 @@ def maxabs(x,y):
     #yule_AR_est(s, order, Nfreqs)
     wnt, spdnt = LD_AR_est(rvs, 10, 512)
     plt.figure()
-    print 'spdnt.shape', spdnt.shape
+    print('spdnt.shape', spdnt.shape)
     _ = plt.plot(spdnt.ravel())
-    print spdnt[:10]
+    print(spdnt[:10])
     plt.title('nitime')
 
     fig = plt.figure()
     arma1.plot4(fig)
 
-
     #plt.show()
diff --git a/statsmodels/sandbox/tsa/garch.py b/statsmodels/sandbox/tsa/garch.py
index 9af422f220d..f5125083bfb 100644
--- a/statsmodels/sandbox/tsa/garch.py
+++ b/statsmodels/sandbox/tsa/garch.py
@@ -72,7 +72,8 @@
 Created on Feb 6, 2010
 @author: "josef pktd"
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import lzip, zip
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -109,7 +110,7 @@ def normloglike(x, mu=0, sigma2=1, returnlls=False, axis=0):
         return LL, lls
     else:
         #Compute the log likelihood
-        #print np.sum(np.log(sigma2),axis)
+        #print(np.sum(np.log(sigma2),axis))
         LL  =  -0.5 * (np.sum(np.log(sigma2),axis) + np.sum((x**2)/sigma2, axis)  +  nobs*np.log(2*np.pi))
         return LL
 
@@ -262,7 +263,7 @@ def score(self, params):
         Score vector for Arma model
         """
         #return None
-        #print params
+        #print(params
         jac = ndt.Jacobian(self.loglike, stepMax=1e-4)
         return jac(params)[-1]
 
@@ -467,7 +468,7 @@ def geth(self, params):
 ##        h = signal.lfilter(ma, ar, etax, zi=zi)[0]
 ##
         h = miso_lfilter(ar, ma, etax, useic=self._icetax)[0]
-        #print 'h.shape', h.shape
+        #print('h.shape', h.shape
         hneg = h<0
         if hneg.any():
             #h[hneg] = 1e-6
@@ -562,15 +563,15 @@ def geterrors(self, params):
         etax[:,0] = 1
         etax[:,1:] = (eta**2)[:,None]
         etax[eta>0,2] = 0
-        #print 'etax.shape', etax.shape
+        #print('etax.shape', etax.shape
         h = miso_lfilter(ar, ma, etax, useic=np.atleast_1d(etax[:,1].mean()))[0]
-        #print 'h.shape', h.shape
+        #print('h.shape', h.shape
         hneg = h<0
         if hneg.any():
             #h[hneg] = 1e-6
             h = np.abs(h)
 
-            #print 'Warning negative variance found'
+            #print('Warning negative variance found'
 
         #check timing, starting time for h and eta, do they match
         #err = np.sqrt(h[:len(eta)])*eta #np.random.standard_t(8, size=len(h))
@@ -606,7 +607,7 @@ def loglike(self, params):
         self.params_converted = params
         self.errorsest, self.h, self.etax = errorsest, h, etax
         #h = h[:-1] #correct this in geterrors
-        #print 'shapes errorsest, h, etax', errorsest.shape, h.shape, etax.shape
+        #print('shapes errorsest, h, etax', errorsest.shape, h.shape, etax.shape
         sigma2 = np.maximum(h, 1e-6)
         axis = 0
         nobs = len(errorsest)
@@ -881,7 +882,7 @@ def score(self, params):
         Score vector for Arma model
         """
         #return None
-        #print params
+        #print(params
         jac = ndt.Jacobian(self.loglike, stepMax=1e-4)
         return jac(params)[-1]
 
@@ -998,7 +999,7 @@ def generate_gjrgarch(nobs, ar, ma, mu=1., scale=0.1, varinnovation=None):
     #h = np.abs(h)
     #h = np.exp(h)
     #err = np.sqrt(h)*np.random.randn(nobs)
-    print 'h.shape', h.shape
+    print('h.shape', h.shape)
     err = np.sqrt(h[:len(eta)])*eta #np.random.standard_t(8, size=len(h))
     return err, h, etax
 
@@ -1189,7 +1190,7 @@ def test_gjrgarch():
 
 
 '''
->>> print signal.correlate(x3, np.array([[-2.0,3,1],[0.0,0.0,0]])[::-1,:],mode='full')[:-1, (x3.shape[1]+1)//2]
+>>> print(signal.correlate(x3, np.array([[-2.0,3,1],[0.0,0.0,0]])[::-1,:],mode='full')[:-1, (x3.shape[1]+1)//2]
 [ -1.   7.  15.  23.  31.  39.  47.  55.  63.  71.]
 >>> (x3*np.array([-2,3,1])).sum(1)
 array([ -1.,   7.,  15.,  23.,  31.,  39.,  47.,  55.,  63.,  71.])
@@ -1216,7 +1217,7 @@ def garchplot(err, h, title='Garch simulation'):
     examples = ['garch']
     if 'arma' in examples:
         arest = tsa.arima.ARIMA()
-        print "\nExample 1"
+        print("\nExample 1")
         ar = [1.0, -0.8]
         ma = [1.0,  0.5]
         y1 = arest.generate_sample(ar,ma,1000,0.1)
@@ -1226,46 +1227,46 @@ def garchplot(err, h, title='Garch simulation'):
         arma1.nar = 1
         arma1.nma = 1
         arma1res = arma1.fit(method='fmin')
-        print arma1res.params
+        print(arma1res.params)
 
         #Warning need new instance otherwise results carry over
         arma2 = Arma(y1)
         res2 = arma2.fit(method='bfgs')
-        print res2.params
-        print res2.model.hessian(res2.params)
-        print ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)
+        print(res2.params)
+        print(res2.model.hessian(res2.params))
+        print(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))
         resls = arest.fit(y1,1,1)
-        print resls[0]
-        print resls[1]
+        print(resls[0])
+        print(resls[1])
 
 
 
-        print '\nparameter estimate'
-        print 'parameter of DGP ar(1), ma(1), sigma_error'
-        print [-0.8, 0.5, 0.1]
-        print 'mle with fmin'
-        print arma1res.params
-        print 'mle with bfgs'
-        print res2.params
-        print 'cond. least squares uses optim.leastsq ?'
+        print('\nparameter estimate')
+        print('parameter of DGP ar(1), ma(1), sigma_error')
+        print([-0.8, 0.5, 0.1])
+        print('mle with fmin')
+        print(arma1res.params)
+        print('mle with bfgs')
+        print(res2.params)
+        print('cond. least squares uses optim.leastsq ?')
         errls = arest.error_estimate
-        print resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0])
+        print(resls[0], np.sqrt(np.dot(errls,errls)/errls.shape[0]))
 
         err = arma1.geterrors(res2.params)
-        print 'cond least squares parameter cov'
-        #print np.dot(err,err)/err.shape[0] * resls[1]
+        print('cond least squares parameter cov')
+        #print(np.dot(err,err)/err.shape[0] * resls[1])
         #errls = arest.error_estimate
-        print np.dot(errls,errls)/errls.shape[0] * resls[1]
-    #    print 'fmin hessian'
-    #    print arma1res.model.optimresults['Hopt'][:2,:2]
-        print 'bfgs hessian'
-        print res2.model.optimresults['Hopt'][:2,:2]
-        print 'numdifftools inverse hessian'
-        print -np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2]
+        print(np.dot(errls,errls)/errls.shape[0] * resls[1])
+    #    print('fmin hessian')
+    #    print(arma1res.model.optimresults['Hopt'][:2,:2])
+        print('bfgs hessian')
+        print(res2.model.optimresults['Hopt'][:2,:2])
+        print('numdifftools inverse hessian')
+        print(-np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2])
 
         arma3 = Arma(y1**2)
         res3 = arma3.fit(method='bfgs')
-        print res3.params
+        print(res3.params)
 
     nobs = 1000
 
@@ -1281,7 +1282,7 @@ def garchplot(err, h, title='Garch simulation'):
 
         seed = 3842774 #91234  #8837708
         seed = np.random.randint(9999999)
-        print 'seed', seed
+        print('seed', seed)
         np.random.seed(seed)
         ar1 = -0.9
         err,h = generate_garch(nobs, [1.0, ar1], [1.0,  0.50], mu=0.0,scale=0.1)
@@ -1336,7 +1337,7 @@ def garchplot(err, h, title='Garch simulation'):
 
     #plt.show()
     seed = np.random.randint(9999999)  # 9188410
-    print 'seed', seed
+    print('seed', seed)
 
     x = np.arange(20).reshape(10,2)
     x3 = np.column_stack((np.ones((x.shape[0],1)),x))
@@ -1361,12 +1362,12 @@ def garchplot(err, h, title='Garch simulation'):
     ggmod.nma = 1
     ggmod._start_params = np.array([-0.6, 0.1, 0.2, 0.0])
     ggres = ggmod.fit(start_params=np.array([-0.6, 0.1, 0.2, 0.0]), maxiter=1000)
-    print 'ggres.params', ggres.params
+    print('ggres.params', ggres.params)
     garchplot(ggmod.errorsest, ggmod.h)
     #plt.show()
 
-    print 'Garch11'
-    print optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2])
+    print('Garch11')
+    print(optimize.fmin(lambda params: -loglike_GARCH11(params, errgjr4-errgjr4.mean())[0], [0.93, 0.9, 0.2]))
 
     ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
     ggmod0.nar = 1
@@ -1374,7 +1375,7 @@ def garchplot(err, h, title='Garch simulation'):
     start_params = np.array([-0.6, 0.2, 0.1])
     ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
     ggres0 = ggmod0.fit(start_params=start_params, maxiter=2000)
-    print 'ggres0.params', ggres0.params
+    print('ggres0.params', ggres0.params)
 
     ggmod0 = Garch0(errgjr4-errgjr4.mean())#hgjr4[:nobs])#-hgjr4.mean()) #errgjr4)
     ggmod0.nar = 1
@@ -1382,7 +1383,7 @@ def garchplot(err, h, title='Garch simulation'):
     start_params = np.array([-0.6, 0.2, 0.1])
     ggmod0._start_params = start_params #np.array([-0.6, 0.1, 0.2, 0.0])
     ggres0 = ggmod0.fit(start_params=start_params, method='bfgs', maxiter=2000)
-    print 'ggres0.params', ggres0.params
+    print('ggres0.params', ggres0.params)
 
 
     if 'rpy' in examples:
@@ -1390,16 +1391,16 @@ def garchplot(err, h, title='Garch simulation'):
         f = r.formula('~garch(1, 1)')
         #fit = r.garchFit(f, data = errgjr4)
         x = r.garchSim( n = 500)
-        print 'R acf', tsa.acf(np.power(x,2))[:15]
+        print('R acf', tsa.acf(np.power(x,2))[:15])
         arma3 = Arma(np.power(x,2))
         arma3res = arma3.fit(start_params=[-0.2,0.1,0.5],maxiter=5000)
-        print arma3res.params
+        print(arma3res.params)
         arma3b = Arma(np.power(x,2))
         arma3bres = arma3b.fit(start_params=[-0.2,0.1,0.5],maxiter=5000, method='bfgs')
-        print arma3bres.params
+        print(arma3bres.params)
 
     llf = loglike_GARCH11([0.93, 0.9, 0.2], errgjr4)
-    print llf[0]
+    print(llf[0])
 
     erro,ho, etaxo = generate_gjrgarch(20, ar, ma, mu=0.04, scale=0.01,
                       varinnovation = np.ones(20))
@@ -1512,8 +1513,8 @@ def garchplot(err, h, title='Garch simulation'):
     llike  =  -0.5 * (np.sum(np.log(sigma2),axis)
                           + np.sum((y**2)/sigma2, axis)
                           +  nobs*np.log(2*np.pi))
-    print lls, llike
-    #print np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum()
+    print(lls, llike)
+    #print(np.log(stats.norm.pdf(y,scale=np.sqrt(ht))).sum())
 
 
 
@@ -1547,5 +1548,5 @@ def garchplot(err, h, title='Garch simulation'):
     # The below don't work yet
     #armodel.fit(method='newton', penalty=True)
     #armodel.fit(method='broyden', penalty=True)
-    print "Unconditional MLE for AR(1) y_t = .9*y_t-1 +.01 * err"
-    print armodel.params
+    print("Unconditional MLE for AR(1) y_t = .9*y_t-1 +.01 * err")
+    print(armodel.params)
diff --git a/statsmodels/sandbox/tsa/movstat.py b/statsmodels/sandbox/tsa/movstat.py
index dba45fc481a..a8937ad4888 100644
--- a/statsmodels/sandbox/tsa/movstat.py
+++ b/statsmodels/sandbox/tsa/movstat.py
@@ -42,7 +42,7 @@
 2009-09-06: cosmetic changes, rearrangements
 '''
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import signal
 
@@ -149,8 +149,8 @@ def check_movorder():
 ##    takeslice = slice(windowsize-1, n + windowsize-1)
 ##    avgkern = (np.ones(windowsize)/float(windowsize))
 ##    m = np.correlate(x, avgkern, 'same')#[takeslice]
-##    print m.shape
-##    print x.shape
+##    print(m.shape)
+##    print(x.shape)
 ##    xm = x - m
 ##    v = np.correlate(x*x, avgkern, 'same') - m**2
 ##    v1 = np.correlate(xm*xm, avgkern, valid) #not correct for var of window
@@ -255,14 +255,14 @@ def movmoment(x, k, windowsize=3, lag='lagged'):
     #Note: expandarr increases the array size by 2*(windsize-1)
 
     #sl = slice(2*(windsize-1)+1+lead or None, -(2*(windsize-1)+lead)+1 or None)
-    print sl
+    print(sl)
 
     if xext.ndim == 1:
         return np.correlate(xext**k, avgkern, 'full')[sl]
         #return np.correlate(xext**k, avgkern, 'same')[windsize-lead:-(windsize+lead)]
     else:
-        print xext.shape
-        print avgkern[:,None].shape
+        print(xext.shape)
+        print(avgkern[:,None].shape)
 
         # try first with 2d along columns, possibly ndim with axis
         return signal.correlate(xext**k, avgkern[:,None], 'full')[sl,:]
@@ -295,7 +295,7 @@ def movmoment(x, k, windowsize=3, lag='lagged'):
 
 if __name__ == '__main__':
 
-    print '\ncheckin moving mean and variance'
+    print('\ncheckin moving mean and variance')
     nobs = 10
     x = np.arange(nobs)
     ws = 3
@@ -314,14 +314,14 @@ def movmoment(x, k, windowsize=3, lag='lagged'):
                    [ 0.22222222,  0.88888889],
                    [ 0.        ,  0.        ]])
     ave2d = np.c_[ave, 2*ave]
-    print movmean(x, windowsize=ws, lag='lagged')
-    print movvar(x, windowsize=ws, lag='lagged')
-    print [np.var(x[i-ws:i]) for i in range(ws, nobs)]
+    print(movmean(x, windowsize=ws, lag='lagged'))
+    print(movvar(x, windowsize=ws, lag='lagged'))
+    print([np.var(x[i-ws:i]) for i in range(ws, nobs)])
     m1 = movmoment(x, 1, windowsize=3, lag='lagged')
     m2 = movmoment(x, 2, windowsize=3, lag='lagged')
-    print m1
-    print m2
-    print m2 - m1*m1
+    print(m1)
+    print(m2)
+    print(m2 - m1*m1)
 
     # this implicitly also tests moment
     assert_array_almost_equal(va[ws-1:,0],
@@ -333,11 +333,11 @@ def movmoment(x, k, windowsize=3, lag='lagged'):
 
 
 
-    print '\nchecking moving moment for 2d (columns only)'
+    print('\nchecking moving moment for 2d (columns only)')
     x2d = np.c_[x, 2*x]
-    print movmoment(x2d, 1, windowsize=3, lag='centered')
-    print movmean(x2d, windowsize=ws, lag='lagged')
-    print movvar(x2d, windowsize=ws, lag='lagged')
+    print(movmoment(x2d, 1, windowsize=3, lag='centered'))
+    print(movmean(x2d, windowsize=ws, lag='lagged'))
+    print(movvar(x2d, windowsize=ws, lag='lagged'))
     assert_array_almost_equal(va[ws-1:,:],
                     movvar(x2d, windowsize=3, lag='leading'))
     assert_array_almost_equal(va[ws//2:-ws//2+1,:],
@@ -353,7 +353,7 @@ def movmoment(x, k, windowsize=3, lag='lagged'):
                     movmean(x2d, windowsize=ws, lag='lagged'))
 
     from scipy import ndimage
-    print ndimage.filters.correlate1d(x2d, np.array([1,1,1])/3., axis=0)
+    print(ndimage.filters.correlate1d(x2d, np.array([1,1,1])/3., axis=0))
 
 
     #regression test check
diff --git a/statsmodels/sandbox/tsa/try_arma_more.py b/statsmodels/sandbox/tsa/try_arma_more.py
index 04c216bbea7..56a22c73575 100644
--- a/statsmodels/sandbox/tsa/try_arma_more.py
+++ b/statsmodels/sandbox/tsa/try_arma_more.py
@@ -11,7 +11,7 @@
 
 Author: josef-pktd
 """
-
+from __future__ import print_function
 import numpy as np
 from scipy import signal, ndimage
 import matplotlib.mlab as mlb
@@ -82,7 +82,7 @@
 
 if np.sum(np.isnan(h)) > 0:
     # this happens with unit root or seasonal unit root'
-    print 'Warning: nan in frequency response h'
+    print('Warning: nan in frequency response h')
     h[np.isnan(h)] = 1.
     rescale = 0
 
@@ -91,7 +91,7 @@
 #replace with signal.order_filter ?
 pm = ndimage.filters.maximum_filter(sd, footprint=np.ones(5))
 maxind = np.nonzero(pm == sd)
-print 'local maxima frequencies'
+print('local maxima frequencies')
 wmax = w[maxind]
 sdmax = sd[maxind]
 
diff --git a/statsmodels/sandbox/tsa/try_fi.py b/statsmodels/sandbox/tsa/try_fi.py
index a96ee39a288..cc771e64959 100644
--- a/statsmodels/sandbox/tsa/try_fi.py
+++ b/statsmodels/sandbox/tsa/try_fi.py
@@ -8,7 +8,7 @@
 only examples left
 
 '''
-
+from __future__ import print_function
 import numpy as np
 #from numpy.testing import assert_array_almost_equal
 from scipy.special import gamma, gammaln
@@ -47,10 +47,10 @@
           -0.00231864])
     >>> # verified for points [[5,10,20,25]] at 4 decimals with Bhardwaj, Swanson, Journal of Eonometrics 2006
     '''
-    print lpol_fiar(0.4, n=20)
-    print lpol_fima(-0.4, n=20)
-    print np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2) #different signs
-    print np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2) #corrected signs
+    print(lpol_fiar(0.4, n=20))
+    print(lpol_fima(-0.4, n=20))
+    print(np.sum((lpol_fima(-0.4, n=n)[1:] + riinv[1:])**2)) #different signs
+    print(np.sum((lpol_fiar(0.4, n=n)[1:] - riinv[1:])**2)) #corrected signs
 
     #test is now in statsmodels.tsa.tests.test_arima_process
     from statsmodels.tsa.tests.test_arima_process import test_fi
@@ -62,19 +62,19 @@
 
     ar_desired = arma_impulse_response(ma_true, ar_true)
     ar_app, ma_app, res = ar2arma(ar_desired, 2,1, n=100, mse='ar', start=[0.1])
-    print ar_app, ma_app
+    print(ar_app, ma_app)
     ar_app, ma_app, res = ar2arma(ar_desired, 2,2, n=100, mse='ar', start=[-0.1, 0.1])
-    print ar_app, ma_app
+    print(ar_app, ma_app)
     ar_app, ma_app, res = ar2arma(ar_desired, 2,3, n=100, mse='ar')#, start = [-0.1, 0.1])
-    print ar_app, ma_app
+    print(ar_app, ma_app)
 
     slow = 1
     if slow:
         ar_desired = lpol_fiar(0.4, n=100)
         ar_app, ma_app, res = ar2arma(ar_desired, 3, 1, n=100, mse='ar')#, start = [-0.1, 0.1])
-        print ar_app, ma_app
+        print(ar_app, ma_app)
         ar_app, ma_app, res = ar2arma(ar_desired, 10, 10, n=100, mse='ar')#, start = [-0.1, 0.1])
-        print ar_app, ma_app
+        print(ar_app, ma_app)
 
 
 
diff --git a/statsmodels/sandbox/tsa/try_var_convolve.py b/statsmodels/sandbox/tsa/try_var_convolve.py
index 927d7ac6af7..4dacfe74d82 100644
--- a/statsmodels/sandbox/tsa/try_var_convolve.py
+++ b/statsmodels/sandbox/tsa/try_var_convolve.py
@@ -13,7 +13,7 @@
 what the results are.
 Runs now without raising exception
 """
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_equal
 from scipy import signal
@@ -39,9 +39,9 @@
 #same result with fftconvolve
 #signal.fftconvolve(x[:,:,None],a3f).shape
 #signal.fftconvolve(x[:,:,None],a3f)[:,1,:]
-print trim_centered(y, x.shape)
+print(trim_centered(y, x.shape))
 # this raises an exception:
-#print trim_centered(yf, (x.shape).shape)
+#print(trim_centered(yf, (x.shape).shape)
 assert_equal(yvalid[:,0], y0.ravel())
 assert_equal(yvalid[:,1], y1.ravel())
 
@@ -139,9 +139,9 @@ def arfilter_old(x, a):
 
 a3f = np.ones((2,3,3))
 y0ar = arfilter(x,a3f[:,:,0])
-print y0ar, x[1:] + x[:-1]
+print(y0ar, x[1:] + x[:-1])
 yres = arfilter(x,a3f[:,:,:2])
-print np.all(yres == (x[1:,:].sum(1) + x[:-1].sum(1))[:,None])
+print(np.all(yres == (x[1:,:].sum(1) + x[:-1].sum(1))[:,None]))
 
 # don't do these imports, here just for copied fftconvolve
 from scipy.fftpack import fft, ifft, ifftshift, fft2, ifft2, fftn, \
@@ -215,27 +215,27 @@ def fftconvolve_old(in1, in2, in3=None, mode="full"):
 for i in range(1,500):
     errar1[i] = rvs[i] - 0.8*rvs[i-1]
 
-#print ar1[-10:]
-#print ar1fft[-11:-1]
-#print ar1lf[-10:]
-#print ar1[:10]
-#print ar1fft[1:11]
-#print ar1lf[:10]
-#print ar1[100:110]
-#print ar1fft[100:110]
-#print ar1lf[100:110]
+#print(ar1[-10:])
+#print(ar1fft[-11:-1])
+#print(ar1lf[-10:])
+#print(ar1[:10])
+#print(ar1fft[1:11])
+#print(ar1lf[:10])
+#print(ar1[100:110])
+#print(ar1fft[100:110])
+#print(ar1lf[100:110])
 #
 #arloop - lfilter - fftp (padded)  are the same
-print '\n compare: \nerrloop - arloop - fft - lfilter - fftp (padded)'
-#print np.column_stack((ar1[1:31],ar1fft[:30], ar1lf[:30]))
-print np.column_stack((errar1[1:31], ar1[1:31],ar1fft[:30], ar1lf[:30],
-                       ar1fftp[100:130]))
+print('\n compare: \nerrloop - arloop - fft - lfilter - fftp (padded)')
+#print(np.column_stack((ar1[1:31],ar1fft[:30], ar1lf[:30]))
+print(np.column_stack((errar1[1:31], ar1[1:31],ar1fft[:30], ar1lf[:30],
+                       ar1fftp[100:130])))
 
 def maxabs(x,y):
     return np.max(np.abs(x-y))
 
-print maxabs(ar1[1:], ar1lf)  #0
-print maxabs(ar1[1:], ar1fftp[100:-1]) # around 1e-15
+print(maxabs(ar1[1:], ar1lf))  #0
+print(maxabs(ar1[1:], ar1fftp[100:-1])) # around 1e-15
 
 rvs3 = np.random.randn(500,3)
 a3n = np.array([[1,1,1],[-0.8,0.5,0.1]])
@@ -356,5 +356,5 @@ def maxabs(x,y):
 nbins = 12
 binProb = np.zeros(nbins) + 1.0/nbins
 binSumProb = np.add.accumulate(binProb)
-print binSumProb
-print stats.gamma.ppf(binSumProb,0.6379,loc=1.6,scale=39.555)
+print(binSumProb)
+print(stats.gamma.ppf(binSumProb,0.6379,loc=1.6,scale=39.555))
diff --git a/statsmodels/sandbox/tsa/varma.py b/statsmodels/sandbox/tsa/varma.py
index 720f0130f0e..93ac18414b8 100644
--- a/statsmodels/sandbox/tsa/varma.py
+++ b/statsmodels/sandbox/tsa/varma.py
@@ -22,7 +22,7 @@
 License : BSD
 '''
 
-
+from __future__ import print_function
 import numpy as np
 from scipy import signal
 #import matplotlib.pylab as plt
@@ -69,10 +69,10 @@ def VAR(x,B, const=0):
     T = x.shape[0]
     xhat = np.zeros(x.shape)
     for t in range(p,T): #[p+2]:#
-##        print p,T
-##        print x[t-p:t,:,np.newaxis].shape
-##        print B.shape
-        #print x[t-p:t,:,np.newaxis]
+##        print(p,T)
+##        print(x[t-p:t,:,np.newaxis].shape)
+##        print(B.shape)
+        #print(x[t-p:t,:,np.newaxis])
         xhat[t,:] = const + (x[t-p:t,:,np.newaxis]*B).sum(axis=1).sum(axis=0)
     return xhat
 
@@ -94,10 +94,10 @@ def VARMA(x,B,C, const=0):
     e = np.zeros(x.shape)
     start = max(P,Q)
     for t in range(start,T): #[p+2]:#
-##        print p,T
-##        print x[t-p:t,:,np.newaxis].shape
-##        print B.shape
-        #print x[t-p:t,:,np.newaxis]
+##        print(p,T
+##        print(x[t-p:t,:,np.newaxis].shape
+##        print(B.shape
+        #print(x[t-p:t,:,np.newaxis]
         xhat[t,:] =  const + (x[t-P:t,:,np.newaxis]*B).sum(axis=1).sum(axis=0) + \
                      (e[t-Q:t,:,np.newaxis]*C).sum(axis=1).sum(axis=0)
         e[t,:] = x[t,:] - xhat[t,:]
@@ -116,8 +116,8 @@ def VARMA(x,B,C, const=0):
     #B[:,:,1] = 2
     B[:,:,1] = [[0,0],[0,0],[0,1]]
     xhat = VAR(x,B)
-    print np.all(xhat[P:,0]==np.correlate(x[:-1,0],np.ones(P))*2)
-    #print xhat
+    print(np.all(xhat[P:,0]==np.correlate(x[:-1,0],np.ones(P))*2))
+    #print(xhat)
 
 
     T = 20
@@ -133,8 +133,8 @@ def VARMA(x,B,C, const=0):
     C = np.zeros((Q,K,K))
     xhat1 = VAR(x,B, const=const)
     xhat2, err2 = VARMA(x,B,C, const=const)
-    print np.all(xhat2 == xhat1)
-    print np.all(xhat2[P:,0] == np.correlate(x[:-1,0],np.ones(P))*2+const)
+    print(np.all(xhat2 == xhat1))
+    print(np.all(xhat2[P:,0] == np.correlate(x[:-1,0],np.ones(P))*2+const))
 
     C[1,1,1] = 0.5
     xhat3, err3 = VARMA(x,B,C)
@@ -145,7 +145,7 @@ def VARMA(x,B,C, const=0):
     C[1,1,1] = 1
     B[:,:,1] = [[0,0],[0,0],[0,1]]
     xhat5, err5 = VARMA(x,B,C)
-    #print err5
+    #print(err5)
 
     #in differences
     #VARMA(np.diff(x,axis=0),B,C)
@@ -164,13 +164,13 @@ def VARMA(x,B,C, const=0):
     xhat0 = VAR(x0,B)
     xcorr00 = signal.correlate(x0,B[:,:,0])#[:,0]
     xcorr01 = signal.correlate(x0,B[:,:,1])
-    print np.all(signal.correlate(x0,B[:,:,0],'valid')[:-1,0]==xhat0[P:,0])
-    print np.all(signal.correlate(x0,B[:,:,1],'valid')[:-1,0]==xhat0[P:,1])
+    print(np.all(signal.correlate(x0,B[:,:,0],'valid')[:-1,0]==xhat0[P:,0]))
+    print(np.all(signal.correlate(x0,B[:,:,1],'valid')[:-1,0]==xhat0[P:,1]))
 
     #import error
     #from movstat import acovf, acf
     from statsmodels.tsa.stattools import acovf, acf
     aav = acovf(x[:,0])
-    print aav[0] == np.var(x[:,0])
+    print(aav[0] == np.var(x[:,0]))
     aac = acf(x[:,0])
 
diff --git a/statsmodels/sandbox/utils_old.py b/statsmodels/sandbox/utils_old.py
index 0dcf4a1340d..8d63060a190 100644
--- a/statsmodels/sandbox/utils_old.py
+++ b/statsmodels/sandbox/utils_old.py
@@ -77,7 +77,7 @@ def fullrank(X, r=None):
         value.append(V[:,order[i]])
     return np.asarray(np.transpose(value)).astype(np.float64)
 
-class StepFunction:
+class StepFunction(object):
     """
     A basic step function: values at the ends are handled in the simplest
     way possible: everything to the left of x[0] is set to ival; everything
@@ -86,7 +86,7 @@ class StepFunction:
     Examples
     --------
     >>> from numpy import arange
-    >>> from nipy.fixes.scipy.stats.models.utils import StepFunction
+    >>> from statsmodels.sandbox.utils_old import StepFunction
     >>>
     >>> x = arange(20)
     >>> y = arange(20)
@@ -105,9 +105,9 @@ def __init__(self, x, y, ival=0., sorted=False):
         _y = np.asarray(y)
 
         if _x.shape != _y.shape:
-            raise ValueError, 'in StepFunction: x and y do not have the same shape'
+            raise ValueError('in StepFunction: x and y do not have the same shape')
         if len(_x.shape) != 1:
-            raise ValueError, 'in StepFunction: x and y must be 1-dimensional'
+            raise ValueError('in StepFunction: x and y must be 1-dimensional')
 
         self.x = np.hstack([[-np.inf], _x])
         self.y = np.hstack([[ival], _y])
diff --git a/statsmodels/stats/adnorm.py b/statsmodels/stats/adnorm.py
index f05bb4dc782..e1abc2beaf6 100644
--- a/statsmodels/stats/adnorm.py
+++ b/statsmodels/stats/adnorm.py
@@ -5,7 +5,8 @@
 Author: Josef Perktold and Scipy developers
 License : BSD-3
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import range
 import numpy as np
 from scipy import stats
 
@@ -50,7 +51,7 @@ def anderson_statistic(x, dist='norm', fit=True, params=(), axis=0):
             params = dist.fit(x)
             #print params
             z = dist.cdf(y, *params)
-            print z
+            print(z)
     else:
         if hasattr(dist, '__call__'):
             z = dist.cdf(y, *params)
@@ -128,8 +129,8 @@ def normal_ad(x, axis=0):
            -1.4423,  1.2489,  0.9182, -0.2331, -0.6182,  0.183 ])
     r_res = np.array([0.58672353588821502, 0.1115380760041617])
     ad2, pval = normal_ad(x)
-    print ad2, pval
-    print r_res - [ad2, pval]
+    print(ad2, pval)
+    print(r_res - [ad2, pval])
 
-    print anderson_statistic((x-x.mean())/x.std(), dist=stats.norm, fit=0)
-    print anderson_statistic(x, dist=stats.norm, fit=True)
+    print(anderson_statistic((x-x.mean())/x.std(), dist=stats.norm, fit=0))
+    print(anderson_statistic(x, dist=stats.norm, fit=True))
diff --git a/statsmodels/stats/anova.py b/statsmodels/stats/anova.py
index 71e991a7c8e..0a5ccc64aa2 100644
--- a/statsmodels/stats/anova.py
+++ b/statsmodels/stats/anova.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import lrange, lmap
 import numpy as np
 from scipy import stats
 from pandas import DataFrame, Index
@@ -133,7 +134,7 @@ def anova1_lm_single(model, endog, exog, nobs, design_info, table, n_rows, test,
                                 model.df_resid)
 
     # fill in residual
-    table.ix[['Residual'], ['sum_sq','df', test, pr_test]] = (model.ssr,
+    table.ix['Residual', ['sum_sq','df', test, pr_test]] = (model.ssr,
                                                             model.df_resid,
                                                             np.nan, np.nan)
     table['mean_sq'] = table['sum_sq'] / table['df']
@@ -180,7 +181,7 @@ def anova2_lm_single(model, design_info, n_rows, test, pr_test, robust):
         # need two hypotheses matrices L1 is most restrictive, ie., term==0
         # L2 is everything except term==0
         cols = design_info.slice(term)
-        L1 = range(cols.start, cols.stop)
+        L1 = lrange(cols.start, cols.stop)
         L2 = []
         term_set = set(term.factors)
         for t in terms_info: # for the term you have
@@ -188,8 +189,8 @@ def anova2_lm_single(model, design_info, n_rows, test, pr_test, robust):
             if term_set.issubset(other_set) and not term_set == other_set:
                 col = design_info.slice(t)
                 # on a higher order term containing current `term`
-                L1.extend(range(col.start, col.stop))
-                L2.extend(range(col.start, col.stop))
+                L1.extend(lrange(col.start, col.stop))
+                L2.extend(lrange(col.start, col.stop))
 
         L1 = np.eye(model.model.exog.shape[1])[L1]
         L2 = np.eye(model.model.exog.shape[1])[L2]
@@ -208,11 +209,11 @@ def anova2_lm_single(model, design_info, n_rows, test, pr_test, robust):
         #from IPython.core.debugger import Pdb; Pdb().set_trace()
         if test == 'F':
             f = model.f_test(L12, cov_p=robust_cov)
-            table.ix[i][test] = test_value = f.fvalue
-            table.ix[i][pr_test] = f.pvalue
+            table.ix[i, test] = test_value = f.fvalue
+            table.ix[i, pr_test] = f.pvalue
 
         # need to back out SSR from f_test
-        table.ix[i]['df'] = r
+        table.ix[i, 'df'] = r
         col_order.append(cols.start)
         index.append(term.name())
 
@@ -222,7 +223,7 @@ def anova2_lm_single(model, design_info, n_rows, test, pr_test, robust):
     ssr = table[test] * table['df'] * model.ssr/model.df_resid
     table['sum_sq'] = ssr
     # fill in residual
-    table.ix['Residual'][['sum_sq','df', test, pr_test]] = (model.ssr,
+    table.ix['Residual', ['sum_sq','df', test, pr_test]] = (model.ssr,
                                                             model.df_resid,
                                                             np.nan, np.nan)
 
@@ -247,11 +248,11 @@ def anova3_lm_single(model, design_info, n_rows, test, pr_test, robust):
 
         if test == 'F':
             f = model.f_test(L12, cov_p=cov)
-            table.ix[i][test] = test_value = f.fvalue
-            table.ix[i][pr_test] = f.pvalue
+            table.ix[i, test] = test_value = f.fvalue
+            table.ix[i, pr_test] = f.pvalue
 
         # need to back out SSR from f_test
-        table.ix[i]['df'] = r
+        table.ix[i, 'df'] = r
         #col_order.append(cols.start)
         index.append(term.name())
 
@@ -262,7 +263,7 @@ def anova3_lm_single(model, design_info, n_rows, test, pr_test, robust):
     ssr = table[test] * table['df'] * model.ssr/model.df_resid
     table['sum_sq'] = ssr
     # fill in residual
-    table.ix['Residual'][['sum_sq','df', test, pr_test]] = (model.ssr,
+    table.ix['Residual', ['sum_sq','df', test, pr_test]] = (model.ssr,
                                                             model.df_resid,
                                                             np.nan, np.nan)
     return table
@@ -350,9 +351,9 @@ def anova_lm(*args, **kwargs):
     if not scale: # assume biggest model is last
         scale = args[-1].scale
 
-    table["ssr"] = map(getattr, args, ["ssr"]*n_models)
-    table["df_resid"] = map(getattr, args, ["df_resid"]*n_models)
-    table.ix[1:]["df_diff"] = np.diff(map(getattr, args, ["df_model"]*n_models))
+    table["ssr"] = lmap(getattr, args, ["ssr"]*n_models)
+    table["df_resid"] = lmap(getattr, args, ["df_resid"]*n_models)
+    table.ix[1:]["df_diff"] = -np.diff(table["df_resid"].values)
     table["ss_diff"] = -table["ssr"].diff()
     if test == "F":
         table["F"] = table["ss_diff"] / table["df_diff"] / scale
diff --git a/statsmodels/stats/api.py b/statsmodels/stats/api.py
index c408058c099..1c5ace727e3 100644
--- a/statsmodels/stats/api.py
+++ b/statsmodels/stats/api.py
@@ -1,5 +1,5 @@
 # pylint: disable=W0611
-import diagnostic
+from . import diagnostic
 from .diagnostic import (
             acorr_ljungbox, acorr_breush_godfrey,
             CompareCox, compare_cox, CompareJ, compare_j,
@@ -11,16 +11,16 @@
             normal_ad, lillifors
             )
 
-import multicomp
+from . import multicomp
 from .multitest import (multipletests, fdrcorrection, fdrcorrection_twostage)
 from .multicomp import tukeyhsd
-import gof
+from . import gof
 from .gof import (powerdiscrepancy, gof_chisquare_discrete,
                   chisquare_effectsize)
-import stattools
+from . import stattools
 from .stattools import durbin_watson, omni_normtest, jarque_bera
 
-import sandwich_covariance
+from . import sandwich_covariance
 from .sandwich_covariance import (
             cov_cluster, cov_cluster_2groups, cov_nw_panel,
             cov_hac, cov_white_simple,
@@ -47,7 +47,7 @@
 
 from .anova import anova_lm
 
-import moment_helpers
+from . import moment_helpers
 from .correlation_tools import corr_nearest, corr_clipped, cov_nearest
 
 from statsmodels.sandbox.stats.runs import (mcnemar, cochrans_q,
diff --git a/statsmodels/stats/base.py b/statsmodels/stats/base.py
index c0fc763bdc3..c6d2f3fa5c1 100644
--- a/statsmodels/stats/base.py
+++ b/statsmodels/stats/base.py
@@ -5,7 +5,7 @@
 
 Author: Josef Perktold
 """
-
+from statsmodels.compat.python import lzip, zip
 import numpy as np
 
 class AllPairsResults(object):
@@ -80,7 +80,7 @@ def pval_table(self):
         k = self.n_levels
         pvals_mat = np.zeros((k, k))
         # if we don't assume we have all pairs
-        pvals_mat[zip(*self.all_pairs)] = self.pval_corrected()
+        pvals_mat[lzip(*self.all_pairs)] = self.pval_corrected()
         #pvals_mat[np.triu_indices(k, 1)] = self.pval_corrected()
         return pvals_mat
 
diff --git a/statsmodels/stats/contrast.py b/statsmodels/stats/contrast.py
index 0d8169ac966..aab053ffdbd 100644
--- a/statsmodels/stats/contrast.py
+++ b/statsmodels/stats/contrast.py
@@ -1,30 +1,94 @@
+from statsmodels.compat.python import range
 import numpy as np
 from scipy.stats import f as fdist
 from scipy.stats import t as student_t
-from statsmodels.tools.tools import clean0, rank, fullrank
+from scipy import stats
+from statsmodels.tools.tools import clean0, fullrank
+from statsmodels.compat.numpy import np_matrix_rank
 
 
 #TODO: should this be public if it's just a container?
 class ContrastResults(object):
     """
-    Container class for looking at contrasts of coefficients in a model.
+    Class for results of tests oflinear restrictions on coefficients in a model.
+
+    This class functions mainly as a container for `t_test`, `f_test` and
+    `wald_test` for the parameters of a model.
+
+    The attributes depend on the statistical test and are either based on the
+    normal, the t, the F or the chisquare distribution.
 
-    The class does nothing, it is a container for the results from T and F.
     """
 
     def __init__(self, t=None, F=None, sd=None, effect=None, df_denom=None,
-                 df_num=None):
+                 df_num=None, alpha=0.05, **kwds):
+
+        self.effect = effect  # Let it be None for F
         if F is not None:
+            self.distribution = 'F'
             self.fvalue = F
             self.df_denom = df_denom
             self.df_num = df_num
+            self.dist = fdist
+            self.dist_args = (df_num, df_denom)
             self.pvalue = fdist.sf(F, df_num, df_denom)
-        else:
+        elif t is not None:
+            self.distribution = 't'
             self.tvalue = t
+            self.statistic = t  # generic alias
             self.sd = sd
-            self.effect = effect
             self.df_denom = df_denom
-            self.pvalue = student_t.sf(np.abs(t), df_denom) * 2
+            self.dist = student_t
+            self.dist_args = (df_denom,)
+            self.pvalue = self.dist.sf(np.abs(t), df_denom) * 2
+        elif 'statistic' in kwds:
+            # TODO: currently targeted to normal distribution, and chi2
+            self.distribution = kwds['distribution']
+            self.statistic = kwds['statistic']
+            self.tvalue = value = kwds['statistic']  # keep alias
+            # TODO: for results instance we decided to use tvalues also for normal
+            self.sd = sd
+            self.dist = getattr(stats, self.distribution)
+            self.dist_args = ()
+            if self.distribution is 'chi2':
+                self.pvalue = self.dist.sf(self.statistic, df_denom)
+            else:
+                "normal"
+                self.pvalue = self.dist.sf(np.abs(value)) * 2
+
+        # cleanup
+        # should we return python scalar?
+        self.pvalue = np.squeeze(self.pvalue)
+
+
+    def conf_int(self, alpha=0.05):
+        """
+        Returns the confidence interval of the value, `effect` of the constraint.
+
+        This is currently only available for t and z tests.
+
+        Parameters
+        ----------
+        alpha : float, optional
+            The significance level for the confidence interval.
+            ie., The default `alpha` = .05 returns a 95% confidence interval.
+
+        Returns
+        -------
+        ci : ndarray, (k_constraints, 2)
+            The array has the lower and the upper limit of the confidence
+            interval in the columns.
+
+        """
+        if self.effect is not None:
+            # confidence intervals
+            q = self.dist.ppf(1 - alpha / 2., *self.dist_args)
+            lower = self.effect - q * self.sd
+            upper = self.effect + q * self.sd
+            return np.column_stack((lower, upper))
+        else:
+            raise NotImplementedError('Confidence Interval not available')
+
 
     def __array__(self):
         if hasattr(self, "fvalue"):
@@ -33,17 +97,91 @@ def __array__(self):
             return self.tvalue
 
     def __str__(self):
-        if hasattr(self, 'fvalue'):
-            return '<F test: F=%s, p=%s, df_denom=%d, df_num=%d>' % \
-                   (`self.fvalue`, self.pvalue, self.df_denom, self.df_num)
-        else:
-            return '<T test: effect=%s, sd=%s, t=%s, p=%s, df_denom=%d>' % \
-                   (`self.effect`, `self.sd`, `self.tvalue`, `self.pvalue`,
-                           self.df_denom)
+        return self.summary().__str__()
+
 
     def __repr__(self):
         return str(self.__class__) + '\n' + self.__str__()
 
+
+    def summary(self, xname=None, alpha=0.05, title=None):
+        """Summarize the Results of the hypothesis test
+
+        Parameters
+        -----------
+
+        xname : list of strings, optional
+            Default is `c_##` for ## in p the number of regressors
+        alpha : float
+            significance level for the confidence intervals. Default is
+            alpha = 0.05 which implies a confidence level of 95%.
+        title : string, optional
+            Title for the params table. If not None, then this replaces the
+            default title
+
+        Returns
+        -------
+        smry : string or Summary instance
+            This contains a parameter results table in the case of t or z test
+            in the same form as the parameter results table in the model
+            results summary.
+            For F or Wald test, the return is a string.
+
+        """
+        if self.effect is not None:
+            # TODO: should also add some extra information, e.g. robust cov ?
+            # TODO: can we infer names for constraints, xname in __init__ ?
+            if title is None:
+                title = 'Test for Constraints'
+            elif title == '':
+                # don't add any title,
+                # I think SimpleTable skips on None - check
+                title = None
+            # we have everything for a params table
+            use_t = (self.distribution == 't')
+            yname='constraints' # Not used in params_frame
+            if xname is None:
+                xname = ['c%d'%ii for ii in range(len(self.effect))]
+            from statsmodels.iolib.summary import summary_params
+            pvalues = np.atleast_1d(self.pvalue)
+            summ = summary_params((self, self.effect, self.sd, self.statistic,
+                                   pvalues, self.conf_int(alpha)),
+                                  yname=yname, xname=xname, use_t=use_t,
+                                  title=title)
+            return summ
+        elif hasattr(self, 'fvalue'):
+            # TODO: create something nicer for these casee
+            return '<F test: F=%s, p=%s, df_denom=%d, df_num=%d>' % \
+                   (repr(self.fvalue), self.pvalue, self.df_denom, self.df_num)
+        else:
+            # generic
+            return '<Wald test: statistic=%s, p-value=%s>' % \
+                   (self.statistic, self.pvalue)
+
+
+    def summary_frame(self, xname=None, alpha=0.05):
+        """Return the parameter table as a pandas DataFrame
+
+        This is only available for t and normal tests
+        """
+        if self.effect is not None:
+            # we have everything for a params table
+            use_t = (self.distribution == 't')
+            yname='constraints'  # Not used in params_frame
+            if xname is None:
+                xname = ['c%d'%ii for ii in range(len(self.effect))]
+            from statsmodels.iolib.summary import summary_params_frame
+            summ = summary_params_frame((self, self.effect, self.sd,
+                                         self.statistic,self.pvalue,
+                                         self.conf_int(alpha)), yname=yname,
+                                         xname=xname, use_t=use_t)
+            return summ
+        else:
+            # TODO: create something nicer
+            raise NotImplementedError('only available for t and z')
+
+
+
 class Contrast(object):
     """
     This class is used to construct contrast matrices in regression models.
@@ -200,7 +338,7 @@ def contrastfromcols(L, D, pseudo=None):
     if len(Lp.shape) == 1:
         Lp.shape = (n, 1)
 
-    if rank(Lp) != Lp.shape[1]:
+    if np_matrix_rank(Lp) != Lp.shape[1]:
         Lp = fullrank(Lp)
         C = np.dot(pseudo, Lp).T
 
diff --git a/statsmodels/stats/correlation_tools.py b/statsmodels/stats/correlation_tools.py
index 66645666a1a..f54683a18f4 100644
--- a/statsmodels/stats/correlation_tools.py
+++ b/statsmodels/stats/correlation_tools.py
@@ -7,7 +7,12 @@
 License: BSD-3
 """
 
+from statsmodels.tools.sm_exceptions import (IterationLimitWarning,
+    iteration_limit_doc)
 import numpy as np
+import scipy.sparse as sparse
+from scipy.sparse.linalg import svds
+from scipy.optimize import fminbound
 
 def clip_evals(x, value=0): #threshold=0, value=0):
     evals, evecs = np.linalg.eigh(x)
@@ -79,7 +84,7 @@ def corr_nearest(corr, threshold=1e-15, n_fact=100):
         x_new[diag_idx, diag_idx] = 1
     else:
         import warnings
-        warnings.warn('maximum iteration reached')
+        warnings.warn(iteration_limit_doc, IterationLimitWarning)
 
     return x_new
 
@@ -92,7 +97,7 @@ def corr_clipped(corr, threshold=1e-15):
     diagonal elements are one.
     Compared to corr_nearest, the distance between the original correlation
     matrix and the positive definite correlation matrix is larger, however,
-    it is much faster since it only computes eigenvalues only once.
+    it is much faster since it only computes eigenvalues once.
 
     Parameters
     ----------
@@ -213,5 +218,632 @@ def cov_nearest(cov, method='clipped', threshold=1e-15, n_fact=100,
     else:
         return cov_
 
+def _nmono_linesearch(obj, grad, x, d, obj_hist, M=10, sig1=0.1,
+                      sig2=0.9, gam=1e-4, maxiter=100):
+    """
+    Implements the non-monotone line search of Grippo et al. (1986),
+    as described in Birgin, Martinez and Raydan (2013).
+
+    Parameters
+    ----------
+    obj : real-valued function
+        The objective function, to be minimized
+    grad : vector-valued function
+        The gradient of the objective function
+    x : array_like
+        The starting point for the line search
+    d : array_like
+        The search direction
+    obj_hist : array_like
+        Objective function history (must contain at least one value)
+    M : positive integer
+        Number of previous function points to consider (see references
+        for details).
+    sig1 : real
+        Tuning parameter, see references for details.
+    sig2 : real
+        Tuning parameter, see references for details.
+    gam : real
+        Tuning parameter, see references for details.
+    maxiter : positive integer
+        The maximum number of iterations; returns Nones if convergence
+        does not occur by this point
+
+    Returns
+    -------
+    alpha : real
+        The step value
+    x : Array_like
+        The function argument at the final step
+    obval : Real
+        The function value at the final step
+    g : Array_like
+        The gradient at the final step
+
+    Notes
+    -----
+    The basic idea is to take a big step in the direction of the
+    gradient, even if the function value is not decreased (but there
+    is a maximum allowed increase in terms of the recent history of
+    the iterates).
+
+    References
+    ----------
+    Grippo L, Lampariello F, Lucidi S (1986). A Nonmonotone Line
+    Search Technique for Newton's Method. SIAM Journal on Numerical
+    Analysis, 23, 707-716.
+
+    E. Birgin, J.M. Martinez, and M. Raydan. Spectral projected
+    gradient methods: Review and perspectives. Journal of Statistical
+    Software (preprint).
+    """
+
+    alpha = 1.
+    last_obval = obj(x)
+    obj_max = max(obj_hist[-M:])
+
+    for iter in range(maxiter):
+
+        obval = obj(x + alpha*d)
+        g = grad(x)
+        gtd = (g * d).sum()
+
+        if obval <= obj_max + gam*alpha*gtd:
+            return alpha, x + alpha*d, obval, g
+
+        a1 = -0.5*alpha**2*gtd / (obval - last_obval - alpha*gtd)
+
+        if (sig1 <= a1) and (a1 <= sig2*alpha):
+            alpha = a1
+        else:
+            alpha /= 2.
+
+        last_obval = obval
+
+    return None, None, None, None
+
+
+def _spg_optim(func, grad, start, project, maxiter=1e4, M=10,
+               ctol=1e-3, maxiter_nmls=200, lam_min=1e-30,
+               lam_max=1e30, sig1=0.1, sig2=0.9, gam=1e-4):
+    """
+    Implements the spectral projected gradient method for minimizing a
+    differentiable function on a convex domain.
+
+    Parameters
+    ----------
+    func : real valued function
+        The objective function to be minimized.
+    grad : real array-valued function
+        The gradient of the objective function
+    start : array_like
+        The starting point
+    project : function
+        In-place projection of the argument to the domain
+        of func.
+    ... See notes regarding additional arguments
+
+    Returns
+    -------
+    rslt : Bunch
+        rslt.params is the final iterate, other fields describe
+        convergence status.
+
+    Notes
+    -----
+    This can be an effective heuristic algorithm for problems where no
+    gauranteed algorithm for computing a global minimizer is known.
+
+    There are a number of tuning parameters, but these generally
+    should not be changed except for `maxiter` (positive integer) and
+    `ctol` (small positive real).  See the Birgin et al reference for
+    more information about the tuning parameters.
+
+    Reference
+    ---------
+    E. Birgin, J.M. Martinez, and M. Raydan. Spectral projected
+    gradient methods: Review and perspectives. Journal of Statistical
+    Software (preprint).  Available at:
+    http://www.ime.usp.br/~egbirgin/publications/bmr5.pdf
+    """
+
+    lam = min(10*lam_min, lam_max)
+
+    params = start.copy()
+    gval = grad(params)
+
+    obj_hist = [func(params),]
+
+    for itr in range(int(maxiter)):
+
+        # Check convergence
+        df = params - gval
+        project(df)
+        df -= params
+        if np.max(np.abs(df)) < ctol:
+            return Bunch(**{"Converged": True, "params": params,
+                            "Message": "Converged successfully"})
+
+        # The line search direction
+        d = params - lam*gval
+        project(d)
+        d -= params
+
+        # Carry out the nonmonotone line search
+        alpha, params1, fval, gval1 = _nmono_linesearch(func, grad, params, d,
+                                                 obj_hist, M=M,
+                                                 sig1=sig1,
+                                                 sig2=sig2,
+                                                 gam=gam,
+                                                 maxiter=maxiter_nmls)
+        if alpha is None:
+            return Bunch(**{"Converged": False, "params": params, "Message":
+                            "Failed in nmono_linesearch"})
+
+        obj_hist.append(fval)
+        s = params1 - params
+        y = gval1 - gval
+
+        sy = (s*y).sum()
+        if sy <= 0:
+            lam = lam_max
+        else:
+            ss = (s*s).sum()
+            lam = max(lam_min, min(ss/sy, lam_max))
+
+        params = params1
+        gval = gval1
+
+    return Bunch(**{"Converged": False, "params": params,
+                    "Message": "spg_optim did not converge"})
+
+
+def _project_correlation_factors(X):
+    """
+    Project a matrix into the domain of matrices whose row-wise sums
+    of squares are less than or equal to 1.
+
+    The input matrix is modified in-place.
+    """
+    nm = np.sqrt((X*X).sum(1))
+    ii = np.flatnonzero(nm > 1)
+    if len(ii) > 0:
+        X[ii,:] /= nm[ii][:, None]
+
+
+#TODO does this belong in a tools module somewhere?
+class Bunch(object):
+
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+
+
+class FactoredPSDMatrix:
+    """
+    Representation of a positive semidefinite matrix in factored form.
+
+    The representation is constructed based on a vector `diag` and
+    rectangular matrix `root`, such that the PSD matrix represented by
+    the class instance is Diag + root * root', where Diag is the
+    square diagonal matrix with `diag` on its main diagonal.
+
+    Parameters
+    ----------
+    diag : 1d array-like
+        See above
+    root : 2d array-like
+        See above
+
+    Notes
+    -----
+    The matrix is represented internally in the form Diag^{1/2}(I +
+    factor * scales * factor')Diag^{1/2}, where `Diag` and `scales`
+    are diagonal matrices, and `factor` is an orthogonal matrix.
+    """
+
+    def __init__(self, diag, root):
+        self.diag = diag
+        self.root = root
+        root = root / np.sqrt(diag)[:, None]
+        u, s, vt = np.linalg.svd(root, 0)
+        self.factor = u
+        self.scales = s**2
+
+
+    def to_matrix(self):
+        """
+        Returns the PSD matrix represented by this instance as a full
+        (square) matrix.
+        """
+        return np.diag(self.diag) + np.dot(self.root, self.root.T)
+
+
+    def decorrelate(self, rhs):
+        """
+        Decorrelate the columns of `rhs`.
+
+        Parameters
+        ----------
+        rhs : array-like
+            A 2 dimensional array with the same number of rows as the
+            PSD matrix represented by the class instance.
+
+        Returns
+        -------
+        C^{-1/2} * rhs, where C is the covariance matrix represented
+        by this class instance.
+
+        Notes
+        -----
+        The returned matrix has the identity matrix as its row-wise
+        population covariance matrix.
+
+        This function exploits the factor structure for efficiency.
+        """
+
+        # I + factor * qval * factor' is the inverse square root of
+        # the covariance matrix in the homogeneous case where diag =
+        # 1.
+        qval = -1 + 1 / np.sqrt(1 + self.scales)
+
+        # Decorrelate in the general case.
+        rhs = rhs / np.sqrt(self.diag)[:, None]
+        rhs1 = np.dot(self.factor.T, rhs)
+        rhs1 *= qval[:, None]
+        rhs1 = np.dot(self.factor, rhs1)
+        rhs += rhs1
+
+        return rhs
+
+    def solve(self, rhs):
+        """
+        Solve a linear system of equations with factor-structured
+        coefficients.
+
+        Parameters
+        ----------
+        rhs : array-like
+            A 2 dimensional array with the same number of rows as the
+            PSD matrix represented by the class instance.
+
+        Returns
+        -------
+        C^{-1} * rhs, where C is the covariance matrix represented
+        by this class instance.
+
+        Notes
+        -----
+        This function exploits the factor structure for efficiency.
+        """
+
+        qval = -self.scales / (1 + self.scales)
+        dr = np.sqrt(self.diag)
+        rhs = rhs / dr[:, None]
+        mat = qval[:, None] * np.dot(self.factor.T, rhs)
+        rhs = rhs + np.dot(self.factor, mat)
+        return rhs / dr[:, None]
+
+    def logdet(self):
+        """
+        Returns the logarithm of the determinant of a
+        factor-structured matrix.
+        """
+
+        logdet = np.sum(np.log(self.diag))
+        logdet += np.sum(np.log(self.scales))
+        logdet += np.sum(np.log(1 + 1 / self.scales))
+
+        return logdet
+
+
+
+def corr_nearest_factor(corr, rank, ctol=1e-6, lam_min=1e-30,
+                        lam_max=1e30, maxiter=1000):
+    """
+    Find the nearest correlation matrix with factor structure to a
+    given square matrix.
+
+    Parameters
+    ----------
+    corr : square array
+        The target matrix (to which the nearest correlation matrix is
+        sought).  Must be square, but need not be positive
+        semidefinite.
+    rank : positive integer
+        The rank of the factor structure of the solution, i.e., the
+        number of linearly independent columns of X.
+    ctol : positive real
+        Convergence criterion.
+    lam_min : float
+        Tuning parameter for spectral projected gradient optimization
+        (smallest allowed step in the search direction).
+    lam_max : float
+        Tuning parameter for spectral projected gradient optimization
+        (largest allowed step in the search direction).
+    maxiter : integer
+        Maximum number of iterations in spectral projected gradient
+        optimization.
+
+    Returns
+    -------
+    rslt : Bunch
+        rslt.corr is a FactoredPSDMatrix defining the estimated
+        correlation structure.  Other fields of `rslt` contain
+        returned values from spg_optim.
+
+    Notes
+    -----
+    A correlation matrix has factor structure if it can be written in
+    the form I + XX' - diag(XX'), where X is n x k with linearly
+    independent columns, and with each row having sum of squares at
+    most equal to 1.  The approximation is made in terms of the
+    Frobenius norm.
+
+    This routine is useful when one has an approximate correlation
+    matrix that is not positive semidefinite, and there is need to
+    estimate the inverse, square root, or inverse square root of the
+    population correlation matrix.  The factor structure allows these
+    tasks to be done without constructing any n x n matrices.
+
+    This is a non-convex problem with no known gauranteed globally
+    convergent algorithm for computing the solution.  Borsdof, Higham
+    and Raydan (2010) compared several methods for this problem and
+    found the spectral projected gradient (SPG) method (used here) to
+    perform best.
+
+    The input matrix `corr` can be a dense numpy array or any scipy
+    sparse matrix.  The latter is useful if the input matrix is
+    obtained by thresholding a very large sample correlation matrix.
+    If `corr` is sparse, the calculations are optimized to save
+    memory, so no working matrix with more than 10^6 elements is
+    constructed.
+
+    References
+    ----------
+    R Borsdof, N Higham, M Raydan (2010).  Computing a nearest
+    correlation matrix with factor structure. SIAM J Matrix Anal
+    Appl, 31:5, 2603-2622.
+    http://eprints.ma.man.ac.uk/1523/01/covered/MIMS_ep2009_87.pdf
+
+    Examples
+    --------
+    Hard thresholding a correlation matrix may result in a matrix that
+    is not positive semidefinite.  We can approximate a hard
+    thresholded correlation matrix with a PSD matrix as follows, where
+    `corr` is the input correlation matrix.
+
+    >>> corr = corr * (np.abs(corr) >= 0.3)
+    >>> rslt = corr_nearest_factor(corr, 3)
+    """
+
+    p, _ = corr.shape
+
+    # Starting values (following the PCA method in BHR).
+    u,s,vt = svds(corr, rank)
+    X = u * np.sqrt(s)
+    nm = np.sqrt((X**2).sum(1))
+    ii = np.flatnonzero(nm > 1e-5)
+    X[ii,:] /= nm[ii][:, None]
+
+    # Zero the diagonal
+    corr1 = corr.copy()
+    if type(corr1) == np.ndarray:
+        np.fill_diagonal(corr1, 0)
+    elif sparse.issparse(corr1):
+        corr1.setdiag(np.zeros(corr1.shape[0]))
+        corr1.eliminate_zeros()
+        corr1.sort_indices()
+    else:
+        raise ValueError("Matrix type not supported")
+
+    # The gradient, from lemma 4.1 of BHR.
+    def grad(X):
+        gr = np.dot(X, np.dot(X.T, X))
+        if type(corr1) == np.ndarray:
+            gr -= np.dot(corr1, X)
+        else:
+            gr -= corr1.dot(X)
+        gr -= (X*X).sum(1)[:, None] * X
+        return 4*gr
+
+    # The objective function (sum of squared deviations between fitted
+    # and observed arrays).
+    def func(X):
+        if type(corr1) == np.ndarray:
+            M = np.dot(X, X.T)
+            np.fill_diagonal(M, 0)
+            M -= corr1
+            fval = (M*M).sum()
+            return fval
+        else:
+            fval = 0.
+            # Control the size of intermediates
+            max_ws = 1e6
+            bs = int(max_ws / X.shape[0])
+            ir = 0
+            while ir < X.shape[0]:
+                ir2 = min(ir+bs, X.shape[0])
+                u = np.dot(X[ir:ir2, :], X.T)
+                ii = np.arange(u.shape[0])
+                u[ii, ir+ii] = 0
+                u -= np.asarray(corr1[ir:ir2, :].todense())
+                fval += (u*u).sum()
+                ir += bs
+            return fval
+
+    rslt = _spg_optim(func, grad, X, _project_correlation_factors)
+    root = rslt.params
+    diag = 1 - (root**2).sum(1)
+    soln = FactoredPSDMatrix(diag, root)
+    rslt.corr = soln
+    del rslt.params
+    return rslt
+
+
+def cov_nearest_factor_homog(cov, rank):
+    """
+    Approximate an arbitrary square matrix with a factor-structured
+    matrix of the form k*I + XX'.
+
+    Parameters
+    ----------
+    cov : array-like
+        The input array, must be square but need not be positive
+        semidefinite
+    rank : positive integer
+        The rank of the fitted factor structure
+
+    Returns
+    -------
+    A FactoredPSDMatrix instance containing the fitted matrix
+
+    Notes
+    -----
+    This routine is useful if one has an estimated covariance matrix
+    that is not SPD, and the ultimate goal is to estimate the inverse,
+    square root, or inverse square root of the true covariance
+    matrix. The factor structure allows these tasks to be performed
+    without constructing any n x n matrices.
+
+    The calculations use the fact that if k is known, then X can be
+    determined from the eigen-decomposition of cov - k*I, which can
+    in turn be easily obtained form the eigen-decomposition of `cov`.
+    Thus the problem can be reduced to a 1-dimensional search for k
+    that does not require repeated eigen-decompositions.
+
+    If the input matrix is sparse, then cov - k*I is also sparse, so
+    the eigen-decomposition can be done effciciently using sparse
+    routines.
+
+    The one-dimensional search for the optimal value of k is not
+    convex, so a local minimum could be obtained.
+
+    Examples
+    --------
+    Hard thresholding a covariance matrix may result in a matrix that
+    is not positive semidefinite.  We can approximate a hard
+    thresholded covariance matrix with a PSD matrix as follows:
+
+    >>> cov = cov * (np.abs(cov) >= 0.3)
+    >>> rslt = cov_nearest_factor_homog(cov, 3)
+    """
+
+    m, n = cov.shape
+
+    Q, Lambda, _ = svds(cov, rank)
+
+    if sparse.issparse(cov):
+        QSQ = np.dot(Q.T, cov.dot(Q))
+        ts = cov.diagonal().sum()
+        tss = cov.dot(cov).diagonal().sum()
+    else:
+        QSQ = np.dot(Q.T, np.dot(cov, Q))
+        ts = np.trace(cov)
+        tss = np.trace(np.dot(cov, cov))
+
+    def fun(k):
+        Lambda_t = Lambda - k
+        v = tss + m*(k**2) + np.sum(Lambda_t**2) - 2*k*ts
+        v += 2*k*np.sum(Lambda_t) - 2*np.sum(np.diag(QSQ) * Lambda_t)
+        return v
+
+    # Get the optimal decomposition
+    k_opt = fminbound(fun, 0, 1e5)
+    Lambda_opt = Lambda - k_opt
+    fac_opt = Q * np.sqrt(Lambda_opt)
+
+    diag = k_opt * np.ones(m, dtype=np.float64) #- (fac_opt**2).sum(1)
+    return FactoredPSDMatrix(diag, fac_opt)
+
+def corr_thresholded(data, minabs=None, max_elt=1e7):
+    """
+    Construct a sparse matrix containing the thresholded row-wise
+    correlation matrix from a data array.
+
+    Parameters
+    ----------
+    data : array_like
+        The data from which the row-wise thresholded correlation
+        matrix is to be computed.
+    minabs : non-negative real
+        The threshold value; correlation coefficients smaller in
+        magnitude than minabs are set to zero.  If None, defaults
+        to 1 / sqrt(n), see Notes for more information.
+
+    Returns
+    -------
+    cormat : sparse.coo_matrix
+        The thresholded correlation matrix, in COO format.
+
+    Notes
+    -----
+    This is an alternative to C = np.corrcoef(data); C *= (np.abs(C)
+    >= absmin), suitable for very tall data matrices.
+
+    If the data are jointly Gaussian, the marginal sampling
+    distributions of the elements of the sample correlation matrix are
+    approximately Gaussian with standard deviation 1 / sqrt(n).  The
+    default value of `minabs` is thus equal to 1 standard error, which
+    will set to zero approximately 68% of the estimated correlation
+    coefficients for which the population value is zero.
+
+    No intermediate matrix with more than `max_elt` values will be
+    constructed.  However memory use could still be high if a large
+    number of correlation values exceed `minabs` in magnitude.
+
+    The thresholded matrix is returned in COO format, which can easily
+    be converted to other sparse formats.
+
+    Examples
+    --------
+    Here X is a tall data matrix (e.g. with 100,000 rows and 50
+    columns).  The row-wise correlation matrix of X is calculated
+    and stored in sparse form, with all entries smaller than 0.3
+    treated as 0.
+
+    >>> cmat = corr_thresholded(X, 0.3)
+    """
+
+    nrow, ncol = data.shape
+
+    if minabs is None:
+        minabs = 1. / float(ncol)
+
+    # Row-standardize the data
+    data = data.copy()
+    data -= data.mean(1)[:, None]
+    sd = data.std(1, ddof=1)
+    ii = np.flatnonzero(sd > 1e-5)
+    data[ii, :] /= sd[ii][:, None]
+    ii = np.flatnonzero(sd <= 1e-5)
+    data[ii, :] = 0
+
+    # Number of rows to process in one pass
+    bs = int(np.floor(max_elt / nrow))
+
+    ipos_all, jpos_all, cor_values = [], [], []
+
+    ir = 0
+    while ir < nrow:
+        ir2 = min(data.shape[0], ir + bs)
+        cm = np.dot(data[ir:ir2,:], data.T) / (ncol - 1)
+        cma = np.abs(cm)
+        ipos, jpos = np.nonzero(cma >= minabs)
+        ipos_all.append(ipos + ir)
+        jpos_all.append(jpos)
+        cor_values.append(cm[ipos, jpos])
+        ir += bs
+
+    ipos = np.concatenate(ipos_all)
+    jpos = np.concatenate(jpos_all)
+    cor_values = np.concatenate(cor_values)
+
+    cmat = sparse.coo_matrix((cor_values, (ipos, jpos)), (nrow, nrow))
+
+    return cmat
+
+
+
 if __name__ == '__main__':
     pass
diff --git a/statsmodels/stats/descriptivestats.py b/statsmodels/stats/descriptivestats.py
index 0d377cc4961..cbbfff88b13 100644
--- a/statsmodels/stats/descriptivestats.py
+++ b/statsmodels/stats/descriptivestats.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import lrange, lmap, iterkeys, iteritems
 import sys
 import numpy as np
 from scipy import stats
@@ -230,14 +231,14 @@ def _fun(per):
         if 'percentiles' in stats:
             self.univariate.update(perdict)
             idx = stats.index('percentiles')
-            stats[idx:idx+1] = sorted(perdict.keys())
+            stats[idx:idx+1] = sorted(iterkeys(perdict))
 
 
 
         #JP: this doesn't allow a change in sequence, sequence in stats is
         #ignored
         #this is just an if condition
-        if any([aitem[1] for aitem in self.univariate.items() if aitem[0] in
+        if any([aitem[1] for aitem in iteritems(self.univariate) if aitem[0] in
                 stats]):
             if columns == 'all':
                 self._columns_list = []
@@ -247,7 +248,7 @@ def _fun(per):
                     #                      self.dataset.dtype.names if
                             #(self._is_dtype_like(col)=='number')]
                 else:
-                    self._columns_list = range(self.dataset.shape[1])
+                    self._columns_list = lrange(self.dataset.shape[1])
             else:
                 self._columns_list = columns
                 if self._arraytype == 'sctruct':
@@ -287,7 +288,7 @@ def print_summary(self, stats, orientation='auto'):
         if (orientation == 'varcols') or \
            (orientation == 'auto' and len(stubs) < len(header)):
             #swap rows and columns
-            data = map(lambda *row: list(row), *data)
+            data = lmap(lambda *row: list(row), *data)
             header, stubs = stubs, header
 
         part_fmt = dict(data_fmts = ["%#8.4g"]*(len(header)-1))
diff --git a/statsmodels/stats/gof.py b/statsmodels/stats/gof.py
index 24951578803..81c0affc5bc 100644
--- a/statsmodels/stats/gof.py
+++ b/statsmodels/stats/gof.py
@@ -17,7 +17,7 @@
 2013-02-25 : add chisquare_power, effectsize and "value"
 
 '''
-
+from statsmodels.compat.python import range, lrange
 import numpy as np
 from scipy import stats
 
@@ -208,7 +208,7 @@ def gof_chisquare_discrete(distfn, arg, rvs, alpha, msg):
 
     # construct intervals with minimum mass 1/nsupp
     # intervalls are left-half-open as in a cdf difference
-    distsupport = xrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
+    distsupport = lrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
     last = 0
     distsupp = [max(distfn.a, -1000)]
     distmass = []
@@ -297,7 +297,7 @@ def gof_binning_discrete(rvs, distfn, arg, nsupp=20):
 
     # construct intervals with minimum mass 1/nsupp
     # intervalls are left-half-open as in a cdf difference
-    distsupport = xrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
+    distsupport = lrange(max(distfn.a, -1000), min(distfn.b, 1000) + 1)
     last = 0
     distsupp = [max(distfn.a, -1000)]
     distmass = []
diff --git a/statsmodels/stats/libqsturng/__init__.py b/statsmodels/stats/libqsturng/__init__.py
index e35755d1717..1afd4175f39 100644
--- a/statsmodels/stats/libqsturng/__init__.py
+++ b/statsmodels/stats/libqsturng/__init__.py
@@ -1,5 +1,5 @@
 
-from qsturng_ import psturng, qsturng, p_keys, v_keys
+from .qsturng_ import psturng, qsturng, p_keys, v_keys
 
 from numpy.testing import Tester
 test = Tester().test
diff --git a/statsmodels/stats/libqsturng/make_tbls.py b/statsmodels/stats/libqsturng/make_tbls.py
index 785c6e0a0db..f9656c00b43 100644
--- a/statsmodels/stats/libqsturng/make_tbls.py
+++ b/statsmodels/stats/libqsturng/make_tbls.py
@@ -1,12 +1,12 @@
 """this script builds the T table and A table for the upper
    quantile stundentized range algorithm"""
-
+from statsmodels.compat.python import iterkeys, lrange, lmap, zip
 import math
 import scipy.stats
 from scipy.optimize import leastsq
 
 import numpy as np
-from statsmodels.compatnp.collections import OrderedDict
+from statsmodels.compat.collections import OrderedDict
 from numpy.random import random
 
 # The values for p in [.5, .75, .9, .95, .975, .99, .995, .999]
@@ -387,33 +387,33 @@
 #                 [alpha keys]        [v keys]
 #                   [table values as lists of floats]
 T = OrderedDict([(0.100, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0100.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0100.split('\n')])),
                  (0.500, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0500.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0500.split('\n')])),
                  (0.675, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0675.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0675.split('\n')])),
                  (0.750, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0750.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0750.split('\n')])),
                  (0.800, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0800.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0800.split('\n')])),
                  (0.850, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0850.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0850.split('\n')])),
                  (0.900, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0900.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0900.split('\n')])),
                  (0.950, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0950.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0950.split('\n')])),
                  (0.975, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0975.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0975.split('\n')])),
                  (0.990, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0990.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0990.split('\n')])),
                  (0.995, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0995.split('\n')])),
+                    lmap(float, L.split()[1:])) for L in q0995.split('\n')])),
                  (0.999, OrderedDict([(float(L.split()[0]),
-                    map(float, L.split()[1:])) for L in q0999.split('\n')]))])
+                    lmap(float, L.split()[1:])) for L in q0999.split('\n')]))])
 
 # This dict maps r values to the correct list index
 R = OrderedDict(zip([2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,
-                     17,18,19,20,30,40,60,80,100], range(24)))
+                     17,18,19,20,30,40,60,80,100], lrange(24)))
 
 inf = np.inf
 # we will need a tinv function
@@ -453,7 +453,7 @@ def qhat(a, p, r, v):
         #eq. 2.4
         a0 = random(4)
         a1, success = leastsq(errfunc, a0,
-                              args=(p, np.array(R.keys()),
+                              args=(p, np.array(list(iterkeys(R))),
                                     v, np.array(T[p][v])))
 
         if v == 1e38:
diff --git a/statsmodels/stats/libqsturng/qsturng_.py b/statsmodels/stats/libqsturng/qsturng_.py
index 4dd97771c08..084d9508ff0 100644
--- a/statsmodels/stats/libqsturng/qsturng_.py
+++ b/statsmodels/stats/libqsturng/qsturng_.py
@@ -22,7 +22,8 @@
     Studentized range distribution.
     http://www.stata.com/stb/stb46/dm64/sturng.pdf
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange, map
 import math
 import scipy.stats
 import numpy as np
@@ -371,7 +372,7 @@
 p_keys = [.1,.5,.675,.75,.8,.85,.9,.95,.975,.99,.995,.999]
 
 # v values that are defined in the A table
-v_keys = range(2, 21) + [24, 30, 40, 60, 120, inf]
+v_keys = lrange(2, 21) + [24, 30, 40, 60, 120, inf]
 
 def _isfloat(x):
     """
@@ -527,7 +528,7 @@ def _interpolate_p(p, r, v):
     try:
         y0 = _func(A[(p0, v)], p0, r, v) + 1.
     except:
-        print p,r,v
+        print(p,r,v)
     y1 = _func(A[(p1, v)], p1, r, v) + 1.
     y2 = _func(A[(p2, v)], p2, r, v) + 1.
 
@@ -687,7 +688,7 @@ def _qsturng(p, r, v):
     p = float(p)
     if isinstance(v, np.ndarray):
         v = v.item()
-    if A.has_key((p,v)):
+    if (p,v) in A:
         y = _func(A[(p,v)], p, r, v) + 1.
 
     elif p not in p_keys and v not in v_keys+([],[1])[p>=.90]:
diff --git a/statsmodels/stats/libqsturng/tests/test_qsturng.py b/statsmodels/stats/libqsturng/tests/test_qsturng.py
index d1debe95fbf..dfbd7582c04 100644
--- a/statsmodels/stats/libqsturng/tests/test_qsturng.py
+++ b/statsmodels/stats/libqsturng/tests/test_qsturng.py
@@ -5,7 +5,7 @@
 @dec.slow are empirical (test within error limits) and intended to more
 extensively ensure the stability and accuracy of the functions"""
 
-from __future__ import with_statement
+from statsmodels.compat.python import iterkeys, lzip, lmap
 
 from numpy.testing import TestCase, rand, assert_, assert_equal, \
     assert_almost_equal, assert_array_almost_equal, assert_array_equal, \
@@ -18,16 +18,16 @@
 def read_ch(fname):
     with open(fname) as f:
         lines = f.readlines()
-    ps,rs,vs,qs = zip(*[L.split(',') for L in lines])
-    return map(float, ps), map(float, rs),map(float, vs), map(float, qs)
+    ps,rs,vs,qs = lzip(*[L.split(',') for L in lines])
+    return lmap(float, ps), lmap(float, rs),lmap(float, vs), lmap(float, qs)
 
 class test_qsturng(TestCase):
     def test_scalar(self):
-        "scalar input -> scalar output"
+        # scalar input -> scalar output
         assert_almost_equal(4.43645545899562, qsturng(.9,5,6), 5)
 
     def test_vector(self):
-        "vector input -> vector output"
+        # vector input -> vector output
         assert_array_almost_equal(np.array([3.98832389,
                                             4.56835318,
                                             6.26400894]),
@@ -81,7 +81,7 @@ def t_est_all_to_tbl(self):
         ps, rs, vs, qs = [], [], [], []
         for p in T:
             for v in T[p]:
-                for r in R.keys():
+                for r in iterkeys(R):
                     ps.append(p)
                     vs.append(v)
                     rs.append(r)
diff --git a/statsmodels/stats/lilliefors.py b/statsmodels/stats/lilliefors.py
index b98a1bbd567..d4fd1b9d9fd 100644
--- a/statsmodels/stats/lilliefors.py
+++ b/statsmodels/stats/lilliefors.py
@@ -16,7 +16,7 @@
 Journal of the American Statistical Association, Vol. 62, No. 318. (Jun., 1967), pp. 399-402.
 
 """
-
+from statsmodels.compat.python import string_types
 import numpy as np
 from scipy.interpolate import interp1d
 from scipy import stats
@@ -68,7 +68,7 @@ def ksstat(x, cdf, alternative='two_sided', args=()):
     """
     nobs = float(len(x))
 
-    if isinstance(cdf, basestring):
+    if isinstance(cdf, string_types):
         cdf = getattr(stats.distributions, cdf).cdf
     elif hasattr(cdf, 'cdf'):
         cdf = getattr(cdf, 'cdf')
@@ -95,7 +95,7 @@ def ksstat(x, cdf, alternative='two_sided', args=()):
 
 def get_lilliefors_table():
     #function just to keep things together
-    from tabledist import TableDist
+    from .tabledist import TableDist
     #for this test alpha is sf probability, i.e. right tail probability
 
     alpha = np.array([ 0.2  ,  0.15 ,  0.1  ,  0.05 ,  0.01 ,  0.001])[::-1]
diff --git a/statsmodels/stats/moment_helpers.py b/statsmodels/stats/moment_helpers.py
index 36cd79c4f54..fb01cd40b63 100644
--- a/statsmodels/stats/moment_helpers.py
+++ b/statsmodels/stats/moment_helpers.py
@@ -12,6 +12,7 @@
 
 '''
 
+from statsmodels.compat.python import range
 import numpy as np
 from scipy.misc import comb
 
diff --git a/statsmodels/stats/multitest.py b/statsmodels/stats/multitest.py
index 1317b92dfc3..7d5abf6a682 100644
--- a/statsmodels/stats/multitest.py
+++ b/statsmodels/stats/multitest.py
@@ -6,8 +6,8 @@
 
 '''
 
-
-from statsmodels.compatnp.collections import OrderedDict
+from statsmodels.compat.python import range
+from statsmodels.compat.collections import OrderedDict
 
 import numpy as np
 
@@ -57,7 +57,8 @@ def _ecdf(x):
     for a in m[1:]:
         multitest_alias[a] = m[0]
 
-def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
+def multipletests(pvals, alpha=0.05, method='hs', is_sorted=False,
+                  returnsorted=False):
     '''test results and p-value correction for multiple tests
 
 
@@ -82,6 +83,10 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
         `fdr_tsbh` : two stage fdr correction (non-negative)
         `fdr_tsbky` : two stage fdr correction (non-negative)
 
+    is_sorted : bool
+        If False (default), the p_values will be sorted, but the corrected
+        pvalues are in the original order. If True, then it assumed that the
+        pvalues are already sorted in ascending order.
     returnsorted : bool
          not tested, return sorted p-values instead of original sequence
 
@@ -104,17 +109,28 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
     the corrected p-values are specific to the given alpha, see
     ``fdrcorrection_twostage``.
 
-    all corrected pvalues now tested against R.
+    all corrected p-values now tested against R.
     insufficient "cosmetic" tests yet
-    new procedure 'fdr_gbs' not verified yet, p-values derived from scratch not
-    reference
+    The 'fdr_gbs' procedure is not verified against another package, p-values
+    are derived from scratch and are not derived in the reference. In Monte
+    Carlo experiments the method worked correctly and maintained the false
+    discovery rate.
 
     All procedures that are included, control FWER or FDR in the independent
     case, and most are robust in the positively correlated case.
 
-    fdr_gbs: high power, fdr control for independent case and only small
+    `fdr_gbs`: high power, fdr control for independent case and only small
     violation in positively correlated case
 
+    **Timing**:
+
+    Most of the time with large arrays is spent in `argsort`. When
+    we want to calculate the p-value for several methods, then it is more
+    efficient to presort the pvalues, and put the results back into the
+    original order outside of the function.
+
+    Method='hommel' is very slow for large arrays, since it requires the
+    evaluation of n partitions, where n is the number of p-values.
 
     there will be API changes.
 
@@ -123,11 +139,14 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
     ----------
 
     '''
+    import gc
     pvals = np.asarray(pvals)
     alphaf = alpha  # Notation ?
-    sortind = np.argsort(pvals)
-    pvals = pvals[sortind]
-    sortrevind = sortind.argsort()
+
+    if not is_sorted:
+        sortind = np.argsort(pvals)
+        pvals = np.take(pvals, sortind)
+
     ntests = len(pvals)
     alphacSidak = 1 - np.power((1. - alphaf), 1./ntests)
     alphacBonf = alphaf / float(ntests)
@@ -143,6 +162,7 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
         alphacSidak_all = 1 - np.power((1. - alphaf),
                                        1./np.arange(ntests, 0, -1))
         notreject = pvals > alphacSidak_all
+        del alphacSidak_all
 
         nr_index = np.nonzero(notreject)[0]
         if nr_index.size == 0:
@@ -152,9 +172,12 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
             notrejectmin = np.min(nr_index)
         notreject[notrejectmin:] = True
         reject = ~notreject
+        del notreject
+
         pvals_corrected_raw = 1 - np.power((1. - pvals),
                                            np.arange(ntests, 0, -1))
         pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)
+        del pvals_corrected_raw
 
     elif method.lower() in ['h', 'holm']:
         notreject = pvals > alphaf / np.arange(ntests, 0, -1)
@@ -168,6 +191,8 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
         reject = ~notreject
         pvals_corrected_raw = pvals * np.arange(ntests, 0, -1)
         pvals_corrected = np.maximum.accumulate(pvals_corrected_raw)
+        del pvals_corrected_raw
+        gc.collect()
 
     elif method.lower() in ['sh', 'simes-hochberg']:
         alphash = alphaf / np.arange(ntests, 0, -1)
@@ -178,9 +203,11 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
             reject[:rejectmax] = True
         pvals_corrected_raw = np.arange(ntests, 0, -1) * pvals
         pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
+        del pvals_corrected_raw
 
     elif method.lower() in ['ho', 'hommel']:
-        a=pvals.copy()
+        # we need a copy because we overwrite it in a loop
+        a = pvals.copy()
         for m in range(ntests, 1, -1):
             cim = np.min(m * pvals[-m:] / np.arange(1,m+1.))
             a[-m:] = np.maximum(a[-m:], cim)
@@ -191,19 +218,23 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
     elif method.lower() in ['fdr_bh', 'fdr_i', 'fdr_p', 'fdri', 'fdrp']:
         # delegate, call with sorted pvals
         reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,
-                                                 method='indep')
+                                                 method='indep',
+                                                 is_sorted=True)
     elif method.lower() in ['fdr_by', 'fdr_n', 'fdr_c', 'fdrn', 'fdrcorr']:
         # delegate, call with sorted pvals
         reject, pvals_corrected = fdrcorrection(pvals, alpha=alpha,
-                                                 method='n')
+                                                 method='n',
+                                                 is_sorted=True)
     elif method.lower() in ['fdr_tsbky', 'fdr_2sbky', 'fdr_twostage']:
         # delegate, call with sorted pvals
         reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,
-                                                         method='bky')[:2]
+                                                         method='bky',
+                                                         is_sorted=True)[:2]
     elif method.lower() in ['fdr_tsbh', 'fdr_2sbh']:
         # delegate, call with sorted pvals
         reject, pvals_corrected = fdrcorrection_twostage(pvals, alpha=alpha,
-                                                         method='bh')[:2]
+                                                         method='bh',
+                                                         is_sorted=True)[:2]
 
     elif method.lower() in ['fdr_gbs']:
         #adaptive stepdown in Gavrilov, Benjamini, Sarkar, Annals of Statistics 2009
@@ -217,24 +248,26 @@ def multipletests(pvals, alpha=0.05, method='hs', returnsorted=False):
         pvals_corrected_raw = np.maximum.accumulate(q) #up requirementd
 
         pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
+        del pvals_corrected_raw
         reject = pvals_corrected <= alpha
 
     else:
         raise ValueError('method not recognized')
 
-
     if not pvals_corrected is None: #not necessary anymore
         pvals_corrected[pvals_corrected>1] = 1
-    if returnsorted:
+    if is_sorted or returnsorted:
         return reject, pvals_corrected, alphacSidak, alphacBonf
     else:
-        if pvals_corrected is None:
-            return reject[sortrevind], pvals_corrected, alphacSidak, alphacBonf
-        else:
-            return reject[sortrevind], pvals_corrected[sortrevind], alphacSidak, alphacBonf
+        pvals_corrected_ = np.empty_like(pvals_corrected)
+        pvals_corrected_[sortind] = pvals_corrected
+        del pvals_corrected
+        reject_ = np.empty_like(reject)
+        reject_[sortind] = reject
+        return reject_, pvals_corrected_, alphacSidak, alphacBonf
 
-#TODO: rename drop 0 at end
-def fdrcorrection(pvals, alpha=0.05, method='indep'):
+
+def fdrcorrection(pvals, alpha=0.05, method='indep', is_sorted=False):
     '''pvalue correction for false discovery rate
 
     This covers Benjamini/Hochberg for independent or positively correlated and
@@ -275,9 +308,11 @@ def fdrcorrection(pvals, alpha=0.05, method='indep'):
     '''
     pvals = np.asarray(pvals)
 
-    pvals_sortind = np.argsort(pvals)
-    pvals_sorted = pvals[pvals_sortind]
-    sortrevind = pvals_sortind.argsort()
+    if not is_sorted:
+        pvals_sortind = np.argsort(pvals)
+        pvals_sorted = np.take(pvals, pvals_sortind)
+    else:
+        pvals_sorted = pvals  # alias
 
     if method in ['i', 'indep', 'p', 'poscorr']:
         ecdffactor = _ecdf(pvals_sorted)
@@ -296,11 +331,21 @@ def fdrcorrection(pvals, alpha=0.05, method='indep'):
 
     pvals_corrected_raw = pvals_sorted / ecdffactor
     pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
+    del pvals_corrected_raw
     pvals_corrected[pvals_corrected>1] = 1
-    return reject[sortrevind], pvals_corrected[sortrevind]
-    #return reject[pvals_sortind.argsort()]
+    if not is_sorted:
+        pvals_corrected_ = np.empty_like(pvals_corrected)
+        pvals_corrected_[pvals_sortind] = pvals_corrected
+        del pvals_corrected
+        reject_ = np.empty_like(reject)
+        reject_[pvals_sortind] = reject
+        return reject_, pvals_corrected_
+    else:
+        return reject, pvals_corrected
+
 
-def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False):
+def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False,
+                           is_sorted=False):
     '''(iterated) two stage linear step-up procedure with estimation of number of true
     hypotheses
 
@@ -352,6 +397,12 @@ def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False):
     TODO: What should be returned?
 
     '''
+    pvals = np.asarray(pvals)
+
+    if not is_sorted:
+        pvals_sortind = np.argsort(pvals)
+        pvals = np.take(pvals, pvals_sortind)
+
     ntests = len(pvals)
     if method == 'bky':
         fact = (1.+alpha)
@@ -363,18 +414,20 @@ def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False):
         raise ValueError("only 'bky' and 'bh' are available as method")
 
     alpha_stages = [alpha_prime]
-    rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_prime, method='indep')
+    rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_prime, method='indep',
+                                   is_sorted=True)
     r1 = rej.sum()
     if (r1 == 0) or (r1 == ntests):
         return rej, pvalscorr * fact, ntests - r1, alpha_stages
     ri_old = r1
 
-    while 1:
+    while True:
         ntests0 = 1.0 * ntests - ri_old
         alpha_star = alpha_prime * ntests / ntests0
         alpha_stages.append(alpha_star)
         #print ntests0, alpha_star
-        rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_star, method='indep')
+        rej, pvalscorr = fdrcorrection(pvals, alpha=alpha_star, method='indep',
+                                       is_sorted=True)
         ri = rej.sum()
         if (not iter) or ri == ri_old:
             break
@@ -389,4 +442,12 @@ def fdrcorrection_twostage(pvals, alpha=0.05, method='bky', iter=False):
     if method == 'bky':
         pvalscorr *= (1. + alpha)
 
-    return rej, pvalscorr, ntests - ri, alpha_stages
+    if not is_sorted:
+        pvalscorr_ = np.empty_like(pvalscorr)
+        pvalscorr_[pvals_sortind] = pvalscorr
+        del pvalscorr
+        reject = np.empty_like(rej)
+        reject[pvals_sortind] = rej
+        return reject, pvalscorr_, ntests - ri, alpha_stages
+    else:
+        return rej, pvalscorr, ntests - ri, alpha_stages
diff --git a/statsmodels/stats/multivariate_tools.py b/statsmodels/stats/multivariate_tools.py
new file mode 100644
index 00000000000..4743e1d403a
--- /dev/null
+++ b/statsmodels/stats/multivariate_tools.py
@@ -0,0 +1,251 @@
+'''Tools for multivariate analysis
+
+
+Author : Josef Perktold
+License : BSD-3
+
+
+
+TODO:
+
+- names of functions, currently just "working titles"
+
+'''
+
+
+
+import numpy as np
+
+# temporarily here, used in return
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+
+def partial_project(endog, exog):
+    '''helper function to get linear projection or partialling out of variables
+
+    endog variables are projected on exog variables
+
+    Parameters
+    ----------
+    endog : ndarray
+        array of variables where the effect of exog is partialled out.
+    exog : ndarray
+        array of variables on which the endog variables are projected.
+
+    Returns
+    -------
+    res : instance of Bunch with
+
+        - params : OLS parameter estimates from projection of endog on exog
+        - fittedvalues : predicted values of endog given exog
+        - resid : residual of the regression, values of endog with effect of
+          exog partialled out
+
+    Notes
+    -----
+    This is no-frills mainly for internal calculations, no error checking or
+    array conversion is performed, at least for now.
+
+    '''
+    x1, x2 = endog, exog
+    params = np.linalg.pinv(x2).dot(x1)
+    predicted = x2.dot(params)
+    residual = x1 - predicted
+    res = Bunch(params=params,
+                fittedvalues=predicted,
+                resid=residual)
+
+    return res
+
+
+
+def cancorr(x1, x2, demean=True, standardize=False):
+    '''canonical correlation coefficient beween 2 arrays
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays, 2_D
+        two 2-dimensional data arrays, observations in rows, variables in columns
+    demean : bool
+         If demean is true, then the mean is subtracted from each variable
+    standardize : bool
+         If standardize is true, then each variable is demeaned and divided by
+         its standard deviation. Rescaling does not change the canonical
+         correlation coefficients.
+
+    Returns
+    -------
+    ccorr : ndarray, 1d
+        canonical correlation coefficients, sorted from largest to smallest.
+        Note, that these are the square root of the eigenvalues.
+
+    Notes
+    -----
+    This is a helper function for other statistical functions. It only
+    calculates the canonical correlation coefficients and does not do a full
+    canoncial correlation analysis
+
+    The canonical correlation coefficient is calculated with the generalized
+    matrix inverse and does not raise an exception if one of the data arrays
+    have less than full column rank.
+
+    See Also
+    --------
+    cc_ranktest
+    cc_stats
+    CCA not yet
+
+    '''
+    #x, y = x1, x2
+    if demean or standardize:
+        x1 = (x1 - x1.mean(0))
+        x2 = (x2 - x2.mean(0))
+
+    if standardize:
+        #std doesn't make a difference to canonical correlation coefficients
+        x1 /= x1.std(0)
+        x2 /= x2.std(0)
+
+    t1 = np.linalg.pinv(x1).dot(x2)
+    t2 = np.linalg.pinv(x2).dot(x1)
+    m = t1.dot(t2)
+    cc = np.sqrt(np.linalg.eigvals(m))
+    cc.sort()
+    return cc[::-1]
+
+
+def cc_ranktest(x1, x2, demean=True, fullrank=False):
+    '''rank tests based on smallest canonical correlation coefficients
+
+    Anderson canonical correlations test (LM test) and
+    Cragg-Donald test (Wald test)
+    Assumes homoskedasticity and independent observations, overrejects if
+    there is heteroscedasticity or autocorrelation.
+
+    The Null Hypothesis is that the rank is k - 1, the alternative hypothesis
+    is that the rank is at least k.
+
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays, 2_D
+        two 2-dimensional data arrays, observations in rows, variables in columns
+    demean : bool
+         If demean is true, then the mean is subtracted from each variable.
+    fullrank : bool
+         If true, then only the test that the matrix has full rank is returned.
+         If false, the test for all possible ranks are returned. However, no
+         the p-values are not corrected for the multiplicity of tests.
+
+    Returns
+    -------
+    value : float
+        value of the test statistic
+    p-value : float
+        p-value for the test Null Hypothesis tha the smallest canonical
+        correlation coefficient is zero. based on chi-square distribution
+    df : int
+        degrees of freedom for thechi-square distribution in the hypothesis test
+    ccorr : ndarray, 1d
+        All canonical correlation coefficients sorted from largest to smallest.
+
+    Notes
+    -----
+    Degrees of freedom for the distribution of the test statistic are based on
+    number of columns of x1 and x2 and not on their matrix rank.
+    (I'm not sure yet what the interpretation of the test is if x1 or x2 are of
+    reduced rank.)
+
+    See Also
+    --------
+    cancorr
+    cc_stats
+
+    '''
+
+    from scipy import stats
+
+    nobs1, k1 = x1.shape
+    nobs2, k2 = x2.shape
+
+    cc = cancorr(x1, x2, demean=demean)
+    cc2 = cc * cc
+    if fullrank:
+        df = np.abs(k1 - k2) + 1
+        value = nobs1 * cc2[-1]
+        w_value = nobs1 * (cc2[-1] / (1. - cc2[-1]))
+        return value, stats.chi2.sf(value, df), df, cc, w_value, stats.chi2.sf(w_value, df)
+    else:
+        r = np.arange(min(k1, k2))[::-1]
+        df = (k1 - r) * (k2 - r)
+        values = nobs1 * cc2[::-1].cumsum()
+        w_values = nobs1 * (cc2 / (1. - cc2))[::-1].cumsum()
+        return values, stats.chi2.sf(values, df), df, cc, w_values, stats.chi2.sf(w_values, df)
+
+
+def cc_stats(x1, x2, demean=True):
+    '''MANOVA statistics based on canonical correlation coefficient
+
+    Calculates Pillai's Trace, Wilk's Lambda, Hotelling's Trace and
+    Roy's Largest Root.
+
+    Parameters
+    ----------
+    x1, x2 : ndarrays, 2_D
+        two 2-dimensional data arrays, observations in rows, variables in columns
+    demean : bool
+         If demean is true, then the mean is subtracted from each variable.
+
+    Returns
+    -------
+    res : dict
+        Dictionary containing the test statistics.
+
+    Notes
+    -----
+
+    same as `canon` in Stata
+
+    missing: F-statistics and p-values
+
+    TODO: should return a results class instead
+    produces nans sometimes, singular, perfect correlation of x1, x2 ?
+
+    '''
+
+    nobs1, k1 = x1.shape  # endogenous ?
+    nobs2, k2 = x2.shape
+    cc = cancorr(x1, x2, demean=demean)
+    cc2 = cc**2
+    lam = (cc2 / (1 - cc2))  # what if max cc2 is 1 ?
+    # Problem: ccr might not care if x1 or x2 are reduced rank,
+    #          but df will depend on rank
+    df_model = k1 * k2  # df_hypothesis (we do not include mean in x1, x2)
+    df_resid = k1 * (nobs1 - k2 - demean)
+    s = min(df_model, k1)
+    m = 0.5 * (df_model - k1)
+    n = 0.5 * (df_resid - k1 - 1)
+
+    df1 = k1 * df_model
+    df2 = k2
+
+
+    pt_value = cc2.sum()    # Pillai's trace
+    wl_value = np.product(1 / (1 + lam))   # Wilk's Lambda
+    ht_value = lam.sum()    # Hotelling's Trace
+    rm_value = lam.max()    # Roy's largest root
+    #from scipy import stats
+    # what's the distribution, the test statistic ?
+    res = {}
+    res['canonical correlation coefficient'] = cc
+    res['eigenvalues'] = lam
+    res["Pillai's Trace"] = pt_value
+    res["Wilk's Lambda"] = wl_value
+    res["Hotelling's Trace"] = ht_value
+    res["Roy's Largest Root"] = rm_value
+    res['df_resid'] = df_resid
+    res['df_m'] = m
+    return res
diff --git a/statsmodels/stats/outliers_influence.py b/statsmodels/stats/outliers_influence.py
index 9ebc71bcd82..040aeef2d84 100644
--- a/statsmodels/stats/outliers_influence.py
+++ b/statsmodels/stats/outliers_influence.py
@@ -6,7 +6,7 @@
 Author: Josef Perktold
 License: BSD-3
 """
-
+from statsmodels.compat.python import lzip
 from collections import defaultdict
 import numpy as np
 
@@ -650,7 +650,7 @@ def summary_table(self, float_fmt="%6.3f"):
                       ("ext.stud.\nresidual", self.resid_studentized_external),
                       ('dffits', self.dffits[0])
                       ]
-        colnames, data = zip(*table_raw) #unzip
+        colnames, data = lzip(*table_raw) #unzip
         data = np.column_stack(data)
         self.table_data = data
         from statsmodels.iolib.table import SimpleTable, default_html_fmt
@@ -721,7 +721,7 @@ def summary_table(res, alpha=0.05):
                                   ])
 
 
-    #colnames, data = zip(*table_raw) #unzip
+    #colnames, data = lzip(*table_raw) #unzip
     data = table_sm
     ss2 = ['Obs', 'Dep Var\nPopulation', 'Predicted\nValue', 'Std Error\nMean Predict', 'Mean ci\n95% low', 'Mean ci\n95% upp', 'Predict ci\n95% low', 'Predict ci\n95% upp', 'Residual', 'Std Error\nResidual', 'Student\nResidual', "Cook's\nD"]
     colnames = ss2
diff --git a/statsmodels/stats/power.py b/statsmodels/stats/power.py
index 7e9c61afa06..0c9c22c2a34 100644
--- a/statsmodels/stats/power.py
+++ b/statsmodels/stats/power.py
@@ -30,7 +30,8 @@
 
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import iteritems
 import numpy as np
 from scipy import stats, optimize
 from statsmodels.tools.rootfinding import brentq_expanding
@@ -208,7 +209,7 @@ def solve_power(self, **kwds):
         #TODO: maybe use explicit kwds,
         #    nicer but requires inspect? and not generic across tests
         #    I'm duplicating this in the subclass to get informative docstring
-        key = [k for k,v in kwds.iteritems() if v is None]
+        key = [k for k,v in iteritems(kwds) if v is None]
         #print kwds, key;
         if len(key) != 1:
             raise ValueError('need exactly one keyword that is None')
@@ -236,7 +237,7 @@ def func(x):
             start_value = self.start_ttp[key]
         except KeyError:
             start_value = 0.9
-            print 'Warning: using default start_value for', key
+            print('Warning: using default start_value for {0}'.format(key))
 
         fit_kwds = self.start_bqexp[key]
         fit_res = []
@@ -274,8 +275,9 @@ def func(x):
 
         if not success == 1:
             import warnings
-            from statsmodels.tools.sm_exceptions import ConvergenceWarning
-            warnings.warn('finding solution failed', ConvergenceWarning)
+            from statsmodels.tools.sm_exceptions import (ConvergenceWarning,
+                convergence_doc)
+            warnings.warn(convergence_doc, ConvergenceWarning)
 
         #attach fit_res, for reading only, should be needed only for debugging
         fit_res.insert(0, success)
@@ -925,7 +927,7 @@ def func(x):
 
         val, r = optimize.brentq(func, 1e-8, 1-1e-8, full_output=True)
         if not r.converged:
-            print r
+            print(r)
         return val
 
 
diff --git a/statsmodels/stats/proportion.py b/statsmodels/stats/proportion.py
index 236d31afbf3..9071fdd6bac 100644
--- a/statsmodels/stats/proportion.py
+++ b/statsmodels/stats/proportion.py
@@ -6,7 +6,7 @@
 Author: Josef Perktold
 License: BSD-3
 """
-
+from statsmodels.compat.python import lzip, range
 import numpy as np
 from scipy import stats, optimize
 
@@ -249,7 +249,7 @@ def _power_ztost(mean_low, var_low, mean_upp, var_upp, mean_alt, var_alt,
     #print mean_upp, np.sqrt(var_upp), crit, var_upp
     if np.any(k_low > k_upp):   #vectorize
         import warnings
-        warnings.warn("no overlap, power is zero")
+        warnings.warn("no overlap, power is zero", UserWarning)
     std_alt = np.sqrt(var_alt)
     z_low = (k_low - mean_alt - continuity[0] * 0.5 / nobs) / std_alt
     z_upp = (k_upp - mean_alt + continuity[1] * 0.5 / nobs) / std_alt
@@ -729,8 +729,8 @@ def proportions_chisquare_allpairs(count, nobs, multitest_method='hs'):
     -----
     Yates continuity correction is not available.
     '''
-    #all_pairs = map(list, zip(*np.triu_indices(4, 1)))
-    all_pairs = zip(*np.triu_indices(4, 1))
+    #all_pairs = lmap(list, lzip(*np.triu_indices(4, 1)))
+    all_pairs = lzip(*np.triu_indices(len(count), 1))
     pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)])[1]
                for pair in all_pairs]
     return AllPairsResults(pvals, all_pairs, multitest_method=multitest_method)
@@ -779,7 +779,7 @@ def proportions_chisquare_pairscontrol(count, nobs, value=None,
     '''
     if (value is not None) or (not alternative in ['two-sided', '2s']):
         raise NotImplementedError
-    #all_pairs = map(list, zip(*np.triu_indices(4, 1)))
+    #all_pairs = lmap(list, lzip(*np.triu_indices(4, 1)))
     all_pairs = [(0, k) for k in range(1, len(count))]
     pvals = [proportions_chisquare(count[list(pair)], nobs[list(pair)],
                                    #alternative=alternative)[1]
diff --git a/statsmodels/stats/sandwich_covariance.py b/statsmodels/stats/sandwich_covariance.py
index 48ee8e3e168..408465f387b 100644
--- a/statsmodels/stats/sandwich_covariance.py
+++ b/statsmodels/stats/sandwich_covariance.py
@@ -101,7 +101,7 @@
 Statistics 90, no. 3 (2008): 414–427.
 
 """
-
+from statsmodels.compat.python import range
 import numpy as np
 
 from statsmodels.tools.grouputils import Group
@@ -216,6 +216,35 @@ def cov_hc3(results):
 
 #---------------------------------------
 
+def _get_sandwich_arrays(results):
+    """Helper function to get scores from results
+
+    Parameters
+
+    """
+
+    if isinstance(results, tuple):
+        # assume we have jac and hessian_inv
+        jac, hessian_inv = results
+        jac = np.asarray(jac)
+        hessian_inv = np.asarray(hessian_inv)
+    elif hasattr(results, 'model'):
+        if hasattr(results, '_results'):
+            # remove wrapper
+            results = results._results
+        # assume we have a results instance
+        if hasattr(results.model, 'jac'):
+            xu = results.model.jac(results.params)
+        else:
+            xu = results.model.exog * results.resid[:, None]
+
+        hessian_inv = np.asarray(results.normalized_cov_params)
+    else:
+        raise ValueError('need either tuple of (jac, hessian_inv) or results' +
+                         'instance')
+
+    return xu, hessian_inv
+
 
 def _HCCM1(results, scale):
     '''
@@ -245,7 +274,7 @@ def _HCCM1(results, scale):
                    np.dot(scale, results.model.pinv_wexog.T))
     return H
 
-def _HCCM2(results, scale):
+def _HCCM2(hessian_inv, scale):
     '''
     sandwich with (X'X)^(-1) * scale * (X'X)^(-1)
 
@@ -268,7 +297,7 @@ def _HCCM2(results, scale):
     if scale.ndim == 1:
         scale = scale[:,None]
 
-    xxi = results.normalized_cov_params
+    xxi = hessian_inv
     H = np.dot(np.dot(xxi, scale), xxi.T)
     return H
 
@@ -399,8 +428,8 @@ def group_sums(x, group):
     #TODO: remove this, already copied to tools/grouputils
     '''
 
-    #TODO: transpose return in grou_sum, need test coverage first
-    return np.array([np.bincount(group, weights=x[:,col])
+    #TODO: transpose return in group_sum, need test coverage first
+    return np.array([np.bincount(group, weights=x[:, col])
                             for col in range(x.shape[1])])
 
 
@@ -494,16 +523,23 @@ def cov_cluster(results, group, use_correction=True):
 
     '''
     #TODO: currently used version of groupsums requires 2d resid
-    xu = results.model.exog * results.resid[:, None]
+    xu, hessian_inv = _get_sandwich_arrays(results)
+
+    if not hasattr(group, 'dtype') or group.dtype != np.dtype('int'):
+        clusters, group = np.unique(group, return_inverse=True)
+    else:
+        clusters = np.unique(group)
+
     scale = S_crosssection(xu, group)
 
-    nobs, k_vars = results.model.exog.shape
-    n_groups = len(np.unique(group)) #replace with stored group attributes if available
+    nobs, k_params = xu.shape
+    n_groups = len(clusters) #replace with stored group attributes if available
 
-    cov_c = _HCCM2(results, scale)
+    cov_c = _HCCM2(hessian_inv, scale)
 
     if use_correction:
-        cov_c *= n_groups / (n_groups - 1.) * ((nobs-1.) / float(nobs - k_vars))
+        cov_c *= (n_groups / (n_groups - 1.) *
+                  ((nobs-1.) / float(nobs - k_params)))
 
     return cov_c
 
@@ -593,14 +629,14 @@ def cov_white_simple(results, use_correction=True):
         with small sample corrections
 
     '''
-    xu = results.model.exog * results.resid[:, None]
+    xu, hessian_inv = _get_sandwich_arrays(results)
     sigma = S_white_simple(xu)
 
-    cov_w = _HCCM2(results, sigma)  #add bread to sandwich
+    cov_w = _HCCM2(hessian_inv, sigma)  #add bread to sandwich
 
     if use_correction:
-        nobs, k_vars = results.model.exog.shape
-        cov_w *= nobs / float(nobs - k_vars)
+        nobs, k_params = xu.shape
+        cov_w *= nobs / float(nobs - k_params)
 
     return cov_w
 
@@ -639,14 +675,14 @@ def cov_hac_simple(results, nlags=None, weights_func=weights_bartlett,
     options might change when other kernels besides Bartlett are available.
 
     '''
-    xu = results.model.exog * results.resid[:, None]
+    xu, hessian_inv = _get_sandwich_arrays(results)
     sigma = S_hac_simple(xu, nlags=nlags, weights_func=weights_func)
 
-    cov_hac = _HCCM2(results, sigma)
+    cov_hac = _HCCM2(hessian_inv, sigma)
 
     if use_correction:
-        nobs, k_vars = results.model.exog.shape
-        cov_hac *= nobs / float(nobs - k_vars)
+        nobs, k_params = xu.shape
+        cov_hac *= nobs / float(nobs - k_params)
 
     return cov_hac
 
@@ -749,21 +785,22 @@ def cov_nw_panel(results, nlags, groupidx, weights_func=weights_bartlett,
     else:
         weights = weights_func(nlags)
 
-    xw = (results.model.exog * results.resid[:,None])
+    xu, hessian_inv = _get_sandwich_arrays(results)
 
-    S_hac = S_nw_panel(xw, weights, groupidx)
-    cov_hac = _HCCM2(results, S_hac)
+    S_hac = S_nw_panel(xu, weights, groupidx)
+    cov_hac = _HCCM2(hessian_inv, S_hac)
     if use_correction:
-        nobs, k_vars = results.model.exog.shape
+        nobs, k_params = xu.shape
         if use_correction == 'hac':
-            cov_hac *= nobs / float(nobs - k_vars)
+            cov_hac *= nobs / float(nobs - k_params)
         elif use_correction in ['c', 'clu', 'cluster']:
             n_groups = len(groupidx)
             cov_hac *= n_groups / (n_groups - 1.)
-            cov_hac *= ((nobs-1.) / float(nobs - k_vars))
+            cov_hac *= ((nobs-1.) / float(nobs - k_params))
 
     return cov_hac
 
+
 def cov_nw_groupsum(results, nlags, time, weights_func=weights_bartlett,
                  use_correction=0):
     '''Driscoll and Kraay Panel robust covariance matrix
@@ -824,19 +861,19 @@ def cov_nw_groupsum(results, nlags, time, weights_func=weights_bartlett,
 
     '''
 
-    xw = (results.model.exog * results.resid[:,None])
+    xu, hessian_inv = _get_sandwich_arrays(results)
 
     #S_hac = S_nw_panel(xw, weights, groupidx)
-    S_hac = S_hac_groupsum(xw, time, nlags=nlags, weights_func=weights_func)
-    cov_hac = _HCCM2(results, S_hac)
+    S_hac = S_hac_groupsum(xu, time, nlags=nlags, weights_func=weights_func)
+    cov_hac = _HCCM2(hessian_inv, S_hac)
     if use_correction:
-        nobs, k_vars = results.model.exog.shape
+        nobs, k_params = xu.shape
         if use_correction == 'hac':
-            cov_hac *= nobs / float(nobs - k_vars)
+            cov_hac *= nobs / float(nobs - k_params)
         elif use_correction in ['c', 'cluster']:
             n_groups = len(np.unique(time))
             cov_hac *= n_groups / (n_groups - 1.)
-            cov_hac *= ((nobs-1.) / float(nobs - k_vars))
+            cov_hac *= ((nobs-1.) / float(nobs - k_params))
 
     return cov_hac
 
diff --git a/statsmodels/stats/stattools.py b/statsmodels/stats/stattools.py
index d8cc1cc89c2..c6c9e2a8a6b 100644
--- a/statsmodels/stats/stattools.py
+++ b/statsmodels/stats/stattools.py
@@ -10,8 +10,8 @@
 import numpy as np
 
 
-#TODO: these are pretty straightforward but they should be tested
-def durbin_watson(resids):
+# TODO: these are pretty straightforward but they should be tested
+def durbin_watson(resids, axis=0):
     """
     Calculates the Durbin-Watson statistic
 
@@ -20,15 +20,33 @@ def durbin_watson(resids):
     resids : array-like
 
     Returns
-        --------
-    Durbin Watson statistic.  This is defined as
-    sum_(t=2)^(T)((e_t - e_(t-1))^(2))/sum_(t=1)^(T)e_t^(2)
+    --------
+    dw : float, array-like
+
+    The Durbin-Watson statistic.
+
+    Notes
+    -----
+    The null hypothesis of the test is that there is no serial correlation.
+    The Durbin-Watson test statistics is defined as:
+
+    .. math::
+
+       \sum_{t=2}^T((e_t - e_{t-1})^2)/\sum_{t=1}^Te_t^2
+
+    The test statistic is approximately equal to 2*(1-r) where ``r`` is the
+    sample autocorrelation of the residuals. Thus, for r == 0, indicating no
+    serial correlation, the test statistic equals 2. This statistic will
+    always be between 0 and 4. The closer to 0 the statistic, the more
+    evidence for positive serial correlation. The closer to 4, the more
+    evidence for negative serial correlation.
     """
-    resids=np.asarray(resids)
-    diff_resids = np.diff(resids, 1)
-    dw = np.dot(diff_resids, diff_resids) / np.dot(resids, resids)
+    resids = np.asarray(resids)
+    diff_resids = np.diff(resids, 1, axis=axis)
+    dw = np.sum(diff_resids**2, axis=axis) / np.sum(resids**2, axis=axis)
     return dw
 
+
 def omni_normtest(resids, axis=0):
     """
     Omnibus test for normality
@@ -48,23 +66,24 @@ def omni_normtest(resids, axis=0):
     resids = np.asarray(resids)
     n = resids.shape[axis]
     if n < 8:
+        from warnings import warn
+
+        warn("omni_normtest is not valid with less than 8 observations; %i "
+             "samples were given." % int(n))
         return np.nan, np.nan
-        return_shape = list(resids.shape)
-        del return_shape[axis]
-        return np.nan * np.zeros(return_shape), np.nan * np.zeros(return_shape)
-        raise ValueError(
-            "skewtest is not valid with less than 8 observations; %i samples"
-            " were given." % int(n))
 
     return stats.normaltest(resids, axis=axis)
 
-def jarque_bera(resids):
+
+def jarque_bera(resids, axis=0):
     """
     Calculate residual skewness, kurtosis, and do the JB test for normality
 
     Parameters
     -----------
     resids : array-like
+    axis : int, optional
+        Default is 0
 
     Returns
     -------
@@ -81,12 +100,335 @@ def jarque_bera(resids):
     """
     resids = np.asarray(resids)
     # Calculate residual skewness and kurtosis
-    skew = stats.skew(resids)
-    kurtosis = 3 + stats.kurtosis(resids)
+    skew = stats.skew(resids, axis=axis)
+    kurtosis = 3 + stats.kurtosis(resids, axis=axis)
 
     # Calculate the Jarque-Bera test for normality
-    JB = (resids.shape[0] / 6.) * (skew**2 + (1 / 4.) * (kurtosis-3)**2)
-    JBpv = stats.chi2.sf(JB,2)
+    n = resids.shape[axis]
+    jb = (n / 6.) * (skew**2 + (1 / 4.) * (kurtosis - 3)**2)
+    jb_pv = stats.chi2.sf(jb, 2)
+
+    return jb, jb_pv, skew, kurtosis
+
+
+def robust_skewness(y, axis=0):
+    """
+    Calculates the four skewness measures in Kim & White
+
+    Parameters
+    ----------
+    y : array-like
+
+    axis : int or None, optional
+        Axis along which the skewness measures are computed.  If `None`, the
+        entire array is used.
+
+    Returns
+    -------
+    sk1 : ndarray
+          The standard skewness estimator.
+    sk2 : ndarray
+          Skewness estimator based on quartiles.
+    sk3 : ndarray
+          Skewness estimator based on mean-median difference, standardized by
+          absolute deviation.
+    sk4 : ndarray
+          Skewness estimator based on mean-median difference, standardized by
+          standard deviation.
+
+    Notes
+    -----
+    The robust skewness measures are defined
+
+    .. math ::
+
+        SK_{2}=\\frac{\\left(q_{.75}-q_{.5}\\right)
+        -\\left(q_{.5}-q_{.25}\\right)}{q_{.75}-q_{.25}}
+
+    .. math ::
+
+        SK_{3}=\\frac{\\mu-\\hat{q}_{0.5}}
+        {\\hat{E}\\left[\\left|y-\\hat{\\mu}\\right|\\right]}
+
+    .. math ::
+
+        SK_{4}=\\frac{\\mu-\\hat{q}_{0.5}}{\\hat{\\sigma}}
+
+    .. [1] Tae-Hwan Kim and Halbert White, "On more robust estimation of
+    skewness and kurtosis," Finance Research Letters, vol. 1, pp. 56-73,
+    March 2004.
+    """
+
+    if axis is None:
+        y = y.ravel()
+        axis = 0
+
+    y = np.sort(y, axis)
+
+    q1, q2, q3 = np.percentile(y, [25.0, 50.0, 75.0], axis=axis)
+
+    mu = y.mean(axis)
+    shape = (y.size,)
+    if axis is not None:
+        shape = list(mu.shape)
+        shape.insert(axis, 1)
+        shape = tuple(shape)
+
+    mu_b = np.reshape(mu, shape)
+    q2_b = np.reshape(q2, shape)
+
+    sigma = np.mean(((y - mu_b)**2), axis)
+
+    sk1 = stats.skew(y, axis=axis)
+    sk2 = (q1 + q3 - 2.0 * q2) / (q3 - q1)
+    sk3 = (mu - q2) / np.mean(abs(y - q2_b), axis=axis)
+    sk4 = (mu - q2) / sigma
+
+    return sk1, sk2, sk3, sk4
+
+
+def _kr3(y, alpha=5.0, beta=50.0):
+    """
+    KR3 estimator from Kim & White
+
+    Parameters
+    ----------
+    y : array-like, 1-d
+    alpha : float, optional
+        Lower cut-off for measuring expectation in tail.
+    beta :  float, optional
+        Lower cut-off for measuring expectation in center.
+
+    Returns
+    -------
+    kr3 : float
+        Robust kurtosis estimator based on standardized lower- and upper-tail
+        expected values
+
+    Notes
+    -----
+    .. [1] Tae-Hwan Kim and Halbert White, "On more robust estimation of
+    skewness and kurtosis," Finance Research Letters, vol. 1, pp. 56-73,
+    March 2004.
+    """
+    perc = (alpha, 100.0 - alpha, beta, 100.0 - beta)
+    lower_alpha, upper_alpha, lower_beta, upper_beta = np.percentile(y, perc)
+    l_alpha = np.mean(y[y < lower_alpha])
+    u_alpha = np.mean(y[y > upper_alpha])
+
+    l_beta = np.mean(y[y < lower_beta])
+    u_beta = np.mean(y[y > upper_beta])
+
+    return (u_alpha - l_alpha) / (u_beta - l_beta)
+
+
+def expected_robust_kurtosis(ab=(5.0, 50.0), dg=(2.5, 25.0)):
+    """
+    Calculates the expected value of the robust kurtosis measures in Kim and
+    White assuming the data are normally distributed.
+
+    Parameters
+    ----------
+    ab: iterable, optional
+        Contains 100*(alpha, beta) in the kr3 measure where alpha is the tail
+        quantile cut-off for measuring the extreme tail and beta is the central
+        quantile cutoff for the standardization of the measure
+    db: iterable, optional
+        Contains 100*(delta, gamma) in the kr4 measure where delta is the tail
+        quantile for measuring extreme values and gamma is the central quantile
+        used in the the standardization of the measure
+
+    Returns
+    -------
+    ekr: array, 4-element
+        Contains the expected values of the 4 robust kurtosis measures
+
+    Notes
+    -----
+    See `robust_kurtosis` for definitions of the robust kurtosis measures
+    """
+
+    alpha, beta = ab
+    delta, gamma = dg
+    expected_value = np.zeros(4)
+    ppf = stats.norm.ppf
+    pdf = stats.norm.pdf
+    q1, q2, q3, q5, q6, q7 = ppf(np.array((1.0, 2.0, 3.0, 5.0, 6.0, 7.0)) / 8)
+    expected_value[0] = 3
+
+    expected_value[1] = ((q7 - q5) + (q3 - q1)) / (q6 - q2)
+
+    q_alpha, q_beta = ppf(np.array((alpha / 100.0, beta / 100.0)))
+    expected_value[2] = (2 * pdf(q_alpha) / alpha) / (2 * pdf(q_beta) / beta)
+
+    q_delta, q_gamma = ppf(np.array((delta / 100.0, gamma / 100.0)))
+    expected_value[3] = (-2.0 * q_delta) / (-2.0 * q_gamma)
+
+    return expected_value
+
+
+def robust_kurtosis(y, axis=0, ab=(5.0, 50.0), dg=(2.5, 25.0), excess=True):
+    """
+    Calculates the four kurtosis measures in Kim & White
+
+    Parameters
+    ----------
+    y : array-like
+    axis : int or None, optional
+        Axis along which the kurtoses are computed.  If `None`, the
+        entire array is used.
+    ab: iterable, optional
+        Contains 100*(alpha, beta) in the kr3 measure where alpha is the tail
+        quantile cut-off for measuring the extreme tail and beta is the central
+        quantile cutoff for the standardization of the measure
+    db: iterable, optional
+        Contains 100*(delta, gamma) in the kr4 measure where delta is the tail
+        quantile for measuring extreme values and gamma is the central quantile
+        used in the the standardization of the measure
+    excess : bool, optional
+        If true (default), computed values are excess of those for a standard
+        normal distribution.
+
+    Returns
+    -------
+    kr1 : ndarray
+          The standard kurtosis estimator.
+    kr2 : ndarray
+          Kurtosis estimator based on octiles.
+    kr3 : ndarray
+          Kurtosis estimators based on exceedence expectations.
+    kr4 : ndarray
+          Kurtosis measure based on the spread between high and low quantiles.
+
+    Notes
+    -----
+    The robust kurtosis measures are defined
+
+    .. math::
+
+        KR_{2}=\\frac{\\left(\\hat{q}_{.875}-\\hat{q}_{.625}\\right)
+        +\\left(\\hat{q}_{.375}-\\hat{q}_{.125}\\right)}
+        {\\hat{q}_{.75}-\\hat{q}_{.25}}
+
+    .. math::
+
+        KR_{3}=\\frac{\\hat{E}\\left(y|y>\\hat{q}_{1-\\alpha}\\right)
+        -\\hat{E}\\left(y|y<\\hat{q}_{\\alpha}\\right)}
+        {\\hat{E}\\left(y|y>\\hat{q}_{1-\\beta}\\right)
+        -\\hat{E}\\left(y|y<\\hat{q}_{\\beta}\\right)}
+
+    .. math::
+
+        KR_{4}=\\frac{\\hat{q}_{1-\\delta}-\\hat{q}_{\\delta}}
+        {\\hat{q}_{1-\\gamma}-\\hat{q}_{\\gamma}}
+
+    where :math:`\\hat{q}_{p}` is the estimated quantile at :math:`p`.
+
+    .. [1] Tae-Hwan Kim and Halbert White, "On more robust estimation of
+    skewness and kurtosis," Finance Research Letters, vol. 1, pp. 56-73,
+    March 2004.
+    """
+    if (axis is None or
+            (y.squeeze().ndim == 1 and y.ndim != 1)):
+        y = y.ravel()
+        axis = 0
+
+    alpha, beta = ab
+    delta, gamma = dg
+
+    perc = (12.5, 25.0, 37.5, 62.5, 75.0, 87.5,
+            delta, 100.0 - delta, gamma, 100.0 - gamma)
+    e1, e2, e3, e5, e6, e7, fd, f1md, fg, f1mg = np.percentile(y, perc,
+                                                               axis=axis)
 
-    return JB, JBpv, skew, kurtosis
+    expected_value = expected_robust_kurtosis(ab, dg) if excess else np.zeros(4)
+
+    kr1 = stats.kurtosis(y, axis, False) - expected_value[0]
+    kr2 = ((e7 - e5) + (e3 - e1)) / (e6 - e2) - expected_value[1]
+    if y.ndim == 1:
+        kr3 = _kr3(y, alpha, beta)
+    else:
+        kr3 = np.apply_along_axis(_kr3, axis, y, alpha, beta)
+    kr3 -= expected_value[2]
+    kr4 = (f1md - fd) / (f1mg - fg) - expected_value[3]
+    return kr1, kr2, kr3, kr4
+
+
+def _medcouple_1d(y):
+    """
+    Calculates the medcouple robust measure of skew.
+
+    Parameters
+    ----------
+    y : array-like, 1-d
+
+    Returns
+    -------
+    mc : float
+        The medcouple statistic
+
+    Notes
+    -----
+    The current algorithm requires a O(N**2) memory allocations, and so may
+    not work for very large arrays (N>10000).
+
+    .. [1] M. Huberta and E. Vandervierenb, "An adjusted boxplot for skewed
+    distributions" Computational Statistics & Data Analysis, vol. 52,
+    pp. 5186-5201, August 2008.
+    """
+
+    # Parameter changes the algorithm to the slower for large n
+
+    y = np.squeeze(np.asarray(y))
+    if y.ndim != 1:
+        raise ValueError("y must be squeezable to a 1-d array")
+
+    y = np.sort(y)
+
+    n = y.shape[0]
+    if n % 2 == 0:
+        mf = (y[n // 2 - 1] + y[n // 2]) / 2
+    else:
+        mf = y[(n - 1) // 2]
+
+    z = y - mf
+    lower = z[z <= 0.0]
+    upper = z[z >= 0.0]
+    upper = upper[:, None]
+    standardization = upper - lower
+    is_zero = np.logical_and(lower == 0.0, upper == 0.0)
+    standardization[is_zero] = np.inf
+    spread = upper + lower
+    return np.median(spread / standardization)
+
+
+def medcouple(y, axis=0):
+    """
+    Calculates the medcouple robust measure of skew.
+
+    Parameters
+    ----------
+    y : array-like
+    axis : int or None, optional
+        Axis along which the medcouple statistic is computed.  If `None`, the
+        entire array is used.
+
+    Returns
+    -------
+    mc : ndarray
+        The medcouple statistic with the same shape as `y`, with the specified
+        axis removed.
+
+    Notes
+    -----
+    The current algorithm requires a O(N**2) memory allocations, and so may
+    not work for very large arrays (N>10000).
+
+    .. [1] M. Huberta and E. Vandervierenb, "An adjusted boxplot for skewed
+    distributions" Computational Statistics & Data Analysis, vol. 52,
+    pp. 5186-5201, August 2008.
+    """
+    if axis is None:
+        return _medcouple_1d(y.ravel())
 
+    return np.apply_along_axis(_medcouple_1d, axis, y)
diff --git a/statsmodels/stats/tabledist.py b/statsmodels/stats/tabledist.py
index 58db1a874c2..60ea4a36588 100644
--- a/statsmodels/stats/tabledist.py
+++ b/statsmodels/stats/tabledist.py
@@ -15,7 +15,8 @@
 
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import range
 import numpy as np
 from scipy.interpolate import interp1d, interp2d, Rbf
 from statsmodels.tools.decorators import cache_readonly
@@ -287,33 +288,33 @@ def crit3(self, prob, n):
 
 
     lf = TableDist(alpha, size, crit_lf)
-    print lf.prob(0.166, 20), 'should be:', 0.15
-    print
-    print lf.crit2(0.15, 20), 'should be:', 0.166, 'interp2d bad'
-    print lf.crit(0.15, 20), 'should be:', 0.166, 'two 1d'
-    print lf.crit3(0.15, 20), 'should be:', 0.166, 'Rbf'
-    print
-    print lf.crit2(0.17, 20), 'should be in:', (.159, .166), 'interp2d bad'
-    print lf.crit(0.17, 20), 'should be in:', (.159, .166), 'two 1d'
-    print lf.crit3(0.17, 20), 'should be in:', (.159, .166), 'Rbf'
-    print
-    print lf.crit2(0.19, 20), 'should be in:', (.159, .166), 'interp2d bad'
-    print lf.crit(0.19, 20), 'should be in:', (.159, .166), 'two 1d'
-    print lf.crit3(0.19, 20), 'should be in:', (.159, .166), 'Rbf'
-    print
-    print lf.crit2(0.199, 20), 'should be in:', (.159, .166), 'interp2d bad'
-    print lf.crit(0.199, 20), 'should be in:', (.159, .166), 'two 1d'
-    print lf.crit3(0.199, 20), 'should be in:', (.159, .166), 'Rbf'
+    print(lf.prob(0.166, 20), 'should be:', 0.15)
+    print('')
+    print(lf.crit2(0.15, 20), 'should be:', 0.166, 'interp2d bad')
+    print(lf.crit(0.15, 20), 'should be:', 0.166, 'two 1d')
+    print(lf.crit3(0.15, 20), 'should be:', 0.166, 'Rbf')
+    print('')
+    print(lf.crit2(0.17, 20), 'should be in:', (.159, .166), 'interp2d bad')
+    print(lf.crit(0.17, 20), 'should be in:', (.159, .166), 'two 1d')
+    print(lf.crit3(0.17, 20), 'should be in:', (.159, .166), 'Rbf')
+    print('')
+    print(lf.crit2(0.19, 20), 'should be in:', (.159, .166), 'interp2d bad')
+    print(lf.crit(0.19, 20), 'should be in:', (.159, .166), 'two 1d')
+    print(lf.crit3(0.19, 20), 'should be in:', (.159, .166), 'Rbf')
+    print('')
+    print(lf.crit2(0.199, 20), 'should be in:', (.159, .166), 'interp2d bad')
+    print(lf.crit(0.199, 20), 'should be in:', (.159, .166), 'two 1d')
+    print(lf.crit3(0.199, 20), 'should be in:', (.159, .166), 'Rbf')
     #testing
-    print np.max(np.abs(np.array([lf.prob(c, size[i]) for i in range(len(size)) for c in crit_lf[i]]).reshape(-1,lf.n_alpha) - lf.alpha))
+    print(np.max(np.abs(np.array([lf.prob(c, size[i]) for i in range(len(size)) for c in crit_lf[i]]).reshape(-1,lf.n_alpha) - lf.alpha)))
     #1.6653345369377348e-16
-    print np.max(np.abs(np.array([lf.crit(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf))
-    #6.9388939039072284e-18
-    print np.max(np.abs(np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf))
-    #4.0615705243496336e-12
-    print (np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) < crit_lf[:,:-1]).all()
-    print (np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) > crit_lf[:,1:]).all()
-    print (np.array([lf.prob(c*0.9, size[i]) for i in range(len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all()
-    print (np.array([lf.prob(c*1.1, size[i]) for i in range(len(size)) for c in crit_lf[i,1:]]).reshape(-1,lf.n_alpha-1) < lf.alpha[1:]).all()
+    print(np.max(np.abs(np.array([lf.crit(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf)))
+    #6.9388939039072284e-18)
+    print(np.max(np.abs(np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha]).reshape(-1,lf.n_alpha) - crit_lf)))
+    #4.0615705243496336e-12)
+    print((np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) < crit_lf[:,:-1]).all())
+    print((np.array([lf.crit3(c, size[i]) for i in range(len(size)) for c in lf.alpha[:-1]*1.1]).reshape(-1,lf.n_alpha-1) > crit_lf[:,1:]).all())
+    print((np.array([lf.prob(c*0.9, size[i]) for i in range(len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all())
+    print((np.array([lf.prob(c*1.1, size[i]) for i in range(len(size)) for c in crit_lf[i,1:]]).reshape(-1,lf.n_alpha-1) < lf.alpha[1:]).all())
     #start at size_idx=2 because of non-monotonicity of lf_crit
-    print (np.array([lf.prob(c, size[i]*0.9) for i in range(2,len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all()
+    print((np.array([lf.prob(c, size[i]*0.9) for i in range(2,len(size)) for c in crit_lf[i,:-1]]).reshape(-1,lf.n_alpha-1) > lf.alpha[:-1]).all())
diff --git a/statsmodels/stats/tests/test_anova.py b/statsmodels/stats/tests/test_anova.py
index 7ed1c445583..09c0a0ad67c 100644
--- a/statsmodels/stats/tests/test_anova.py
+++ b/statsmodels/stats/tests/test_anova.py
@@ -1,4 +1,6 @@
-from StringIO import StringIO
+# -*- coding: utf-8 -*-
+
+from statsmodels.compat.python import StringIO
 
 import numpy as np
 
@@ -92,6 +94,46 @@ def test_results(self):
         np.testing.assert_almost_equal(results['F'].values, f_value, 4)
         np.testing.assert_almost_equal(results['PR(>F)'].values, pr_f)
 
+
+class TestAnovaLMNoconstant(object):
+    @classmethod
+    def setupClass(cls):
+        # kidney data taken from JT's course
+        # don't know the license
+        kidney_table.seek(0)
+        cls.data = read_table(kidney_table, sep="\s+")
+        cls.kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight) - 1',
+                        data=cls.data).fit()
+
+    def test_results(self):
+        Df = np.array([2, 2, 2, 54])
+        sum_sq = np.array([158.6415227, 16.97129, 0.6356584, 28.9892])
+        mean_sq = np.array([79.3207613, 8.485645, 0.3178292, 0.536837])
+        f_value = np.array([147.7557648, 15.80674, 0.5920404, np.nan])
+        pr_f = np.array([1.262324e-22, 3.944502e-06, 0.5567479, np.nan])
+
+        results = anova_lm(self.kidney_lm)
+        np.testing.assert_equal(results['df'].values, Df)
+        np.testing.assert_almost_equal(results['sum_sq'].values, sum_sq, 4)
+        np.testing.assert_almost_equal(results['F'].values, f_value, 4)
+        np.testing.assert_almost_equal(results['PR(>F)'].values, pr_f)
+
+        # > sum2.lm = lm(logDays ~ Duration * Weight - 1, contrasts=list(Duration=contr.sum, Weight=contr.sum))
+        # > anova.lm.sum2 <- anova(sum2.lm)
+        # > anova.lm.sum2
+        # Analysis of Variance Table
+        #
+        # Response: logDays
+        #                 Df  Sum Sq Mean Sq F value    Pr(>F)
+        # Duration         2 158.642  79.321 147.756 < 2.2e-16 ***
+        # Weight           2  16.971   8.486  15.807 3.945e-06 ***
+        # Duration:Weight  2   0.636   0.318   0.592    0.5567
+        # Residuals       54  28.989   0.537
+        # ---
+        # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+
+
 class TestAnovaLMCompare(TestAnovaLM):
     def test_results(self):
         new_model = ols("np.log(Days+1) ~ C(Duration) + C(Weight)",
@@ -124,6 +166,40 @@ def test_results(self):
         np.testing.assert_almost_equal(results["F"].values, F)
         np.testing.assert_almost_equal(results["Pr(>F)"].values, PrF)
 
+
+class TestAnovaLMCompareNoconstant(TestAnovaLM):
+    def test_results(self):
+        new_model = ols("np.log(Days+1) ~ C(Duration) + C(Weight) - 1",
+                        self.data).fit()
+        results = anova_lm(new_model, self.kidney_lm)
+
+        Res_Df = np.array([
+             56, 54
+            ])
+        RSS = np.array([
+             29.62486, 28.9892
+            ])
+        Df = np.array([
+             0, 2
+            ])
+        Sum_of_Sq = np.array([
+             np.nan, 0.6356584
+            ])
+        F = np.array([
+             np.nan, 0.5920404
+            ])
+        PrF = np.array([
+             np.nan, 0.5567479
+            ])
+
+        np.testing.assert_equal(results["df_resid"].values, Res_Df)
+        np.testing.assert_almost_equal(results["ssr"].values, RSS, 4)
+        np.testing.assert_almost_equal(results["df_diff"].values, Df)
+        np.testing.assert_almost_equal(results["ss_diff"].values, Sum_of_Sq)
+        np.testing.assert_almost_equal(results["F"].values, F)
+        np.testing.assert_almost_equal(results["Pr(>F)"].values, PrF)
+
+
 class TestAnova2(TestAnovaLM):
     # drop some observations to make an unbalanced, disproportionate panel
     # to make sure things are okay
@@ -151,6 +227,48 @@ def test_results(self):
         np.testing.assert_almost_equal(results['F'].values, F_value, 4)
         np.testing.assert_almost_equal(results['PR(>F)'].values, PrF)
 
+
+class TestAnova2Noconstant(TestAnovaLM):
+    # drop some observations to make an unbalanced, disproportionate panel
+    # to make sure things are okay
+    def test_results(self):
+        data = self.data.drop([0,1,2])
+        anova_ii = ols("np.log(Days+1) ~ C(Duration, Sum)*C(Weight, Sum) - 1",
+                                data).fit()
+
+        Sum_Sq = np.array([
+             154.7131692, 13.27205, 0.1905093, 27.60181
+            ])
+        Df = np.array([
+             2, 2, 2, 51
+            ])
+        F_value = np.array([
+             142.9321191, 12.26141, 0.1760025, np.nan
+            ])
+        PrF = np.array([
+             1.238624e-21, 4.487909e-05, 0.8391231, np.nan
+            ])
+
+        results = anova_lm(anova_ii, typ="II")
+        np.testing.assert_equal(results['df'].values, Df)
+        np.testing.assert_almost_equal(results['sum_sq'].values, Sum_Sq, 4)
+        np.testing.assert_almost_equal(results['F'].values, F_value, 4)
+        np.testing.assert_almost_equal(results['PR(>F)'].values, PrF)
+
+        # > sum2.lm.dropped <- lm(logDays ~ Duration * Weight - 1,  dta.dropped,
+        #                 contrasts=list(Duration=contr.sum, Weight=contr.sum))
+        # > anova.ii.dropped2 <- Anova(sum2.lm.dropped, type='II')
+        # > anova.ii.dropped2
+        # Anova Table (Type II tests)
+        #
+        # Response: logDays
+        #                  Sum Sq Df F value    Pr(>F)
+        # Duration        154.713  2 142.932 < 2.2e-16 ***
+        # Weight           13.272  2  12.261 4.488e-05 ***
+        # Duration:Weight   0.191  2   0.176    0.8391
+        # Residuals        27.602 51
+
+
 class TestAnova2HC0(TestAnovaLM):
     #NOTE: R doesn't return SSq with robust covariance. Why?
     # drop some observations to make an unbalanced, disproportionate panel
diff --git a/statsmodels/stats/tests/test_corrpsd.py b/statsmodels/stats/tests/test_corrpsd.py
index ecbd51771d3..67b204a9b53 100644
--- a/statsmodels/stats/tests/test_corrpsd.py
+++ b/statsmodels/stats/tests/test_corrpsd.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Tests for findind a positive semi-definite correlation of covariance matrix
+"""Tests for finding a positive semi-definite correlation or covariance matrix
 
 Created on Mon May 27 12:07:02 2013
 
@@ -7,9 +7,14 @@
 """
 
 import numpy as np
+import scipy.sparse as sparse
 from numpy.testing import assert_almost_equal, assert_allclose
 from statsmodels.stats.correlation_tools import (
-                 corr_nearest, corr_clipped, cov_nearest)
+    corr_nearest, corr_clipped, cov_nearest,
+    _project_correlation_factors, corr_nearest_factor, _spg_optim,
+    corr_thresholded, cov_nearest_factor_homog, FactoredPSDMatrix)
+
+import warnings
 
 def norm_f(x, y):
     '''Frobenious norm (squared sum) of difference between two arrays
@@ -129,7 +134,9 @@ def test_clipped(self):
     def test_cov_nearest(self):
         x = self.x
         res_r = self.res
-        y = cov_nearest(x, method='nearest', threshold=1e-7)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            y = cov_nearest(x, method='nearest', threshold=1e-7)
         #print np.max(np.abs(x - y))
         assert_almost_equal(y, res_r.mat, decimal=2)
         d = norm_f(x, y)
@@ -205,3 +212,191 @@ def test_corrpsd_threshold():
         #print 'evals', evals, threshold
         #print evals[0] / threshold - 1
         assert_allclose(evals[0], threshold, rtol=0.25, atol=1e-15)
+
+class Test_Factor(object):
+
+    def test_corr_nearest_factor(self):
+
+        d = 100
+
+        for dm in 1,2:
+
+            # Construct a test matrix with exact factor structure
+            X = np.zeros((d,dm), dtype=np.float64)
+            x = np.linspace(0, 2*np.pi, d)
+            for j in range(dm):
+                X[:,j] = np.sin(x*(j+1))
+            _project_correlation_factors(X)
+            X *= 0.7
+            mat = np.dot(X, X.T)
+            np.fill_diagonal(mat, 1.)
+
+            # Try to recover the structure
+            rslt = corr_nearest_factor(mat, dm)
+            C = rslt.corr
+            mat1 = C.to_matrix()
+
+            assert(np.abs(mat - mat1).max() < 1e-3)
+
+
+    # Test that we get the same result if the input is dense or sparse
+    def test_corr_nearest_factor_sparse(self):
+
+        d = 100
+
+        for dm in 1,2:
+
+            # Generate a test matrix of factors
+            X = np.zeros((d,dm), dtype=np.float64)
+            x = np.linspace(0, 2*np.pi, d)
+            for j in range(dm):
+                X[:,j] = np.sin(x*(j+1))
+
+            # Get the correlation matrix
+            _project_correlation_factors(X)
+            X *= 0.7
+            mat = np.dot(X, X.T)
+            np.fill_diagonal(mat, 1)
+
+            # Threshold it
+            mat *= (np.abs(mat) >= 0.4)
+            smat = sparse.csr_matrix(mat)
+
+            fac_dense = corr_nearest_factor(smat, dm).corr
+            mat_dense = fac_dense.to_matrix()
+
+            fac_sparse = corr_nearest_factor(smat, dm).corr
+            mat_sparse = fac_sparse.to_matrix()
+
+            assert_allclose(mat_dense, mat_sparse, rtol=0.25,
+                            atol=1e-3)
+
+
+    # Test on a quadratic function.
+    def test_spg_optim(self):
+
+        dm = 100
+
+        ind = np.arange(dm)
+        indmat = np.abs(ind[:,None] - ind[None,:])
+        M = 0.8**indmat
+
+        def obj(x):
+            return np.dot(x, np.dot(M, x))
+
+        def grad(x):
+            return 2*np.dot(M, x)
+
+        def project(x):
+            return x
+
+        x = np.random.normal(size=dm)
+        rslt = _spg_optim(obj, grad, x, project)
+        xnew = rslt.params
+        assert(obj(xnew) < 1e-4)
+
+    def test_decorrelate(self):
+
+        d = 30
+        dg = np.linspace(1, 2, d)
+        root = np.random.normal(size=(d, 4))
+        fac = FactoredPSDMatrix(dg, root)
+        mat = fac.to_matrix()
+        rmat = np.linalg.cholesky(mat)
+        dcr = fac.decorrelate(rmat)
+        idm = np.dot(dcr, dcr.T)
+        assert_almost_equal(idm, np.eye(d))
+
+        rhs = np.random.normal(size=(d, 5))
+        mat2 = np.dot(rhs.T, np.linalg.solve(mat, rhs))
+        mat3 = fac.decorrelate(rhs)
+        mat3 = np.dot(mat3.T, mat3)
+        assert_almost_equal(mat2, mat3)
+
+    def test_logdet(self):
+
+        d = 30
+        dg = np.linspace(1, 2, d)
+        root = np.random.normal(size=(d, 4))
+        fac = FactoredPSDMatrix(dg, root)
+        mat = fac.to_matrix()
+
+        _, ld = np.linalg.slogdet(mat)
+        ld2 = fac.logdet()
+
+        assert_almost_equal(ld, ld2)
+
+    def test_solve(self):
+
+        d = 30
+        dg = np.linspace(1, 2, d)
+        root = np.random.normal(size=(d, 2))
+        fac = FactoredPSDMatrix(dg, root)
+        rhs = np.random.normal(size=(d, 5))
+        sr1 = fac.solve(rhs)
+        mat = fac.to_matrix()
+        sr2 = np.linalg.solve(mat, rhs)
+        assert_almost_equal(sr1, sr2)
+
+    def test_cov_nearest_factor_homog(self):
+
+        d = 100
+
+        for dm in 1,2:
+
+            # Construct a test matrix with exact factor structure
+            X = np.zeros((d,dm), dtype=np.float64)
+            x = np.linspace(0, 2*np.pi, d)
+            for j in range(dm):
+                X[:,j] = np.sin(x*(j+1))
+            mat = np.dot(X, X.T)
+            np.fill_diagonal(mat, np.diag(mat) + 3.1)
+
+            # Try to recover the structure
+            rslt = cov_nearest_factor_homog(mat, dm)
+            mat1 = rslt.to_matrix()
+
+            assert(np.abs(mat - mat1).max() < 1e-4)
+
+
+    # Check that dense and sparse inputs give the same result
+    def test_cov_nearest_factor_homog_sparse(self):
+
+        d = 100
+
+        for dm in 1,2:
+
+            # Construct a test matrix with exact factor structure
+            X = np.zeros((d,dm), dtype=np.float64)
+            x = np.linspace(0, 2*np.pi, d)
+            for j in range(dm):
+                X[:,j] = np.sin(x*(j+1))
+            mat = np.dot(X, X.T)
+            np.fill_diagonal(mat, np.diag(mat) + 3.1)
+
+            # Fit to dense
+            rslt = cov_nearest_factor_homog(mat, dm)
+            mat1 = rslt.to_matrix()
+
+            # Fit to sparse
+            smat = sparse.csr_matrix(mat)
+            rslt = cov_nearest_factor_homog(smat, dm)
+            mat2 = rslt.to_matrix()
+
+            assert_allclose(mat1, mat2, rtol=0.25, atol=1e-3)
+
+    def test_corr_thresholded(self):
+
+        import datetime
+
+        t1 = datetime.datetime.now()
+        X = np.random.normal(size=(2000,10))
+        tcor = corr_thresholded(X, 0.2, max_elt=4e6)
+        t2 = datetime.datetime.now()
+        ss = (t2-t1).seconds
+
+        fcor = np.corrcoef(X)
+        fcor *= (np.abs(fcor) >= 0.2)
+
+        assert_allclose(tcor.todense(), fcor, rtol=0.25, atol=1e-3)
+
diff --git a/statsmodels/stats/tests/test_diagnostic.py b/statsmodels/stats/tests/test_diagnostic.py
index 8af512b8426..0fd0024654e 100644
--- a/statsmodels/stats/tests/test_diagnostic.py
+++ b/statsmodels/stats/tests/test_diagnostic.py
@@ -23,6 +23,8 @@
 
 import statsmodels.stats.sandwich_covariance as sw
 import statsmodels.stats.diagnostic as smsdia
+import json
+
 #import statsmodels.sandbox.stats.diagnostic as smsdia
 import statsmodels.stats.outliers_influence as oi
 
@@ -588,11 +590,6 @@ def test_influence(self):
         #this test is slow
         infl = oi.OLSInfluence(res)
 
-        try:
-            import json
-        except ImportError:
-            raise SkipTest
-
         fp = open(os.path.join(cur_dir,"results/influence_lsdiag_R.json"))
         lsdiag = json.load(fp)
 
@@ -624,7 +621,7 @@ def test_influence(self):
         fn = os.path.join(cur_dir,"results/influence_measures_bool_R.csv")
         #not used yet:
         #infl_bool_r  = pandas.read_csv(fn, index_col=0,
-        #                                converters=dict(zip(range(7),[conv]*7)))
+        #                                converters=dict(zip(lrange(7),[conv]*7)))
         infl_r2 = np.asarray(infl_r)
         assert_almost_equal(infl.dfbetas, infl_r2[:,:3], decimal=13)
         assert_almost_equal(infl.cov_ratio, infl_r2[:,4], decimal=14)
@@ -733,10 +730,6 @@ def test_influence_wrapped():
     assert_(isinstance(df, DataFrame))
 
     #this test is slow
-    try:
-        import json
-    except ImportError:
-        raise SkipTest
     fp = open(os.path.join(cur_dir,"results/influence_lsdiag_R.json"))
     lsdiag = json.load(fp)
 
@@ -762,7 +755,7 @@ def test_influence_wrapped():
     fn = os.path.join(cur_dir,"results/influence_measures_bool_R.csv")
     #not used yet:
     #infl_bool_r  = pandas.read_csv(fn, index_col=0,
-    #                                converters=dict(zip(range(7),[conv]*7)))
+    #                                converters=dict(zip(lrange(7),[conv]*7)))
     infl_r2 = np.asarray(infl_r)
     #TODO: finish wrapping this stuff
     assert_almost_equal(infl.dfbetas, infl_r2[:,:3], decimal=13)
diff --git a/statsmodels/stats/tests/test_gof.py b/statsmodels/stats/tests/test_gof.py
index fed836bb262..23fbb4bdf9b 100644
--- a/statsmodels/stats/tests/test_gof.py
+++ b/statsmodels/stats/tests/test_gof.py
@@ -5,6 +5,7 @@
 
 Author: Josef Perktold
 """
+from statsmodels.compat.python import itervalues
 
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_equal
@@ -16,8 +17,8 @@ class Holder(object):
     pass
 
 def test_chisquare_power():
-    from results.results_power import pwr_chisquare
-    for case in pwr_chisquare.values():
+    from .results.results_power import pwr_chisquare
+    for case in itervalues(pwr_chisquare):
         power = chisquare_power(case.w, case.N, case.df + 1,
                                 alpha=case.sig_level)
         assert_almost_equal(power, case.power, decimal=6,
diff --git a/statsmodels/stats/tests/test_multi.py b/statsmodels/stats/tests/test_multi.py
index fb79ed5f627..dc500bcbae1 100644
--- a/statsmodels/stats/tests/test_multi.py
+++ b/statsmodels/stats/tests/test_multi.py
@@ -12,9 +12,10 @@
     consistency only
 
 '''
-
+from statsmodels.compat.python import iteritems
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_equal, assert_
+from numpy.testing import (assert_almost_equal, assert_equal, assert_,
+                          assert_allclose)
 
 from statsmodels.stats.multitest import (multipletests, fdrcorrection,
                                          fdrcorrection_twostage)
@@ -183,7 +184,7 @@ def test_multi_pvalcorrection(self):
         res_multtest = self.res2
         pval0 = res_multtest[:,0]
 
-        for k,v in rmethods.items():
+        for k,v in iteritems(rmethods):
             if v[1] in self.methods:
                 reject, pvalscorr = multipletests(pval0,
                                                   alpha=self.alpha,
@@ -241,8 +242,8 @@ def test_pvalcorrection_reject():
                 #print 'reject.sum', v[1], reject.sum()
                 msg = 'case %s %3.2f rejected:%d\npval_raw=%r\npvalscorr=%r' % (
                                  method, alpha, reject.sum(), pval1, pvalscorr)
-                #assert_equal(reject, pvalscorr <= alpha, err_msg=msg)
-                yield assert_equal, reject, pvalscorr <= alpha, msg
+                assert_equal(reject, pvalscorr <= alpha, err_msg=msg)
+                #yield assert_equal, reject, pvalscorr <= alpha  #, msg
 
 
 def test_hommel():
@@ -293,6 +294,25 @@ def test_fdr_bky():
     assert_equal(8, res_tst[0].sum())
     #print fdrcorrection_twostage(pvals, alpha=0.05, iter=True)
 
+def test_issorted():
+    # test that is_sorted keyword works correctly
+    # the fdrcorrection functions are tested indirectly
+    from statsmodels.stats.multitest import multitest_methods_names
+
+    # data generated as random numbers np.random.beta(0.2, 0.5, size=10)
+    pvals = np.array([31, 9958111, 7430818, 8653643, 9892855, 876, 2651691,
+                      145836, 9931, 6174747]) * 1e-7
+    sortind = np.argsort(pvals)
+    sortrevind = sortind.argsort()
+    pvals_sorted = pvals[sortind]
+
+    for method in multitest_methods_names:
+        res1 = multipletests(pvals, method=method, is_sorted=False)
+        res2 = multipletests(pvals_sorted, method=method, is_sorted=True)
+        assert_equal(res2[0][sortrevind], res1[0])
+        assert_allclose(res2[0][sortrevind], res1[0], rtol=1e-10)
+
+
 def test_tukeyhsd():
     #example multicomp in R p 83
 
diff --git a/statsmodels/stats/tests/test_nonparametric.py b/statsmodels/stats/tests/test_nonparametric.py
index 7c480699367..13de0bd9442 100644
--- a/statsmodels/stats/tests/test_nonparametric.py
+++ b/statsmodels/stats/tests/test_nonparametric.py
@@ -5,7 +5,7 @@
 
 Author: Josef Perktold
 """
-
+from statsmodels.compat.python import lzip, range
 import numpy as np
 from numpy.testing import assert_allclose, assert_almost_equal
 from statsmodels.sandbox.stats.runs import (mcnemar, cochrans_q, Runs,
@@ -82,16 +82,16 @@ def test_mcnemar_vectorized():
     ttk = np.random.randint(5,15, size=(2,2,3))
     mcnemar(ttk)
     res = mcnemar(ttk, exact=False)
-    res1 = zip(*[mcnemar(ttk[:,:,i], exact=False) for i in range(3)])
+    res1 = lzip(*[mcnemar(ttk[:,:,i], exact=False) for i in range(3)])
     assert_allclose(res, res1, rtol=1e-13)
 
     res = mcnemar(ttk, exact=False, correction=False)
-    res1 = zip(*[mcnemar(ttk[:,:,i], exact=False, correction=False)
+    res1 = lzip(*[mcnemar(ttk[:,:,i], exact=False, correction=False)
                                                           for i in range(3)])
     assert_allclose(res, res1, rtol=1e-13)
 
     res = mcnemar(ttk, exact=True)
-    res1 = zip(*[mcnemar(ttk[:,:,i], exact=True) for i in range(3)])
+    res1 = lzip(*[mcnemar(ttk[:,:,i], exact=True) for i in range(3)])
     assert_allclose(res, res1, rtol=1e-13)
 
 
diff --git a/statsmodels/stats/tests/test_pairwise.py b/statsmodels/stats/tests/test_pairwise.py
index b5b98898b2e..71f22f882b7 100644
--- a/statsmodels/stats/tests/test_pairwise.py
+++ b/statsmodels/stats/tests/test_pairwise.py
@@ -6,9 +6,9 @@
 Author: Josef Perktold
 """
 
-from statsmodels.compatnp.py3k import BytesIO, asbytes
+from statsmodels.compat.python import BytesIO, asbytes, range
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_equal
+from numpy.testing import assert_almost_equal, assert_equal, assert_
 
 from statsmodels.stats.libqsturng import qsturng
 
@@ -198,9 +198,36 @@ def setup_class(self):
         pvals = tukeyhsd2s[:, 3]
         self.reject2 = pvals < 0.05
 
+    def test_table_names_default_group_order(self):
+        t = self.res._results_table
+        # if the group_order parameter is not used, the groups should
+        # be reported in alphabetical order
+        expected_order = [(b'medical', b'mental'),
+                          (b'medical', b'physical'),
+                          (b'mental', b'physical')]
+        for i in range(1, 4):
+            first_group = t[i][0].data
+            second_group = t[i][1].data
+            assert_((first_group, second_group) == expected_order[i - 1])
+
+    def test_table_names_custom_group_order(self):
+        # if the group_order parameter is used, the groups should
+        # be reported in the specified order
+        mc = MultiComparison(self.endog, self.groups,
+                             group_order=[b'physical', b'medical', b'mental'])
+        res = mc.tukeyhsd(alpha=self.alpha)
+        #print(res)
+        t = res._results_table
+        expected_order = [(b'physical',b'medical'),
+                          (b'physical',b'mental'),
+                          (b'medical', b'mental')]
+        for i in range(1, 4):
+            first_group = t[i][0].data
+            second_group = t[i][1].data
+            assert_((first_group, second_group) == expected_order[i - 1])
 
-class TestTuckeyHSD2s(CheckTuckeyHSDMixin):
 
+class TestTuckeyHSD2s(CheckTuckeyHSDMixin):
     @classmethod
     def setup_class(self):
         #unbalanced case
diff --git a/statsmodels/stats/tests/test_panel_robustcov.py b/statsmodels/stats/tests/test_panel_robustcov.py
index a4389332fff..a685dfc3f6f 100644
--- a/statsmodels/stats/tests/test_panel_robustcov.py
+++ b/statsmodels/stats/tests/test_panel_robustcov.py
@@ -7,6 +7,7 @@
 Author: Josef Perktold
 """
 
+from statsmodels.compat.python import range, lmap
 import numpy as np
 from numpy.testing import assert_almost_equal
 
@@ -18,7 +19,7 @@
 def test_panel_robust_cov():
     import pandas as pa
     import statsmodels.datasets.grunfeld as gr
-    from results.results_panelrobust import results as res_stata
+    from .results.results_panelrobust import results as res_stata
 
     dtapa = gr.data.load_pandas()
     #Stata example/data seems to miss last firm
@@ -57,6 +58,10 @@ def test_panel_robust_cov():
     cov_clu = sw.cov_cluster(res, firm_id)
     assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
 
+    #cluster robust standard errors, non-int groups
+    cov_clu = sw.cov_cluster(res, lmap(str, firm_id))
+    assert_almost_equal(cov_clu, res_stata.cov_clu_stata, decimal=4)
+
     #Driscoll and Kraay panel robust standard errors
     rcov = sw.cov_nw_groupsum(res, 0, time, use_correction=0)
     assert_almost_equal(rcov, res_stata.cov_dk0_stata, decimal=4)
diff --git a/statsmodels/stats/tests/test_power.py b/statsmodels/stats/tests/test_power.py
index 75aa6b3bfea..9368b54540d 100644
--- a/statsmodels/stats/tests/test_power.py
+++ b/statsmodels/stats/tests/test_power.py
@@ -18,6 +18,7 @@
 
 
 import statsmodels.stats.power as smp
+import warnings
 #from .test_weightstats import CheckPowerMixin
 from statsmodels.stats.tests.test_weightstats import Holder
 
@@ -741,7 +742,6 @@ def test_power_solver_warn():
         # this converges with scipy 0.11  ???
         # nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1, alternative='larger')
 
-        import warnings
         with warnings.catch_warnings():  # python >= 2.6
             warnings.simplefilter("ignore")
             val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1,
diff --git a/statsmodels/stats/tests/test_proportion.py b/statsmodels/stats/tests/test_proportion.py
index c30d2fe767e..52a3c553534 100644
--- a/statsmodels/stats/tests/test_proportion.py
+++ b/statsmodels/stats/tests/test_proportion.py
@@ -11,12 +11,13 @@
 
 from statsmodels.stats.proportion import proportion_confint
 import statsmodels.stats.proportion as smprop
+import warnings
 
 class Holder(object):
     pass
 
 def test_confint_proportion():
-    from results.results_proportion import res_binom, res_binom_methods
+    from .results.results_proportion import res_binom, res_binom_methods
     methods = {'agresti_coull' : 'agresti-coull',
                'normal' : 'asymptotic',
                'beta' : 'exact',
@@ -81,6 +82,17 @@ def test_pairwiseproptest(self):
         assert_almost_equal(pptd.pvals_raw, ppt.pvals_raw[:len(self.nobs) - 1],
                             decimal=13)
 
+
+    def test_number_pairs_1493(self):
+        ppt = smprop.proportions_chisquare_allpairs(self.n_success[:3],
+                                                    self.nobs[:3],
+                                                    multitest_method=None)
+
+        assert_equal(len(ppt.pvals_raw), 3)
+        idx = [0, 1, 3]
+        assert_almost_equal(ppt.pvals_raw, self.res_ppt_pvals_raw[idx])
+
+
 class TestProportion(CheckProportionMixin):
     def setup(self):
         self.n_success = np.array([ 73,  90, 114,  75])
@@ -302,7 +314,6 @@ def test_power_ztost_prop():
                          discrete=True, dist='binom')[0]
     assert_almost_equal(power, 0.8204, decimal=4) # PASS example
 
-    import warnings
     with warnings.catch_warnings():  # python >= 2.6
         warnings.simplefilter("ignore")
         power = smprop.power_ztost_prop(0.4, 0.6, np.arange(20, 210, 20),
diff --git a/statsmodels/stats/tests/test_statstools.py b/statsmodels/stats/tests/test_statstools.py
index 2cca39091f6..74379afdefc 100644
--- a/statsmodels/stats/tests/test_statstools.py
+++ b/statsmodels/stats/tests/test_statstools.py
@@ -1,17 +1,17 @@
-
-
+# TODO: Test robust skewness
+# TODO: Test robust kurtosis
 import numpy as np
 import pandas as pd
-from numpy.testing import assert_almost_equal
+from numpy.testing import (assert_almost_equal, assert_raises, TestCase)
 from statsmodels.stats.stattools import (omni_normtest, jarque_bera,
-                            durbin_watson)
+                                         durbin_watson, _medcouple_1d, medcouple,
+                                         robust_kurtosis, robust_skewness)
 from statsmodels.stats.adnorm import normal_ad
 
-
 #a random array, rounded to 4 decimals
-x = np.array([-0.1184, -1.3403,  0.0063, -0.612 , -0.3869, -0.2313, -2.8485,
-       -0.2167,  0.4153,  1.8492, -0.3706,  0.9726, -0.1501, -0.0337,
-       -1.4423,  1.2489,  0.9182, -0.2331, -0.6182,  0.183 ])
+x = np.array([-0.1184, -1.3403, 0.0063, -0.612, -0.3869, -0.2313, -2.8485,
+              -0.2167, 0.4153, 1.8492, -0.3706, 0.9726, -0.1501, -0.0337,
+              -1.4423, 1.2489, 0.9182, -0.2331, -0.6182, 0.183])
 
 
 def test_durbin_watson():
@@ -34,30 +34,31 @@ def test_durbin_watson():
     assert_almost_equal(durbin_watson(x**2), st_R, 14)
 
     st_R = 1.09897993228779
-    assert_almost_equal(durbin_watson(x[1:]+0.5*x[:-1]), st_R, 14)
+    assert_almost_equal(durbin_watson(x[1:] + 0.5 * x[:-1]), st_R, 14)
 
     st_R = 0.937241876707273
-    assert_almost_equal(durbin_watson(x[1:]+0.8*x[:-1]), st_R, 14)
+    assert_almost_equal(durbin_watson(x[1:] + 0.8 * x[:-1]), st_R, 14)
 
     st_R = 0.921488912587806
-    assert_almost_equal(durbin_watson(x[1:]+0.9*x[:-1]), st_R, 14)
+    assert_almost_equal(durbin_watson(x[1:] + 0.9 * x[:-1]), st_R, 14)
 
 
 def test_omni_normtest():
     #tests against R fBasics
     from scipy import stats
+
     st_pv_R = np.array(
               [[3.994138321207883, -1.129304302161460,  1.648881473704978],
                [0.1357325110375005, 0.2587694866795507, 0.0991719192710234]])
 
     nt = omni_normtest(x)
-    assert_almost_equal(nt, st_pv_R[:,0], 14)
+    assert_almost_equal(nt, st_pv_R[:, 0], 14)
 
     st = stats.skewtest(x)
-    assert_almost_equal(st, st_pv_R[:,1], 14)
+    assert_almost_equal(st, st_pv_R[:, 1], 14)
 
     kt = stats.kurtosistest(x)
-    assert_almost_equal(kt, st_pv_R[:,2], 11)
+    assert_almost_equal(kt, st_pv_R[:, 2], 11)
 
     st_pv_R = np.array(
               [[34.523210399523926,  4.429509162503833,  3.860396220444025],
@@ -66,23 +67,25 @@ def test_omni_normtest():
     x2 = x**2
     #TODO: fix precision in these test with relative tolerance
     nt = omni_normtest(x2)
-    assert_almost_equal(nt, st_pv_R[:,0], 12)
+    assert_almost_equal(nt, st_pv_R[:, 0], 12)
 
     st = stats.skewtest(x2)
-    assert_almost_equal(st, st_pv_R[:,1], 12)
+    assert_almost_equal(st, st_pv_R[:, 1], 12)
 
     kt = stats.kurtosistest(x2)
-    assert_almost_equal(kt, st_pv_R[:,2], 12)
+    assert_almost_equal(kt, st_pv_R[:, 2], 12)
+
 
 def test_omni_normtest_axis():
     #test axis of omni_normtest
-    x = np.random.randn(15, 3)
+    x = np.random.randn(25, 3)
     nt1 = omni_normtest(x)
     nt2 = omni_normtest(x, axis=0)
     nt3 = omni_normtest(x.T, axis=1)
     assert_almost_equal(nt2, nt1, decimal=13)
     assert_almost_equal(nt3, nt1, decimal=13)
 
+
 def test_jarque_bera():
     #tests against R fBasics
     st_pv_R = np.array([1.9662677226861689, 0.3741367669648314])
@@ -101,6 +104,7 @@ def test_jarque_bera():
     jb = jarque_bera(np.exp(-x**2))[:2]
     assert_almost_equal(jb, st_pv_R, 14)
 
+
 def test_shapiro():
     #tests against R fBasics
     #testing scipy.stats
@@ -112,18 +116,19 @@ def test_shapiro():
 
     #st is ok -7.15e-06, pval agrees at -3.05e-10
     st_pv_R = np.array([5.799574255943298e-01, 1.838456834681376e-06 * 1e4])
-    sh = shapiro(x**2)*np.array([1,1e4])
+    sh = shapiro(x**2) * np.array([1, 1e4])
     assert_almost_equal(sh, st_pv_R, 5)
 
-    st_pv_R = np.array([0.91730442643165588, 0.08793704167882448 ])
+    st_pv_R = np.array([0.91730442643165588, 0.08793704167882448])
     sh = shapiro(np.log(x**2))
     assert_almost_equal(sh, st_pv_R, 5)
 
     #diff is [  9.38773155e-07,   5.48221246e-08]
-    st_pv_R = np.array([0.818361863493919373, 0.001644620895206969 ])
+    st_pv_R = np.array([0.818361863493919373, 0.001644620895206969])
     sh = shapiro(np.exp(-x**2))
     assert_almost_equal(sh, st_pv_R, 5)
 
+
 def test_adnorm():
     #tests against R fBasics
     st_pv = []
@@ -147,19 +152,156 @@ def test_adnorm():
     assert_almost_equal(ad, st_pv_R, 12)
     st_pv.append(st_pv_R)
 
-    ad = normal_ad(np.column_stack((x,x**2, np.log(x**2),np.exp(-x**2))).T,
+    ad = normal_ad(np.column_stack((x, x**2, np.log(x**2), np.exp(-x**2))).T,
                    axis=1)
     assert_almost_equal(ad, np.column_stack(st_pv), 11)
 
+
 def test_durbin_watson_pandas():
-    x=np.random.randn(50)
-    x_series=pd.Series(x)
+    x = np.random.randn(50)
+    x_series = pd.Series(x)
     assert_almost_equal(durbin_watson(x), durbin_watson(x_series), decimal=13)
 
-if __name__ == '__main__':
-    test_durbin_watson()
-    test_omni_normtest()
-    test_jarque_bera()
-    test_shapiro()
-    test_adnorm()
-    test_durbin_watson_pandas()
+
+class TestStattools(TestCase):
+    @classmethod
+    def setup_class(cls):
+        x = np.random.standard_normal(1000)
+        e1, e2, e3, e4, e5, e6, e7 = np.percentile(x, (12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5))
+        c05, c50, c95 = np.percentile(x, (5.0, 50.0, 95.0))
+        f025, f25, f75, f975 = np.percentile(x, (2.5, 25.0, 75.0, 97.5))
+        mean = np.mean
+        kr1 = mean(((x - mean(x)) / np.std(x))**4.0) - 3.0
+        kr2 = ((e7 - e5) + (e3 - e1)) / (e6 - e2) - 1.2330951154852172
+        kr3 = (mean(x[x > c95]) - mean(x[x < c05])) / (mean(x[x > c50]) - mean(x[x < c50])) - 2.5852271228708048
+        kr4 = (f975 - f025) / (f75 - f25) - 2.9058469516701639
+        cls.kurtosis_x = x
+        cls.expected_kurtosis = np.array([kr1, kr2, kr3, kr4])
+        cls.kurtosis_constants = np.array([3.0,1.2330951154852172,2.5852271228708048,2.9058469516701639])
+
+    def test_medcouple_no_axis(self):
+        x = np.reshape(np.arange(100.0), (50, 2))
+        mc = medcouple(x, axis=None)
+        assert_almost_equal(mc, medcouple(x.ravel()))
+
+    def test_medcouple_1d(self):
+        x = np.reshape(np.arange(100.0),(50,2))
+        assert_raises(ValueError, _medcouple_1d, x)
+
+    def test_medcouple_symmetric(self):
+        mc = medcouple(np.arange(5.0))
+        assert_almost_equal(mc, 0)
+
+
+    def test_medcouple_nonzero(self):
+        mc = medcouple(np.array([1, 2, 7, 9, 10.0]))
+        assert_almost_equal(mc, -0.3333333)
+
+
+    def test_medcouple_symmetry(self):
+        x = np.random.standard_normal(100)
+        mcp = medcouple(x)
+        mcn = medcouple(-x)
+        assert_almost_equal(mcp + mcn, 0)
+
+
+    def test_durbin_watson(self):
+        x = np.random.standard_normal(100)
+        dw = sum(np.diff(x)**2.0) / np.dot(x, x)
+        assert_almost_equal(dw, durbin_watson(x))
+
+
+    def test_durbin_watson_2d(self):
+        shape = (1, 10)
+        x = np.random.standard_normal(100)
+        dw = sum(np.diff(x)**2.0) / np.dot(x, x)
+        x = np.tile(x[:, None], shape)
+        assert_almost_equal(np.squeeze(dw * np.ones(shape)), durbin_watson(x))
+
+
+    def test_durbin_watson_3d(self):
+        shape = (10, 1, 10)
+        x = np.random.standard_normal(100)
+        dw = sum(np.diff(x)**2.0) / np.dot(x, x)
+        x = np.tile(x[None, :, None], shape)
+        assert_almost_equal(np.squeeze(dw * np.ones(shape)), durbin_watson(x, axis=1))
+
+
+    def test_robust_skewness_1d(self):
+        x = np.arange(21.0)
+        sk = robust_skewness(x)
+        assert_almost_equal(np.array(sk), np.zeros(4))
+
+    def test_robust_skewness_1d_2d(self):
+        x = np.random.randn(21)
+        y = x[:, None]
+        sk_x = robust_skewness(x)
+        sk_y = robust_skewness(y, axis=None)
+        assert_almost_equal(np.array(sk_x), np.array(sk_y))
+
+    def test_robust_skewness_symmetric(self):
+        x = np.random.standard_normal(100)
+        x = np.hstack([x, np.zeros(1), -x])
+        sk = robust_skewness(x)
+        assert_almost_equal(np.array(sk), np.zeros(4))
+
+
+    def test_robust_skewness_3d(self):
+        x = np.random.standard_normal(100)
+        x = np.hstack([x, np.zeros(1), -x])
+        x = np.tile(x, (10, 10, 1))
+        sk_3d = robust_skewness(x, axis=2)
+        result = np.zeros((10, 10))
+        for sk in sk_3d:
+            assert_almost_equal(sk, result)
+
+    def test_robust_kurtosis_1d_2d(self):
+        x = np.random.randn(100)
+        y = x[:, None]
+        kr_x = np.array(robust_kurtosis(x))
+        kr_y = np.array(robust_kurtosis(y, axis=None))
+        assert_almost_equal(kr_x, kr_y)
+
+    def test_robust_kurtosis(self):
+        x = self.kurtosis_x
+        assert_almost_equal(np.array(robust_kurtosis(x)), self.expected_kurtosis)
+
+    def test_robust_kurtosis_3d(self):
+        x = np.tile(self.kurtosis_x, (10, 10, 1))
+        kurtosis = np.array(robust_kurtosis(x, axis=2))
+        for i, r in enumerate(self.expected_kurtosis):
+            assert_almost_equal(r * np.ones((10, 10)), kurtosis[i])
+
+
+    def test_robust_kurtosis_excess_false(self):
+        x = self.kurtosis_x
+        expected = self.expected_kurtosis + self.kurtosis_constants
+        kurtosis = np.array(robust_kurtosis(x, excess=False))
+        assert_almost_equal(expected, kurtosis)
+
+    def test_robust_kurtosis_ab(self):
+        """Test custom alpha, beta in kr3"""
+        x = self.kurtosis_x
+        alpha, beta = (10.0, 45.0)
+        kurtosis = robust_kurtosis(self.kurtosis_x, ab=(alpha,beta), excess=False)
+        num = np.mean(x[x>np.percentile(x,100.0 - alpha)]) - np.mean(x[x<np.percentile(x,alpha)])
+        denom = np.mean(x[x>np.percentile(x,100.0 - beta)]) - np.mean(x[x<np.percentile(x,beta)])
+        assert_almost_equal(kurtosis[2], num/denom)
+
+    def test_robust_kurtosis_dg(self):
+        """Test custom delta, gamma in kr4"""
+        x = self.kurtosis_x
+        delta, gamma = (10.0, 45.0)
+        kurtosis = robust_kurtosis(self.kurtosis_x, dg=(delta,gamma), excess=False)
+        q = np.percentile(x,[delta, 100.0-delta, gamma, 100.0-gamma])
+        assert_almost_equal(kurtosis[3], (q[1] - q[0]) / (q[3] - q[2]))
+
+
+if __name__ == "__main__":
+    import nose
+
+    nose.runmodule(argv=[__file__, '-vvs', '-x'], exit=False) #, '--pdb'
+    # run_module_suite()
+    #nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
+    #               exit=False)
+
diff --git a/statsmodels/tests/rmodelwrap.py b/statsmodels/tests/rmodelwrap.py
index c42a861a100..fd164e4387c 100644
--- a/statsmodels/tests/rmodelwrap.py
+++ b/statsmodels/tests/rmodelwrap.py
@@ -1,9 +1,10 @@
 ''' Wrapper for R models to allow comparison to scipy models '''
 
+from statsmodels.compat.python import iterkeys, range
 import numpy as np
 
 import rpy
-from check_for_rpy import skip_rpy
+from .check_for_rpy import skip_rpy
 skipR = skip_rpy()
 if not skipR:
     from rpy import r
@@ -43,8 +44,8 @@ def __init__(self, y, design, model_type=r.lm, **kwds):
 #        self.beta0 = np.array([coeffs[c] for c in self._design_cols])
         self.nobs = len(self.results['residuals'])
         if isinstance(self.results['residuals'], dict):
-            self.resid = np.zeros((len(self.results['residuals'].keys())))
-            for i in self.results['residuals'].keys():
+            self.resid = np.zeros((len(iterkeys(self.results['residuals']))))
+            for i in iterkeys(self.results['residuals']):
                 self.resid[int(i)-1] = self.results['residuals'][i]
         else:
             self.resid = self.results['residuals']
@@ -87,7 +88,7 @@ def getglm(self):
                 for k in range(1, 1+self.nobs)]
         if isinstance(self.resid, dict):
             tmp = np.zeros(len(self.resid))
-            for i in self.resid.keys():
+            for i in iterkeys(self.resid):
                 tmp[int(i)-1] = self.resid[i]
             self.resid = tmp
         self.predict = [self.results['linear.predictors'][str(k)] \
@@ -99,7 +100,7 @@ def getglm(self):
         self.resid_deviance = self.rsum['deviance.resid']
         if isinstance(self.resid_deviance, dict):
             tmp = np.zeros(len(self.resid_deviance))
-            for i in self.resid_deviance.keys():
+            for i in iterkeys(self.resid_deviance):
                 tmp[int(i)-1] = self.resid_deviance[i]
             self.resid_deviance = tmp
         self.null_deviance = self.rsum['null.deviance']
@@ -107,8 +108,8 @@ def getglm(self):
     def getrlm(self):
         self.k2 = self.results['k2']
         if isinstance(self.results['w'], dict):
-            tmp = np.zeros((len(self.results['w'].keys())))
-            for i in self.results['w'].keys():
+            tmp = np.zeros((len(list(iterkeys(self.results['w'])))))
+            for i in iterkeys(self.results['w']):
                 tmp[int(i)-1] = self.results['w'][i]
             self.weights = tmp
         else: self.weights = self.results['w']
diff --git a/statsmodels/tools/__init__.py b/statsmodels/tools/__init__.py
index 70ae753c6e5..08bdbeb2a4c 100644
--- a/statsmodels/tools/__init__.py
+++ b/statsmodels/tools/__init__.py
@@ -1,4 +1,4 @@
-from tools import add_constant, categorical
+from .tools import add_constant, categorical
 
 from statsmodels import NoseWrapper as Tester
 test = Tester().test
diff --git a/statsmodels/tools/catadd.py b/statsmodels/tools/catadd.py
index 4fe63603b2e..e7e51d4c52c 100644
--- a/statsmodels/tools/catadd.py
+++ b/statsmodels/tools/catadd.py
@@ -1,7 +1,6 @@
-
-
+from __future__ import print_function
 import numpy as np
-from statsmodels.tools.tools import rank as smrank
+from statsmodels.compat.numpy import np_matrix_rank
 
 
 def add_indep(x, varnames, dtype=None):
@@ -19,7 +18,7 @@ def add_indep(x, varnames, dtype=None):
 
     nvars_orig = len(x)
     nobs = len(x[0])
-    #print 'nobs, nvars_orig', nobs, nvars_orig
+    #print('nobs, nvars_orig', nobs, nvars_orig)
     if not dtype:
         dtype = np.asarray(x[0]).dtype
     xout = np.zeros((nobs, nvars_orig), dtype=dtype)
@@ -29,10 +28,10 @@ def add_indep(x, varnames, dtype=None):
     varnames_dropped = []
     keepindx = []
     for (xi, ni) in zip(x, varnames):
-        #print xi.shape, xout.shape
+        #print(xi.shape, xout.shape)
         xout[:,count] = xi
-        rank_new = smrank(xout)
-        #print rank_new
+        rank_new = np_matrix_rank(xout)
+        #print(rank_new)
         if  rank_new > rank_old:
             varnames_new.append(ni)
             rank_old = rank_new
@@ -50,7 +49,7 @@ def add_indep(x, varnames, dtype=None):
     varnames = ['const'] + ['var1_%d' %i for i in np.arange(3)] \
                          + ['var2_%d' %i for i in np.arange(2)]
     xo,vo = add_indep(x, varnames)
-    print xo.shape
+    print(xo.shape)
 
 
 
diff --git a/statsmodels/tools/compatibility.py b/statsmodels/tools/compatibility.py
index 0f269161ddb..45a680f1a90 100644
--- a/statsmodels/tools/compatibility.py
+++ b/statsmodels/tools/compatibility.py
@@ -1,18 +1,13 @@
 
 import numpy as np
-
-try:
-    from numpy.linalg import slogdet as np_slogdet
-except:
-    def np_slogdet(x):
-        return 1, np.log(np.linalg.det(x))
-
+from statsmodels.compat.python import StringIO
+from numpy.linalg import slogdet
 
 
 def getZipFile():
     '''return ZipFile class with open method for python < 2.6
 
-    for python < 2.6, the open method returns a StringIO.StringIO file_like
+    for python < 2.6, the open method returns a StringIO file_like
 
     Examples
     --------
@@ -31,6 +26,5 @@ class ZipFile(zipfile.ZipFile):
 
             def open(self, filename):
                 fullfilename = [f for f in self.namelist() if filename in f][0]
-                import StringIO
-                return StringIO.StringIO(self.read(fullfilename))
+                return StringIO(self.read(fullfilename))
         return ZipFile
diff --git a/statsmodels/tools/data.py b/statsmodels/tools/data.py
index c505c540a15..6a31c3f5962 100644
--- a/statsmodels/tools/data.py
+++ b/statsmodels/tools/data.py
@@ -8,38 +8,25 @@
 # good: if ndarray check passes then loading pandas is not triggered,
 
 
+from statsmodels.compat.python import range
 import numpy as np
+import pandas as pd
 
-def have_pandas():
-    try:
-        import pandas
-        return True
-    except ImportError:
-        return False
-    except Exception:
-        return False
+def _check_period_index(x, freq="M"):
+    from pandas import PeriodIndex, DatetimeIndex
+    if not isinstance(x.index, (DatetimeIndex, PeriodIndex)):
+        raise ValueError("The index must be a DatetimeIndex or PeriodIndex")
 
-def have_patsy():
-    try:
-        import patsy
-        return True
-    except ImportError:
-        return False
-    except Exception:
-        return False
+    from statsmodels.tsa.base.datetools import _infer_freq
+    inferred_freq = _infer_freq(x.index)
+    if not inferred_freq.startswith(freq):
+        raise ValueError("Expected frequency {}. Got {}".format(inferred_freq,
+                                                                freq))
 
 def is_data_frame(obj):
-    if not have_pandas():
-        return False
-
-    import pandas as pn
-
-    return isinstance(obj, pn.DataFrame)
+    return isinstance(obj, pd.DataFrame)
 
 def is_design_matrix(obj):
-    if not have_patsy():
-        return False
-
     from patsy import DesignMatrix
     return isinstance(obj, DesignMatrix)
 
@@ -106,10 +93,7 @@ def _is_using_ndarray(endog, exog):
             (isinstance(exog, np.ndarray) or exog is None))
 
 def _is_using_pandas(endog, exog):
-    if not have_pandas():
-        return False
-    from pandas import Series, DataFrame, WidePanel
-    klasses = (Series, DataFrame, WidePanel)
+    klasses = (pd.Series, pd.DataFrame, pd.WidePanel)
     return (isinstance(endog, klasses) or isinstance(exog, klasses))
 
 def _is_array_like(endog, exog):
diff --git a/statsmodels/tools/decorators.py b/statsmodels/tools/decorators.py
index e64569d9b41..57d0756fa3b 100644
--- a/statsmodels/tools/decorators.py
+++ b/statsmodels/tools/decorators.py
@@ -1,12 +1,11 @@
+from __future__ import print_function
+from statsmodels.tools.sm_exceptions import CacheWriteWarning
 from numpy.testing import assert_equal
+from statsmodels.compat.python import get_function_name
 import warnings
 
 __all__ = ['resettable_cache','cache_readonly', 'cache_writable']
 
-class CacheWriteWarning(UserWarning):
-    pass
-
-
 class ResettableCache(dict):
     """
     Dictionary whose elements mey depend one from another.
@@ -30,7 +29,7 @@ class ResettableCache(dict):
     >>> cache = resettable_cache(a=0, b=1, c=2, reset=reset)
     >>> assert_equal(cache, dict(a=0, b=1, c=2))
 
-    >>> print "Try resetting a"
+    >>> print("Try resetting a")
     >>> cache['a'] = 1
     >>> assert_equal(cache, dict(a=1, b=None, c=None))
     >>> cache['c'] = 2
@@ -38,7 +37,7 @@ class ResettableCache(dict):
     >>> cache['b'] = 0
     >>> assert_equal(cache, dict(a=1, b=0, c=None))
 
-    >>> print "Try deleting b"
+    >>> print("Try deleting b")
     >>> del(cache['a'])
     >>> assert_equal(cache, {})
     """
@@ -60,11 +59,11 @@ def __delitem__(self, key):
             del(self[mustreset])
 
 #    def __getstate__(self):
-#        print 'pickling wrapper', self.__dict__
+#        print('pickling wrapper', self.__dict__)
 #        return self.__dict__
 #
 #    def __setstate__(self, dict_):
-#        print 'unpickling wrapper', dict_
+#        print('unpickling wrapper', dict_)
 #        self.__dict__.update(dict_)
 
 resettable_cache = ResettableCache
@@ -89,12 +88,12 @@ def __get__(self, obj, type=None):
         # Get the name of the attribute to set and cache
         name = self.name
         _cachedval = _cache.get(name, None)
-#        print "[_cachedval=%s]" % _cachedval
+#        print("[_cachedval=%s]" % _cachedval)
         if _cachedval is None:
             # Call the "fget" function
             _cachedval = self.fget(obj)
             # Set the attribute in obj
-#            print "Setting %s in cache to %s" % (name, _cachedval)
+#            print("Setting %s in cache to %s" % (name, _cachedval))
             try:
                 _cache[name] = _cachedval
             except KeyError:
@@ -107,7 +106,7 @@ def __get__(self, obj, type=None):
                 except AttributeError:
                     pass
 #        else:
-#            print "Reading %s from cache (%s)" % (name, _cachedval)
+#            print("Reading %s from cache (%s)" % (name, _cachedval))
         return _cachedval
 
     def __set__(self, obj, value):
@@ -173,7 +172,7 @@ def __init__(self,func):
              the value of this computation.
              """
         self.getter = func
-        self.name = func.func_name
+        self.name = get_function_name(func)
 
     def __get__(self,obj,type=None):
         """This will be called on attribute access on the class or instance. """
@@ -182,11 +181,11 @@ def __get__(self,obj,type=None):
             # Being called on the class, return the original function. This way,
             # introspection works on the class.
             #return func
-            #print 'class access'
+            #print('class access')
             return self.getter
 
         val = self.getter(obj)
-        #print "** auto_attr - loading '%s'" % self.name  # dbg
+        #print("** auto_attr - loading '%s'" % self.name  # dbg)
         setattr(obj, self.name, val)
         return val
 
@@ -205,7 +204,7 @@ def nottest(fn):
     cache = resettable_cache(a=0, b=1, c=2, reset=reset)
     assert_equal(cache, dict(a=0, b=1, c=2))
     #
-    print "Try resetting a"
+    print("Try resetting a")
     cache['a'] = 1
     assert_equal(cache, dict(a=1, b=None, c=None))
     cache['c'] = 2
@@ -213,7 +212,7 @@ def nottest(fn):
     cache['b'] = 0
     assert_equal(cache, dict(a=1, b=0, c=None))
     #
-    print "Try deleting b"
+    print("Try deleting b")
     del(cache['a'])
     assert_equal(cache, {})
 ### ---------------------------------------------------------------------------
@@ -243,21 +242,21 @@ def f(self):
             return self.e + 1
 
     ex = Example()
-    print "(attrs  : %s)" % str(ex.__dict__)
-    print "(cached : %s)" % str(ex._cache)
-    print "Try a   :", ex.a
-    print "Try accessing/setting a readonly attribute"
+    print("(attrs  : %s)" % str(ex.__dict__))
+    print("(cached : %s)" % str(ex._cache))
+    print("Try a   :", ex.a)
+    print("Try accessing/setting a readonly attribute")
     assert_equal(ex.__dict__, dict(a=0, _cache={}))
-    print "Try b #1:", ex.b
+    print("Try b #1:", ex.b)
     b = ex.b
     assert_equal(b, 1)
     assert_equal(ex.__dict__, dict(a=0, _cache=dict(b=1,)))
 #   assert_equal(ex.__dict__, dict(a=0, b=1, _cache=dict(b=1)))
     ex.b = -1
-    print "Try dict", ex.__dict__
+    print("Try dict", ex.__dict__)
     assert_equal(ex._cache, dict(b=1,))
     #
-    print "Try accessing/resetting a cachewritable attribute"
+    print("Try accessing/resetting a cachewritable attribute")
     c = ex.c
     assert_equal(c, 2)
     assert_equal(ex._cache, dict(b=1, c=2))
diff --git a/statsmodels/tools/dump2module.py b/statsmodels/tools/dump2module.py
index f813bf306aa..8ce29aff57f 100644
--- a/statsmodels/tools/dump2module.py
+++ b/statsmodels/tools/dump2module.py
@@ -2,7 +2,8 @@
 
 Author : Josef Perktold
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import iterkeys
 import numpy as np
 
 class HoldIt(object):
@@ -121,7 +122,7 @@ def verify(self):
         for d in self._what:
             self_item = getattr(data, d)
             saved_item = getattr(data, d)
-            #print d,
+            #print(d)
             #try simple equality
             correct = np.all(self.item == saved_item)
             #try allclose
@@ -130,14 +131,14 @@ def verify(self):
                                   rtol=1e-16, atol=1e-16)
                 if not correct:
                     import warnings
-                    warnings.warm("inexact precision in "+d)
+                    warnings.warn("inexact precision in "+d, RuntimeWarning)
             #try iterating, if object array
             if not correct:
                 correlem =[np.all(data[d].item()[k] ==
                                   getattr(testsave.var_results, d).item()[k])
-                           for k in data[d].item().keys()]
+                           for k in iterkeys(data[d].item())]
                 if not correlem:
-                    #print d, "wrong"
+                    #print(d, "wrong")
                     incorrectli.append(d)
             correctli.append(d)
 
@@ -157,18 +158,18 @@ def verify(self):
     import testsave
 
     for d in data:
-        print d,
+        print(d)
         correct = np.all(data[d] == getattr(testsave.var_results, d))
         if not correct and not data[d].dtype == np.dtype('object'):
             correct = np.allclose(data[d], getattr(testsave.var_results, d),
                               rtol=1e-16, atol=1e-16)
-            if not correct: print "inexact precision"
+            if not correct: print("inexact precision")
         if not correct:
             correlem =[np.all(data[d].item()[k] ==
                               getattr(testsave.var_results, d).item()[k])
-                       for k in data[d].item().keys()]
+                       for k in iterkeys(data[d].item())]
             if not correlem:
-                print d, "wrong"
+                print(d, "wrong")
 
-    print res_var.verify()
+    print(res_var.verify())
 
diff --git a/statsmodels/tools/grouputils.py b/statsmodels/tools/grouputils.py
index 0ff051808a4..b0a86e89d54 100644
--- a/statsmodels/tools/grouputils.py
+++ b/statsmodels/tools/grouputils.py
@@ -30,9 +30,15 @@
 
 
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import lrange, lzip, range
 import numpy as np
-from statsmodels.compatnp.np_compat import npc_unique
+import pandas as pd
+from statsmodels.compat.numpy import npc_unique
+import statsmodels.tools.data as data_util
+from statsmodels.tools.decorators import cache_readonly
+from pandas.core.index import Index, MultiIndex
+
 
 def combine_indices(groups, prefix='', sep='.', return_labels=False):
     '''use np.unique to get integer group indices for product, intersection
@@ -44,35 +50,35 @@ def combine_indices(groups, prefix='', sep='.', return_labels=False):
         groups = np.asarray(groups)
 
     dt = groups.dtype
-    #print dt
+    #print(dt)
 
-    is2d = (groups.ndim == 2) #need to store
+    is2d = (groups.ndim == 2)  # need to store
 
     if is2d:
         ncols = groups.shape[1]
         if not groups.flags.c_contiguous:
             groups = np.array(groups, order='C')
 
-        groups_ = groups.view([('',groups.dtype)]*groups.shape[1])
+        groups_ = groups.view([('', groups.dtype)] * groups.shape[1])
     else:
         groups_ = groups
 
     uni, uni_idx, uni_inv = npc_unique(groups_, return_index=True,
-                                      return_inverse=True)
+                                       return_inverse=True)
 
     if is2d:
         uni = uni.view(dt).reshape(-1, ncols)
 
         #avoiding a view would be
-#        for t in uni.dtype.fields.values():
-#            assert (t[0] == dt)
-#
-#        uni.dtype = dt
-#        uni.shape = (uni.size//ncols, ncols)
+        #for t in uni.dtype.fields.values():
+        #    assert (t[0] == dt)
+        #
+        #uni.dtype = dt
+        #uni.shape = (uni.size//ncols, ncols)
 
     if return_labels:
         label = [(prefix+sep.join(['%s']*len(uni[0]))) % tuple(ii)
-                                           for ii in uni]
+                 for ii in uni]
         return uni_inv, uni_idx, uni, label
     else:
         return uni_inv, uni_idx, uni
@@ -93,18 +99,18 @@ def group_sums(x, group, use_bincount=True):
     '''
     x = np.asarray(x)
     if x.ndim == 1:
-        x = x[:,None]
+        x = x[:, None]
     elif x.ndim > 2 and use_bincount:
         raise ValueError('not implemented yet')
 
     if use_bincount:
-        return np.array([np.bincount(group, weights=x[:,col])
-                               for col in range(x.shape[1])])
+        return np.array([np.bincount(group, weights=x[:, col])
+                         for col in range(x.shape[1])])
     else:
         uniques = np.unique(group)
         result = np.zeros([len(uniques)] + list(x.shape[1:]))
         for ii, cat in enumerate(uniques):
-            result[ii] = x[g==cat].sum(0)
+            result[ii] = x[g == cat].sum(0)
         return result
 
 
@@ -113,20 +119,21 @@ def group_sums_dummy(x, group_dummy):
 
     group_dummy can be either ndarray or sparse matrix
     '''
-    if type(group_dummy) is np.ndarray:
+    if data_util._is_using_ndarray_type(group_dummy, None):
         return np.dot(x.T, group_dummy)
-    else:  #check for sparse
+    else:  # check for sparse
         return x.T * group_dummy
 
+
 def dummy_sparse(groups):
     '''create a sparse indicator from a group array with integer labels
 
     Parameters
     ----------
     groups: ndarray, int, 1d (nobs,)
-        an array of group indicators for each observation. Group levels are assumed
-        to be defined as consecutive integers, i.e. range(n_groups) where
-        n_groups is the number of group levels. A group level with no
+        an array of group indicators for each observation. Group levels are
+        assumed to be defined as consecutive integers, i.e. range(n_groups)
+        where n_groups is the number of group levels. A group level with no
         observations for it will still produce a column of zeros.
 
     Returns
@@ -206,7 +213,7 @@ def labels(self):
 
         if uni.ndim > 1:
             label = [(prefix+sep.join(['%s']*len(uni[0]))) % tuple(ii)
-            for ii in uni]
+                     for ii in uni]
         else:
             label = [prefix + '%s' % ii for ii in uni]
         return label
@@ -219,15 +226,14 @@ def dummy(self, drop_idx=None, sparse=False, dtype=int):
         '''
         uni = self.uni
         if drop_idx is not None:
-            idx = range(len(uni))
+            idx = lrange(len(uni))
             del idx[drop_idx]
             uni = uni[idx]
 
         group = self.group
 
         if not sparse:
-            return (group[:,None] == uni[None,:]).astype(dtype)
-
+            return (group[:, None] == uni[None, :]).astype(dtype)
         else:
             return dummy_sparse(self.group_int)
 
@@ -240,19 +246,19 @@ def group_sums(self, x, use_bincount=True):
         return group_sums(x, self.group_int, use_bincount=use_bincount)
 
     def group_demean(self, x, use_bincount=True):
-        means_g = group_demean(x/float(nobs), self.group_int,
-                               use_bincount=use_bincount)
-        x_demeaned = x - means_g[self.group_int]  #check reverse_index?
+        nobs = float(len(x))
+        means_g = group_sums(x / nobs, self.group_int,
+                             use_bincount=use_bincount)
+        x_demeaned = x - means_g[self.group_int]  # check reverse_index?
         return x_demeaned, means_g
 
 
 class GroupSorted(Group):
-
     def __init__(self, group, name=''):
         super(self.__class__, self).__init__(group, name=name)
 
         idx = (np.nonzero(np.diff(group))[0]+1).tolist()
-        self.groupidx = groupidx = zip([0]+idx, idx+[len(group)])
+        self.groupidx = groupidx = lzip([0]+idx, idx+[len(group)])
 
         ngroups = len(groupidx)
 
@@ -276,26 +282,287 @@ def lag_indices(self, lag):
         not tested yet
 
         '''
-        lag_idx = np.asarray(self.groupidx)[:,1] - lag   #asarray or already?
-        mask_ok = (low <= lag_idx)
+        lag_idx = np.asarray(self.groupidx)[:, 1] - lag  # asarray or already?
+        mask_ok = (lag <= lag_idx)
         #still an observation that belongs to the same individual
 
         return lag_idx[mask_ok]
 
 
+def _is_hierarchical(x):
+    """
+    Checks if the first item of an array-like object is also array-like
+    If so, we have a MultiIndex and returns True. Else returns False.
+    """
+    item = x[0]
+    # is there a better way to do this?
+    if isinstance(item, (list, tuple, np.ndarray, pd.Series, pd.DataFrame)):
+        return True
+    else:
+        return False
+
+
+def _make_hierarchical_index(index, names):
+    return MultiIndex.from_tuples(*[index], names=names)
+
+
+def _make_generic_names(index):
+    n_names = len(index.names)
+    pad = str(len(str(n_names)))  # number of digits
+    return [("group{0:0"+pad+"}").format(i) for i in range(n_names)]
+
+
+class Grouping(object):
+    def __init__(self, index, names=None):
+        '''
+        index : index-like
+            Can be pandas MultiIndex or Index or array-like. If array-like
+            and is a MultipleIndex (more than one grouping variable),
+            groups are expected to be in each row. E.g., [('red', 1),
+            ('red', 2), ('green', 1), ('green', 2)]
+        names : list or str, optional
+            The names to use for the groups. Should be a str if only
+            one grouping variable is used.
+
+        Notes
+        -----
+        If index is already a pandas Index then there is no copy.
+        '''
+        if isinstance(index, (Index, MultiIndex)):
+            if names is not None:
+                if hasattr(index, 'set_names'): # newer pandas
+                    index.set_names(names, inplace=True)
+                else:
+                    index.names = names
+            self.index = index
+        else:  # array-like
+            if _is_hierarchical(index):
+                self.index = _make_hierarchical_index(index, names)
+            else:
+                self.index = Index(index, name=names)
+            if names is None:
+                names = _make_generic_names(self.index)
+                if hasattr(self.index, 'set_names'):
+                    self.index.set_names(names, inplace=True)
+                else:
+                    self.index.names = names
+
+        self.nobs = len(self.index)
+        self.nlevels = len(self.index.names)
+        self.slices = None
+
+    @property
+    def index_shape(self):
+        if hasattr(self.index, 'levshape'):
+            return self.index.levshape
+        else:
+            return self.index.shape
+
+    @property
+    def levels(self):
+        if hasattr(self.index, 'levels'):
+            return self.index.levels
+        else:
+            return pd.Categorical(self.index).levels
+
+    @property
+    def labels(self):
+        # this was index_int, but that's not a very good name...
+        if hasattr(self.index, 'labels'):
+            return self.index.labels
+        else:  # pandas version issue here
+            return pd.Categorical(self.index).labels[None]
+
+    @property
+    def group_names(self):
+        return self.index.names
+
+    def reindex(self, index=None, names=None):
+        """
+        Resets the index in-place.
+        """
+        #NOTE: this isn't of much use if the rest of the data doesn't change
+        #This needs to reset cache
+        if names is None:
+            names = self.group_names
+        self = Grouping(index, names)
+
+    def get_slices(self, level=0):
+        '''
+        Sets the slices attribute to be a list of indices of the sorted
+        groups for the first index level. I.e., self.slices[0] is the
+        index where each observation is in the first (sorted) group.
+        '''
+        #TODO: refactor this
+        groups = self.index.get_level_values(level).unique()
+        groups.sort()
+        if isinstance(self.index, MultiIndex):
+            self.slices = [self.index.get_loc_level(x, level=level)[0]
+                           for x in groups]
+        else:
+            self.slices = [self.index.get_loc(x) for x in groups]
+
+    def count_categories(self, level=0):
+        """
+        Sets the attribute counts to equal the bincount of the (integer-valued)
+        labels.
+        """
+        #TODO: refactor this not to set an attribute. Why would we do this?
+        self.counts = np.bincount(self.labels[level])
+
+    def check_index(self, is_sorted=True, unique=True, index=None):
+        '''Sanity checks'''
+        if not index:
+            index = self.index
+        if is_sorted:
+            test = pd.DataFrame(lrange(len(index)), index=index)
+            test_sorted = test.sort()
+            if not test.index.equals(test_sorted.index):
+                raise Exception('Data is not be sorted')
+        if unique:
+            if len(index) != len(index.unique()):
+                raise Exception('Duplicate index entries')
+
+    def sort(self, data, index=None):
+        '''Applies a (potentially hierarchical) sort operation on a numpy array
+        or pandas series/dataframe based on the grouping index or a
+        user-supplied index.  Returns an object of the same type as the
+        original data as well as the matching (sorted) Pandas index.
+        '''
+
+        if index is None:
+            index = self.index
+        if data_util._is_using_ndarray_type(data, None):
+            if data.ndim == 1:
+                out = pd.Series(data, index=index, copy=True)
+                out = out.sort_index()
+            else:
+                out = pd.DataFrame(data, index=index)
+                out = out.sort(inplace=False)  # copies
+            return np.array(out), out.index
+        elif data_util._is_using_pandas(data, None):
+            out = data
+            out = out.reindex(index)  # copies?
+            out = out.sort_index()
+            return out, out.index
+        else:
+            msg = 'data must be a Numpy array or a Pandas Series/DataFrame'
+            raise ValueError(msg)
+
+    def transform_dataframe(self, dataframe, function, level=0, **kwargs):
+        '''Apply function to each column, by group
+        Assumes that the dataframe already has a proper index'''
+        if dataframe.shape[0] != self.nobs:
+            raise Exception('dataframe does not have the same shape as index')
+        out = dataframe.groupby(level=level).apply(function, **kwargs)
+        if 1 in out.shape:
+            return np.ravel(out)
+        else:
+            return np.array(out)
+
+    def transform_array(self, array, function, level=0, **kwargs):
+        '''Apply function to each column, by group'''
+        if array.shape[0] != self.nobs:
+            raise Exception('array does not have the same shape as index')
+        dataframe = pd.DataFrame(array, index=self.index)
+        return self.transform_dataframe(dataframe, function, level=level,
+                                        **kwargs)
+
+    def transform_slices(self, array, function, level=0, **kwargs):
+        '''Apply function to each group. Similar to transform_array but does
+        not coerce array to a DataFrame and back and only works on a 1D or 2D
+        numpy array. function is called function(group, group_idx, **kwargs).
+        '''
+        array = np.asarray(array)
+        if array.shape[0] != self.nobs:
+            raise Exception('array does not have the same shape as index')
+        # always reset because level is given. need to refactor this.
+        self.get_slices(level=level)
+        processed = []
+        for s in self.slices:
+            if array.ndim == 2:
+                subset = array[s, :]
+            elif array.ndim == 1:
+                subset = array[s]
+            processed.append(function(subset, s, **kwargs))
+        processed = np.array(processed)
+        return processed.reshape(-1, processed.shape[-1])
+
+    #TODO: this isn't general needs to be a PanelGrouping object
+    def dummies_time(self):
+        self.dummy_sparse(level=1)
+        return self._dummies
+
+    def dummies_groups(self, level=0):
+        self.dummy_sparse(level=level)
+        return self._dummies
+
+    def dummy_sparse(self, level=0):
+        '''create a sparse indicator from a group array with integer labels
+
+        Parameters
+        ----------
+        groups: ndarray, int, 1d (nobs,) an array of group indicators for each
+            observation. Group levels are assumed to be defined as consecutive
+            integers, i.e. range(n_groups) where n_groups is the number of
+            group levels. A group level with no observations for it will still
+            produce a column of zeros.
+
+        Returns
+        -------
+        indi : ndarray, int8, 2d (nobs, n_groups)
+            an indicator array with one row per observation, that has 1 in the
+            column of the group level for that observation
+
+        Examples
+        --------
+
+        >>> g = np.array([0, 0, 2, 1, 1, 2, 0])
+        >>> indi = dummy_sparse(g)
+        >>> indi
+        <7x3 sparse matrix of type '<type 'numpy.int8'>'
+            with 7 stored elements in Compressed Sparse Row format>
+        >>> indi.todense()
+        matrix([[1, 0, 0],
+                [1, 0, 0],
+                [0, 0, 1],
+                [0, 1, 0],
+                [0, 1, 0],
+                [0, 0, 1],
+                [1, 0, 0]], dtype=int8)
+
+
+        current behavior with missing groups
+        >>> g = np.array([0, 0, 2, 0, 2, 0])
+        >>> indi = dummy_sparse(g)
+        >>> indi.todense()
+        matrix([[1, 0, 0],
+                [1, 0, 0],
+                [0, 0, 1],
+                [1, 0, 0],
+                [0, 0, 1],
+                [1, 0, 0]], dtype=int8)
+        '''
+        from scipy import sparse
+        groups = self.labels[level]
+        indptr = np.arange(len(groups)+1)
+        data = np.ones(len(groups), dtype=np.int8)
+        self._dummies = sparse.csr_matrix((data, groups, indptr))
+
+
 if __name__ == '__main__':
 
     #---------- examples combine_indices
     from numpy.testing import assert_equal
 
     np.random.seed(985367)
-    groups = np.random.randint(0,2,size=(10,2))
+    groups = np.random.randint(0, 2, size=(10, 2))
     uv, ux, u, label = combine_indices(groups, return_labels=True)
     uv, ux, u, label = combine_indices(groups, prefix='g1,g2=', sep=',',
                                        return_labels=True)
 
-    group0 = np.array(['sector0', 'sector1'])[groups[:,0]]
-    group1 = np.array(['region0', 'region1'])[groups[:,1]]
+    group0 = np.array(['sector0', 'sector1'])[groups[:, 0]]
+    group1 = np.array(['region0', 'region1'])[groups[:, 1]]
     uv, ux, u, label = combine_indices((group0, group1),
                                        prefix='sector,region=',
                                        sep=',',
@@ -340,42 +607,43 @@ def lag_indices(self, lag):
     from scipy import sparse
 
     g = np.array([0, 0, 1, 2, 1, 1, 2, 0])
-    u = range(3)
+    u = lrange(3)
     indptr = np.arange(len(g)+1)
     data = np.ones(len(g), dtype=np.int8)
     a = sparse.csr_matrix((data, g, indptr))
-    print a.todense()
-    print np.all(a.todense() == (g[:,None] == np.arange(3)).astype(int))
+    print(a.todense())
+    print(np.all(a.todense() == (g[:, None] == np.arange(3)).astype(int)))
 
     x = np.arange(len(g)*3).reshape(len(g), 3, order='F')
 
-    print 'group means'
-    print x.T * a
-    print np.dot(x.T, g[:,None] == np.arange(3))
-    print np.array([np.bincount(g, weights=x[:,col]) for col in range(3)])
+    print('group means')
+    print(x.T * a)
+    print(np.dot(x.T, g[:, None] == np.arange(3)))
+    print(np.array([np.bincount(g, weights=x[:, col]) for col in range(3)]))
+    for cat in u:
+        print(x[g == cat].sum(0))
     for cat in u:
-        print x[g==cat].sum(0)
-    for cat in u: x[g==cat].sum(0)
+        x[g == cat].sum(0)
 
     cc = sparse.csr_matrix([[0, 1, 0, 1, 0, 0, 0, 0, 0],
-                [1, 0, 1, 0, 1, 0, 0, 0, 0],
-                [0, 1, 0, 0, 0, 1, 0, 0, 0],
-                [1, 0, 0, 0, 1, 0, 1, 0, 0],
-                [0, 1, 0, 1, 0, 1, 0, 1, 0],
-                [0, 0, 1, 0, 1, 0, 0, 0, 1],
-                [0, 0, 0, 1, 0, 0, 0, 1, 0],
-                [0, 0, 0, 0, 1, 0, 1, 0, 1],
-                [0, 0, 0, 0, 0, 1, 0, 1, 0]])
+                            [1, 0, 1, 0, 1, 0, 0, 0, 0],
+                            [0, 1, 0, 0, 0, 1, 0, 0, 0],
+                            [1, 0, 0, 0, 1, 0, 1, 0, 0],
+                            [0, 1, 0, 1, 0, 1, 0, 1, 0],
+                            [0, 0, 1, 0, 1, 0, 0, 0, 1],
+                            [0, 0, 0, 1, 0, 0, 0, 1, 0],
+                            [0, 0, 0, 0, 1, 0, 1, 0, 1],
+                            [0, 0, 0, 0, 0, 1, 0, 1, 0]])
 
     #------------- groupsums
-    print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2), g,
-                    use_bincount=False).T
-    print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2)[:,:,0], g)
-    print group_sums(np.arange(len(g)*3*2).reshape(len(g),3,2)[:,:,1], g)
+    print(group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2), g,
+                     use_bincount=False).T)
+    print(group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2)[:, :, 0], g))
+    print(group_sums(np.arange(len(g)*3*2).reshape(len(g), 3, 2)[:, :, 1], g))
 
     #------------- examples class
     x = np.arange(len(g)*3).reshape(len(g), 3, order='F')
     mygroup = Group(g)
-    print mygroup.group_int
-    print mygroup.group_sums(x)
-    print mygroup.labels()
+    print(mygroup.group_int)
+    print(mygroup.group_sums(x))
+    print(mygroup.labels())
diff --git a/statsmodels/tools/linalg.py b/statsmodels/tools/linalg.py
index 707fc1af05e..f3255e630dc 100644
--- a/statsmodels/tools/linalg.py
+++ b/statsmodels/tools/linalg.py
@@ -5,7 +5,8 @@
 The only changes are that additional results are returned
 
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import lmap, range
 import numpy as np
 from scipy.linalg import svd as decomp_svd
 #decomp_svd
@@ -58,7 +59,7 @@ def lstsq(a, b, cond=None, overwrite_a=0, overwrite_b=0):
     Raises LinAlgError if computation does not converge
 
     """
-    a1, b1 = map(asarray_chkfinite,(a,b))
+    a1, b1 = lmap(asarray_chkfinite,(a,b))
     if len(a1.shape) != 2:
         raise ValueError('expected matrix')
     m,n = a1.shape
@@ -202,8 +203,8 @@ def pinv2(a, cond=None, rcond=None):
     pinv2(a0)
     x = pinv(a0)
     x2=scipy.linalg.pinv(a0)
-    print np.max(np.abs(x-x2))
+    print(np.max(np.abs(x-x2)))
     x = pinv2(a0)
     x2 = scipy.linalg.pinv2(a0)
-    print np.max(np.abs(x-x2))
+    print(np.max(np.abs(x-x2)))
 
diff --git a/statsmodels/tools/numdiff.py b/statsmodels/tools/numdiff.py
index 0fee170a3e4..2c0e9a33d75 100644
--- a/statsmodels/tools/numdiff.py
+++ b/statsmodels/tools/numdiff.py
@@ -3,7 +3,8 @@
 Author : josef-pkt
 License : BSD
 """
-
+from __future__ import print_function
+from statsmodels.compat.python import range
 #These are simple forward differentiation, so that we have them available
 #without dependencies.
 #
@@ -368,12 +369,12 @@ def fun(beta, x):
         return np.dot(x, beta).sum(0)
 
     def fun1(beta, y, x):
-        #print beta.shape, x.shape
+        #print(beta.shape, x.shape)
         xb = np.dot(x, beta)
         return (y-xb)**2 #(xb-xb.mean(0))**2
 
     def fun2(beta, y, x):
-        #print beta.shape, x.shape
+        #print(beta.shape, x.shape)
         return fun1(beta, y, x).sum(0)
 
     nobs = 200
@@ -392,43 +393,43 @@ def fun2(beta, y, x):
     args = (y,x)
     from scipy import optimize
     xfmin = optimize.fmin(fun2, (0,0,0), args)
-    print approx_fprime((1,2,3),fun,epsilon,x)
+    print(approx_fprime((1,2,3),fun,epsilon,x))
     jac = approx_fprime(xk,fun1,epsilon,args)
     jacmin = approx_fprime(xk,fun1,-epsilon,args)
-    #print jac
-    print jac.sum(0)
-    print '\nnp.dot(jac.T, jac)'
-    print np.dot(jac.T, jac)
-    print '\n2*np.dot(x.T, x)'
-    print 2*np.dot(x.T, x)
+    #print(jac)
+    print(jac.sum(0))
+    print('\nnp.dot(jac.T, jac)')
+    print(np.dot(jac.T, jac))
+    print('\n2*np.dot(x.T, x)')
+    print(2*np.dot(x.T, x))
     jac2 = (jac+jacmin)/2.
-    print np.dot(jac2.T, jac2)
+    print(np.dot(jac2.T, jac2))
 
     #he = approx_hess(xk,fun2,epsilon,*args)
-    print approx_hess_old(xk,fun2,1e-3,args)
+    print(approx_hess_old(xk,fun2,1e-3,args))
     he = approx_hess_old(xk,fun2,None,args)
-    print 'hessfd'
-    print he
-    print 'epsilon =', None
-    print he[0] - 2*np.dot(x.T, x)
+    print('hessfd')
+    print(he)
+    print('epsilon =', None)
+    print(he[0] - 2*np.dot(x.T, x))
 
     for eps in [1e-3,1e-4,1e-5,1e-6]:
-        print 'eps =', eps
-        print approx_hess_old(xk,fun2,eps,args)[0] - 2*np.dot(x.T, x)
+        print('eps =', eps)
+        print(approx_hess_old(xk,fun2,eps,args)[0] - 2*np.dot(x.T, x))
 
     hcs2 = approx_hess_cs(xk,fun2,args=args)
-    print 'hcs2'
-    print hcs2 - 2*np.dot(x.T, x)
+    print('hcs2')
+    print(hcs2 - 2*np.dot(x.T, x))
 
     hfd3 = approx_hess(xk,fun2,args=args)
-    print 'hfd3'
-    print hfd3 - 2*np.dot(x.T, x)
+    print('hfd3')
+    print(hfd3 - 2*np.dot(x.T, x))
 
     import numdifftools as nd
     hnd = nd.Hessian(lambda a: fun2(a, y, x))
     hessnd = hnd(xk)
-    print 'numdiff'
-    print hessnd - 2*np.dot(x.T, x)
+    print('numdiff')
+    print(hessnd - 2*np.dot(x.T, x))
     #assert_almost_equal(hessnd, he[0])
     gnd = nd.Gradient(lambda a: fun2(a, y, x))
     gradnd = gnd(xk)
diff --git a/statsmodels/tools/parallel.py b/statsmodels/tools/parallel.py
index 5648596a200..9e0a880ad79 100644
--- a/statsmodels/tools/parallel.py
+++ b/statsmodels/tools/parallel.py
@@ -1,4 +1,4 @@
-'''Parallel utility function using joblib
+"""Parallel utility function using joblib
 
 copied from https://github.com/mne-tools/mne-python
 
@@ -8,7 +8,11 @@
 changes for statsmodels (Josef Perktold)
 - try import from joblib directly, (doesn't import all of sklearn)
 
-'''
+"""
+from __future__ import print_function
+
+from statsmodels.tools.sm_exceptions import (ModuleUnavailableWarning,
+                                           module_unavailable_doc)
 
 
 def parallel_func(func, n_jobs, verbose=5):
@@ -39,7 +43,7 @@ def parallel_func(func, n_jobs, verbose=5):
     >>> from math import sqrt
     >>> from statsmodels.tools.parallel import parallel_func
     >>> parallel, p_func, n_jobs = parallel_func(sqrt, n_jobs=-1, verbose=0)
-    >>> print n_jobs
+    >>> print(n_jobs)
     >>> parallel(p_func(i**2) for i in range(10))
     """
     try:
@@ -56,11 +60,15 @@ def parallel_func(func, n_jobs, verbose=5):
                 import multiprocessing
                 n_jobs = multiprocessing.cpu_count()
             except (ImportError, NotImplementedError):
-                print "multiprocessing not installed. Cannot run in parallel."
+                import warnings
+                warnings.warn(module_unavailable_doc.format('multiprocessing'),
+                              ModuleUnavailableWarning)
                 n_jobs = 1
 
     except ImportError:
-        print "joblib not installed. Cannot run in parallel."
+        import warnings
+        warnings.warn(module_unavailable_doc.format('joblib'),
+                      ModuleUnavailableWarning)
         n_jobs = 1
         my_func = func
         parallel = list
diff --git a/statsmodels/tools/print_version.py b/statsmodels/tools/print_version.py
index 41e8e8c646d..b55a51d5ef1 100755
--- a/statsmodels/tools/print_version.py
+++ b/statsmodels/tools/print_version.py
@@ -1,4 +1,6 @@
 #!/usr/bin/env python
+from __future__ import print_function
+from statsmodels.compat.python import reduce
 import sys
 from os.path import dirname
 
@@ -10,7 +12,128 @@ def safe_version(module, attr='__version__'):
     except AttributeError:
         return "Cannot detect version"
 
-def show_versions():
+
+def _show_versions_only():
+    print("\nINSTALLED VERSIONS")
+    print("------------------")
+    print("Python: %d.%d.%d.%s.%s" % sys.version_info[:])
+    try:
+        import os
+        (sysname, nodename, release, version, machine) = os.uname()
+        print("OS: %s %s %s %s" % (sysname, release, version,machine))
+        print("byteorder: %s" % sys.byteorder)
+        print("LC_ALL: %s" % os.environ.get('LC_ALL',"None"))
+        print("LANG: %s" % os.environ.get('LANG',"None"))
+    except:
+        pass
+
+    try:
+        import statsmodels
+        from statsmodels import version
+        has_sm = True
+    except ImportError:
+        has_sm = False
+
+    print('\nStatsmodels\n===========\n')
+    if has_sm:
+        print('Installed: %s' % safe_version(version, 'full_version'))
+    else:
+        print('Not installed')
+
+    print("\nRequired Dependencies\n=====================\n")
+    try:
+        import Cython
+        print("cython: %s" % safe_version(Cython))
+    except ImportError:
+        print("cython: Not installed")
+
+    try:
+        import numpy
+        print("numpy: %s" % safe_version(numpy, ['version', 'version']))
+    except ImportError:
+        print("numpy: Not installed")
+
+    try:
+        import scipy
+        print("scipy: %s" % safe_version(scipy, ['version', 'version']))
+    except ImportError:
+        print("scipy: Not installed")
+
+    try:
+        import pandas
+        print("pandas: %s" % safe_version(pandas, ['version', 'version']))
+    except ImportError:
+        print("pandas: Not installed")
+
+    try:
+        import dateutil
+        print("    dateutil: %s" % safe_version(dateutil))
+    except ImportError:
+        print("    dateutil: not installed")
+
+    try:
+        import patsy
+        print("patsy: %s" % safe_version(patsy))
+    except ImportError:
+        print("patsy: Not installed")
+
+    print("\nOptional Dependencies\n=====================\n")
+
+    try:
+        import matplotlib as mpl
+        print("matplotlib: %s" % safe_version(mpl))
+    except ImportError:
+        print("matplotlib: Not installed")
+
+    try:
+        from cvxopt import info
+        print("cvxopt: %s" % safe_version(info, 'version'))
+    except ImportError:
+        print("cvxopt: Not installed")
+
+    print("\nDeveloper Tools\n================\n")
+
+    try:
+        import IPython
+        print("IPython: %s" % safe_version(IPython))
+    except ImportError:
+        print("IPython: Not installed")
+    try:
+        import jinja2
+        print("    jinja2: %s" % safe_version(jinja2))
+    except ImportError:
+        print("    jinja2: Not installed")
+
+    try:
+        import sphinx
+        print("sphinx: %s" % safe_version(sphinx))
+    except ImportError:
+        print("sphinx: Not installed")
+
+    try:
+        import pygments
+        print("    pygments: %s" % safe_version(pygments))
+    except ImportError:
+        print("    pygments: Not installed")
+
+    try:
+        import nose
+        print("nose: %s" % safe_version(nose))
+    except ImportError:
+        print("nose: Not installed")
+
+    try:
+        import virtualenv
+        print("virtualenv: %s" % safe_version(virtualenv))
+    except ImportError:
+        print("virtualenv: Not installed")
+
+    print("\n")
+
+
+def show_versions(show_dirs=True):
+    if not show_dirs:
+        _show_versions_only()
     print("\nINSTALLED VERSIONS")
     print("------------------")
     print("Python: %d.%d.%d.%s.%s" % sys.version_info[:])
diff --git a/statsmodels/tools/rootfinding.py b/statsmodels/tools/rootfinding.py
index 12cdeae3f73..d9d8eea5f10 100644
--- a/statsmodels/tools/rootfinding.py
+++ b/statsmodels/tools/rootfinding.py
@@ -10,7 +10,7 @@
   - rewrite core loop to use for...except instead of while.
 
 """
-
+from __future__ import print_function
 import numpy as np
 from scipy import optimize
 
@@ -133,7 +133,7 @@ def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
             f_low = func(sl, *args)
             increasing = (f_low < f_upp)
             if DEBUG:
-                print 'symm', sl, su, f_low, f_upp
+                print('symm', sl, su, f_low, f_upp)
 
 
         # possibly func returns nan
@@ -168,9 +168,9 @@ def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
 
 
     if DEBUG:
-        print 'low, upp', low, upp, func(sl, *args), func(su, *args)
-        print 'increasing', increasing
-        print 'sl, su', sl, su
+        print('low, upp', low, upp, func(sl, *args), func(su, *args))
+        print('increasing', increasing)
+        print('sl, su', sl, su)
 
     if not increasing:
         sl, su = su, sl
@@ -198,7 +198,7 @@ def brentq_expanding(func, low=None, upp=None, args=(), xtol=1e-5,
         # right is now such that func(right) > q
 
     if n_it >= max_it:
-        #print 'Warning: max_it reached'
+        #print('Warning: max_it reached')
         #TODO: use Warnings, Note: brentq might still work even with max_it
         f_low = func(sl, *args)
         f_upp = func(su, *args)
diff --git a/statsmodels/tools/sm_exceptions.py b/statsmodels/tools/sm_exceptions.py
index b9f5f60c87f..ad7d1869c3a 100644
--- a/statsmodels/tools/sm_exceptions.py
+++ b/statsmodels/tools/sm_exceptions.py
@@ -1,5 +1,54 @@
+"""
+Contains custom errors and warnings.
+
+Errors should derive from Exception or another custom error. Custom errors are
+only needed it standard errors, for example ValueError or TypeError, are not
+accurate descriptions of the reason for the error.
+
+Warnings should derive from either an existing warning or another custom
+warning, and should usually be accompanied by a sting using the format
+warning_name_doc that services as a generic message to use when the warning is
+raised.
+"""
+
+# Errors
 class PerfectSeparationError(Exception):
     pass
 
+class MissingDataError(Exception):
+    pass
+# Warning
+class ModuleUnavailableWarning(Warning):
+    pass
+
+
+module_unavailable_doc = """
+The module {0} is not available. Cannot run in parallel.
+"""
+
+
+
 class ConvergenceWarning(UserWarning):
     pass
+
+
+convergence_doc = """
+Failed to converge on a solution.
+"""
+
+
+class CacheWriteWarning(UserWarning):
+    pass
+
+
+class IterationLimitWarning(UserWarning):
+    pass
+
+
+iteration_limit_doc = """
+Maximum iteration reached.
+"""
+
+
+class InvalidTestWarning(UserWarning):
+    pass
diff --git a/statsmodels/tools/tests/test_eval_measures.py b/statsmodels/tools/tests/test_eval_measures.py
index dd7b10097a8..d3e810b311e 100644
--- a/statsmodels/tools/tests/test_eval_measures.py
+++ b/statsmodels/tools/tests/test_eval_measures.py
@@ -5,6 +5,7 @@
 @author: josef
 """
 
+from statsmodels.compat.python import zip
 import numpy as np
 from numpy.testing import assert_equal, assert_almost_equal, assert_
 
diff --git a/statsmodels/tools/tests/test_grouputils.py b/statsmodels/tools/tests/test_grouputils.py
new file mode 100644
index 00000000000..4a5f77fce9e
--- /dev/null
+++ b/statsmodels/tools/tests/test_grouputils.py
@@ -0,0 +1,250 @@
+import numpy as np
+import pandas as pd
+from statsmodels.tools.grouputils import Grouping
+from statsmodels.tools.tools import categorical
+from statsmodels.datasets import grunfeld, anes96
+from pandas.util import testing as ptesting
+
+
+class CheckGrouping(object):
+
+    def test_reindex(self):
+        # smoke test
+        self.grouping.reindex(self.grouping.index)
+
+    def test_count_categories(self):
+        self.grouping.count_categories(level=0)
+        np.testing.assert_equal(self.grouping.counts, self.expected_counts)
+
+    def test_sort(self):
+        # data frame
+        sorted_data, index = self.grouping.sort(self.data)
+        expected_sorted_data = self.data.sort_index()
+
+        ptesting.assert_frame_equal(sorted_data, expected_sorted_data)
+        np.testing.assert_(isinstance(sorted_data, pd.DataFrame))
+        np.testing.assert_(not index.equals(self.grouping.index))
+
+        # make sure it copied
+        if hasattr(sorted_data, 'equals'): # newer pandas
+            np.testing.assert_(not sorted_data.equals(self.data))
+
+        # 2d arrays
+        sorted_data, index = self.grouping.sort(self.data.values)
+        np.testing.assert_array_equal(sorted_data,
+                                      expected_sorted_data.values)
+        np.testing.assert_(isinstance(sorted_data, np.ndarray))
+
+        # 1d series
+        series = self.data[self.data.columns[0]]
+        sorted_data, index = self.grouping.sort(series)
+
+        expected_sorted_data = series.sort_index()
+        ptesting.assert_series_equal(sorted_data, expected_sorted_data)
+        np.testing.assert_(isinstance(sorted_data, pd.Series))
+        if hasattr(sorted_data, 'equals'):
+            np.testing.assert_(not sorted_data.equals(series))
+
+        # 1d array
+        array = series.values
+        sorted_data, index = self.grouping.sort(array)
+
+        expected_sorted_data = series.sort_index().values
+        np.testing.assert_array_equal(sorted_data, expected_sorted_data)
+        np.testing.assert_(isinstance(sorted_data, np.ndarray))
+
+    def test_transform_dataframe(self):
+        names = self.data.index.names
+        transformed_dataframe = self.grouping.transform_dataframe(
+                                            self.data,
+                                            lambda x : x.mean(),
+                                            level=0)
+        expected = self.data.reset_index().groupby(names[0]
+                                            ).apply(lambda x : x.mean())[
+                                                    self.data.columns]
+        np.testing.assert_array_equal(transformed_dataframe,
+                                      expected.values)
+
+        if len(names) > 1:
+            transformed_dataframe = self.grouping.transform_dataframe(
+                                            self.data, lambda x : x.mean(),
+                                            level=1)
+            expected = self.data.reset_index().groupby(names[1]
+                                                      ).apply(lambda x :
+                                                              x.mean())[
+                                                        self.data.columns]
+            np.testing.assert_array_equal(transformed_dataframe,
+                                          expected.values)
+
+    def test_transform_array(self):
+        names = self.data.index.names
+        transformed_array = self.grouping.transform_array(
+                                            self.data.values,
+                                            lambda x : x.mean(),
+                                            level=0)
+        expected = self.data.reset_index().groupby(names[0]
+                                            ).apply(lambda x : x.mean())[
+                                                    self.data.columns]
+        np.testing.assert_array_equal(transformed_array,
+                                      expected.values)
+
+        if len(names) > 1:
+            transformed_array = self.grouping.transform_array(
+                                            self.data.values,
+                                            lambda x : x.mean(), level=1)
+            expected = self.data.reset_index().groupby(names[1]
+                                                      ).apply(lambda x :
+                                                              x.mean())[
+                                                        self.data.columns]
+            np.testing.assert_array_equal(transformed_array,
+                                          expected.values)
+
+
+    def test_transform_slices(self):
+        names = self.data.index.names
+        transformed_slices = self.grouping.transform_slices(
+                                            self.data.values,
+                                            lambda x, idx : x.mean(0),
+                                            level=0)
+        expected = self.data.reset_index().groupby(names[0]).mean()[
+                                                    self.data.columns]
+        np.testing.assert_allclose(transformed_slices, expected.values,
+                                   rtol=1e-12, atol=1e-25)
+
+        if len(names) > 1:
+            transformed_slices = self.grouping.transform_slices(
+                                            self.data.values,
+                                            lambda x, idx : x.mean(0),
+                                            level=1)
+            expected = self.data.reset_index().groupby(names[1]
+                                                       ).mean()[
+                                                        self.data.columns]
+            np.testing.assert_allclose(transformed_slices, expected.values,
+                                       rtol=1e-12, atol=1e-25)
+
+    def test_dummies_groups(self):
+        # smoke test, calls dummy_sparse under the hood
+        self.grouping.dummies_groups()
+
+        if len(self.grouping.group_names) > 1:
+            self.grouping.dummies_groups(level=1)
+
+    def test_dummy_sparse(self):
+        data = self.data
+        self.grouping.dummy_sparse()
+        expected = categorical(data.index.get_level_values(0).values,
+                               drop=True)
+        np.testing.assert_equal(self.grouping._dummies.toarray(), expected)
+
+        if len(self.grouping.group_names) > 1:
+            self.grouping.dummy_sparse(level=1)
+            expected = categorical(data.index.get_level_values(1).values,
+                    drop=True)
+            np.testing.assert_equal(self.grouping._dummies.toarray(),
+                                    expected)
+
+
+class TestMultiIndexGrouping(CheckGrouping):
+    @classmethod
+    def setupClass(cls):
+        grun_data = grunfeld.load_pandas().data
+        multi_index_data = grun_data.set_index(['firm', 'year'])
+        multi_index_panel = multi_index_data.index
+        cls.grouping = Grouping(multi_index_panel)
+        cls.data = multi_index_data
+
+        cls.expected_counts = [20] * 11
+
+
+class TestIndexGrouping(CheckGrouping):
+    @classmethod
+    def setupClass(cls):
+        grun_data = grunfeld.load_pandas().data
+        index_data = grun_data.set_index(['firm'])
+        index_group = index_data.index
+        cls.grouping = Grouping(index_group)
+        cls.data = index_data
+
+        cls.expected_counts = [20] * 11
+
+
+def test_init_api():
+    # make a multi-index panel
+    grun_data = grunfeld.load_pandas().data
+    multi_index_panel = grun_data.set_index(['firm', 'year']).index
+    grouping = Grouping(multi_index_panel)
+    # check group_names
+    np.testing.assert_array_equal(grouping.group_names, ['firm', 'year'])
+    # check shape
+    np.testing.assert_array_equal(grouping.index_shape, (11, 20))
+    # check index_int
+    np.testing.assert_array_equal(grouping.labels,
+      [[ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+         5, 5, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
+         8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
+         4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2,
+         2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,
+         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7,
+         7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
+         7, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
+         9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
+         10, 10, 10, 10, 10, 10, 10, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
+         6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3,
+         3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0,
+         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+       [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+         17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
+         14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+         11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7,
+         8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4,
+         5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1,
+         2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+         19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+         16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+         13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+         10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3, 4, 5, 6,
+         7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0, 1, 2, 3,
+         4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]])
+    grouping = Grouping(multi_index_panel, names=['firms', 'year'])
+    np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
+
+    # make a multi-index grouping
+    anes_data = anes96.load_pandas().data
+    multi_index_groups = anes_data.set_index(['educ', 'income',
+                                              'TVnews']).index
+    grouping = Grouping(multi_index_groups)
+    np.testing.assert_array_equal(grouping.group_names,
+                                  ['educ', 'income', 'TVnews'])
+    np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
+
+    # make a list multi-index panel
+    list_panel = multi_index_panel.tolist()
+    grouping = Grouping(list_panel, names=['firms', 'year'])
+    np.testing.assert_array_equal(grouping.group_names, ['firms', 'year'])
+    np.testing.assert_array_equal(grouping.index_shape, (11, 20))
+
+    # make a list multi-index grouping
+    list_groups = multi_index_groups.tolist()
+    grouping = Grouping(list_groups, names=['educ', 'income', 'TVnews'])
+    np.testing.assert_array_equal(grouping.group_names,
+                                  ['educ', 'income', 'TVnews'])
+    np.testing.assert_array_equal(grouping.index_shape, (7, 24, 8))
+
+
+    # single-variable index grouping
+    index_group = multi_index_panel.get_level_values(0)
+    grouping = Grouping(index_group)
+    # the original multi_index_panel had it's name changed inplace above
+    np.testing.assert_array_equal(grouping.group_names, ['firms'])
+    np.testing.assert_array_equal(grouping.index_shape, (220,))
+
+    # single variable list grouping
+    list_group = multi_index_panel.get_level_values(0).tolist()
+    grouping = Grouping(list_group)
+    np.testing.assert_array_equal(grouping.group_names, ["group0"])
+    np.testing.assert_array_equal(grouping.index_shape, 11*20)
+
+    # test generic group names
+    grouping = Grouping(list_groups)
+    np.testing.assert_array_equal(grouping.group_names,
+                                  ['group0', 'group1', 'group2'])
diff --git a/statsmodels/tools/tests/test_numdiff.py b/statsmodels/tools/tests/test_numdiff.py
index 27a5f813d65..50e4c6e4bcf 100644
--- a/statsmodels/tools/tests/test_numdiff.py
+++ b/statsmodels/tools/tests/test_numdiff.py
@@ -6,7 +6,7 @@
 Should Hessian also work per observation, if fun returns 2d
 
 '''
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_almost_equal, assert_allclose
 import statsmodels.api as sm
@@ -29,12 +29,12 @@ def fun(beta, x):
     return np.dot(x, beta).sum(0)
 
 def fun1(beta, y, x):
-    #print beta.shape, x.shape
+    #print(beta.shape, x.shape)
     xb = np.dot(x, beta)
     return (y-xb)**2 #(xb-xb.mean(0))**2
 
 def fun2(beta, y, x):
-    #print beta.shape, x.shape
+    #print(beta.shape, x.shape)
     return fun1(beta, y, x).sum(0)
 
 
@@ -91,7 +91,7 @@ def test_hess(self):
 
 class TestGradMNLogit(CheckGradLoglikeMixin):
     def __init__(self):
-        #from results.results_discrete import Anes
+        #from .results.results_discrete import Anes
         data = sm.datasets.anes96.load()
         exog = data.exog
         exog = sm.add_constant(exog, prepend=False)
@@ -299,21 +299,21 @@ def hesstrue(self, params):
     y = np.dot(x, beta) + 0.1*np.random.randn(nobs)
     xkols = np.dot(np.linalg.pinv(x),y)
 
-    print approx_fprime((1,2,3),fun,epsilon,x)
+    print(approx_fprime((1,2,3),fun,epsilon,x))
     gradtrue = x.sum(0)
-    print x.sum(0)
+    print(x.sum(0))
     gradcs = approx_fprime_cs((1,2,3), fun, (x,), h=1.0e-20)
-    print gradcs, maxabs(gradcs, gradtrue)
-    print approx_hess_cs((1,2,3), fun, (x,), h=1.0e-20)  #this is correctly zero
+    print(gradcs, maxabs(gradcs, gradtrue))
+    print(approx_hess_cs((1,2,3), fun, (x,), h=1.0e-20))  #this is correctly zero
 
-    print approx_hess_cs((1,2,3), fun2, (y,x), h=1.0e-20)-2*np.dot(x.T, x)
-    print numdiff.approx_hess(xk,fun2,1e-3, (y,x))[0] - 2*np.dot(x.T, x)
+    print(approx_hess_cs((1,2,3), fun2, (y,x), h=1.0e-20)-2*np.dot(x.T, x))
+    print(numdiff.approx_hess(xk,fun2,1e-3, (y,x))[0] - 2*np.dot(x.T, x))
 
     gt = (-x*2*(y-np.dot(x, [1,2,3]))[:,None])
     g = approx_fprime_cs((1,2,3), fun1, (y,x), h=1.0e-20)#.T   #this shouldn't be transposed
     gd = numdiff.approx_fprime((1,2,3),fun1,epsilon,(y,x))
-    print maxabs(g, gt)
-    print maxabs(gd, gt)
+    print(maxabs(g, gt))
+    print(maxabs(gd, gt))
 
 
     import statsmodels.api as sm
@@ -331,25 +331,25 @@ def hesstrue(self, params):
     #cs doesn't work for Probit because special.ndtr doesn't support complex
     #maybe calculating ndtr for real and imag parts separately, if we need it
     #and if it still works in this case
-    print 'sm', score(test_params)
-    print 'fd', numdiff.approx_fprime(test_params,loglike,epsilon)
-    print 'cs', numdiff.approx_fprime_cs(test_params,loglike)
-    print 'sm', hess(test_params)
-    print 'fd', numdiff.approx_fprime(test_params,score,epsilon)
-    print 'cs', numdiff.approx_fprime_cs(test_params, score)
-
-    #print 'fd', numdiff.approx_hess(test_params, loglike, epsilon) #TODO: bug
+    print('sm', score(test_params))
+    print('fd', numdiff.approx_fprime(test_params,loglike,epsilon))
+    print('cs', numdiff.approx_fprime_cs(test_params,loglike))
+    print('sm', hess(test_params))
+    print('fd', numdiff.approx_fprime(test_params,score,epsilon))
+    print('cs', numdiff.approx_fprime_cs(test_params, score))
+
+    #print('fd', numdiff.approx_hess(test_params, loglike, epsilon)) #TODO: bug
     '''
     Traceback (most recent call last):
       File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\test_numdiff.py", line 74, in <module>
-        print 'fd', numdiff.approx_hess(test_params, loglike, epsilon)
+        print('fd', numdiff.approx_hess(test_params, loglike, epsilon))
       File "C:\Josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\statsmodels\sandbox\regression\numdiff.py", line 118, in approx_hess
         xh = x + h
     TypeError: can only concatenate list (not "float") to list
     '''
     hesscs = numdiff.approx_hess_cs(test_params, loglike)
-    print 'cs', hesscs
-    print maxabs(hess(test_params), hesscs)
+    print('cs', hesscs)
+    print(maxabs(hess(test_params), hesscs))
 
     data = sm.datasets.anes96.load()
     exog = data.exog
diff --git a/statsmodels/tools/tests/test_parallel.py b/statsmodels/tools/tests/test_parallel.py
index 7af5fb28d69..6c5866aba5d 100644
--- a/statsmodels/tools/tests/test_parallel.py
+++ b/statsmodels/tools/tests/test_parallel.py
@@ -1,9 +1,13 @@
+from statsmodels.compat.python import range
+import warnings
 from statsmodels.tools.parallel import parallel_func
 from numpy import arange, testing
 from math import sqrt
 
 def test_parallel():
     x = arange(10.)
-    parallel, p_func, n_jobs = parallel_func(sqrt, n_jobs=-1, verbose=0)
-    y = parallel(p_func(i**2) for i in range(10))
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        parallel, p_func, n_jobs = parallel_func(sqrt, n_jobs=-1, verbose=0)
+        y = parallel(p_func(i**2) for i in range(10))
     testing.assert_equal(x,y)
diff --git a/statsmodels/tools/tests/test_tools.py b/statsmodels/tools/tests/test_tools.py
index a2ae9568124..21d2548932b 100644
--- a/statsmodels/tools/tests/test_tools.py
+++ b/statsmodels/tools/tests/test_tools.py
@@ -1,7 +1,7 @@
 """
 Test functions for models.tools
 """
-
+from statsmodels.compat.python import lrange, range
 import numpy as np
 from numpy.random import standard_normal
 from numpy.testing import (assert_equal, assert_array_equal,
@@ -10,11 +10,14 @@
 
 from statsmodels.datasets import longley
 from statsmodels.tools import tools
+from statsmodels.tools.tools import pinv_extended
+from statsmodels.compat.numpy import np_matrix_rank
+
 
 class TestTools(TestCase):
 
     def test_add_constant_list(self):
-        x = range(1,5)
+        x = lrange(1,5)
         x = tools.add_constant(x)
         y = np.asarray([[1,1,1,1],[1,2,3,4.]]).T
         assert_equal(x, y)
@@ -46,24 +49,48 @@ def test_recipr0(self):
         assert_almost_equal(Y, np.array([[0.5,1],[-0.25,0]]))
 
     def test_rank(self):
-        X = standard_normal((40,10))
-        self.assertEquals(tools.rank(X), 10)
-
-        X[:,0] = X[:,1] + X[:,2]
-        self.assertEquals(tools.rank(X), 9)
+        import warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            X = standard_normal((40,10))
+            self.assertEquals(tools.rank(X), np_matrix_rank(X))
+
+            X[:,0] = X[:,1] + X[:,2]
+            self.assertEquals(tools.rank(X), np_matrix_rank(X))
+
+    def test_extendedpinv(self):
+        X = standard_normal((40, 10))
+        np_inv = np.linalg.pinv(X)
+        np_sing_vals = np.linalg.svd(X, 0, 0)
+        sm_inv, sing_vals = pinv_extended(X)
+        assert_almost_equal(np_inv, sm_inv)
+        assert_almost_equal(np_sing_vals, sing_vals)
+
+    def test_extendedpinv_singular(self):
+        X = standard_normal((40, 10))
+        X[:, 5] = X[:, 1] + X[:, 3]
+        np_inv = np.linalg.pinv(X)
+        np_sing_vals = np.linalg.svd(X, 0, 0)
+        sm_inv, sing_vals = pinv_extended(X)
+        assert_almost_equal(np_inv, sm_inv)
+        assert_almost_equal(np_sing_vals, sing_vals)
 
     def test_fullrank(self):
-        X = standard_normal((40,10))
-        X[:,0] = X[:,1] + X[:,2]
-
-        Y = tools.fullrank(X)
-        self.assertEquals(Y.shape, (40,9))
-        self.assertEquals(tools.rank(Y), 9)
-
-        X[:,5] = X[:,3] + X[:,4]
-        Y = tools.fullrank(X)
-        self.assertEquals(Y.shape, (40,8))
-        self.assertEquals(tools.rank(Y), 8)
+        import warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            X = standard_normal((40,10))
+            X[:,0] = X[:,1] + X[:,2]
+
+            Y = tools.fullrank(X)
+            self.assertEquals(Y.shape, (40,9))
+            self.assertEquals(tools.rank(Y), 9)
+
+            X[:,5] = X[:,3] + X[:,4]
+            Y = tools.fullrank(X)
+            self.assertEquals(Y.shape, (40,8))
+            warnings.simplefilter("ignore")
+            self.assertEquals(tools.rank(Y), 8)
 
 
 def test_estimable():
diff --git a/statsmodels/tools/tests/test_transform_model.py b/statsmodels/tools/tests/test_transform_model.py
new file mode 100644
index 00000000000..285d8b6492e
--- /dev/null
+++ b/statsmodels/tools/tests/test_transform_model.py
@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 27 13:26:01 2014
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy import stats
+
+from statsmodels.regression.linear_model import OLS
+from statsmodels.tools.transform_model import StandardizeTransform
+
+
+def test_standardize1():
+
+    np.random.seed(123)
+    x = 1 + np.random.randn(5, 4)
+
+    transf = StandardizeTransform(x)
+    xs1 = transf(x)
+
+    assert_allclose(transf.mean, x.mean(0), rtol=1e-13)
+    assert_allclose(transf.scale, x.std(0, ddof=1), rtol=1e-13)
+
+    xs2 = stats.zscore(x, ddof=1)
+    assert_allclose(xs1, xs2, rtol=1e-13, atol=1e-20)
+
+    # check we use stored transformation
+    xs4 = transf(2 * x)
+    assert_allclose(xs4, (2*x - transf.mean) / transf.scale, rtol=1e-13, atol=1e-20)
+
+
+    # affine transform doesn't change standardized
+    x2 = 2 * x + np.random.randn(4)
+    transf2 = StandardizeTransform(x2)
+    xs3 = transf2(x2)
+    assert_allclose(xs3, xs1, rtol=1e-13, atol=1e-20)
+
+    # check constant
+    x5 = np.column_stack((np.ones(x.shape[0]), x))
+    transf5 = StandardizeTransform(x5)
+    xs5 = transf5(x5)
+
+    assert_equal(transf5.const_idx, 0)
+    assert_equal(xs5[:, 0], np.ones(x.shape[0]))
+    assert_allclose(xs5[:, 1:], xs1, rtol=1e-13, atol=1e-20)
+
+
+def test_standardize_ols():
+
+    np.random.seed(123)
+    nobs = 20
+    x = 1 + np.random.randn(nobs, 4)
+    exog = np.column_stack((np.ones(nobs), x))
+    endog = exog.sum(1) + np.random.randn(nobs)
+
+    res2 = OLS(endog, exog).fit()
+    transf = StandardizeTransform(exog)
+    exog_st = transf(exog)
+    res1 = OLS(endog, exog_st).fit()
+    params = transf.transform_params(res1.params)
+    assert_allclose(params, res2.params, rtol=1e-13)
+
+
+
+test_standardize1()
+test_standardize_ols()
diff --git a/statsmodels/tools/tools.py b/statsmodels/tools/tools.py
index 1d6d40a9a60..a2cdf24e851 100644
--- a/statsmodels/tools/tools.py
+++ b/statsmodels/tools/tools.py
@@ -1,7 +1,8 @@
 '''
 Utility functions models code
 '''
-
+from statsmodels.compat.python import (reduce, lzip, lmap, asstr2, urlopen, urljoin,
+                                StringIO, range)
 import numpy as np
 import numpy.lib.recfunctions as nprf
 import numpy.linalg as L
@@ -10,7 +11,7 @@
 from statsmodels.distributions import (ECDF, monotone_fn_inverter,
                                                StepFunction)
 from statsmodels.tools.data import _is_using_pandas
-from statsmodels.compatnp.py3k import asstr2
+from statsmodels.compat.numpy import np_matrix_rank
 from pandas import DataFrame
 
 def _make_dictnames(tmp_arr, offset=0):
@@ -182,9 +183,9 @@ def categorical(data, col=None, dictnames=False, drop=False, ):
             if len(data.dtype) <= 1:
                 if tmp_dummy.shape[0] < tmp_dummy.shape[1]:
                     tmp_dummy = np.squeeze(tmp_dummy).swapaxes(1,0)
-                dt = zip(tmp_arr, [tmp_dummy.dtype.str]*len(tmp_arr))
+                dt = lzip(tmp_arr, [tmp_dummy.dtype.str]*len(tmp_arr))
                 # preserve array type
-                return np.array(map(tuple, tmp_dummy.tolist()),
+                return np.array(lmap(tuple, tmp_dummy.tolist()),
                         dtype=dt).view(type(data))
 
             data=nprf.drop_fields(data, col, usemask=False,
@@ -340,11 +341,34 @@ def isestimable(C, D):
     if C.shape[1] != D.shape[1]:
         raise ValueError('Contrast should have %d columns' % D.shape[1])
     new = np.vstack([C, D])
-    if rank(new) != rank(D):
+    if np_matrix_rank(new) != np_matrix_rank(D):
         return False
     return True
 
 
+def pinv_extended(X, rcond=1e-15):
+    """
+    Return the pinv of an array X as well as the singular values
+    used in computation.
+
+    Code adapted from numpy.
+    """
+    X = np.asarray(X)
+    X = X.conjugate()
+    u, s, vt = np.linalg.svd(X, 0)
+    s_orig = np.copy(s)
+    m = u.shape[0]
+    n = vt.shape[1]
+    cutoff = rcond * np.maximum.reduce(s)
+    for i in range(min(n, m)):
+        if s[i] > cutoff:
+            s[i] = 1./s[i]
+        else:
+            s[i] = 0.
+    res = np.dot(np.transpose(vt), np.multiply(s[:, np.core.newaxis], np.transpose(u)))
+    return res, s_orig
+
+
 def recipr(X):
     """
     Return the reciprocal of an array, setting all entries less than or
@@ -376,6 +400,9 @@ def rank(X, cond=1.0e-12):
     Return the rank of a matrix X based on its generalized inverse,
     not the SVD.
     """
+    from warnings import warn
+    warn("rank is deprecated and will be removed in 0.7."
+         " Use np.linalg.matrix_rank instead.", FutureWarning)
     X = np.asarray(X)
     if len(X.shape) == 2:
         D = svdvals(X)
@@ -393,7 +420,7 @@ def fullrank(X, r=None):
     """
 
     if r is None:
-        r = rank(X)
+        r = np_matrix_rank(X)
 
     V, D, U = L.svd(X, full_matrices=0)
     order = np.argsort(D)
@@ -486,9 +513,6 @@ def webuse(data, baseurl='http://www.stata-press.com/data/r11/', as_df=True):
     """
     # lazy imports
     from statsmodels.iolib import genfromdta
-    from urllib2 import urlopen
-    from urlparse import urljoin
-    from StringIO import StringIO
 
     url = urljoin(baseurl, data+'.dta')
     dta = urlopen(url)
@@ -530,3 +554,11 @@ def maybe_unwrap_results(results):
     routines.
     """
     return getattr(results, '_results', results)
+
+class Bunch(dict):
+    """
+    Returns a dict-like object with keys accessible via attribute lookup.
+    """
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
diff --git a/statsmodels/tools/transform_model.py b/statsmodels/tools/transform_model.py
new file mode 100644
index 00000000000..397c7656220
--- /dev/null
+++ b/statsmodels/tools/transform_model.py
@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 27 13:23:24 2014
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+
+
+class StandardizeTransform(object):
+    """class to reparameterize a model for standardized exog
+
+    Parameters
+    ----------
+    data : array_like
+        data that is standardized along axis=0
+    ddof : None or int
+        degrees of freedom for calculation of standard deviation.
+        default is 1, in contrast to numpy.std
+    const_idx : None or int
+        If None, then the presence of a constant is detected if the standard
+        deviation of a column is **equal** to zero. A constant column is
+        not transformed. If this is an integer, then the corresponding column
+        will not be transformed.
+    demean : bool, default is True
+        If demean is true, then the data will be demeaned, otherwise it will
+        only be rescaled.
+
+    Notes
+    -----
+    Warning: Not all options are tested and it is written for one use case.
+    API changes are expected.
+
+    This can be used to transform only the design matrix, exog, in a model,
+    which is required in some discrete models when the endog cannot be rescaled
+    or demeaned.
+    The transformation is full rank and does not drop the constant.
+
+    """
+
+    def __init__(self, data, ddof=1, const_idx=None, demean=True):
+        data = np.asarray(data)
+        self.mean = data.mean(0)
+        self.scale = data.std(0, ddof=1)
+
+        # do not transform a constant
+        if const_idx is None:
+            const_idx = np.nonzero(self.scale == 0)[0]
+            if len(const_idx) == 0:
+                const_idx = 'nc'
+
+        if const_idx != 'nc':
+            self.mean[const_idx] = 0
+            self.scale[const_idx] = 1
+
+
+        if demean is False:
+            self.mean = None
+
+        self.const_idx = const_idx
+
+    def transform(self, data):
+        """standardize the data using the stored transformation
+
+        """
+        # could use scipy.stats.zscore instead
+        if self.mean is None:
+            return np.asarray(data) / self.scale
+        else:
+            return (np.asarray(data) - self.mean) / self.scale
+
+    def transform_params(self, params):
+        """Transform parameters of the standardized model to the original model
+
+        Parameters
+        ----------
+        params : ndarray
+            parameters estimated with the standardized model
+
+        Returns
+        -------
+        params_new : ndarray
+            parameters transformed to the parameterization of the original model
+
+        """
+
+        params_new = params / self.scale
+        if self.const_idx != 'nc':
+            params_new[self.const_idx] -= (params_new * self.mean).sum()
+
+        return params_new
+
+
+    __call__ = transform
+
diff --git a/statsmodels/tsa/api.py b/statsmodels/tsa/api.py
index bfe0f84a1e8..0c02057ef9b 100644
--- a/statsmodels/tsa/api.py
+++ b/statsmodels/tsa/api.py
@@ -1,14 +1,15 @@
 from .ar_model import AR
 from .arima_model import ARMA, ARIMA
-import vector_ar as var
+from . import vector_ar as var
 from .vector_ar.var_model import VAR
 from .vector_ar.svar_model import SVAR
 from .vector_ar.dynamic import DynamicVAR
-import filters
-import tsatools
+from .filters import api as filters
+from . import tsatools
 from .tsatools import (add_trend, detrend, lagmat, lagmat2ds, add_lag)
-import interp
-import stattools
-from .stattools import (adfuller, acovf, q_stat, acf, pacf_yw, pacf_ols, pacf,
-                            ccovf, ccf, periodogram, grangercausalitytests)
+from . import interp
+from . import stattools
+from .stattools import *
 from .base import datetools
+from .seasonal import seasonal_decompose
+from ..graphics import tsaplots as graphics
diff --git a/statsmodels/tsa/ar_model.py b/statsmodels/tsa/ar_model.py
index 23e0a917ba5..f0af7ee670c 100644
--- a/statsmodels/tsa/ar_model.py
+++ b/statsmodels/tsa/ar_model.py
@@ -1,20 +1,18 @@
 from __future__ import division
+from statsmodels.compat.python import iteritems, range, string_types, lmap
 
 import numpy as np
-from numpy import (dot, identity, atleast_2d, atleast_1d, zeros)
-from numpy.linalg import inv
-from scipy import optimize
-from scipy.stats import t, norm, ss as sumofsq
+from numpy import dot, identity
+from numpy.linalg import inv, slogdet
+from scipy.stats import norm, ss as sumofsq
 from statsmodels.regression.linear_model import OLS
 from statsmodels.tsa.tsatools import (lagmat, add_trend,
-                _ar_transparams, _ar_invtransparams)
+                                      _ar_transparams, _ar_invtransparams)
 import statsmodels.tsa.base.tsa_model as tsbase
 import statsmodels.base.model as base
 from statsmodels.tools.decorators import (resettable_cache,
-        cache_readonly, cache_writable)
-from statsmodels.tools.compatibility import np_slogdet
-from statsmodels.tools.numdiff import (approx_fprime, approx_hess,
-        approx_hess_cs)
+                                          cache_readonly, cache_writable)
+from statsmodels.tools.numdiff import approx_fprime, approx_hess
 from statsmodels.tsa.kalmanf.kalmanfilter import KalmanFilter
 import statsmodels.base.wrapper as wrap
 from statsmodels.tsa.vector_ar import util
@@ -23,30 +21,33 @@
 
 __all__ = ['AR']
 
+
 def _check_ar_start(start, k_ar, method, dynamic):
     if (method == 'cmle' or dynamic) and start < k_ar:
         raise ValueError("Start must be >= k_ar for conditional MLE "
-                "or dynamic forecast. Got %d" % start)
+                         "or dynamic forecast. Got %d" % start)
+
 
 def _validate(start, k_ar, dates, method):
     """
     Checks the date and then returns an integer
     """
     from datetime import datetime
-    if isinstance(start, (basestring, datetime)):
+    if isinstance(start, (string_types, datetime)):
         start_date = start
         start = _index_date(start, dates)
     if 'mle' not in method and start < k_ar:
         raise ValueError("Start must be >= k_ar for conditional MLE or "
-                "dynamic forecast. Got %s" % start_date)
+                         "dynamic forecast. Got %s" % start_date)
     return start
 
+
 def _ar_predict_out_of_sample(y, params, p, k_trend, steps, start=0):
-    mu = params[:k_trend] or 0 # only have to worry about constant
-    arparams = params[k_trend:][::-1] # reverse for dot
+    mu = params[:k_trend] or 0  # only have to worry about constant
+    arparams = params[k_trend:][::-1]  # reverse for dot
 
     # dynamic endogenous variable
-    endog = np.zeros(p + steps) # this is one too big but doesn't matter
+    endog = np.zeros(p + steps)  # this is one too big but doesn't matter
     if start:
         endog[:p] = y[start-p:start]
     else:
@@ -61,18 +62,18 @@ def _ar_predict_out_of_sample(y, params, p, k_trend, steps, start=0):
     return forecast
 
 
-
 class AR(tsbase.TimeSeriesModel):
     __doc__ = tsbase._tsa_doc % {"model" : "Autoregressive AR(p) model",
-            "params" : """endog : array-like
+                                 "params" : """endog : array-like
         1-d endogenous response variable. The independent variable.""",
-        "extra_params" : base._missing_param_doc,
-        "extra_sections" : ""}
+                                 "extra_params" : base._missing_param_doc,
+                                 "extra_sections" : ""}
+
     def __init__(self, endog, dates=None, freq=None, missing='none'):
         super(AR, self).__init__(endog, None, dates, freq, missing=missing)
-        endog = self.endog # original might not have been an ndarray
+        endog = self.endog  # original might not have been an ndarray
         if endog.ndim == 1:
-            endog = endog[:,None]
+            endog = endog[:, None]
             self.endog = endog  # to get shapes right
         elif endog.ndim > 1 and endog.shape[1] != 1:
             raise ValueError("Only the univariate case is implemented")
@@ -119,25 +120,25 @@ def _presample_fit(self, params, start, p, end, y, predictedvalues):
         R_mat = KalmanFilter.R(params, p, k, 0, p)
 
         # Initial State mean and variance
-        alpha = np.zeros((p,1))
-        Q_0 = dot(inv(identity(p**2)-np.kron(T_mat,T_mat)),dot(R_mat,
-                R_mat.T).ravel('F'))
+        alpha = np.zeros((p, 1))
+        Q_0 = dot(inv(identity(p**2)-np.kron(T_mat, T_mat)),
+                  dot(R_mat, R_mat.T).ravel('F'))
 
-        Q_0 = Q_0.reshape(p,p, order='F') #TODO: order might need to be p+k
+        Q_0 = Q_0.reshape(p, p, order='F')  # TODO: order might need to be p+k
         P = Q_0
         Z_mat = KalmanFilter.Z(p)
-        for i in xrange(end): #iterate p-1 times to fit presample
-            v_mat = y[i] - dot(Z_mat,alpha)
+        for i in range(end):  # iterate p-1 times to fit presample
+            v_mat = y[i] - dot(Z_mat, alpha)
             F_mat = dot(dot(Z_mat, P), Z_mat.T)
-            Finv = 1./F_mat # inv. always scalar
-            K = dot(dot(dot(T_mat,P),Z_mat.T),Finv)
+            Finv = 1./F_mat  # inv. always scalar
+            K = dot(dot(dot(T_mat, P), Z_mat.T), Finv)
             # update state
-            alpha = dot(T_mat, alpha) + dot(K,v_mat)
-            L = T_mat - dot(K,Z_mat)
+            alpha = dot(T_mat, alpha) + dot(K, v_mat)
+            L = T_mat - dot(K, Z_mat)
             P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)
-    #            P[0,0] += 1 # for MA part, R_mat.R_mat.T above
-            if i >= start-1: #only record if we ask for it
-                predictedvalues[i+1-start] = dot(Z_mat,alpha)
+            #P[0,0] += 1 # for MA part, R_mat.R_mat.T above
+            if i >= start - 1:  # only record if we ask for it
+                predictedvalues[i + 1 - start] = dot(Z_mat, alpha)
 
     def _get_predict_start(self, start, dynamic):
         method = getattr(self, 'method', 'mle')
@@ -145,11 +146,11 @@ def _get_predict_start(self, start, dynamic):
         if start is None:
             if method == 'mle' and not dynamic:
                 start = 0
-            else: # can't do presample fit for cmle or dynamic
+            else:  # can't do presample fit for cmle or dynamic
                 start = k_ar
         elif isinstance(start, int):
             start = super(AR, self)._get_predict_start(start)
-        else: # should be a date
+        else:  # should be a date
             start = _validate(start, k_ar, self.data.dates, method)
             start = super(AR, self)._get_predict_start(start)
         _check_ar_start(start, k_ar, method, dynamic)
@@ -204,20 +205,19 @@ def predict(self, params, start=None, end=None, dynamic=False):
         if dynamic:
             out_of_sample += end - start + 1
             return _ar_predict_out_of_sample(endog, params, k_ar,
-                    k_trend, out_of_sample, start)
+                                             k_trend, out_of_sample, start)
 
-
-        predictedvalues = np.zeros(end+1-start)
+        predictedvalues = np.zeros(end + 1 - start)
 
         # fit pre-sample
-        if method == 'mle': # use Kalman Filter to get initial values
+        if method == 'mle':  # use Kalman Filter to get initial values
             if k_trend:
                 mu = params[0]/(1-np.sum(params[k_trend:]))
 
             # modifies predictedvalues in place
             if start < k_ar:
                 self._presample_fit(params, start, k_ar, min(k_ar-1, end),
-                        endog[:k_ar]-mu, predictedvalues)
+                                    endog[:k_ar] - mu, predictedvalues)
                 predictedvalues[:k_ar-start] += mu
 
         if end < k_ar:
@@ -233,7 +233,8 @@ def predict(self, params, start=None, end=None, dynamic=False):
 
         if out_of_sample:
             forecastvalues = _ar_predict_out_of_sample(endog, params,
-                                        k_ar, k_trend, out_of_sample)
+                                                       k_ar, k_trend,
+                                                       out_of_sample)
             predictedvalues = np.r_[predictedvalues, forecastvalues]
 
         return predictedvalues
@@ -253,12 +254,12 @@ def _presample_varcov(self, params):
         # get inv(Vp) Hamilton 5.3.7
         params0 = np.r_[-1, params[k:]]
 
-        Vpinv = np.zeros((p,p), dtype=params.dtype)
-        for i in range(1,p1):
-            Vpinv[i-1,i-1:] = np.correlate(params0, params0[:i],
-                                           old_behavior=False)[:-1]
-            Vpinv[i-1,i-1:] -= np.correlate(params0[-i:], params0,
+        Vpinv = np.zeros((p, p), dtype=params.dtype)
+        for i in range(1, p1):
+            Vpinv[i-1, i-1:] = np.correlate(params0, params0[:i],
                                             old_behavior=False)[:-1]
+            Vpinv[i-1, i-1:] -= np.correlate(params0[-i:], params0,
+                                             old_behavior=False)[:-1]
 
         Vpinv = Vpinv + Vpinv.T - np.diag(Vpinv.diagonal())
         return Vpinv
@@ -270,17 +271,16 @@ def _loglike_css(self, params):
         nobs = self.nobs
         Y = self.Y
         X = self.X
-        ssr = sumofsq(Y.squeeze()-np.dot(X,params))
+        ssr = sumofsq(Y.squeeze() - np.dot(X, params))
         sigma2 = ssr/nobs
-        return -nobs/2 * (np.log(2*np.pi) + np.log(sigma2)) -\
-                    ssr/(2*sigma2)
+        return (-nobs/2 * (np.log(2 * np.pi) + np.log(sigma2)) -
+                ssr/(2 * sigma2))
 
     def _loglike_mle(self, params):
         """
         Loglikelihood of AR(p) process using exact maximum likelihood
         """
         nobs = self.nobs
-        Y = self.Y
         X = self.X
         endog = self.endog
         k_ar = self.k_ar
@@ -296,24 +296,23 @@ def _loglike_mle(self, params):
             c = [params[0]] * k_ar
         else:
             c = [0]
-        mup = np.asarray(c/(1-np.sum(params[k_trend:])))
-        diffp = yp-mup[:,None]
+        mup = np.asarray(c / (1 - np.sum(params[k_trend:])))
+        diffp = yp - mup[:, None]
 
         # get inv(Vp) Hamilton 5.3.7
         Vpinv = self._presample_varcov(params)
 
-        diffpVpinv = np.dot(np.dot(diffp.T,Vpinv),diffp).item()
-        ssr = sumofsq(endog[k_ar:].squeeze() -np.dot(X,params))
+        diffpVpinv = np.dot(np.dot(diffp.T, Vpinv), diffp).item()
+        ssr = sumofsq(endog[k_ar:].squeeze() - np.dot(X, params))
 
         # concentrating the likelihood means that sigma2 is given by
         sigma2 = 1./nobs * (diffpVpinv + ssr)
         self.sigma2 = sigma2
-        logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity
-        loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \
-                logdet + diffpVpinv/sigma2 + ssr/sigma2)
+        logdet = slogdet(Vpinv)[1]  # TODO: add check for singularity
+        loglike = -1/2. * (nobs * (np.log(2 * np.pi) + np.log(sigma2)) -
+                           logdet + diffpVpinv / sigma2 + ssr / sigma2)
         return loglike
 
-
     def loglike(self, params):
         """
         The loglikelihood of an AR(p) process
@@ -369,7 +368,6 @@ def score(self, params):
         loglike = self.loglike
         return approx_fprime(params, loglike, epsilon=1e-8)
 
-
     def information(self, params):
         """
         Not Implemented Yet
@@ -422,30 +420,30 @@ def select_order(self, maxlag, ic, trend='c', method='mle'):
         # make Y and X with same nobs to compare ICs
         Y = endog[maxlag:]
         self.Y = Y  # attach to get correct fit stats
-        X = self._stackX(maxlag, trend) # sets k_trend
+        X = self._stackX(maxlag, trend)  # sets k_trend
         self.X = X
-        k = self.k_trend # k_trend set in _stackX
-        k = max(1,k) # handle if startlag is 0
+        k = self.k_trend  # k_trend set in _stackX
+        k = max(1, k)  # handle if startlag is 0
         results = {}
 
         if ic != 't-stat':
-            for lag in range(k,maxlag+1):
+            for lag in range(k, maxlag+1):
                 # have to reinstantiate the model to keep comparable models
                 endog_tmp = endog[maxlag-lag:]
                 fit = AR(endog_tmp).fit(maxlag=lag, method=method,
-                        full_output=0, trend=trend,
-                        maxiter=100, disp=0)
+                                        full_output=0, trend=trend,
+                                        maxiter=100, disp=0)
                 results[lag] = eval('fit.'+ic)
-            bestic, bestlag = min((res, k) for k,res in results.iteritems())
+            bestic, bestlag = min((res, k) for k, res in iteritems(results))
 
-        else: # choose by last t-stat.
-            stop = 1.6448536269514722 # for t-stat, norm.ppf(.95)
-            for lag in range(maxlag,k-1,-1):
+        else:  # choose by last t-stat.
+            stop = 1.6448536269514722  # for t-stat, norm.ppf(.95)
+            for lag in range(maxlag, k - 1, -1):
                 # have to reinstantiate the model to keep comparable models
-                endog_tmp = endog[maxlag-lag:]
+                endog_tmp = endog[maxlag - lag:]
                 fit = AR(endog_tmp).fit(maxlag=lag, method=method,
-                        full_output=0, trend=trend,
-                        maxiter=35, disp=-1)
+                                        full_output=0, trend=trend,
+                                        maxiter=35, disp=-1)
 
                 if np.abs(fit.tvalues[-1]) >= stop:
                     bestlag = lag
@@ -453,7 +451,7 @@ def select_order(self, maxlag, ic, trend='c', method='mle'):
         return bestlag
 
     def fit(self, maxlag=None, method='cmle', ic=None, trend='c',
-            transparams=True, start_params=None, solver=None, maxiter=35,
+            transparams=True, start_params=None, solver='lbfgs', maxiter=35,
             full_output=1, disp=1, callback=None, **kwargs):
         """
         Fit the unconditional maximum likelihood of an AR(p) process.
@@ -491,14 +489,11 @@ def fit(self, maxlag=None, method='cmle', ic=None, trend='c',
         start_params : array-like, optional
             A first guess on the parameters.  Default is cmle estimates.
         solver : str or None, optional
-            Solver to be used.  The default is 'l_bfgs' (limited memory Broyden-
-            Fletcher-Goldfarb-Shanno).  Other choices are 'bfgs', 'newton'
-            (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient),
-            'ncg' (non-conjugate gradient), and 'powell'.
-            The limited memory BFGS uses m=30 to approximate the Hessian,
-            projected gradient tolerance of 1e-7 and factr = 1e3.  These
-            cannot currently be changed for l_bfgs.  See notes for more
-            information.
+            Solver to be used if method is 'mle'.  The default is 'lbfgs'
+            (limited memory Broyden-Fletcher-Goldfarb-Shanno).  Other choices
+            are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead),
+            'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient),
+            and 'powell'.
         maxiter : int, optional
             The maximum number of function evaluations. Default is 35.
         tol : float
@@ -523,78 +518,71 @@ def fit(self, maxlag=None, method='cmle', ic=None, trend='c',
 
         See also
         --------
-        statsmodels.base.model.LikelihoodModel.fit : for more information on using
-            the solvers.
-
+        statsmodels.base.model.LikelihoodModel.fit
         """
         method = method.lower()
-        if method not in ['cmle','yw','mle']:
+        if method not in ['cmle', 'yw', 'mle']:
             raise ValueError("Method %s not recognized" % method)
         self.method = method
         self.trend = trend
         self.transparams = transparams
-        nobs = len(self.endog) # overwritten if method is 'cmle'
+        nobs = len(self.endog)  # overwritten if method is 'cmle'
         endog = self.endog
 
         if maxlag is None:
             maxlag = int(round(12*(nobs/100.)**(1/4.)))
-        k_ar = maxlag # stays this if ic is None
+        k_ar = maxlag  # stays this if ic is None
 
         # select lag length
         if ic is not None:
             ic = ic.lower()
-            if ic not in ['aic','bic','hqic','t-stat']:
+            if ic not in ['aic', 'bic', 'hqic', 't-stat']:
                 raise ValueError("ic option %s not understood" % ic)
             k_ar = self.select_order(k_ar, ic, trend, method)
 
-        self.k_ar = k_ar # change to what was chosen by ic
+        self.k_ar = k_ar  # change to what was chosen by ic
 
         # redo estimation for best lag
         # make LHS
-        Y = endog[k_ar:,:]
+        Y = endog[k_ar:, :]
         # make lagged RHS
-        X = self._stackX(k_ar, trend) # sets self.k_trend
+        X = self._stackX(k_ar, trend)  # sets self.k_trend
         k_trend = self.k_trend
-        k = k_trend
         self.exog_names = util.make_lag_names(self.endog_names, k_ar, k_trend)
         self.Y = Y
         self.X = X
 
-        if solver:
-            solver = solver.lower()
         if method == "cmle":     # do OLS
-            arfit = OLS(Y,X).fit()
+            arfit = OLS(Y, X).fit()
             params = arfit.params
             self.nobs = nobs - k_ar
-            self.sigma2 = arfit.ssr/arfit.nobs #needed for predict fcasterr
-        if method == "mle":
+            self.sigma2 = arfit.ssr/arfit.nobs  # needed for predict fcasterr
+
+        elif method == "mle":
+            solver = solver.lower()
             self.nobs = nobs
             if start_params is None:
-                start_params = OLS(Y,X).fit().params
+                start_params = OLS(Y, X).fit().params
             else:
                 if len(start_params) != k_trend + k_ar:
                     raise ValueError("Length of start params is %d. There"
-                            " are %d parameters." % (len(start_params),
-                                                     k_trend + k_ar))
+                                     " are %d parameters." %
+                                     (len(start_params), k_trend + k_ar))
             start_params = self._invtransparams(start_params)
-            loglike = lambda params : -self.loglike(params)
-            if solver == None:  # use limited memory bfgs
-                bounds = [(None,)*2]*(k_ar+k)
-                mlefit = optimize.fmin_l_bfgs_b(loglike, start_params,
-                    approx_grad=True, m=12, pgtol=1e-8, factr=1e2,
-                    bounds=bounds, iprint=disp)
-                self.mlefit = mlefit
-                params = mlefit[0]
-            else:
-                mlefit = super(AR, self).fit(start_params=start_params,
-                            method=solver, maxiter=maxiter,
-                            full_output=full_output, disp=disp,
-                            callback = callback, **kwargs)
-                self.mlefit = mlefit
-                params = mlefit.params
+            if solver == 'lbfgs':
+                kwargs.setdefault('pgtol', 1e-8)
+                kwargs.setdefault('factr', 1e2)
+                kwargs.setdefault('m', 12)
+                kwargs.setdefault('approx_grad', True)
+            mlefit = super(AR, self).fit(start_params=start_params,
+                                         method=solver, maxiter=maxiter,
+                                         full_output=full_output, disp=disp,
+                                         callback=callback, **kwargs)
+
+            params = mlefit.params
             if self.transparams:
                 params = self._transparams(params)
-                self.transparams = False # turn off now for other results
+                self.transparams = False  # turn off now for other results
 
         # don't use yw, because we can't estimate the constant
         #elif method == "yw":
@@ -607,6 +595,9 @@ def fit(self, maxlag=None, method='cmle', ic=None, trend='c',
         pinv_exog = np.linalg.pinv(X)
         normalized_cov_params = np.dot(pinv_exog, pinv_exog.T)
         arfit = ARResults(self, params, normalized_cov_params)
+        if method == 'mle':
+            arfit.mle_retvals = mlefit.mle_retvals
+            arfit.mle_settings = mlefit.mle_settings
         return ARResultsWrapper(arfit)
 
 
@@ -666,8 +657,9 @@ class ARResults(tsbase.TimeSeriesModelResults):
     pvalues : array
         The p values associated with the standard errors.
     resid : array
-        The residuals of the model. If the model is fit by 'mle' then the pre-sample
-        residuals are calculated using fittedvalues from the Kalman Filter.
+        The residuals of the model. If the model is fit by 'mle' then the
+        pre-sample residuals are calculated using fittedvalues from the Kalman
+        Filter.
     roots : array
         The roots of the AR process are the solution to
         (1 - arparams[0]*z - arparams[1]*z**2 -...- arparams[p-1]*z**k_ar) = 0
@@ -678,22 +670,22 @@ class ARResults(tsbase.TimeSeriesModelResults):
     sigma2 : float
         The variance of the innovations (residuals).
     trendorder : int
-        The polynomial order of the trend. 'nc' = None, 'c' or 't' = 0, 'ct' = 1,
-        etc.
+        The polynomial order of the trend. 'nc' = None, 'c' or 't' = 0,
+        'ct' = 1, etc.
     tvalues : array
         The t-values associated with `params`.
     """
 
-    _cache = {} # for scale setter
+    _cache = {}  # for scale setter
 
     def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         super(ARResults, self).__init__(model, params, normalized_cov_params,
-                scale)
+                                        scale)
         self._cache = resettable_cache()
         self.nobs = model.nobs
         n_totobs = len(model.endog)
         self.n_totobs = n_totobs
-        self.X = model.X # copy?
+        self.X = model.X  # copy?
         self.Y = model.Y
         k_ar = model.k_ar
         self.k_ar = k_ar
@@ -702,7 +694,7 @@ def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         trendorder = None
         if k_trend > 0:
             trendorder = k_trend - 1
-        self.trendorder = 1
+        self.trendorder = trendorder
         #TODO: cmle vs mle?
         self.df_model = k_ar + k_trend
         self.df_resid = self.model.df_resid = n_totobs - self.df_model
@@ -710,8 +702,8 @@ def __init__(self, model, params, normalized_cov_params=None, scale=1.):
     @cache_writable()
     def sigma2(self):
         model = self.model
-        if model.method == "cmle": # do DOF correction
-            return 1./self.nobs * sumofsq(self.resid)
+        if model.method == "cmle":  # do DOF correction
+            return 1. / self.nobs * sumofsq(self.resid)
         else:
             return self.model.sigma2
 
@@ -720,11 +712,11 @@ def scale(self):
         return self.sigma2
 
     @cache_readonly
-    def bse(self): # allow user to specify?
-        if self.model.method == "cmle": # uses different scale/sigma definition
+    def bse(self):  # allow user to specify?
+        if self.model.method == "cmle":  # uses different scale/sigma def.
             resid = self.resid
-            ssr = np.dot(resid,resid)
-            ols_scale = ssr/(self.nobs - self.k_ar - self.k_trend)
+            ssr = np.dot(resid, resid)
+            ols_scale = ssr / (self.nobs - self.k_ar - self.k_trend)
             return np.sqrt(np.diag(self.cov_params(scale=ols_scale)))
         else:
             hess = approx_hess(self.params, self.model.loglike)
@@ -751,8 +743,8 @@ def hqic(self):
         # Lutkepohl
         # return np.log(self.sigma2)+ 2 * np.log(np.log(nobs))/nobs * self.k_ar
         # R uses all estimated parameters rather than just lags
-        return np.log(self.sigma2) + 2 * np.log(np.log(nobs))/nobs * \
-                (1 + self.df_model)
+        return (np.log(self.sigma2) + 2 * np.log(np.log(nobs))/nobs *
+                (1 + self.df_model))
         # Stata
         #nobs = self.nobs
         #return -2 * self.llf/nobs + 2 * np.log(np.log(nobs))/nobs * \
@@ -781,7 +773,7 @@ def resid(self):
         #NOTE: uses fittedvalues because it calculate presample values for mle
         model = self.model
         endog = model.endog.squeeze()
-        if model.method == "cmle": # elimate pre-sample
+        if model.method == "cmle":  # elimate pre-sample
             return endog[self.k_ar:] - self.fittedvalues
         else:
             return model.endog.squeeze() - self.fittedvalues
@@ -801,7 +793,7 @@ def fittedvalues(self):
 
     def predict(self, start=None, end=None, dynamic=False):
         params = self.params
-        predictedvalues =  self.model.predict(params, start, end, dynamic)
+        predictedvalues = self.model.predict(params, start, end, dynamic)
         return predictedvalues
 
         #start = self.model._get_predict_start(start)
@@ -812,25 +804,26 @@ def predict(self, start=None, end=None, dynamic=False):
         #ma_rep = arma2ma(np.r_[1,-params[::-1]], [1], out_of_sample)
         #fcasterr = np.sqrt(self.sigma2 * np.cumsum(ma_rep**2))
 
-
     preddoc = AR.predict.__doc__.split('\n')
-    extra_doc = """        confint : bool, float
+    extra_doc = ("""        confint : bool, float
             Whether to return confidence intervals.  If `confint` == True,
             95 % confidence intervals are returned.  Else if `confint` is a
             float, then it is assumed to be the alpha value of the confidence
             interval.  That is confint == .05 returns a 95% confidence
-            interval, and .10 would return a 90% confidence interval.""".split('\n')
+            interval, and .10 would return a 90% confidence interval."""
+                 ).split('\n')
     #ret_doc = """
     #    fcasterr : array-like
     #    confint : array-like
     #"""
     predict.__doc__ = '\n'.join(preddoc[:5] + preddoc[7:20] + extra_doc +
-            preddoc[20:])
+                                preddoc[20:])
+
 
 class ARResultsWrapper(wrap.ResultsWrapper):
     _attrs = {}
     _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
-                                    _attrs)
+                                   _attrs)
     _methods = {}
     _wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
                                      _methods)
@@ -845,7 +838,7 @@ class ARResultsWrapper(wrap.ResultsWrapper):
     res_ols = ar_ols.fit(maxlag=9)
     ar_mle = AR(sunspots.endog)
     res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs",
-                    maxiter=500, gtol=1e-10)
+                              maxiter=500, gtol=1e-10)
 #    res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True,
 #            tol=1e-13)
 
@@ -889,27 +882,24 @@ class ARResultsWrapper(wrap.ResultsWrapper):
     #NOTE: not anymore, it's end of year now
     ts_dr = ts.date_array(start_date=d1, length=len(sunspots.endog))
     pandas_dr = pandas.DateRange(start=d1.datetime,
-                    periods=len(sunspots.endog), timeRule='A@DEC')
+                                 periods=len(sunspots.endog), timeRule='A@DEC')
     #pandas_dr = pandas_dr.shift(-1, pandas.datetools.yearBegin)
 
-
-
-    dates = np.arange(1700,1700+len(sunspots.endog))
+    dates = np.arange(1700, 1700 + len(sunspots.endog))
     dates = ts.date_array(dates, freq='A')
     #sunspots = pandas.TimeSeries(sunspots.endog, index=dates)
 
     #NOTE: pandas only does business days for dates it looks like
     import datetime
-    dt_dates = np.asarray(map(datetime.datetime.fromordinal,
-                ts_dr.toordinal().astype(int)))
+    dt_dates = np.asarray(lmap(datetime.datetime.fromordinal,
+                              ts_dr.toordinal().astype(int)))
     sunspots = pandas.TimeSeries(sunspots.endog, index=dt_dates)
 
     #NOTE: pandas can't handle pre-1900 dates
     mod = AR(sunspots, freq='A')
     res = mod.fit(method='mle', maxlag=9)
 
-
 # some data for an example in Box Jenkins
-    IBM = np.asarray([460,457,452,459,462,459,463,479,493,490.])
+    IBM = np.asarray([460, 457, 452, 459, 462, 459, 463, 479, 493, 490.])
     w = np.diff(IBM)
     theta = .5
diff --git a/statsmodels/tsa/arima_model.py b/statsmodels/tsa/arima_model.py
index 4e6f8aff276..699277734a6 100644
--- a/statsmodels/tsa/arima_model.py
+++ b/statsmodels/tsa/arima_model.py
@@ -1,4 +1,11 @@
+# Note: The information criteria add 1 to the number of parameters
+#       whenever the model has an AR or MA term since, in principle,
+#       the variance could be treated as a free parameter and restricted
+#       This code does not allow this, but it adds consistency with other
+#       packages such as gretl and X12-ARIMA
+
 from __future__ import absolute_import
+from statsmodels.compat.python import string_types, range
 # for 2to3 with extensions
 
 from datetime import datetime
@@ -7,27 +14,23 @@
 from scipy import optimize
 from scipy.stats import t, norm
 from scipy.signal import lfilter
-from numpy import (dot, identity, kron, log, zeros, pi, exp, eye, abs, empty,
-                   zeros_like)
-from numpy.linalg import inv, pinv
+from numpy import dot, log, zeros, pi
+from numpy.linalg import inv
 
 from statsmodels.tools.decorators import (cache_readonly,
-        cache_writable, resettable_cache)
-import statsmodels.base.model as base
+                                          resettable_cache)
 import statsmodels.tsa.base.tsa_model as tsbase
 import statsmodels.base.wrapper as wrap
 from statsmodels.regression.linear_model import yule_walker, GLS
 from statsmodels.tsa.tsatools import (lagmat, add_trend,
-        _ar_transparams, _ar_invtransparams, _ma_transparams,
-        _ma_invtransparams)
+                                      _ar_transparams, _ar_invtransparams,
+                                      _ma_transparams, _ma_invtransparams)
 from statsmodels.tsa.vector_ar import util
 from statsmodels.tsa.ar_model import AR
 from statsmodels.tsa.arima_process import arma2ma
-from statsmodels.tools.numdiff import (approx_fprime, approx_fprime_cs,
-        approx_hess_cs)
+from statsmodels.tools.numdiff import approx_hess_cs, approx_fprime_cs
 from statsmodels.tsa.base.datetools import _index_date
 from statsmodels.tsa.kalmanf import KalmanFilter
-from .kalmanf import kalman_loglike
 
 _armax_notes = """
 
@@ -43,9 +46,10 @@
         operator, :math:`L`. This is the regression model with ARMA errors,
         or ARMAX model. This specification is used, whether or not the model
         is fit using conditional sum of square or maximum-likelihood, using
-        the `method` argument in :meth:`statsmodels.tsa.arima_model.%(Model)s.fit`. Therefore, for now, `css`
-        and `mle` refer to estimation methods only. This may change for the
-        case of the `css` model in future versions.
+        the `method` argument in
+        :meth:`statsmodels.tsa.arima_model.%(Model)s.fit`. Therefore, for
+        now, `css` and `mle` refer to estimation methods only. This may
+        change for the case of the `css` model in future versions.
 """
 
 _arma_params = """\
@@ -140,47 +144,48 @@
 """
 
 _arma_predict = _predict % {"Model" : "ARMA",
-        "params" : """
-        params : array-like
+                            "params" : """
+            params : array-like
             The fitted parameters of the model.""",
-        "extra_params" : "",
-        "extra_section" : _predict_notes}
+                            "extra_params" : "",
+                            "extra_section" : _predict_notes}
 
 _arma_results_predict = _predict % {"Model" : "ARMA", "params" : "",
-        "extra_params" : "", "extra_section" : _results_notes}
+                                    "extra_params" : "", "extra_section" :
+                                    _results_notes}
 
 _arima_predict = _predict % {"Model" : "ARIMA",
-        "params" : """
+                             "params" : """
         params : array-like
             The fitted parameters of the model.""",
-        "extra_params" :
-"""
+                             "extra_params" : """
         typ : str {'linear', 'levels'}
             - 'linear' : Linear prediction in terms of the differenced
               endogenous variables.
             - 'levels' : Predict the levels of the original endogenous
               variables.""",
-        "extra_section" : _predict_notes}
+                             "extra_section" : _predict_notes}
 
 _arima_results_predict = _predict % {"Model" : "ARIMA",
-        "params" : "",
-        "extra_params" : """typ : str {'linear', 'levels'}
+                                     "params" : "",
+                                     "extra_params" :
+                                     """typ : str {'linear', 'levels'}
             - 'linear' : Linear prediction in terms of the differenced
               endogenous variables.
             - 'levels' : Predict the levels of the original endogenous
               variables.
 """,
-        "extra_section" : _results_notes}
-
+                                     "extra_section" : _results_notes}
 
 
 def _check_arima_start(start, k_ar, k_diff, method, dynamic):
     if start < 0:
         raise ValueError("The start index %d of the original series "
-                             "has been differenced away" % start)
+                         "has been differenced away" % start)
     elif (dynamic or 'mle' not in method) and start < k_ar:
         raise ValueError("Start must be >= k_ar for conditional MLE "
-                "or dynamic forecast. Got %d" % start)
+                         "or dynamic forecast. Got %d" % start)
+
 
 def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors,
                                trendparam, exparams, arparams, maparams, steps,
@@ -218,7 +223,7 @@ def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors,
             X = lagmat(np.dot(exog, exparams), p, original='in', trim='both')
             mu = trendparam * (1 - arparams.sum())
             # arparams were reversed in unpack for ease later
-            mu = mu + (np.r_[1, -arparams[::-1]]*X).sum(1)[:,None]
+            mu = mu + (np.r_[1, -arparams[::-1]] * X).sum(1)[:, None]
         else:
             mu = trendparam * (1 - arparams.sum())
             mu = np.array([mu]*steps)
@@ -226,7 +231,7 @@ def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors,
         X = np.dot(exog, exparams)
         #NOTE: you shouldn't have to give in-sample exog!
         X = lagmat(X, p, original='in', trim='both')
-        mu = (np.r_[1, -arparams[::-1]]*X).sum(1)[:,None]
+        mu = (np.r_[1, -arparams[::-1]] * X).sum(1)[:, None]
     else:
         mu = np.zeros(steps)
 
@@ -239,16 +244,17 @@ def _get_predict_out_of_sample(endog, p, q, k_trend, k_exog, start, errors,
 
     return endog, resid, mu
 
+
 def _arma_predict_out_of_sample(params, steps, errors, p, q, k_trend, k_exog,
                                 endog, exog=None, start=0, method='mle'):
     (trendparam, exparams,
-     arparams, maparams) = _unpack_params(params, (p,q), k_trend,
-                                k_exog, reverse=True)
+     arparams, maparams) = _unpack_params(params, (p, q), k_trend,
+                                          k_exog, reverse=True)
     endog, resid, mu = _get_predict_out_of_sample(endog, p, q, k_trend, k_exog,
-                                                   start, errors, trendparam,
-                                                   exparams, arparams,
-                                                   maparams, steps, method,
-                                                   exog)
+                                                  start, errors, trendparam,
+                                                  exparams, arparams,
+                                                  maparams, steps, method,
+                                                  exog)
 
     forecast = np.zeros(steps)
     if steps == 1:
@@ -259,45 +265,44 @@ def _arma_predict_out_of_sample(params, steps, errors, p, q, k_trend, k_exog,
             return mu[0] + np.dot(arparams, endog[:p])
 
     if q:
-        i = 0 # if q == 1
+        i = 0  # if q == 1
     else:
         i = -1
 
-    for i in range(min(q,steps-1)):
-        fcast = mu[i] + np.dot(arparams,endog[i:i+p]) + \
-                np.dot(maparams[:q-i],resid[i:i+q])
+    for i in range(min(q, steps - 1)):
+        fcast = (mu[i] + np.dot(arparams, endog[i:i + p]) +
+                 np.dot(maparams[:q - i], resid[i:i + q]))
         forecast[i] = fcast
         endog[i+p] = fcast
 
-    for i in range(i+1,steps-1):
-        fcast = mu[i] + np.dot(arparams,endog[i:i+p])
+    for i in range(i + 1, steps - 1):
+        fcast = mu[i] + np.dot(arparams, endog[i:i+p])
         forecast[i] = fcast
         endog[i+p] = fcast
 
     #need to do one more without updating endog
-    forecast[-1] = mu[-1] + np.dot(arparams,endog[steps-1:])
+    forecast[-1] = mu[-1] + np.dot(arparams, endog[steps - 1:])
     return forecast
 
-def _arma_predict_in_sample(start, end, endog, resid, k_ar,
-                            method):
+
+def _arma_predict_in_sample(start, end, endog, resid, k_ar, method):
     """
     Pre- and in-sample fitting for ARMA.
     """
     if 'mle' in method:
-        fittedvalues = endog - resid #get them all then trim
-    elif k_ar > 0:
+        fittedvalues = endog - resid  # get them all then trim
+    else:
         fittedvalues = endog[k_ar:] - resid
 
     fv_start = start
     if 'mle' not in method:
-        fv_start -= k_ar # start is in terms of endog index
-    predictedvalues = np.zeros(end + 1 - fv_start)
+        fv_start -= k_ar  # start is in terms of endog index
     fv_end = min(len(fittedvalues), end + 1)
     return fittedvalues[fv_start:fv_end]
 
+
 def _validate(start, k_ar, k_diff, dates, method):
-    if isinstance(start, (basestring, datetime)):
-        start_date = start
+    if isinstance(start, (string_types, datetime)):
         start = _index_date(start, dates)
         start -= k_diff
     if 'mle' not in method and start < k_ar - k_diff:
@@ -306,6 +311,7 @@ def _validate(start, k_ar, k_diff, dates, method):
 
     return start
 
+
 def _unpack_params(params, order, k_trend, k_exog, reverse=False):
     p, q = order
     k = k_trend + k_exog
@@ -317,28 +323,30 @@ def _unpack_params(params, order, k_trend, k_exog, reverse=False):
         return trend, exparams, arparams[::-1], maparams[::-1]
     return trend, exparams, arparams, maparams
 
+
 def _unpack_order(order):
     k_ar, k_ma, k = order
     k_lags = max(k_ar, k_ma+1)
     return k_ar, k_ma, order, k_lags
 
+
 def _make_arma_names(data, k_trend, order, exog_names):
     k_ar, k_ma = order
     exog_names = exog_names or []
     ar_lag_names = util.make_lag_names([data.ynames], k_ar, 0)
-    ar_lag_names = [''.join(('ar.', i))
-                              for i in ar_lag_names]
+    ar_lag_names = [''.join(('ar.', i)) for i in ar_lag_names]
     ma_lag_names = util.make_lag_names([data.ynames], k_ma, 0)
     ma_lag_names = [''.join(('ma.', i)) for i in ma_lag_names]
     trend_name = util.make_lag_names('', 0, k_trend)
     exog_names = trend_name + exog_names + ar_lag_names + ma_lag_names
     return exog_names
 
+
 def _make_arma_exog(endog, exog, trend):
-    k_trend = 1 # overwritten if no constant
+    k_trend = 1  # overwritten if no constant
     if exog is None and trend == 'c':   # constant only
-        exog = np.ones((len(endog),1))
-    elif exog is not None and trend == 'c': # constant plus exogenous
+        exog = np.ones((len(endog), 1))
+    elif exog is not None and trend == 'c':  # constant plus exogenous
         exog = add_trend(exog, trend='c', prepend=True)
     elif exog is not None and trend == 'nc':
         # make sure it's not holding constant from last run
@@ -349,32 +357,35 @@ def _make_arma_exog(endog, exog, trend):
         k_trend = 0
     return k_trend, exog
 
+
 def _check_estimable(nobs, n_params):
     if nobs <= n_params:
         raise ValueError("Insufficient degrees of freedom to estimate")
 
+
 class ARMA(tsbase.TimeSeriesModel):
 
     __doc__ = tsbase._tsa_doc % {"model" : _arma_model,
-                    "params" : _arma_params, "extra_params" : "",
-                    "extra_sections" : _armax_notes % {"Model" : "ARMA"}}
+                                 "params" : _arma_params, "extra_params" : "",
+                                 "extra_sections" : _armax_notes %
+                                 {"Model" : "ARMA"}}
 
     def __init__(self, endog, order=None, exog=None, dates=None, freq=None,
-                        missing='none'):
-        super(ARMA, self).__init__(endog, exog, dates, freq)
-        exog = self.data.exog # get it after it's gone through processing
+                 missing='none'):
+        super(ARMA, self).__init__(endog, exog, dates, freq, missing=missing)
+        exog = self.data.exog  # get it after it's gone through processing
         if order is None:
             import warnings
             warnings.warn("In the next release order will not be optional "
-                    "in the model constructor.", FutureWarning)
+                          "in the model constructor.", FutureWarning)
         else:
             _check_estimable(len(self.endog), sum(order))
             self.k_ar = k_ar = order[0]
             self.k_ma = k_ma = order[1]
-            self.k_lags = k_lags = max(k_ar,k_ma+1)
+            self.k_lags = max(k_ar, k_ma+1)
         if exog is not None:
             if exog.ndim == 1:
-                exog = exog[:,None]
+                exog = exog[:, None]
             k_exog = exog.shape[1]  # number of exog. variables excl. const
         else:
             k_exog = 0
@@ -407,9 +418,9 @@ def _fit_start_params_hr(self, order):
         Hannan, E.J. and Rissanen, J.  1982.  "Recursive estimation of mixed
             autoregressive-moving average order."  `Biometrika`.  69.1.
         """
-        p,q,k = order
+        p, q, k = order
         start_params = zeros((p+q+k))
-        endog = self.endog.copy() # copy because overwritten
+        endog = self.endog.copy()  # copy because overwritten
         exog = self.exog
         if k != 0:
             ols_params = GLS(endog, exog).fit().params
@@ -429,11 +440,12 @@ def _fit_start_params_hr(self, order):
                 # doesn't leave enough obs. No consistent way to fix.
                 if p_tmp + q >= len(endog):
                     raise ValueError("Proper starting parameters cannot"
-                            " be found for this order with this number "
-                            "of observations. Use the start_params "
-                            "argument.")
+                                     " be found for this order with this "
+                                     "number of observations. Use the "
+                                     "start_params argument.")
                 resid = endog[p_tmp:] - np.dot(lagmat(endog, p_tmp,
-                                trim='both'), arcoefs_tmp)
+                                                      trim='both'),
+                                               arcoefs_tmp)
                 if p < p_tmp + q:
                     endog_start = p_tmp + q - p
                     resid_start = 0
@@ -444,7 +456,7 @@ def _fit_start_params_hr(self, order):
                 lag_resid = lagmat(resid, q, 'both')[resid_start:]
                 # stack ar lags and resids
                 X = np.column_stack((lag_endog, lag_resid))
-                coefs = GLS(endog[max(p_tmp+q,p):], X).fit().params
+                coefs = GLS(endog[max(p_tmp + q, p):], X).fit().params
                 start_params[k:k+p+q] = coefs
             else:
                 start_params[k+p:k+p+q] = yule_walker(endog, order=q)[0]
@@ -453,15 +465,15 @@ def _fit_start_params_hr(self, order):
             start_params[k:k+p] = arcoefs
 
         # check AR coefficients
-        if p and not np.all(np.abs(np.roots(np.r_[1,
-                                        -start_params[k:k+p]])) < 1):
+        if p and not np.all(np.abs(np.roots(np.r_[1, -start_params[k:k + p]]
+                                            )) < 1):
             raise ValueError("The computed initial AR coefficients are not "
                              "stationary\nYou should induce stationarity, "
                              "choose a different model order, or you can\n"
                              "pass your own start_params.")
         # check MA coefficients
-        elif q and not np.all(np.abs(np.roots(np.r_[1,
-                                            start_params[k+p:]])) < 1):
+        elif q and not np.all(np.abs(np.roots(np.r_[1, start_params[k + p:]]
+                                              )) < 1):
             raise ValueError("The computed initial MA coefficients are not "
                              "invertible\nYou should induce invertibility, "
                              "choose a different model order, or you can\n"
@@ -471,9 +483,9 @@ def _fit_start_params_hr(self, order):
         return start_params
 
     def _fit_start_params(self, order, method):
-        if method != 'css-mle': # use Hannan-Rissanen to get start params
+        if method != 'css-mle':  # use Hannan-Rissanen to get start params
             start_params = self._fit_start_params_hr(order)
-        else: # use CSS to get start params
+        else:  # use CSS to get start params
             func = lambda params: -self.loglike_css(params)
             #start_params = [.1]*(k_ar+k_ma+k_exog) # different one for k?
             start_params = self._fit_start_params_hr(order)
@@ -481,12 +493,12 @@ def _fit_start_params(self, order, method):
                 start_params = self._invtransparams(start_params)
             bounds = [(None,)*2]*sum(order)
             mlefit = optimize.fmin_l_bfgs_b(func, start_params,
-                        approx_grad=True, m=12, pgtol=1e-7, factr=1e3,
-                        bounds = bounds, iprint=-1)
+                                            approx_grad=True, m=12,
+                                            pgtol=1e-7, factr=1e3,
+                                            bounds=bounds, iprint=-1)
             start_params = self._transparams(mlefit[0])
         return start_params
 
-
     def score(self, params):
         """
         Compute the score function at params.
@@ -495,11 +507,7 @@ def score(self, params):
         -----
         This is a numerical approximation.
         """
-        loglike = self.loglike
-        #if self.transparams:
-        #    params = self._invtransparams(params)
-        #return approx_fprime(params, loglike, epsilon=1e-5)
-        return approx_fprime_cs(params, loglike)
+        return approx_fprime_cs(params, self.loglike, args=(False,))
 
     def hessian(self, params):
         """
@@ -509,10 +517,7 @@ def hessian(self, params):
         -----
         This is a numerical approximation.
         """
-        loglike = self.loglike
-        #if self.transparams:
-        #    params = self._invtransparams(params)
-        return approx_hess_cs(params, loglike)
+        return approx_hess_cs(params, self.loglike, args=(False,))
 
     def _transparams(self, params):
         """
@@ -567,10 +572,10 @@ def _get_predict_start(self, start, dynamic):
                 start = 0
             else:
                 start = k_ar
-            self._set_predict_start_date(start) # else it's done in super
+            self._set_predict_start_date(start)  # else it's done in super
         elif isinstance(start, int):
             start = super(ARMA, self)._get_predict_start(start)
-        else: # should be on a date
+        else:  # should be on a date
             #elif 'mle' not in method or dynamic: # should be on a date
             start = _validate(start, k_ar, k_diff, self.data.dates,
                               method)
@@ -601,16 +606,18 @@ def geterrors(self, params):
         k_ar, k_ma = self.k_ar, self.k_ma
         k = self.k_exog + self.k_trend
 
-
         method = getattr(self, 'method', 'mle')
-        if 'mle' in method: # use KalmanFilter to get errors
+        if 'mle' in method:  # use KalmanFilter to get errors
             (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat,
-            T_mat, paramsdtype) = KalmanFilter._init_kalman_state(params, self)
-            errors = KalmanFilter.geterrors(y,k,k_ar,k_ma, k_lags, nobs,
-                    Z_mat, m, R_mat, T_mat, paramsdtype)
+             T_mat, paramsdtype) = KalmanFilter._init_kalman_state(params,
+                                                                   self)
+
+            errors = KalmanFilter.geterrors(y, k, k_ar, k_ma, k_lags, nobs,
+                                            Z_mat, m, R_mat, T_mat,
+                                            paramsdtype)
             if isinstance(errors, tuple):
-                errors = errors[0] # non-cython version returns a tuple
-        else: # use scipy.signal.lfilter
+                errors = errors[0]  # non-cython version returns a tuple
+        else:  # use scipy.signal.lfilter
             y = self.endog.copy()
             k = self.k_exog + self.k_trend
             if k > 0:
@@ -619,21 +626,20 @@ def geterrors(self, params):
             k_ar = self.k_ar
             k_ma = self.k_ma
 
-
             (trendparams, exparams,
              arparams, maparams) = _unpack_params(params, (k_ar, k_ma),
-                                        self.k_trend, self.k_exog,
-                                        reverse=False)
-            b,a = np.r_[1,-arparams], np.r_[1,maparams]
+                                                  self.k_trend, self.k_exog,
+                                                  reverse=False)
+            b, a = np.r_[1, -arparams], np.r_[1, maparams]
             zi = zeros((max(k_ar, k_ma)))
             for i in range(k_ar):
                 zi[i] = sum(-b[:i+1][::-1]*y[:i+1])
-            e = lfilter(b,a,y,zi=zi)
+            e = lfilter(b, a, y, zi=zi)
             errors = e[0][k_ar:]
         return errors.squeeze()
 
     def predict(self, params, start=None, end=None, exog=None, dynamic=False):
-        method = getattr(self, 'method', 'mle') # don't assume fit
+        method = getattr(self, 'method', 'mle')  # don't assume fit
         #params = np.asarray(params)
 
         # will return an index of a date
@@ -648,34 +654,36 @@ def predict(self, params, start=None, end=None, exog=None, dynamic=False):
 
         if out_of_sample != 0 and self.k_exog > 0:
             if self.k_exog == 1 and exog.ndim == 1:
-                exog = exog[:,None]
+                exog = exog[:, None]
                 # we need the last k_ar exog for the lag-polynomial
             if self.k_exog > 0:
                 # need the last k_ar exog for the lag-polynomial
                 exog = np.vstack((self.exog[-k_ar:, self.k_trend:], exog))
 
-
         if dynamic:
             #TODO: now that predict does dynamic in-sample it should
             # also return error estimates and confidence intervals
             # but how? len(endog) is not tot_obs
             out_of_sample += end - start + 1
             return _arma_predict_out_of_sample(params, out_of_sample, resid,
-                    k_ar, self.k_ma, self.k_trend, self.k_exog, endog, exog,
-                    start, method)
+                                               k_ar, self.k_ma, self.k_trend,
+                                               self.k_exog, endog, exog,
+                                               start, method)
 
         predictedvalues = _arma_predict_in_sample(start, end, endog, resid,
-                            k_ar, method)
+                                                  k_ar, method)
         if out_of_sample:
             forecastvalues = _arma_predict_out_of_sample(params, out_of_sample,
-                                        resid, k_ar, self.k_ma, self.k_trend,
-                                        self.k_exog, endog, exog,
-                                        method=method)
+                                                         resid, k_ar,
+                                                         self.k_ma,
+                                                         self.k_trend,
+                                                         self.k_exog, endog,
+                                                         exog, method=method)
             predictedvalues = np.r_[predictedvalues, forecastvalues]
         return predictedvalues
     predict.__doc__ = _arma_predict
 
-    def loglike(self, params):
+    def loglike(self, params, set_sigma2=True):
         """
         Compute the log-likelihood for ARMA(p,q) model
 
@@ -685,19 +693,19 @@ def loglike(self, params):
         """
         method = self.method
         if method in ['mle', 'css-mle']:
-            return self.loglike_kalman(params)
+            return self.loglike_kalman(params, set_sigma2)
         elif method == 'css':
-            return self.loglike_css(params)
+            return self.loglike_css(params, set_sigma2)
         else:
             raise ValueError("Method %s not understood" % method)
 
-    def loglike_kalman(self, params):
+    def loglike_kalman(self, params, set_sigma2=True):
         """
-        Compute exact loglikelihood for ARMA(p,q) model using the Kalman Filter.
+        Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.
         """
-        return KalmanFilter.loglike(params, self)
+        return KalmanFilter.loglike(params, self, set_sigma2)
 
-    def loglike_css(self, params):
+    def loglike_css(self, params, set_sigma2=True):
         """
         Conditional Sum of Squares likelihood function.
         """
@@ -714,20 +722,21 @@ def loglike_css(self, params):
         if k > 0:
             y -= dot(self.exog, newparams[:k])
         # the order of p determines how many zeros errors to set for lfilter
-        b,a = np.r_[1,-newparams[k:k+k_ar]], np.r_[1,newparams[k+k_ar:]]
-        zi = np.zeros((max(k_ar,k_ma)), dtype=params.dtype)
+        b, a = np.r_[1, -newparams[k:k + k_ar]], np.r_[1, newparams[k + k_ar:]]
+        zi = np.zeros((max(k_ar, k_ma)), dtype=params.dtype)
         for i in range(k_ar):
-            zi[i] = sum(-b[:i+1][::-1] * y[:i+1])
-        errors = lfilter(b,a, y, zi=zi)[0][k_ar:]
+            zi[i] = sum(-b[:i + 1][::-1] * y[:i + 1])
+        errors = lfilter(b, a, y, zi=zi)[0][k_ar:]
 
-        ssr = np.dot(errors,errors)
+        ssr = np.dot(errors, errors)
         sigma2 = ssr/nobs
-        self.sigma2 = sigma2
+        if set_sigma2:
+            self.sigma2 = sigma2
         llf = -nobs/2.*(log(2*pi) + log(sigma2)) - ssr/(2*sigma2)
         return llf
 
-    def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
-            transparams=True, solver=None, maxiter=35, full_output=1,
+    def fit(self, order=None, start_params=None, trend='c', method="css-mle",
+            transparams=True, solver='lbfgs', maxiter=50, full_output=1,
             disp=5, callback=None, **kwargs):
         """
         Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter.
@@ -735,7 +744,7 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
         Parameters
         ----------
         start_params : array-like, optional
-            Starting parameters for ARMA(p,q).  If None, the default is given
+            Starting parameters for ARMA(p,q). If None, the default is given
             by ARMA._fit_start_params.  See there for more information.
         transparams : bool, optional
             Whehter or not to transform the parameters to ensure stationarity.
@@ -751,10 +760,10 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
             `start_params` as starting parameters.  See above for more
             information.
         trend : str {'c','nc'}
-            Whehter to include a constant or not.  'c' includes constant,
+            Whether to include a constant or not.  'c' includes constant,
             'nc' no constant.
         solver : str or None, optional
-            Solver to be used.  The default is 'l_bfgs' (limited memory
+            Solver to be used.  The default is 'lbfgs' (limited memory
             Broyden-Fletcher-Goldfarb-Shanno).  Other choices are 'bfgs',
             'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' -
             (conjugate gradient), 'ncg' (non-conjugate gradient), and
@@ -762,7 +771,7 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
             approximate the Hessian, projected gradient tolerance of 1e-8 and
             factr = 1e2. You can change these by using kwargs.
         maxiter : int, optional
-            The maximum number of function evaluations. Default is 35.
+            The maximum number of function evaluations. Default is 50.
         tol : float
             The convergence tolerance.  Default is 1e-08.
         full_output : bool, optional
@@ -800,37 +809,42 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
         if order is not None:
             import warnings
             warnings.warn("The order argument to fit is deprecated. "
-                    "Please use the model constructor argument order. "
-                    "This will overwrite any order given in the model "
-                    "constructor.", FutureWarning)
+                          "Please use the model constructor argument order. "
+                          "This will overwrite any order given in the model "
+                          "constructor.", FutureWarning)
 
             _check_estimable(len(self.endog), sum(order))
             # get model order and constants
-            self.k_ar = k_ar = int(order[0])
-            self.k_ma = k_ma = int(order[1])
-            self.k_lags = max(k_ar,k_ma+1)
+            self.k_ar = int(order[0])
+            self.k_ma = int(order[1])
+            self.k_lags = max(k_ar, k_ma + 1)
         else:
             try:
                 assert hasattr(self, "k_ar")
                 assert hasattr(self, "k_ma")
             except:
                 raise ValueError("Please give order to the model constructor "
-                        "before calling fit.")
-            k_ar = self.k_ar
-            k_ma = self.k_ma
+                                 "before calling fit.")
+        k_ar = self.k_ar
+        k_ma = self.k_ma
 
         # enforce invertibility
         self.transparams = transparams
 
-        self.method = method.lower()
-
         endog, exog = self.endog, self.exog
         k_exog = self.k_exog
-        self.nobs = len(endog) # this is overwritten if method is 'css'
+        self.nobs = len(endog)  # this is overwritten if method is 'css'
 
         # (re)set trend and handle exogenous variables
         # always pass original exog
         k_trend, exog = _make_arma_exog(endog, self.exog, trend)
+
+        # Check has something to estimate
+        if k_ar == 0 and k_ma == 0 and k_trend == 0 and k_exog == 0:
+            raise ValueError("Estimation requires the inclusion of least one "
+                         "AR term, MA term, a constant or an exogenous "
+                         "variable.")
+
         # check again now that we know the trend
         _check_estimable(len(endog), k_ar + k_ma + k_exog + k_trend)
 
@@ -842,48 +856,46 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
                                            self.exog_names)
         k = k_trend + k_exog
 
-
         # choose objective function
-        method = method.lower()
+        if k_ma == 0 and k_ar == 0:
+            method = "css"  # Always CSS when no AR or MA terms
+
+        self.method = method = method.lower()
+
         # adjust nobs for css
         if method == 'css':
             self.nobs = len(self.endog) - k_ar
-        loglike = lambda params: -self.loglike(params)
 
         if start_params is not None:
             start_params = np.asarray(start_params)
 
-        else: # estimate starting parameters
-            start_params = self._fit_start_params((k_ar,k_ma,k), method)
+        else:  # estimate starting parameters
+            start_params = self._fit_start_params((k_ar, k_ma, k), method)
 
-        if transparams: # transform initial parameters to ensure invertibility
+        if transparams:  # transform initial parameters to ensure invertibility
             start_params = self._invtransparams(start_params)
 
-        if solver is None:  # use default limited memory bfgs
-            bounds = [(None,)*2]*(k_ar+k_ma+k)
-            pgtol = kwargs.get('pgtol', 1e-8)
-            factr = kwargs.get('factr', 1e2)
-            m = kwargs.get('m', 12)
-            mlefit = optimize.fmin_l_bfgs_b(loglike, start_params,
-                    approx_grad=True, m=m, pgtol=pgtol, factr=factr,
-                    bounds=bounds, iprint=disp)
-            self.mlefit = mlefit
-            params = mlefit[0]
-
-        else:   # call the solver from LikelihoodModel
-            mlefit = super(ARMA, self).fit(start_params, method=solver,
-                        maxiter=maxiter, full_output=full_output, disp=disp,
-                        callback = callback, **kwargs)
-            self.mlefit = mlefit
-            params = mlefit.params
-
-        if transparams: # transform parameters back
+        if solver == 'lbfgs':
+            kwargs.setdefault('pgtol', 1e-8)
+            kwargs.setdefault('factr', 1e2)
+            kwargs.setdefault('m', 12)
+            kwargs.setdefault('approx_grad', True)
+        mlefit = super(ARMA, self).fit(start_params, method=solver,
+                                       maxiter=maxiter,
+                                       full_output=full_output, disp=disp,
+                                       callback=callback, **kwargs)
+        params = mlefit.params
+
+        if transparams:  # transform parameters back
             params = self._transparams(params)
 
-        self.transparams = False # set to false so methods don't expect transf.
+        self.transparams = False  # so methods don't expect transf.
 
-        normalized_cov_params = None #TODO: fix this
+        normalized_cov_params = None  # TODO: fix this
         armafit = ARMAResults(self, params, normalized_cov_params)
+        armafit.mle_retvals = mlefit.mle_retvals
+        armafit.mle_settings = mlefit.mle_settings
+        armafit.mlefit = mlefit
         return ARMAResultsWrapper(armafit)
 
 
@@ -893,23 +905,24 @@ def fit(self, order=None, start_params=None, trend='c', method = "css-mle",
 class ARIMA(ARMA):
 
     __doc__ = tsbase._tsa_doc % {"model" : _arima_model,
-            "params" : _arima_params, "extra_params" : "",
-            "extra_sections" : _armax_notes % {"Model" : "ARIMA"}}
+                                 "params" : _arima_params, "extra_params" : "",
+                                 "extra_sections" : _armax_notes %
+                                 {"Model" : "ARIMA"}}
 
     def __new__(cls, endog, order, exog=None, dates=None, freq=None,
-                       missing='none'):
+                missing='none'):
         p, d, q = order
-        if d == 0: # then we just use an ARMA model
-            return ARMA(endog, (p,q), exog, dates, freq, missing)
+        if d == 0:  # then we just use an ARMA model
+            return ARMA(endog, (p, q), exog, dates, freq, missing)
         else:
             mod = super(ARIMA, cls).__new__(cls)
             mod.__init__(endog, order, exog, dates, freq, missing)
             return mod
 
     def __init__(self, endog, order, exog=None, dates=None, freq=None,
-                       missing='none'):
-        p,d,q = order
-        super(ARIMA, self).__init__(endog, (p,q), exog, dates, freq, missing)
+                 missing='none'):
+        p, d, q = order
+        super(ARIMA, self).__init__(endog, (p, q), exog, dates, freq, missing)
         self.k_diff = d
         self.endog = np.diff(self.endog, n=d)
         #NOTE: will check in ARMA but check again since differenced now
@@ -935,13 +948,13 @@ def _get_predict_start(self, start, dynamic):
                 start = k_ar
         elif isinstance(start, int):
                 start -= k_diff
-                try: # catch when given an integer outside of dates index
+                try:  # catch when given an integer outside of dates index
                     start = super(ARIMA, self)._get_predict_start(start,
                                                                   dynamic)
-                except IndexError, err:
+                except IndexError:
                     raise ValueError("start must be in series. "
                                      "got %d" % (start + k_diff))
-        else: # received a date
+        else:  # received a date
             start = _validate(start, k_ar, k_diff, self.data.dates,
                               method)
             start = super(ARIMA, self)._get_predict_start(start, dynamic)
@@ -960,8 +973,8 @@ def _get_predict_end(self, end, dynamic=False):
 
         return end - self.k_diff, out_of_sample
 
-    def fit(self, start_params=None, trend='c', method = "css-mle",
-            transparams=True, solver=None, maxiter=35, full_output=1,
+    def fit(self, start_params=None, trend='c', method="css-mle",
+            transparams=True, solver='lbfgs', maxiter=50, full_output=1,
             disp=5, callback=None, **kwargs):
         """
         Fits ARIMA(p,d,q) model by exact maximum likelihood via Kalman filter.
@@ -988,7 +1001,7 @@ def fit(self, start_params=None, trend='c', method = "css-mle",
             Whether to include a constant or not.  'c' includes constant,
             'nc' no constant.
         solver : str or None, optional
-            Solver to be used.  The default is 'l_bfgs' (limited memory
+            Solver to be used.  The default is 'lbfgs' (limited memory
             Broyden-Fletcher-Goldfarb-Shanno).  Other choices are 'bfgs',
             'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' -
             (conjugate gradient), 'ncg' (non-conjugate gradient), and
@@ -996,7 +1009,7 @@ def fit(self, start_params=None, trend='c', method = "css-mle",
             approximate the Hessian, projected gradient tolerance of 1e-8 and
             factr = 1e2. You can change these by using kwargs.
         maxiter : int, optional
-            The maximum number of function evaluations. Default is 35.
+            The maximum number of function evaluations. Default is 50.
         tol : float
             The convergence tolerance.  Default is 1e-08.
         full_output : bool, optional
@@ -1032,24 +1045,25 @@ def fit(self, start_params=None, trend='c', method = "css-mle",
 
         """
         arima_fit = super(ARIMA, self).fit(None, start_params, trend,
-                               method, transparams, solver, maxiter,
-                               full_output, disp, callback, **kwargs)
-        normalized_cov_params = None #TODO: fix this?
+                                           method, transparams, solver,
+                                           maxiter, full_output, disp,
+                                           callback, **kwargs)
+        normalized_cov_params = None  # TODO: fix this?
         arima_fit = ARIMAResults(self, arima_fit._results.params,
-                                       normalized_cov_params)
+                                 normalized_cov_params)
         arima_fit.k_diff = self.k_diff
         return ARIMAResultsWrapper(arima_fit)
 
     def predict(self, params, start=None, end=None, exog=None, typ='linear',
                 dynamic=False):
         # go ahead and convert to an index for easier checking
-        if isinstance(start, (basestring, datetime)):
+        if isinstance(start, (string_types, datetime)):
             start = _index_date(start, self.data.dates)
         if typ == 'linear':
             if not dynamic or (start != self.k_ar + self.k_diff and
-                                                    start is not None):
+                               start is not None):
                 return super(ARIMA, self).predict(params, start, end, exog,
-                                              dynamic)
+                                                  dynamic)
             else:
                 # need to assume pre-sample residuals are zero
                 # do this by a hack
@@ -1072,17 +1086,17 @@ def predict(self, params, start=None, end=None, exog=None, typ='linear',
                     # add each predicted diff to lagged endog
                     if out_of_sample:
                         fv = predict[:-out_of_sample] + endog[start:end+1]
-                        fv = np.r_[fv,
-                              endog[-1] + np.cumsum(predict[-out_of_sample:])]
+                        fv = np.r_[fv, endog[-1] +
+                                   np.cumsum(predict[-out_of_sample:])]
                     else:
                         fv = predict + endog[start:end + 1]
                 else:
                     k_ar = self.k_ar
                     if out_of_sample:
                         fv = (predict[:-out_of_sample] +
-                                endog[max(start, self.k_ar-1):end+k_ar+1])
-                        fv = np.r_[fv,
-                              endog[-1] + np.cumsum(predict[-out_of_sample:])]
+                              endog[max(start, self.k_ar-1):end+k_ar+1])
+                        fv = np.r_[fv, endog[-1] +
+                                   np.cumsum(predict[-out_of_sample:])]
                     else:
                         fv = predict + endog[max(start, k_ar):end+k_ar+1]
             else:
@@ -1096,7 +1110,7 @@ def predict(self, params, start=None, end=None, exog=None, typ='linear',
                     k_trend = self.k_trend
                     k_diff = self.k_diff
                     (trendparam, exparams,
-                     arparams, maparams) = _unpack_params(params, (p,q),
+                     arparams, maparams) = _unpack_params(params, (p, q),
                                                           k_trend,
                                                           k_exog,
                                                           reverse=True)
@@ -1116,11 +1130,12 @@ def predict(self, params, start=None, end=None, exog=None, typ='linear',
                     return endog[start-1] + np.cumsum(predict)
             return fv
 
-        else: # pragma : no cover
+        else:  # pragma : no cover
             raise ValueError("typ %s not understood" % typ)
 
     predict.__doc__ = _arima_predict
 
+
 class ARMAResults(tsbase.TimeSeriesModelResults):
     """
     Class to hold results from fitting an ARMA model.
@@ -1142,7 +1157,9 @@ class ARMAResults(tsbase.TimeSeriesModelResults):
 
     aic : float
         Akaike Information Criterion
-        :math:`-2*llf+2*(df_model+1)`
+        :math:`-2*llf+2* df_model`
+        where `df_model` includes all AR parameters, MA parameters, constant
+        terms parameters on constant terms and the variance.
     arparams : array
         The parameters associated with the AR coefficients in the model.
     arroots : array
@@ -1152,7 +1169,7 @@ class ARMAResults(tsbase.TimeSeriesModelResults):
         circle.
     bic : float
         Bayes Information Criterion
-        -2*llf + log(nobs)*(df_model+1)
+        -2*llf + log(nobs)*df_model
         Where if the model is fit using conditional sum of squares, the
         number of observations `nobs` does not include the `p` pre-sample
         observations.
@@ -1225,10 +1242,9 @@ class ARMAResults(tsbase.TimeSeriesModelResults):
 
     #TODO: use this for docstring when we fix nobs issue
 
-
     def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         super(ARMAResults, self).__init__(model, params, normalized_cov_params,
-                scale)
+                                          scale)
         self.sigma2 = model.sigma2
         nobs = model.nobs
         self.nobs = nobs
@@ -1242,17 +1258,18 @@ def __init__(self, model, params, normalized_cov_params=None, scale=1.):
         k_ma = model.k_ma
         self.k_ma = k_ma
         df_model = k_exog + k_trend + k_ar + k_ma
+        self._ic_df_model = df_model + 1
         self.df_model = df_model
         self.df_resid = self.nobs - df_model
         self._cache = resettable_cache()
 
     @cache_readonly
     def arroots(self):
-        return np.roots(np.r_[1,-self.arparams])**-1
+        return np.roots(np.r_[1, -self.arparams])**-1
 
     @cache_readonly
     def maroots(self):
-        return np.roots(np.r_[1,self.maparams])**-1
+        return np.roots(np.r_[1, self.maparams])**-1
 
     @cache_readonly
     def arfreq(self):
@@ -1299,35 +1316,35 @@ def llf(self):
     def bse(self):
         params = self.params
         hess = self.model.hessian(params)
-        if len(params) == 1: # can't take an inverse
-            return np.sqrt(-1./hess)
+        if len(params) == 1:  # can't take an inverse, ensure 1d
+            return np.sqrt(-1./hess[0])
         return np.sqrt(np.diag(-inv(hess)))
 
-    def cov_params(self): # add scale argument?
+    def cov_params(self):  # add scale argument?
         params = self.params
         hess = self.model.hessian(params)
         return -inv(hess)
 
     @cache_readonly
     def aic(self):
-        return -2*self.llf + 2*(self.df_model+1)
+        return -2 * self.llf + 2 * self._ic_df_model
 
     @cache_readonly
     def bic(self):
         nobs = self.nobs
-        return -2*self.llf + np.log(nobs)*(self.df_model+1)
+        return -2 * self.llf + np.log(nobs) * self._ic_df_model
 
     @cache_readonly
     def hqic(self):
         nobs = self.nobs
-        return -2*self.llf + 2*(self.df_model+1)*np.log(np.log(nobs))
+        return -2 * self.llf + 2 * np.log(np.log(nobs)) * self._ic_df_model
 
     @cache_readonly
     def fittedvalues(self):
         model = self.model
         endog = model.endog.copy()
         k_ar = self.k_ar
-        exog = model.exog # this is a copy
+        exog = model.exog  # this is a copy
         if exog is not None:
             if model.method == "css" and k_ar > 0:
                 exog = exog[k_ar:]
@@ -1335,11 +1352,10 @@ def fittedvalues(self):
             endog = endog[k_ar:]
         fv = endog - self.resid
         # add deterministic part back in
-        k = self.k_exog + self.k_trend
-    #TODO: this needs to be commented out for MLE with constant
-
-    #    if k != 0:
-    #        fv += dot(exog, self.params[:k])
+        #k = self.k_exog + self.k_trend
+        #TODO: this needs to be commented out for MLE with constant
+        #if k != 0:
+        #    fv += dot(exog, self.params[:k])
         return fv
 
     @cache_readonly
@@ -1403,18 +1419,20 @@ def forecast(self, steps=1, exog=None, alpha=.05):
         arparams = self.arparams
         maparams = self.maparams
         forecast = _arma_predict_out_of_sample(self.params,
-                steps, self.resid, self.k_ar, self.k_ma, self.k_trend,
-                self.k_exog, self.model.endog, exog, method=self.model.method)
+                                               steps, self.resid, self.k_ar,
+                                               self.k_ma, self.k_trend,
+                                               self.k_exog, self.model.endog,
+                                               exog, method=self.model.method)
         # compute the standard errors
         sigma2 = self.sigma2
-        ma_rep = arma2ma(np.r_[1,-arparams],
+        ma_rep = arma2ma(np.r_[1, -arparams],
                          np.r_[1, maparams], nobs=steps)
 
-
         fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
 
-        const = norm.ppf(1 - alpha/2.)
-        conf_int = np.c_[forecast - const*fcasterr, forecast + const*fcasterr]
+        const = norm.ppf(1 - alpha / 2.)
+        conf_int = np.c_[forecast - const * fcasterr,
+                         forecast + const * fcasterr]
 
         return forecast, fcasterr, conf_int
 
@@ -1477,7 +1495,7 @@ def summary(self, alpha=.05):
 
         smry = Summary()
         smry.add_table_2cols(self, gleft=top_left, gright=top_right,
-                                   title=title)
+                             title=title)
         smry.add_table_params(self, alpha=alpha, use_t=False)
 
         # Make the roots table
@@ -1499,16 +1517,22 @@ def summary(self, alpha=.05):
             stubs = arstubs
             roots = self.arroots
             freq = self.arfreq
-        modulus = np.abs(roots)
-        data = np.column_stack((roots.real, roots.imag, modulus, freq))
-        roots_table = SimpleTable(data,
-                headers=['           Real', '         Imaginary',
-                        '         Modulus', '        Frequency'],
-                title="Roots",
-                stubs=stubs, data_fmts=["%17.4f", "%+17.4fj", "%17.4f",
-                    "%17.4f"])
-
-        smry.tables.append(roots_table)
+        else:  # 0,0 model
+            stubs = []
+        if len(stubs):  # not 0, 0
+            modulus = np.abs(roots)
+            data = np.column_stack((roots.real, roots.imag, modulus, freq))
+            roots_table = SimpleTable(data,
+                                      headers=['           Real',
+                                               '         Imaginary',
+                                               '         Modulus',
+                                               '        Frequency'],
+                                      title="Roots",
+                                      stubs=stubs,
+                                      data_fmts=["%17.4f", "%+17.4fj",
+                                                 "%17.4f", "%17.4f"])
+
+            smry.tables.append(roots_table)
         return smry
 
     def summary2(self, title=None, alpha=.05, float_format="%.4f"):
@@ -1536,6 +1560,7 @@ def summary2(self, title=None, alpha=.05, float_format="%.4f"):
             results
 
         """
+        from pandas import DataFrame
         # get sample TODO: make better sample machinery for estimation
         k_diff = getattr(self, 'k_diff', 0)
         if 'mle' in self.model.method:
@@ -1550,10 +1575,6 @@ def summary2(self, title=None, alpha=.05, float_format="%.4f"):
             sample = str(start) + ' - ' + str(len(self.data.orig_endog))
 
         k_ar, k_ma = self.k_ar, self.k_ma
-        if not k_diff:
-            order = str((k_ar, k_ma))
-        else:
-            order = str((k_ar, k_diff, k_ma))
 
         # Roots table
         if k_ma and k_ar:
@@ -1572,11 +1593,15 @@ def summary2(self, title=None, alpha=.05, float_format="%.4f"):
             stubs = arstubs
             roots = self.arroots
             freq = self.arfreq
-        modulus = np.abs(roots)
-        data = np.column_stack((roots.real, roots.imag, modulus, freq))
-        data = pd.DataFrame(data)
-        data.columns = ['Real', 'Imaginary', 'Modulus', 'Frequency']
-        data.index = stubs
+        else:  # 0, 0 order
+            stubs = []
+
+        if len(stubs):
+            modulus = np.abs(roots)
+            data = np.column_stack((roots.real, roots.imag, modulus, freq))
+            data = DataFrame(data)
+            data.columns = ['Real', 'Imaginary', 'Modulus', 'Frequency']
+            data.index = stubs
 
         # Summary
         from statsmodels.iolib import summary2
@@ -1595,21 +1620,23 @@ def summary2(self, title=None, alpha=.05, float_format="%.4f"):
         params = summary2.summary_params(self)
         smry.add_dict(model_info)
         smry.add_df(params, float_format=float_format)
-        smry.add_df(data, float_format="%17.4f")
+        if len(stubs):
+            smry.add_df(data, float_format="%17.4f")
         smry.add_title(results=self, title=title)
 
         return smry
 
+
 class ARMAResultsWrapper(wrap.ResultsWrapper):
     _attrs = {}
     _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
-                                    _attrs)
+                                   _attrs)
     _methods = {}
-    _wrap_methods = wrap.union_dicts(
-                        tsbase.TimeSeriesResultsWrapper._wrap_methods,
-                        _methods)
+    _wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
+                                     _methods)
 wrap.populate_wrapper(ARMAResultsWrapper, ARMAResults)
 
+
 class ARIMAResults(ARMAResults):
     def predict(self, start=None, end=None, exog=None, typ='linear',
                 dynamic=False):
@@ -1648,16 +1675,17 @@ def forecast(self, steps=1, exog=None, alpha=.05):
         """
         if exog is not None:
             if self.k_exog == 1 and exog.ndim == 1:
-                exog = exog[:,None]
+                exog = exog[:, None]
             if exog.shape[0] != steps:
                 raise ValueError("new exog needed for each step")
             # prepend in-sample exog observations
             exog = np.vstack((self.model.exog[-self.k_ar:, self.k_trend:],
                               exog))
         forecast = _arma_predict_out_of_sample(self.params, steps, self.resid,
-                                        self.k_ar, self.k_ma, self.k_trend,
-                                        self.k_exog, self.model.endog,
-                                        exog, method=self.model.method)
+                                               self.k_ar, self.k_ma,
+                                               self.k_trend, self.k_exog,
+                                               self.model.endog,
+                                               exog, method=self.model.method)
         forecast = self.model.data.endog[-1] + np.cumsum(forecast)
         # get forecast errors
         arparams = self.arparams
@@ -1669,61 +1697,61 @@ def forecast(self, steps=1, exog=None, alpha=.05):
         conf_int = np.c_[forecast - const*fcerr, forecast + const*fcerr]
         return forecast, fcerr, conf_int
 
+
 class ARIMAResultsWrapper(ARMAResultsWrapper):
     pass
 wrap.populate_wrapper(ARIMAResultsWrapper, ARIMAResults)
 
 
 if __name__ == "__main__":
-    import numpy as np
     import statsmodels.api as sm
 
     # simulate arma process
     from statsmodels.tsa.arima_process import arma_generate_sample
-    y = arma_generate_sample([1., -.75],[1.,.25], nsample=1000)
+    y = arma_generate_sample([1., -.75], [1., .25], nsample=1000)
     arma = ARMA(y)
-    res = arma.fit(trend='nc', order=(1,1))
+    res = arma.fit(trend='nc', order=(1, 1))
 
     np.random.seed(12345)
-    y_arma22 = arma_generate_sample([1.,-.85,.35],[1,.25,-.9], nsample=1000)
+    y_arma22 = arma_generate_sample([1., -.85, .35], [1, .25, -.9],
+                                    nsample=1000)
     arma22 = ARMA(y_arma22)
-    res22 = arma22.fit(trend = 'nc', order=(2,2))
+    res22 = arma22.fit(trend='nc', order=(2, 2))
 
     # test CSS
     arma22_css = ARMA(y_arma22)
-    res22css = arma22_css.fit(trend='nc', order=(2,2), method='css')
-
+    res22css = arma22_css.fit(trend='nc', order=(2, 2), method='css')
 
     data = sm.datasets.sunspots.load()
     ar = ARMA(data.endog)
-    resar = ar.fit(trend='nc', order=(9,0))
+    resar = ar.fit(trend='nc', order=(9, 0))
 
-    y_arma31 = arma_generate_sample([1,-.75,-.35,.25],[.1], nsample=1000)
+    y_arma31 = arma_generate_sample([1, -.75, -.35, .25], [.1],
+                                    nsample=1000)
 
     arma31css = ARMA(y_arma31)
-    res31css = arma31css.fit(order=(3,1), method="css", trend="nc",
-            transparams=True)
+    res31css = arma31css.fit(order=(3, 1), method="css", trend="nc",
+                             transparams=True)
 
-    y_arma13 = arma_generate_sample([1., -.75],[1,.25,-.5,.8], nsample=1000)
+    y_arma13 = arma_generate_sample([1., -.75], [1, .25, -.5, .8],
+                                    nsample=1000)
     arma13css = ARMA(y_arma13)
-    res13css = arma13css.fit(order=(1,3), method='css', trend='nc')
-
+    res13css = arma13css.fit(order=(1, 3), method='css', trend='nc')
 
 # check css for p < q and q < p
-    y_arma41 = arma_generate_sample([1., -.75, .35, .25, -.3],[1,-.35],
-                    nsample=1000)
+    y_arma41 = arma_generate_sample([1., -.75, .35, .25, -.3], [1, -.35],
+                                    nsample=1000)
     arma41css = ARMA(y_arma41)
-    res41css = arma41css.fit(order=(4,1), trend='nc', method='css')
+    res41css = arma41css.fit(order=(4, 1), trend='nc', method='css')
 
     y_arma14 = arma_generate_sample([1, -.25], [1., -.75, .35, .25, -.3],
-                    nsample=1000)
+                                    nsample=1000)
     arma14css = ARMA(y_arma14)
-    res14css = arma14css.fit(order=(4,1), trend='nc', method='css')
+    res14css = arma14css.fit(order=(4, 1), trend='nc', method='css')
 
     # ARIMA Model
     from statsmodels.tools.tools import webuse
     dta = webuse('wpi1')
     wpi = dta['wpi']
 
-    mod = ARIMA(wpi, (1,1,1)).fit()
-
+    mod = ARIMA(wpi, (1, 1, 1)).fit()
diff --git a/statsmodels/tsa/arima_process.py b/statsmodels/tsa/arima_process.py
index 01d560723ed..760215e9c32 100644
--- a/statsmodels/tsa/arima_process.py
+++ b/statsmodels/tsa/arima_process.py
@@ -52,7 +52,8 @@
 Author: josefpktd
 License: BSD
 '''
-
+from __future__ import print_function
+from statsmodels.compat.python import range
 import numpy as np
 from scipy import signal, optimize, linalg
 from statsmodels.base.model import LikelihoodModel
@@ -190,7 +191,8 @@ def generate_sample(cls, ar, ma, nsample, std=1):
 
 
 def arma_generate_sample(ar, ma, nsample, sigma=1, distrvs=np.random.randn, burnin=0):
-    '''generate an random sample of an ARMA process
+    """
+    Generate a random sample of an ARMA process
 
     Parameters
     ----------
@@ -211,14 +213,32 @@ def arma_generate_sample(ar, ma, nsample, sigma=1, distrvs=np.random.randn, burn
         to reduce the effect of initial conditions, burnin observations at the
         beginning of the sample are dropped
 
-
     Returns
     -------
-    acovf : array
-        autocovariance of ARMA process given by ar, ma
+    sample : array
+        sample of ARMA process given by ar, ma of length nsample
 
+    Notes
+    -----
+    As mentioned above, both the AR and MA components should include the
+    coefficient on the zero-lag. This is typically 1. Further, due to the
+    conventions used in signal processing used in signal.lfilter vs.
+    conventions in statistics for ARMA processes, the AR paramters should
+    have the opposite sign of what you might expect. See the examples below.
 
-    '''
+    Examples
+    --------
+    >>> import numpy as np
+    >>> np.random.seed(12345)
+    >>> ar = np.array([.75, -.25])
+    >>> ma = np.array([.65, .35])
+    >>> arparams = np.r_[1, -ar] # add zero-lag and negate
+    >>> maparams = np.r_[1, ma] # add zero-lag
+    >>> y = sm.tsa.arma_generate_sample(arparams, maparams, 250)
+    >>> model = sm.tsa.ARMA(y, (2, 2)).fit(trend='nc', disp=0)
+    >>> model.params
+    array([ 0.79044189, -0.23140636,  0.70072904,  0.40608028])
+    """
     #TODO: unify with ArmaProcess method
     eta = sigma * distrvs(nsample+burnin)
     return signal.lfilter(ma, ar, eta)[burnin:]
@@ -232,6 +252,8 @@ def arma_acovf(ar, ma, nobs=10):
         coefficient for autoregressive lag polynomial, including zero lag
     ma : array_like, 1d
         coefficient for moving-average lag polynomial, including zero lag
+    nobs : int
+        number of terms (lags plus zero lag) to include in returned acovf
 
     Returns
     -------
@@ -270,7 +292,7 @@ def arma_acovf(ar, ma, nobs=10):
     return acovf[:nobs]
 
 def arma_acf(ar, ma, nobs=10):
-    '''theoretical autocorrelation function of ARMA process
+    '''theoretical autocorrelation function of an ARMA process
 
     Parameters
     ----------
@@ -278,6 +300,8 @@ def arma_acf(ar, ma, nobs=10):
         coefficient for autoregressive lag polynomial, including zero lag
     ma : array_like, 1d
         coefficient for moving-average lag polynomial, including zero lag
+    nobs : int
+        number of terms (lags plus zero lag) to include in returned acf
 
     Returns
     -------
@@ -298,6 +322,20 @@ def arma_acf(ar, ma, nobs=10):
 def arma_pacf(ar, ma, nobs=10):
     '''partial autocorrelation function of an ARMA process
 
+    Parameters
+    ----------
+    ar : array_like, 1d
+        coefficient for autoregressive lag polynomial, including zero lag
+    ma : array_like, 1d
+        coefficient for moving-average lag polynomial, including zero lag
+    nobs : int
+        number of terms (lags plus zero lag) to include in returned pacf
+
+    Returns
+    -------
+    pacf : array
+        partial autocorrelation of ARMA process given by ar, ma
+
     Notes
     -----
     solves yule-walker equation for each lag order up to nobs lags
@@ -351,7 +389,7 @@ def arma_periodogram(ar, ma, worN=None, whole=0):
     sd = np.abs(h)**2/np.sqrt(2*np.pi)
     if np.sum(np.isnan(h)) > 0:
         # this happens with unit root or seasonal unit root'
-        print 'Warning: nan in frequency response h, maybe a unit root'
+        print('Warning: nan in frequency response h, maybe a unit root')
     return w, sd
 
 
@@ -493,17 +531,17 @@ def ar2arma(ar_des, p, q, n=20, mse='ar', start=None):
     def msear_err(arma, ar_des):
         ar, ma = np.r_[1, arma[:p-1]], np.r_[1, arma[p-1:]]
         ar_approx = arma_impulse_response(ma, ar,  n)
-##        print ar,ma
-##        print ar_des.shape, ar_approx.shape
-##        print ar_des
-##        print ar_approx
+##        print(ar,ma)
+##        print(ar_des.shape, ar_approx.shape)
+##        print(ar_des)
+##        print(ar_approx)
         return (ar_des - ar_approx) #((ar - ar_approx)**2).sum()
     if start is None:
         arma0 = np.r_[-0.9* np.ones(p-1), np.zeros(q-1)]
     else:
         arma0 = start
     res = optimize.leastsq(msear_err, arma0, ar_des, maxfev=5000)#, full_output=True)
-    #print res
+    #print(res)
     arma_app = np.atleast_1d(res[0])
     ar_app = np.r_[1, arma_app[:p-1]],
     ma_app = np.r_[1, arma_app[p-1:]]
@@ -917,123 +955,123 @@ def generate_sample(self, size=100, scale=1, distrvs=None, axis=0, burnin=0):
     eta = 0.1 * np.random.randn(1000)
     yar1 = signal.lfilter(ar, ma, eta)
 
-    print "\nExample 0"
+    print("\nExample 0")
     arest = ARIMAProcess(yar1)
     rhohat, cov_x, infodict, mesg, ier = arest.fit((1,0,1))
-    print rhohat
-    print cov_x
+    print(rhohat)
+    print(cov_x)
 
-    print "\nExample 1"
+    print("\nExample 1")
     ar = [1.0,  -0.8]
     ma = [1.0,  0.5]
     y1 = arest.generate_sample(ar,ma,1000,0.1)
     arest = ARIMAProcess(y1)
     rhohat1, cov_x1, infodict, mesg, ier = arest.fit((1,0,1))
-    print rhohat1
-    print cov_x1
+    print(rhohat1)
+    print(cov_x1)
     err1 = arest.errfn(x=y1)
-    print np.var(err1)
+    print(np.var(err1))
     import statsmodels.api as sm
-    print sm.regression.yule_walker(y1, order=2, inv=True)
+    print(sm.regression.yule_walker(y1, order=2, inv=True))
 
-    print "\nExample 2"
+    print("\nExample 2")
     nsample = 1000
     ar = [1.0, -0.6, -0.1]
     ma = [1.0,  0.3,  0.2]
     y2 = ARIMA.generate_sample(ar,ma,nsample,0.1)
     arest2 = ARIMAProcess(y2)
     rhohat2, cov_x2, infodict, mesg, ier = arest2.fit((1,0,2))
-    print rhohat2
-    print cov_x2
+    print(rhohat2)
+    print(cov_x2)
     err2 = arest.errfn(x=y2)
-    print np.var(err2)
-    print arest2.rhoy
-    print arest2.rhoe
-    print "true"
-    print ar
-    print ma
+    print(np.var(err2))
+    print(arest2.rhoy)
+    print(arest2.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
     rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,0,2))
-    print rhohat2a
-    print cov_x2a
+    print(rhohat2a)
+    print(cov_x2a)
     err2a = arest.errfn(x=y2)
-    print np.var(err2a)
-    print arest2.rhoy
-    print arest2.rhoe
-    print "true"
-    print ar
-    print ma
+    print(np.var(err2a))
+    print(arest2.rhoy)
+    print(arest2.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
 
-    print sm.regression.yule_walker(y2, order=2, inv=True)
+    print(sm.regression.yule_walker(y2, order=2, inv=True))
 
-    print "\nExample 20"
+    print("\nExample 20")
     nsample = 1000
     ar = [1.0]#, -0.8, -0.4]
     ma = [1.0,  0.5,  0.2]
     y3 = ARIMA.generate_sample(ar,ma,nsample,0.01)
     arest20 = ARIMAProcess(y3)
     rhohat3, cov_x3, infodict, mesg, ier = arest20.fit((2,0,0))
-    print rhohat3
-    print cov_x3
+    print(rhohat3)
+    print(cov_x3)
     err3 = arest20.errfn(x=y3)
-    print np.var(err3)
-    print np.sqrt(np.dot(err3,err3)/nsample)
-    print arest20.rhoy
-    print arest20.rhoe
-    print "true"
-    print ar
-    print ma
+    print(np.var(err3))
+    print(np.sqrt(np.dot(err3,err3)/nsample))
+    print(arest20.rhoy)
+    print(arest20.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
 
     rhohat3a, cov_x3a, infodict, mesg, ier = arest20.fit((0,0,2))
-    print rhohat3a
-    print cov_x3a
+    print(rhohat3a)
+    print(cov_x3a)
     err3a = arest20.errfn(x=y3)
-    print np.var(err3a)
-    print np.sqrt(np.dot(err3a,err3a)/nsample)
-    print arest20.rhoy
-    print arest20.rhoe
-    print "true"
-    print ar
-    print ma
+    print(np.var(err3a))
+    print(np.sqrt(np.dot(err3a,err3a)/nsample))
+    print(arest20.rhoy)
+    print(arest20.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
 
-    print sm.regression.yule_walker(y3, order=2, inv=True)
+    print(sm.regression.yule_walker(y3, order=2, inv=True))
 
-    print "\nExample 02"
+    print("\nExample 02")
     nsample = 1000
     ar = [1.0, -0.8, 0.4] #-0.8, -0.4]
     ma = [1.0]#,  0.8,  0.4]
     y4 = ARIMA.generate_sample(ar,ma,nsample)
     arest02 = ARIMAProcess(y4)
     rhohat4, cov_x4, infodict, mesg, ier = arest02.fit((2,0,0))
-    print rhohat4
-    print cov_x4
+    print(rhohat4)
+    print(cov_x4)
     err4 = arest02.errfn(x=y4)
-    print np.var(err4)
+    print(np.var(err4))
     sige = np.sqrt(np.dot(err4,err4)/nsample)
-    print sige
-    print sige * np.sqrt(np.diag(cov_x4))
-    print np.sqrt(np.diag(cov_x4))
-    print arest02.rhoy
-    print arest02.rhoe
-    print "true"
-    print ar
-    print ma
+    print(sige)
+    print(sige * np.sqrt(np.diag(cov_x4)))
+    print(np.sqrt(np.diag(cov_x4)))
+    print(arest02.rhoy)
+    print(arest02.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
 
     rhohat4a, cov_x4a, infodict, mesg, ier = arest02.fit((0,0,2))
-    print rhohat4a
-    print cov_x4a
+    print(rhohat4a)
+    print(cov_x4a)
     err4a = arest02.errfn(x=y4)
-    print np.var(err4a)
+    print(np.var(err4a))
     sige = np.sqrt(np.dot(err4a,err4a)/nsample)
-    print sige
-    print sige * np.sqrt(np.diag(cov_x4a))
-    print np.sqrt(np.diag(cov_x4a))
-    print arest02.rhoy
-    print arest02.rhoe
-    print "true"
-    print ar
-    print ma
+    print(sige)
+    print(sige * np.sqrt(np.diag(cov_x4a)))
+    print(np.sqrt(np.diag(cov_x4a)))
+    print(arest02.rhoy)
+    print(arest02.rhoe)
+    print("true")
+    print(ar)
+    print(ma)
     import statsmodels.api as sm
-    print sm.regression.yule_walker(y4, order=2, method='mle', inv=True)
+    print(sm.regression.yule_walker(y4, order=2, method='mle', inv=True))
 
 
     import matplotlib.pyplot as plt
@@ -1043,7 +1081,7 @@ def generate_sample(self, size=100, scale=1, distrvs=None, axis=0, burnin=0):
     ar1, ar2 = ([1, -0.4], [1, 0.5])
     ar2 = [1, -1]
     lagpolyproduct = np.convolve(ar1, ar2)
-    print deconvolve(lagpolyproduct, ar2, n=None)
-    print signal.deconvolve(lagpolyproduct, ar2)
-    print deconvolve(lagpolyproduct, ar2, n=10)
+    print(deconvolve(lagpolyproduct, ar2, n=None))
+    print(signal.deconvolve(lagpolyproduct, ar2))
+    print(deconvolve(lagpolyproduct, ar2, n=10))
 
diff --git a/statsmodels/tsa/arma_mle.py b/statsmodels/tsa/arma_mle.py
index d48539b8fbf..0e8b8f1a37a 100644
--- a/statsmodels/tsa/arma_mle.py
+++ b/statsmodels/tsa/arma_mle.py
@@ -8,6 +8,7 @@
 
 """
 
+from statsmodels.compat.python import range
 import numpy as np
 from scipy import signal, optimize
 from statsmodels.base.model import (LikelihoodModel,
@@ -353,7 +354,7 @@ def forecast3(self, step_ahead=1, start=None): #, end=None):
         '''another try for h-step ahead forecasting
         '''
 
-        from arima_process import arma2ma, ArmaProcess
+        from .arima_process import arma2ma, ArmaProcess
         p,q = self.nar, self.nma
         k=0
         ar = self.params[k:k+p]
diff --git a/statsmodels/tsa/base/datetools.py b/statsmodels/tsa/base/datetools.py
index d63f2364bce..4bc73f7618f 100644
--- a/statsmodels/tsa/base/datetools.py
+++ b/statsmodels/tsa/base/datetools.py
@@ -1,24 +1,25 @@
+from statsmodels.compat.python import (lrange, lzip, lmap, string_types, callable,
+                                asstr, reduce, zip, map)
 import re
 import datetime
+from pandas import Period
+from pandas.tseries.frequencies import to_offset
 from pandas import datetools as pandas_datetools
 import numpy as np
-from statsmodels.compatnp.py3k import asstr
 
 #NOTE: All of these frequencies assume end of period (except wrt time)
-try:
-    from pandas.tseries.frequencies import to_offset
-    class _freq_to_pandas_class(object):
-        # being lazy, don't want to replace dictionary below
-        def __getitem__(self, key):
-            return to_offset(key)
-    _freq_to_pandas = _freq_to_pandas_class()
-except ImportError:
-    _freq_to_pandas = {'B' : pandas_datetools.BDay(1),
-                       'D' : pandas_datetools.day,
-                       'W' : pandas_datetools.Week(weekday=6),
-                       'M' : pandas_datetools.monthEnd,
-                       'A' : pandas_datetools.yearEnd,
-                       'Q' : pandas_datetools.quarterEnd}
+class _freq_to_pandas_class(object):
+    # being lazy, don't want to replace dictionary below
+    def __getitem__(self, key):
+        return to_offset(key)
+_freq_to_pandas = _freq_to_pandas_class()
+
+
+def _is_datetime_index(dates):
+    if isinstance(dates[0], (datetime.datetime, Period)):
+        return True  # TimeStamp is a datetime subclass
+    else:
+        return False
 
 def _index_date(date, dates):
     """
@@ -32,7 +33,7 @@ def _index_date(date, dates):
     If there dates are not of a fixed-frequency and date is not on the
     existing dates, then a ValueError is raised.
     """
-    if isinstance(date, basestring):
+    if isinstance(date, string_types):
         date = date_parser(date)
     try:
         if hasattr(dates, 'indexMap'): # 0.7.x
@@ -44,7 +45,7 @@ def _index_date(date, dates):
                 return np.where(date)[0].item()
             except TypeError: # expected behavior
                 return date
-    except KeyError, err:
+    except KeyError as err:
         freq = _infer_freq(dates)
         if freq is None:
             #TODO: try to intelligently roll forward onto a date in the
@@ -88,13 +89,9 @@ def _idx_from_dates(d1, d2, freq):
     Does not check the start date to see whether it is on the offest but
     assumes that it is.
     """
-    try: # pandas 0.8.x
-        from pandas import DatetimeIndex
-        return len(DatetimeIndex(start=d1, end=d2,
-                                 freq = _freq_to_pandas[freq])) - 1
-    except ImportError, err:
-        from pandas import DateRange
-        return len(DateRange(d1, d2, offset = _freq_to_pandas[freq])) - 1
+    from pandas import DatetimeIndex
+    return len(DatetimeIndex(start=d1, end=d2,
+                             freq = _freq_to_pandas[freq])) - 1
 
 _quarter_to_day = {
         "1" : (3, 31),
@@ -108,8 +105,8 @@ def _idx_from_dates(d1, d2, freq):
         }
 
 _mdays = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
-_months_with_days = zip(range(1,13), _mdays)
-_month_to_day = dict(zip(map(str,range(1,13)), _months_with_days))
+_months_with_days = lzip(lrange(1,13), _mdays)
+_month_to_day = dict(zip(map(str,lrange(1,13)), _months_with_days))
 _month_to_day.update(dict(zip(["I", "II", "III", "IV", "V", "VI",
                                "VII", "VIII", "IX", "X", "XI", "XII"],
                                _months_with_days)))
@@ -214,15 +211,15 @@ def date_range_str(start, end=None, length=None):
         split = 'a'
     else:
         raise ValueError("Date %s not understood" % start)
-    yr1, offset1 = map(int, start.replace(":","").split(split))
+    yr1, offset1 = lmap(int, start.replace(":","").split(split))
     if end is not None:
         end = end.lower()
-        yr2, offset2 = map(int, end.replace(":","").split(split))
+        yr2, offset2 = lmap(int, end.replace(":","").split(split))
         length = (yr2 - yr1) * annual_freq + offset2
     elif length:
         yr2 = yr1 + length // annual_freq
         offset2 = length % annual_freq + (offset1 - 1)
-    years = np.repeat(range(yr1+1, yr2), annual_freq).tolist()
+    years = np.repeat(lrange(yr1+1, yr2), annual_freq).tolist()
     years = np.r_[[str(yr1)]*(annual_freq+1-offset1), years] # tack on first year
     years = np.r_[years, [str(yr2)]*offset2] # tack on last year
     if split != 'a':
@@ -251,7 +248,7 @@ def dates_from_str(dates):
     date_list : array
         A list of datetime types.
     """
-    return map(date_parser, dates)
+    return lmap(date_parser, dates)
 
 def dates_from_range(start, end=None, length=None):
     """
@@ -284,6 +281,8 @@ def _add_datetimes(dates):
     return reduce(lambda x, y: y+x, dates)
 
 def _infer_freq(dates):
+    if hasattr(dates, "freqstr"):
+        return dates.freqstr
     try:
         from pandas.tseries.api import infer_freq
         freq = infer_freq(dates)
diff --git a/statsmodels/tsa/base/tests/test_base.py b/statsmodels/tsa/base/tests/test_base.py
index cf5ee81440d..42c22d9a3c2 100644
--- a/statsmodels/tsa/base/tests/test_base.py
+++ b/statsmodels/tsa/base/tests/test_base.py
@@ -1,16 +1,9 @@
 import numpy as np
 from pandas import Series
-from pandas.util import testing as ptesting
+from pandas import date_range
 from statsmodels.tsa.base.tsa_model import TimeSeriesModel
-from statsmodels.tsa.base.datetools import dates_from_range
 import numpy.testing as npt
 
-try:
-    from pandas import DatetimeIndex
-    _pandas_08x = True
-except ImportError:
-    _pandas_08x = False
-
 def test_pandas_nodates_index():
     from statsmodels.datasets import sunspots
     y = sunspots.load_pandas().data.SUNACTIVITY
@@ -20,61 +13,47 @@ def test_predict_freq():
     # test that predicted dates have same frequency
     x = np.arange(1,36.)
 
-    if _pandas_08x:
-        from pandas import date_range
-
-        # there's a bug in pandas up to 0.10.2 for YearBegin
-        #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
-        dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
-        series = Series(x, index=dates)
-        model = TimeSeriesModel(series)
-        #npt.assert_(model.data.freq == "AS-APR")
-        npt.assert_(model.data.freq == "A-APR")
-
-        start = model._get_predict_start("2006-4-30")
-        end = model._get_predict_end("2016-4-30")
-        model._make_predict_dates()
-
-        predict_dates = model.data.predict_dates
+    # there's a bug in pandas up to 0.10.2 for YearBegin
+    #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
+    dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
+    series = Series(x, index=dates)
+    model = TimeSeriesModel(series)
+    #npt.assert_(model.data.freq == "AS-APR")
+    npt.assert_(model.data.freq == "A-APR")
 
-        #expected_dates = date_range("2006-12-31", "2016-12-31",
-        #                            freq="AS-APR")
-        expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR")
-        npt.assert_equal(predict_dates, expected_dates)
-        #ptesting.assert_series_equal(predict_dates, expected_dates)
+    start = model._get_predict_start("2006-4-30")
+    end = model._get_predict_end("2016-4-30")
+    model._make_predict_dates()
 
-    else:
-        from pandas import DateRange, datetools
-        dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd)
-        series = Series(x, index=dates)
-        model = TimeSeriesModel(series)
-        npt.assert_(model.data.freq == "A")
+    predict_dates = model.data.predict_dates
 
-        start = model._get_predict_start("2006-12-31")
-        end = model._get_predict_end("2016-12-31")
-        model._make_predict_dates()
+    #expected_dates = date_range("2006-12-31", "2016-12-31",
+    #                            freq="AS-APR")
+    expected_dates = date_range("2006-4-30", "2016-4-30", freq="A-APR")
+    npt.assert_equal(predict_dates, expected_dates)
+    #ptesting.assert_series_equal(predict_dates, expected_dates)
 
-        predict_dates = model.data.predict_dates
-
-        expected_dates = DateRange("2006-12-31", "2016-12-31",
-                                    offset=datetools.yearEnd)
-        npt.assert_array_equal(predict_dates, expected_dates)
 
 def test_keyerror_start_date():
     x = np.arange(1,36.)
 
-    if _pandas_08x:
-        from pandas import date_range
+    from pandas import date_range
 
-        # there's a bug in pandas up to 0.10.2 for YearBegin
-        #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
-        dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
-        series = Series(x, index=dates)
-        model = TimeSeriesModel(series)
-    else:
-        from pandas import DateRange, datetools
-        dates = DateRange("1972-1-1", "2007-1-1", offset=datetools.yearEnd)
-        series = Series(x, index=dates)
-        model = TimeSeriesModel(series)
+    # there's a bug in pandas up to 0.10.2 for YearBegin
+    #dates = date_range("1972-4-1", "2007-4-1", freq="AS-APR")
+    dates = date_range("1972-4-30", "2006-4-30", freq="A-APR")
+    series = Series(x, index=dates)
+    model = TimeSeriesModel(series)
 
     npt.assert_raises(ValueError, model._get_predict_start, "1970-4-30")
+
+def test_period_index():
+    # test 1285
+    from pandas import PeriodIndex, TimeSeries
+    dates = PeriodIndex(start="1/1/1990", periods=20, freq="M")
+    x = np.arange(1, 21.)
+
+    model = TimeSeriesModel(Series(x, index=dates))
+    npt.assert_(model.data.freq == "M")
+    model = TimeSeriesModel(TimeSeries(x, index=dates))
+    npt.assert_(model.data.freq == "M")
diff --git a/statsmodels/tsa/base/tests/test_datetools.py b/statsmodels/tsa/base/tests/test_datetools.py
index 5b322593cf5..6e27874225e 100644
--- a/statsmodels/tsa/base/tests/test_datetools.py
+++ b/statsmodels/tsa/base/tests/test_datetools.py
@@ -3,6 +3,7 @@
 from statsmodels.tsa.base.datetools import (_date_from_idx,
                 _idx_from_dates, date_parser, date_range_str, dates_from_str,
                 dates_from_range, _infer_freq, _freq_to_pandas)
+from pandas import DatetimeIndex, PeriodIndex
 
 def test_date_from_idx():
     d1 = datetime(2008, 12, 31)
@@ -107,46 +108,24 @@ def test_dates_from_range():
     dt_range = dates_from_range("1959m3", length=len(results))
 
 
-try:
-    from pandas import DatetimeIndex
-    _pandas_08x = True
-except ImportError, err:
-    _pandas_08x = False
-    d1 = datetime(2008, 12, 31)
-    d2 = datetime(2012, 9, 30)
+
 
 def test_infer_freq():
     d1 = datetime(2008, 12, 31)
     d2 = datetime(2012, 9, 30)
 
-    if _pandas_08x:
-        b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values
-        d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values
-        w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values
-        m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values
-        a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values
-        q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values
-        assert _infer_freq(w) == 'W-SUN'
-        assert _infer_freq(a) == 'A-DEC'
-        assert _infer_freq(q) == 'Q-DEC'
-        assert _infer_freq(w[:3]) == 'W-SUN'
-        assert _infer_freq(a[:3]) == 'A-DEC'
-        assert _infer_freq(q[:3]) == 'Q-DEC'
-    else:
-        from pandas import DateRange
-
-        b = DateRange(d1, d2, offset=_freq_to_pandas['B']).values
-        d = DateRange(d1, d2, offset=_freq_to_pandas['D']).values
-        w = DateRange(d1, d2, offset=_freq_to_pandas['W']).values
-        m = DateRange(d1, d2, offset=_freq_to_pandas['M']).values
-        a = DateRange(d1, d2, offset=_freq_to_pandas['A']).values
-        q = DateRange(d1, d2, offset=_freq_to_pandas['Q']).values
-        assert _infer_freq(w) == 'W'
-        assert _infer_freq(a) == 'A'
-        assert _infer_freq(q) == 'Q'
-        assert _infer_freq(w[:3]) == 'W'
-        assert _infer_freq(a[:3]) == 'A'
-        assert _infer_freq(q[:3]) == 'Q'
+    b = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['B']).values
+    d = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['D']).values
+    w = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['W']).values
+    m = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['M']).values
+    a = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['A']).values
+    q = DatetimeIndex(start=d1, end=d2, freq=_freq_to_pandas['Q']).values
+    assert _infer_freq(w) == 'W-SUN'
+    assert _infer_freq(a) == 'A-DEC'
+    assert _infer_freq(q) == 'Q-DEC'
+    assert _infer_freq(w[:3]) == 'W-SUN'
+    assert _infer_freq(a[:3]) == 'A-DEC'
+    assert _infer_freq(q[:3]) == 'Q-DEC'
 
     assert _infer_freq(b[2:5]) == 'B'
     assert _infer_freq(b[:3]) == 'D'
@@ -155,3 +134,7 @@ def test_infer_freq():
     assert _infer_freq(m) == 'M'
     assert _infer_freq(d[:3]) == 'D'
     assert _infer_freq(m[:3]) == 'M'
+
+def test_period_index():
+    dates = PeriodIndex(start="1/1/1990", periods=20, freq="M")
+    npt.assert_(_infer_freq(dates) == "M")
diff --git a/statsmodels/tsa/base/tsa_model.py b/statsmodels/tsa/base/tsa_model.py
index 5f8c6b1414c..d822c3221f6 100644
--- a/statsmodels/tsa/base/tsa_model.py
+++ b/statsmodels/tsa/base/tsa_model.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import lrange
 import statsmodels.base.model as base
 from statsmodels.base import data
 import statsmodels.base.wrapper as wrap
@@ -45,7 +46,7 @@ def _init_dates(self, dates, freq):
             dates = self.data.row_labels
 
         if dates is not None:
-            if (not isinstance(dates[0], datetime.datetime) and
+            if (not datetools._is_datetime_index(dates) and
                     isinstance(self.data, data.PandasData)):
                 raise ValueError("Given a pandas object and the index does "
                                  "not contain dates")
@@ -102,7 +103,7 @@ def _set_predict_start_date(self, start):
             raise ValueError("Start must be <= len(endog)")
         if start == len(dates):
             self.data.predict_start = datetools._date_from_idx(dates[-1],
-                                                    start, self.data.freq)
+                                                    1, self.data.freq)
         elif start < len(dates):
             self.data.predict_start = dates[start]
         else:
@@ -151,7 +152,7 @@ def _get_predict_end(self, end):
                 dtend = self._str_to_date(end)
                 self.data.predict_end = dtend
                 end = self._get_dates_loc(dates, dtend)
-            except KeyError, err: # end is greater than dates[-1]...probably
+            except KeyError as err: # end is greater than dates[-1]...probably
                 if dtend > self.data.dates[-1]:
                     end = len(self.data.endog) - 1
                     freq = self.data.freq
@@ -171,7 +172,7 @@ def _get_predict_end(self, end):
         elif isinstance(end, int) and dates is not None:
             try:
                 self.data.predict_end = dates[end]
-            except IndexError, err:
+            except IndexError as err:
                 nobs = len(self.data.endog) - 1 # as an index
                 out_of_sample = end - nobs
                 end = nobs
@@ -217,14 +218,14 @@ def _make_predict_dates(self):
                 from pandas import DatetimeIndex
                 dates = DatetimeIndex(start=dtstart, end=dtend,
                                         freq=pandas_freq)
-            except ImportError, err:
+            except ImportError as err:
                 from pandas import DateRange
                 dates = DateRange(dtstart, dtend, offset = pandas_freq).values
         # handle
         elif freq is None and (isinstance(dtstart, int) and
                                isinstance(dtend, int)):
             from pandas import Index
-            dates = Index(range(dtstart, dtend+1))
+            dates = Index(lrange(dtstart, dtend+1))
         # if freq is None and dtstart and dtend aren't integers, we're
         # in sample
         else:
diff --git a/statsmodels/tsa/descriptivestats.py b/statsmodels/tsa/descriptivestats.py
index 7c58b189714..633a9a29b63 100644
--- a/statsmodels/tsa/descriptivestats.py
+++ b/statsmodels/tsa/descriptivestats.py
@@ -8,7 +8,7 @@
 """
 
 import numpy as np
-import stattools as stt
+from . import stattools as stt
 
 
 #todo: check subclassing for descriptive stats classes
@@ -28,12 +28,12 @@ def filter(self, num, den):
         return self.__class__(xfiltered, self.label, self.name + '_filtered')
 
     def detrend(self, order=1):
-        import tsatools
+        from . import tsatools
         xdetrended = tsatools.detrend(self.data, order=order)
         return self.__class__(xdetrended, self.label, self.name + '_detrended')
 
     def fit(self, order=(1,0,1), **kwds):
-        from arima_model import ARMA
+        from .arima_model import ARMA
         self.mod = ARMA(self.data)
         self.res = self.mod.fit(order=order, **kwds)
         #self.estimated_process =
diff --git a/statsmodels/tsa/filters/__init__.py b/statsmodels/tsa/filters/__init__.py
index 2b848e24343..e69de29bb2d 100644
--- a/statsmodels/tsa/filters/__init__.py
+++ b/statsmodels/tsa/filters/__init__.py
@@ -1,4 +0,0 @@
-from .bk_filter import bkfilter
-from .hp_filter import hpfilter
-from .cf_filter import cffilter
-from .filtertools import miso_lfilter, arfilter
diff --git a/statsmodels/tsa/filters/_utils.py b/statsmodels/tsa/filters/_utils.py
new file mode 100644
index 00000000000..0a104510ca5
--- /dev/null
+++ b/statsmodels/tsa/filters/_utils.py
@@ -0,0 +1,149 @@
+from functools import wraps
+
+from statsmodels.tools.data import _is_using_pandas
+from statsmodels.tsa.base import datetools
+from statsmodels.tsa.tsatools import freq_to_period
+
+
+def _get_pandas_wrapper(X, trim_head=None, trim_tail=None, names=None):
+    index = X.index
+    #TODO: allow use index labels
+    if trim_head is None and trim_tail is None:
+        index = index
+    elif trim_tail is None:
+        index = index[trim_head:]
+    elif trim_head is None:
+        index = index[:-trim_tail]
+    else:
+        index = index[trim_head:-trim_tail]
+    if hasattr(X, "columns"):
+        if names is None:
+            names = X.columns
+        return lambda x : X.__class__(x, index=index, columns=names)
+    else:
+        if names is None:
+            names = X.name
+        return lambda x : X.__class__(x, index=index, name=names)
+
+
+def _maybe_get_pandas_wrapper(X, trim_head=None, trim_tail=None):
+    """
+    If using pandas returns a function to wrap the results, e.g., wrapper(X)
+    trim is an integer for the symmetric truncation of the series in some
+    filters.
+    otherwise returns None
+    """
+    if _is_using_pandas(X, None):
+        return _get_pandas_wrapper(X, trim_head, trim_tail)
+    else:
+        return
+
+
+def _maybe_get_pandas_wrapper_freq(X, trim=None):
+    if _is_using_pandas(X, None):
+        index = X.index
+        func = _get_pandas_wrapper(X, trim)
+        freq = index.inferred_freq
+        return func, freq
+    else:
+        return lambda x : x, None
+
+
+def pandas_wrapper(func, trim_head=None, trim_tail=None, names=None, *args,
+                   **kwargs):
+    @wraps(func)
+    def new_func(X, *args, **kwargs):
+        # quick pass-through for do nothing case
+        if not _is_using_pandas(X, None):
+            return func(X, *args, **kwargs)
+
+        wrapper_func = _get_pandas_wrapper(X, trim_head, trim_tail,
+                                           names)
+        ret = func(X, *args, **kwargs)
+        ret = wrapper_func(ret)
+        return ret
+
+    return new_func
+
+
+def pandas_wrapper_bunch(func, trim_head=None, trim_tail=None,
+                         names=None, *args, **kwargs):
+    @wraps(func)
+    def new_func(X, *args, **kwargs):
+        # quick pass-through for do nothing case
+        if not _is_using_pandas(X, None):
+            return func(X, *args, **kwargs)
+
+        wrapper_func = _get_pandas_wrapper(X, trim_head, trim_tail,
+                                           names)
+        ret = func(X, *args, **kwargs)
+        ret = wrapper_func(ret)
+        return ret
+
+    return new_func
+
+
+def pandas_wrapper_predict(func, trim_head=None, trim_tail=None,
+                           columns=None, *args, **kwargs):
+    pass
+
+
+def pandas_wrapper_freq(func, trim_head=None, trim_tail=None,
+                        freq_kw='freq', columns=None, *args, **kwargs):
+    """
+    Return a new function that catches the incoming X, checks if it's pandas,
+    calls the functions as is. Then wraps the results in the incoming index.
+
+    Deals with frequencies. Expects that the function returns a tuple,
+    a Bunch object, or a pandas-object.
+    """
+
+    @wraps(func)
+    def new_func(X, *args, **kwargs):
+        # quick pass-through for do nothing case
+        if not _is_using_pandas(X, None):
+            return func(X, *args, **kwargs)
+
+        wrapper_func = _get_pandas_wrapper(X, trim_head, trim_tail,
+                                           columns)
+        index = X.index
+        freq = index.inferred_freq
+        kwargs.update({freq_kw : freq_to_period(freq)})
+        ret = func(X, *args, **kwargs)
+        ret = wrapper_func(ret)
+        return ret
+
+    return new_func
+
+
+def dummy_func(X):
+    return X
+
+def dummy_func_array(X):
+    return X.values
+
+def dummy_func_pandas_columns(X):
+    return X.values
+
+
+def dummy_func_pandas_series(X):
+    return X['A']
+
+import pandas as pd
+import numpy as np
+
+
+def test_pandas_freq_decorator():
+    X = pd.util.testing.makeDataFrame()
+    # in X, get a function back that returns an X with the same columns
+    func = pandas_wrapper(dummy_func)
+
+    np.testing.assert_equal(func(X.values), X)
+
+    func = pandas_wrapper(dummy_func_array)
+    pd.util.testing.assert_frame_equal(func(X), X)
+
+    expected = X.rename(columns=dict(zip('ABCD', 'EFGH')))
+    func = pandas_wrapper(dummy_func_array, names=list('EFGH'))
+    pd.util.testing.assert_frame_equal(func(X), expected)
+
diff --git a/statsmodels/tsa/filters/api.py b/statsmodels/tsa/filters/api.py
new file mode 100644
index 00000000000..8a7ea852c6c
--- /dev/null
+++ b/statsmodels/tsa/filters/api.py
@@ -0,0 +1,4 @@
+from .bk_filter import bkfilter
+from .hp_filter import hpfilter
+from .cf_filter import cffilter
+from .filtertools import miso_lfilter, convolution_filter, recursive_filter
diff --git a/statsmodels/tsa/filters/bk_filter.py b/statsmodels/tsa/filters/bk_filter.py
index 1f2d1cbf33e..d0c38823701 100644
--- a/statsmodels/tsa/filters/bk_filter.py
+++ b/statsmodels/tsa/filters/bk_filter.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 from scipy.signal import fftconvolve
-from .utils import _maybe_get_pandas_wrapper
+from ._utils import _maybe_get_pandas_wrapper
 
 def bkfilter(X, low=6, high=32, K=12):
     """
@@ -50,15 +50,26 @@ def bkfilter(X, low=6, high=32, K=12):
     Examples
     --------
     >>> import statsmodels.api as sm
-    >>> dta = sm.datasets.macrodata.load()
-    >>> X = dta.data['realinv']
-    >>> Y = sm.tsa.filters.bkfilter(X, 6, 24, 12)
+    >>> import pandas as pd
+    >>> dta = sm.datasets.macrodata.load_pandas().data
+    >>> dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')
+    >>> index = pd.DatetimeIndex(dates)
+    >>> dta.set_index(index, inplace=True)
+
+    >>> cycles = sm.tsa.filters.bkfilter(dta[['realinv']], 6, 24, 12)
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> cycles.plot(ax=ax, style=['r--', 'b-'])
+    >>> plt.show()
+
+    .. plot:: plots/bkf_plot.py
     """
     #TODO: change the docstring to ..math::?
     #TODO: allow windowing functions to correct for Gibb's Phenomenon?
     # adjust bweights (symmetrically) by below before demeaning
     # Lancosz Sigma Factors np.sinc(2*j/(2.*K+1))
-    _pandas_wrapper = _maybe_get_pandas_wrapper(X, K)
+    _pandas_wrapper = _maybe_get_pandas_wrapper(X, K, K)
     X = np.asarray(X)
     omega_1 = 2.*np.pi/high # convert from freq. to periodicity
     omega_2 = 2.*np.pi/low
diff --git a/statsmodels/tsa/filters/cf_filter.py b/statsmodels/tsa/filters/cf_filter.py
index a5b315299f7..ca0d868d8ba 100644
--- a/statsmodels/tsa/filters/cf_filter.py
+++ b/statsmodels/tsa/filters/cf_filter.py
@@ -1,7 +1,7 @@
-from __future__ import absolute_import
+from statsmodels.compat.python import range
 
 import numpy as np
-from .utils import _maybe_get_pandas_wrapper
+from ._utils import _maybe_get_pandas_wrapper
 
 # the data is sampled quarterly, so cut-off frequency of 18
 
@@ -38,6 +38,24 @@ def cffilter(X, low=6, high=32, drift=True):
         The features of `X` between periodicities given by low and high
     trend : array
         The trend in the data with the cycles removed.
+
+    Examples
+    --------
+    >>> import statsmodels.api as sm
+    >>> import pandas as pd
+    >>> dta = sm.datasets.macrodata.load_pandas().data
+    >>> dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')
+    >>> index = pd.DatetimeIndex(dates)
+    >>> dta.set_index(index, inplace=True)
+
+    >>> cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]])
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> cf_cycles.plot(ax=ax, style=['r--', 'b-'])
+    >>> plt.show()
+
+    .. plot:: plots/cff_plot.py
     """
     #TODO: cythonize/vectorize loop?, add ability for symmetric filter,
     #      and estimates of theta other than random walk.
@@ -60,7 +78,7 @@ def cffilter(X, low=6, high=32, drift=True):
     Bj = np.r_[B0,Bj][:,None]
     y = np.zeros((nobs,nseries))
 
-    for i in xrange(nobs):
+    for i in range(nobs):
 
         B = -.5*Bj[0] -np.sum(Bj[1:-i-2])
         A = -Bj[0] - np.sum(Bj[1:-i-2]) - np.sum(Bj[1:i]) - B
diff --git a/statsmodels/tsa/filters/filtertools.py b/statsmodels/tsa/filters/filtertools.py
index 66a8f0489c2..ac27df17de8 100644
--- a/statsmodels/tsa/filters/filtertools.py
+++ b/statsmodels/tsa/filters/filtertools.py
@@ -12,10 +12,36 @@
 #not original copied from various experimental scripts
 #version control history is there
 
+from statsmodels.compat.python import range
 import numpy as np
 import scipy.fftpack as fft
 from scipy import signal
 from scipy.signal.signaltools import _centered as trim_centered
+from ._utils import _maybe_get_pandas_wrapper
+
+
+def _pad_nans(x, head=None, tail=None):
+    if np.ndim(x) == 1:
+        if head is None and tail is None:
+            return x
+        elif head and tail:
+            return np.r_[[np.nan] * head, x, [np.nan] * tail]
+        elif tail is None:
+            return np.r_[[np.nan] * head, x]
+        elif head is None:
+            return np.r_[x, [np.nan] * tail]
+    elif np.ndim(x) == 2:
+        if head is None and tail is None:
+            return x
+        elif head and tail:
+            return np.r_[[[np.nan] * x.shape[1]] * head, x,
+                         [[np.nan] * x.shape[1]] * tail]
+        elif tail is None:
+            return np.r_[[[np.nan] * x.shape[1]] * head, x]
+        elif head is None:
+            return np.r_[x, [[np.nan] * x.shape[1]] * tail]
+    else:
+        raise ValueError("Nan-padding for ndim > 2 not implemented")
 
 #original changes and examples in sandbox.tsa.try_var_convolve
 
@@ -131,101 +157,153 @@ def fftconvolve3(in1, in2=None, in3=None, mode="full"):
 
 #original changes and examples in sandbox.tsa.try_var_convolve
 #examples and tests are there
-def arfilter(x, a):
-    '''apply an autoregressive filter to a series x
+def recursive_filter(x, ar_coeff, init=None):
+    '''
+    Autoregressive, or recursive, filtering.
+
+    Parameters
+    ----------
+    x : array-like
+        Time-series data. Should be 1d or n x 1.
+    ar_coeff : array-like
+        AR coefficients in reverse time order. See Notes
+    init : array-like
+        Initial values of the time-series prior to the first value of y.
+        The default is zero.
+
+    Returns
+    -------
+    y : array
+        Filtered array, number of columns determined by x and ar_coeff. If a
+        pandas object is given, a pandas object is returned.
+
+    Notes
+    -----
+
+    Computes the recursive filter ::
+
+        y[n] = ar_coeff[0] * y[n-1] + ...
+                + ar_coeff[n_coeff - 1] * y[n - n_coeff] + x[n]
+
+    where n_coeff = len(n_coeff).
+    '''
+    _pandas_wrapper = _maybe_get_pandas_wrapper(x)
+    x = np.asarray(x).squeeze()
+    ar_coeff = np.asarray(ar_coeff).squeeze()
 
-    x can be 2d, a can be 1d, 2d, or 3d
+    if x.ndim > 1 or ar_coeff.ndim > 1:
+        raise ValueError('x and ar_coeff have to be 1d')
+
+    if init is not None:  # integer init are treated differently in lfiltic
+        if len(init) != len(ar_coeff):
+            raise ValueError("ar_coeff must be the same length as init")
+        init = np.asarray(init, dtype=float)
+
+    if init is not None:
+        zi = signal.lfiltic([1], np.r_[1, -ar_coeff], init, x)
+    else:
+        zi = None
+
+    y = signal.lfilter([1.], np.r_[1, -ar_coeff], x, zi=zi)
+
+    if init is not None:
+        result = y[0]
+    else:
+        result = y
+
+    if _pandas_wrapper:
+        return _pandas_wrapper(result)
+    return result
+
+
+def convolution_filter(x, filt, nsides=2):
+    '''
+    Linear filtering via convolution. Centered and backward displaced moving
+    weighted average.
 
     Parameters
     ----------
     x : array_like
         data array, 1d or 2d, if 2d then observations in rows
-    a : array_like
-        autoregressive filter coefficients, ar lag polynomial
-        see Notes
+    filt : array_like
+        Linear filter coefficients in reverse time-order. Should have the
+        same number of dimensions as x though if 1d and ``x`` is 2d will be
+        coerced to 2d.
+    nsides : int, optional
+        If 2, a centered moving average is computed using the filter
+        coefficients. If 1, the filter coefficients are for past values only.
+        Both methods use scipy.signal.convolve.
 
     Returns
     -------
     y : ndarray, 2d
-        filtered array, number of columns determined by x and a
+        Filtered array, number of columns determined by x and filt. If a
+        pandas object is given, a pandas object is returned. The index of
+        the return is the exact same as the time period in ``x``
 
     Notes
     -----
+    In nsides == 1, x is filtered ::
 
-    In general form this uses the linear filter ::
+        y[n] = filt[0]*x[n-1] + ... + filt[n_filt-1]*x[n-n_filt]
 
-        y = a(L)x
+    where n_filt is len(filt).
 
-    where
-    x : nobs, nvars
-    a : nlags, nvars, npoly
+    If nsides == 2, x is filtered around lag 0 ::
 
-    Depending on the shape and dimension of a this uses different
-    Lag polynomial arrays
+        y[n] = filt[0]*x[n - n_filt/2] + ... + filt[n_filt / 2] * x[n]
+               + ... + x[n + n_filt/2]
 
-    case 1 : a is 1d or (nlags,1)
-        one lag polynomial is applied to all variables (columns of x)
-    case 2 : a is 2d, (nlags, nvars)
-        each series is independently filtered with its own
-        lag polynomial, uses loop over nvar
-    case 3 : a is 3d, (nlags, nvars, npoly)
-        the ith column of the output array is given by the linear filter
-        defined by the 2d array a[:,:,i], i.e. ::
+    where n_filt is len(filt). If n_filt is even, then more of the filter
+    is forward in time than backward.
 
-            y[:,i] = a(.,.,i)(L) * x
-            y[t,i] = sum_p sum_j a(p,j,i)*x(t-p,j)
-                     for p = 0,...nlags-1, j = 0,...nvars-1,
-                     for all t >= nlags
+    If filt is 1d or (nlags,1) one lag polynomial is applied to all
+    variables (columns of x). If filt is 2d, (nlags, nvars) each series is
+    independently filtered with its own lag polynomial, uses loop over nvar.
+    This is different than the usual 2d vs 2d convolution.
 
-    All filtering is done with scipy.signal.convolve, so it will be reasonably
-    fast for medium sized arrays. For large arrays fft convolution would be
+    Filtering is done with scipy.signal.convolve, so it will be reasonably
+    fast for medium sized data. For large data fft convolution would be
     faster.
-
-    Note: maybe convert to axis=1, Not
-
-    TODO:
-        initial conditions,
-        make sure tests for 3d case are done, I don't remember how much I
-        tested the 3d case
-
     '''
+    # for nsides shift the index instead of using 0 for 0 lag this
+    # allows correct handling of NaNs
+    if nsides == 1:
+        trim_head = len(filt) - 1
+        trim_tail = None
+    elif nsides == 2:
+        trim_head = np.ceil(len(filt)/2.) - 1 or None
+        trim_tail = (np.ceil(len(filt)/2.) - len(filt) % 2) or None
+    else:  # pragma : no cover
+        raise ValueError("nsides must be 1 or 2")
+
+    _pandas_wrapper = _maybe_get_pandas_wrapper(x)
     x = np.asarray(x)
-    a = np.asarray(a)
-    if x.ndim == 1:
-        x = x[:,None]
+    filt = np.asarray(filt)
+    if x.ndim > 1 and filt.ndim == 1:
+        filt = filt[:, None]
     if x.ndim > 2:
         raise ValueError('x array has to be 1d or 2d')
-    nvar = x.shape[1]
-    nlags = a.shape[0]
-    ntrim = nlags//2
-    # for x is 2d with ncols >1
-
-    if a.ndim == 1:
-        # case: identical ar filter (lag polynomial)
-        return signal.convolve(x, a[:,None], mode='valid')
-        # alternative:
-        #return signal.lfilter(a,[1],x.astype(float),axis=0)
-    elif a.ndim == 2:
-        if min(a.shape) == 1:
-            # case: identical ar filter (lag polynomial)
-            return signal.convolve(x, a, mode='valid')
-
-        # case: independent ar
-        #(a bit like recserar in gauss, but no x yet)
-        result = np.zeros((x.shape[0]-nlags+1, nvar))
-        for i in range(nvar):
-            # could also use np.convolve, but easier for swiching to fft
-            result[:,i] = signal.convolve(x[:,i], a[:,i], mode='valid')
-        return result
-
-    elif a.ndim == 3:
-        # case: vector autoregressive with lag matrices
-#        #not necessary:
-#        if np.any(a.shape[1:] != nvar):
-#            raise ValueError('if 3d shape of a has to be (nobs,nvar,nvar)')
-        yf = signal.convolve(x[:,:,None], a)
-        yvalid = yf[ntrim:-ntrim, yf.shape[1]//2,:]
-        return yvalid
+
+    if filt.ndim == 1 or min(filt.shape) == 1:
+        result = signal.convolve(x, filt, mode='valid')
+    elif filt.ndim == 2:
+        nlags = filt.shape[0]
+        nvar = x.shape[1]
+        result = np.zeros((x.shape[0] - nlags + 1, nvar))
+        if nsides == 2:
+            for i in range(nvar):
+                # could also use np.convolve, but easier for swiching to fft
+                result[:, i] = signal.convolve(x[:, i], filt[:, i],
+                                               mode='valid')
+        elif nsides == 1:
+            for i in range(nvar):
+                result[:, i] = signal.convolve(x[:, i], np.r_[0, filt[:, i]],
+                                               mode='valid')
+    result = _pad_nans(result, trim_head, trim_tail)
+    if _pandas_wrapper:
+        return _pandas_wrapper(result)
+    return result
 
 
 #copied from sandbox.tsa.garch
diff --git a/statsmodels/tsa/filters/hp_filter.py b/statsmodels/tsa/filters/hp_filter.py
index a55d5fb71f5..f48676a9ad2 100644
--- a/statsmodels/tsa/filters/hp_filter.py
+++ b/statsmodels/tsa/filters/hp_filter.py
@@ -4,7 +4,7 @@
 from scipy.sparse import dia_matrix, eye as speye
 from scipy.sparse.linalg import spsolve
 import numpy as np
-from .utils import _maybe_get_pandas_wrapper
+from ._utils import _maybe_get_pandas_wrapper
 
 def hpfilter(X, lamb=1600):
     """
@@ -30,9 +30,24 @@ def hpfilter(X, lamb=1600):
     Examples
     ---------
     >>> import statsmodels.api as sm
-    >>> dta = sm.datasets.macrodata.load()
-    >>> X = dta.data['realgdp']
-    >>> cycle, trend = sm.tsa.filters.hpfilter(X,1600)
+    >>> import pandas as pd
+    >>> dta = sm.datasets.macrodata.load_pandas().data
+    >>> dates = sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3')
+    >>> index = pd.DatetimeIndex(dates)
+    >>> dta.set_index(index, inplace=True)
+
+    >>> cycle, trend = sm.tsa.filters.hpfilter(dta.realgdp, 1600)
+    >>> gdp_decomp = dta[['realgdp']]
+    >>> gdp_decomp["cycle"] = cycle
+    >>> gdp_decomp["trend"] = trend
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> gdp_decomp[["realgdp", "trend"]]["2000-03-31":].plot(ax=ax,
+    ...                                                      fontsize=16);
+    >>> plt.show()
+
+    .. plot:: plots/hpf_plot.py
 
     Notes
     -----
diff --git a/statsmodels/tsa/filters/tests/results/__init__.py b/statsmodels/tsa/filters/tests/results/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/statsmodels/tsa/filters/tests/results/filter.R b/statsmodels/tsa/filters/tests/results/filter.R
new file mode 100644
index 00000000000..0ac15b3b354
--- /dev/null
+++ b/statsmodels/tsa/filters/tests/results/filter.R
@@ -0,0 +1,49 @@
+library(R2nparray)
+
+x <- c(-50, 175, 149, 214, 247, 237, 225, 329, 729, 809, 530, 489, 540, 457, 
+         195, 176, 337, 239, 128, 102, 232, 429, 3, 98, 43, -141, -77, -13, 
+         125, 361, -45, 184)
+x <- ts(x, start=c(1951, 1), frequency=4)
+
+
+conv2 <- filter(x, c(.75, .25), method="convolution")
+
+conv1 <- filter(x, c(.75, .25), method="convolution", sides=1)
+
+recurse <- filter(x, c(.75, .25), method="recursive")
+
+recurse.init <- filter(x, c(.75, .25), method="recursive", init=c(150, 100))
+
+conv2.odd <- filter(x, c(.75, .5, .3, .2, .1), method="convolution", 
+                     sides=2)
+conv1.odd <- filter(x, c(.75, .5, .3, .2, .1), method="convolution", 
+                     sides=1)
+recurse.odd <- filter(x, c(.75, .5, .3, .2, .1), method="recursive", 
+                       init=c(150, 100, 125, 135, 145))
+
+# missing values
+
+x[10] = NaN
+
+conv2.na <- filter(x, c(.75, .25), method="convolution")
+
+conv1.na <- filter(x, c(.75, .25), method="convolution", sides=1)
+
+recurse.na <- filter(x, c(.75, .25), method="recursive")
+
+recurse.init.na <- filter(x, c(.75, .25), method="recursive", init=c(150, 100))
+
+
+options(digits=12)
+
+R2nparray(list(conv2=as.numeric(conv2), conv1=as.numeric(conv1), 
+               recurse=as.numeric(recurse), 
+               recurse_init=as.numeric(recurse.init),
+               conv2_na=as.numeric(conv2.na), conv1_na=as.numeric(conv1.na), 
+               recurse_na=as.numeric(recurse.na), 
+               recurse_init_na=as.numeric(recurse.init.na),
+               conv2_odd=as.numeric(conv2.odd),
+               conv1_odd=as.numeric(conv1.odd),
+               recurse_odd=as.numeric(recurse.odd)),
+          fname="filter_results.py")
+
diff --git a/statsmodels/tsa/filters/tests/results/filter_results.py b/statsmodels/tsa/filters/tests/results/filter_results.py
new file mode 100644
index 00000000000..24f914be7b8
--- /dev/null
+++ b/statsmodels/tsa/filters/tests/results/filter_results.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+conv2 = np.array([118.75,155.5,197.75,238.75,239.5,228,303,629,789,599.75,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75,np.nan])
+
+conv1 = np.array([np.nan,118.75,155.5,197.75,238.75,239.5,228,303,629,789,599.75,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75])
+
+recurse = np.array([-50,137.5,239.625,428.09375,627.9765625,815.005859375,993.248535156,1277.68786621,1935.57803345,2580.10549164,2948.97362709,3345.75659323,3786.56085169,4133.35978708,4241.66005323,4390.58498669,4690.35375333,4854.41156167,4941.39710958,5021.6507226,5233.58731935,5609.60317016,5518.59920746,5639.35019814,5652.16245047,5507.95938738,5467.01015315,5464.24746171,5589.93813457,5919.51546636,5792.12113341,6007.96971665])
+
+recurse_init = np.array([87.5,278.125,379.46875,568.1328125,767.966796875,955.008300781,1133.2479248,1417.6880188,2075.5779953,2720.10550117,3088.97362471,3485.75659382,3926.56085154,4273.35978711,4381.66005322,4530.58498669,4830.35375333,4994.41156167,5081.39710958,5161.6507226,5373.58731935,5749.60317016,5658.59920746,5779.35019814,5792.16245047,5647.95938738,5607.01015315,5604.24746171,5729.93813457,6059.51546636,5932.12113341,6147.96971665])
+
+conv2_na = np.array([118.75,155.5,197.75,238.75,239.5,228,303,629,np.nan,np.nan,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75,np.nan])
+
+conv1_na = np.array([np.nan,118.75,155.5,197.75,238.75,239.5,228,303,629,np.nan,np.nan,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75])
+
+recurse_na = np.array([-50,137.5,239.625,428.09375,627.9765625,815.005859375,993.248535156,1277.68786621,1935.57803345,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan])
+
+recurse_init_na = np.array([87.5,278.125,379.46875,568.1328125,767.966796875,955.008300781,1133.2479248,1417.6880188,2075.5779953,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan])
+
+conv2_odd = np.array([np.nan,np.nan,366.95,412.75,419.05,501.15,850.85,1138.65,1109,1053.15,1043.2,946.35,687.55,523.5,544.65,485.25,371.3,297.2,344.9,517.85,319.55,260.3,191.15,-11.35,-95.45,-72.15,40.25,299.85,173.95,247.5,np.nan,np.nan])
+
+conv1_odd = np.array([np.nan,np.nan,np.nan,np.nan,366.95,412.75,419.05,501.15,850.85,1138.65,1109,1053.15,1043.2,946.35,687.55,523.5,544.65,485.25,371.3,297.2,344.9,517.85,319.55,260.3,191.15,-11.35,-95.45,-72.15,40.25,299.85,173.95,247.5])
+
+recurse_odd = np.array([191.5,462.125,668.84375,1044.1453125,1556.46835938,2238.65205078,3175.44806152,4572.56601685,6833.45236176,9776.38394429,13509.7387615,18791.5897613,26145.4239591,36153.4065035,49699.8299323,68480.4947171,94501.551723,130110.583827,179061.168784,246469.715955,339396.406323,467401.785292,643016.749056,885080.436404,1218108.49028,1676305.60832,2307074.11064,3175195.69641,4370080.25182,6014713.24095,8277634.14851,11392536.8578])
+
diff --git a/statsmodels/tsa/filters/tests/test_filters.py b/statsmodels/tsa/filters/tests/test_filters.py
index 463bdacf941..f98d28468cf 100644
--- a/statsmodels/tsa/filters/tests/test_filters.py
+++ b/statsmodels/tsa/filters/tests/test_filters.py
@@ -1,11 +1,14 @@
 from datetime import datetime
-
-from numpy.testing import assert_almost_equal, assert_equal, assert_allclose
+import numpy as np
+from numpy.testing import (assert_almost_equal, assert_equal, assert_allclose,
+                           assert_raises, assert_)
 from numpy import array, column_stack
 from statsmodels.datasets import macrodata
 from statsmodels.tsa.base.datetools import dates_from_range
-from pandas import Series, Index, DataFrame
-from statsmodels.tsa.filters import bkfilter, hpfilter, cffilter
+from pandas import Index, DataFrame, DatetimeIndex, concat
+from statsmodels.tsa.filters.api import (bkfilter, hpfilter, cffilter,
+                                         convolution_filter, recursive_filter)
+
 
 def test_bking1d():
     """
@@ -596,6 +599,144 @@ def test_hpfilter_pandas():
     assert_equal(cycle.index[-1], datetime(2009, 9, 30))
     assert_equal(cycle.name, "realgdp")
 
+
+class TestFilters(object):
+    @classmethod
+    def setupClass(cls):
+        # even
+        data = [-50, 175, 149, 214, 247, 237, 225, 329, 729, 809,
+                530, 489, 540, 457, 195, 176, 337, 239, 128, 102,
+                232, 429, 3, 98, 43, -141, -77, -13, 125, 361, -45, 184]
+        cls.data = DataFrame(data, DatetimeIndex(start='1/1/1951',
+                                                 periods=len(data),
+                                                 freq='Q'))
+        data[9] = np.nan
+        cls.datana = DataFrame(data, DatetimeIndex(start='1/1/1951',
+                                                  periods=len(data),
+                                                  freq='Q'))
+        from .results import filter_results
+        cls.expected = filter_results
+
+    def test_convolution(self):
+        x = self.data.values.squeeze()
+        res = convolution_filter(x, [.75, .25])
+        expected = self.expected.conv2
+        np.testing.assert_almost_equal(res, expected)
+
+        res = convolution_filter(x, [.75, .25], nsides=1)
+        expected = self.expected.conv1
+        np.testing.assert_almost_equal(res, expected)
+
+        x = self.datana.values.squeeze()
+        res = convolution_filter(x, [.75, .25])
+        expected = self.expected.conv2_na
+        np.testing.assert_almost_equal(res, expected)
+
+        res = convolution_filter(x, [.75, .25], nsides=1)
+        expected = self.expected.conv1_na
+        np.testing.assert_almost_equal(res, expected)
+
+    def test_convolution2d(self):
+        x = self.data.values
+        res = convolution_filter(x, [[.75], [.25]])
+        expected = self.expected.conv2
+        np.testing.assert_almost_equal(res, expected[:, None])
+        res = convolution_filter(np.c_[x, x], [[.75, .75], [.25, .25]])
+        np.testing.assert_almost_equal(res, np.c_[expected, expected])
+
+        res = convolution_filter(x, [[.75], [.25]], nsides=1)
+        expected = self.expected.conv1
+        np.testing.assert_almost_equal(res, expected[:, None])
+
+        x = self.datana.values
+        res = convolution_filter(x, [[.75], [.25]])
+        expected = self.expected.conv2_na
+        np.testing.assert_almost_equal(res, expected[:, None])
+
+        res = convolution_filter(x, [[.75], [.25]], nsides=1)
+        expected = self.expected.conv1_na
+        np.testing.assert_almost_equal(res, expected[:, None])
+
+    def test_recursive(self):
+        x = self.data.values.squeeze()
+        res = recursive_filter(x, [.75, .25])
+        expected = self.expected.recurse
+        np.testing.assert_almost_equal(res, expected)
+
+        res = recursive_filter(x, [.75, .25], init=[150, 100])
+        expected = self.expected.recurse_init
+        np.testing.assert_almost_equal(res, expected)
+
+        x = self.datana.values.squeeze()
+        res = recursive_filter(x, [.75, .25])
+        expected = self.expected.recurse_na
+        np.testing.assert_almost_equal(res, expected)
+
+        res = recursive_filter(x, [.75, .25], init=[150, 100])
+        expected = self.expected.recurse_init_na
+        np.testing.assert_almost_equal(res, expected)
+
+        np.testing.assert_raises(ValueError, recursive_filter, x,
+                                 [.75, .25, .5], [150, 100])
+
+    def test_pandas(self):
+        start = datetime(1951, 3, 31)
+        end = datetime(1958, 12, 31)
+        x = self.data[0]
+        res = convolution_filter(x, [.75, .25])
+        assert_(res.index[0] == start)
+        assert_(res.index[-1] == end)
+
+        res = convolution_filter(x, [.75, .25], nsides=1)
+        assert_(res.index[0] == start)
+        # with no nan-padding q1 if not
+        assert_(res.index[-1] == end)
+
+        res = recursive_filter(x, [.75, .25])
+        assert_(res.index[0] == start)
+        assert_(res.index[-1] == end)
+
+        x = self.datana
+        res = recursive_filter(x, [.75, .25])
+        assert_(res.index[0] == start)
+        assert_(res.index[-1] == end)
+
+    def test_pandas2d(self):
+        start = datetime(1951, 3, 31)
+        end = datetime(1958, 12, 31)
+        x = concat((self.data[0], self.data[0]), axis=1)
+        res = convolution_filter(x, [[.75, .75], [.25, .25]])
+        assert_(res.index[0] == start)
+        assert_(res.index[-1] == end)
+
+    def test_odd_length_filter(self):
+        start = datetime(1951, 3, 31)
+        end = datetime(1958, 12, 31)
+        x = self.data[0]
+        res = convolution_filter(x, [.75, .5, .3, .2, .1])
+        expected = self.expected.conv2_odd
+        np.testing.assert_almost_equal(res.values.squeeze(), expected)
+        np.testing.assert_(res.index[0] == start)
+        np.testing.assert_(res.index[-1] == end)
+
+        res = convolution_filter(x, [.75, .5, .3, .2, .1], nsides=1)
+        expected = self.expected.conv1_odd
+        np.testing.assert_almost_equal(res.values.squeeze(), expected)
+        np.testing.assert_(res.index[0] == start)
+        np.testing.assert_(res.index[-1] == end)
+        # with no NAs
+
+        # not a stable filter
+        res = recursive_filter(x, [.75, .5, .3, .2, .1], init=[150, 100,
+                                                               125, 135,
+                                                               145])
+        expected = self.expected.recurse_odd
+        # only have 12 characters in R and this blows up and gets big
+        np.testing.assert_almost_equal(res.values.squeeze(), expected, 4)
+        np.testing.assert_(res.index[0] == start)
+        np.testing.assert_(res.index[-1] == end)
+
+
 if __name__ == "__main__":
     import nose
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False)
diff --git a/statsmodels/tsa/filters/utils.py b/statsmodels/tsa/filters/utils.py
deleted file mode 100644
index dc87fd5c847..00000000000
--- a/statsmodels/tsa/filters/utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from statsmodels.tools.data import _is_using_pandas
-
-def _maybe_get_pandas_wrapper(X, trim=None):
-    """
-    If using pandas returns a function to wrap the results, e.g., wrapper(X)
-    trim is an integer for the symmetric truncation of the series in some
-    filters.
-    otherwise returns None
-    """
-    if _is_using_pandas(X, None):
-        index = X.index
-        if trim is not None:
-            index = X.index[trim:-trim]
-        if hasattr(X, "columns"):
-            return lambda x : X.__class__(x, index=index, columns=X.columns)
-        else:
-            return lambda x : X.__class__(x, index=index, name=X.name)
-    else:
-        return
diff --git a/statsmodels/tsa/interp/__init__.py b/statsmodels/tsa/interp/__init__.py
index 1e29f339244..e5ea7d219ab 100644
--- a/statsmodels/tsa/interp/__init__.py
+++ b/statsmodels/tsa/interp/__init__.py
@@ -1 +1 @@
-from denton import dentonm
+from .denton import dentonm
diff --git a/statsmodels/tsa/kalmanf/__init__.py b/statsmodels/tsa/kalmanf/__init__.py
index ab3b66848d5..cc138042dfe 100644
--- a/statsmodels/tsa/kalmanf/__init__.py
+++ b/statsmodels/tsa/kalmanf/__init__.py
@@ -1 +1 @@
-from kalmanfilter import KalmanFilter
+from .kalmanfilter import KalmanFilter
diff --git a/statsmodels/tsa/kalmanf/blas_lapack.pxd b/statsmodels/tsa/kalmanf/blas_lapack.pxd
new file mode 100644
index 00000000000..4e13669120c
--- /dev/null
+++ b/statsmodels/tsa/kalmanf/blas_lapack.pxd
@@ -0,0 +1,94 @@
+ctypedef int dgemm_t(
+    # Compute C := alpha*A*B + beta*C
+    char *transa,  # {'T','C'}: o(A)=A'; {'N'}: o(A)=A
+    char *transb,  # {'T','C'}: o(B)=B'; {'N'}: o(B)=B
+    int *m,        # Rows of o(A)    (and of C)
+    int *n,        # Columns of o(B) (and of C)
+    int *k,        # Columns of o(A) / Rows of o(B)
+    double *alpha, # Scalar multiple
+    double *a,     # Matrix A: mxk
+    int *lda,      # The size of the first dimension of A (in memory)
+    double *b,     # Matrix B: kxn
+    int *ldb,      # The size of the first dimension of B (in memory)
+    double *beta,  # Scalar multiple
+    double *c,     # Matrix C: mxn
+    int *ldc       # The size of the first dimension of C (in memory)
+)
+
+ctypedef int zgemm_t(
+    # Compute C := alpha*A*B + beta*C
+    char *transa,   # {'T','C'}: o(A)=A'; {'N'}: o(A)=A
+    char *transb,   # {'T','C'}: o(B)=B'; {'N'}: o(B)=B
+    int *m,         # Rows of o(A)    (and of C)
+    int *n,         # Columns of o(B) (and of C)
+    int *k,         # Columns of o(A) / Rows of o(B)
+    complex *alpha, # Scalar multiple
+    void *a,     # Matrix A: mxk
+    int *lda,       # The size of the first dimension of A (in memory)
+    void *b,     # Matrix B: kxn
+    int *ldb,       # The size of the first dimension of B (in memory)
+    complex *beta,  # Scalar multiple
+    void *c,     # Matrix C: mxn
+    int *ldc        # The size of the first dimension of C (in memory)
+)
+
+ctypedef double ddot_t(
+    # Compute DDOT := x.T * y
+    int *n,        # Length of vectors
+    double *x,     # Vector x, min(len(x)) = n
+    int *incx,     # The increment between elements of x (usually 1)
+    double *y,     # Vector y, min(len(y)) = m
+    int *incy      # The increment between elements of y (usually 1)
+)
+
+ctypedef int dgemv_t(
+    # Compute y := alpha*A*x + beta*y
+    char *trans,   # {'T','C'}: o(A)=A'; {'N'}: o(A)=A
+    int *m,        # Rows of A (prior to transpose from *trans)
+    int *n,        # Columns of A / min(len(x))
+    double *alpha, # Scalar multiple
+    double *a,     # Matrix A: mxn
+    int *lda,      # The size of the first dimension of A (in memory)
+    double *x,     # Vector x, min(len(x)) = n
+    int *incx,     # The increment between elements of x (usually 1)
+    double *beta,  # Scalar multiple
+    double *y,     # Vector y, min(len(y)) = m
+    int *incy      # The increment between elements of y (usually 1)
+)
+
+ctypedef int zgemv_t(
+    # Compute C := alpha*A*x + beta*y
+    char *trans,    # {'T','C'}: o(A)=A'; {'N'}: o(A)=A
+    int *m,         # Rows of o(A)
+    int *n,         # Columns of o(A) / min(len(x))
+    complex *alpha, # Scalar multiple
+    complex *a,     # Matrix A: mxn
+    int *lda,       # The size of the first dimension of A (in memory)
+    complex *x,     # Vector x, min(len(x)) = n
+    int *incx,      # The increment between elements of x (usually 1)
+    complex *beta,  # Scalar multiple
+    complex *y,     # Vector y, min(len(y)) = m
+    int *incy       # The increment between elements of y (usually 1)
+)
+
+ctypedef complex zdotu_t(
+    # Compute ZDOT := x.T * y
+    int *n,        # Length of vectors
+    complex *x,     # Vector x, min(len(x)) = n
+    int *incx,     # The increment between elements of x (usually 1)
+    complex *y,     # Vector y, min(len(y)) = m
+    int *incy      # The increment between elements of y (usually 1)
+)
+
+ctypedef double dger_t(
+    # Compute A := alpha*x*y.T + A
+    int *m,         # Length of x
+    int *n,         # Length of y
+    double *alpha,  # Scalar multiple
+    double *x,      # Vector X
+    int *incx,      # Increment between elements of x (usually 1)
+    double *y,      # Vector y
+    int *incy,      # Increment between elements of y (usually 1)
+    double *A,      # Matrix A: m x n
+    int *lda        # The size of the first dimension of A in memory
+)
diff --git a/statsmodels/tsa/kalmanf/capsule.h b/statsmodels/tsa/kalmanf/capsule.h
new file mode 100644
index 00000000000..5a7be16e4f1
--- /dev/null
+++ b/statsmodels/tsa/kalmanf/capsule.h
@@ -0,0 +1,24 @@
+/* partially cribbed from numpy/core/include/numpy/npy_3kcompat.h
+*/
+
+#include <Python.h>
+
+#if PY_VERSION_HEX >= 0x03000000
+
+void* SMCapsule_AsVoidPtr(PyObject *obj)
+{
+    void *ret = PyCapsule_GetPointer(obj, NULL);
+    if (ret == NULL) {
+        PyErr_Clear();
+    }
+    return ret;
+}
+
+#else
+
+void* SMCapsule_AsVoidPtr(PyObject *ptr)
+{
+    return PyCObject_AsVoidPtr(ptr);
+}
+
+#endif
diff --git a/statsmodels/tsa/kalmanf/kalman_loglike.pyx b/statsmodels/tsa/kalmanf/kalman_loglike.pyx
index 69733025bac..d5a5cd70926 100644
--- a/statsmodels/tsa/kalmanf/kalman_loglike.pyx
+++ b/statsmodels/tsa/kalmanf/kalman_loglike.pyx
@@ -1,118 +1,341 @@
-# cython: profile=True
+import scipy.linalg.blas
 from numpy cimport float64_t, ndarray, complex128_t, complex64_t
 from numpy import log as nplog
-from numpy import identity, dot, kron, zeros, pi, exp, eye, sum, empty, ones
+from numpy import (identity, dot, kron, pi, sum, zeros_like, ones, asarray,
+                   complex128, float64, asfortranarray)
 from numpy.linalg import pinv
 cimport cython
+cimport numpy as cnp
+
+cnp.import_array()
+
+# included in Cython numpy headers
+from numpy cimport PyArray_ZEROS
 
 ctypedef float64_t DOUBLE
-ctypedef complex128_t COMPLEX128
+ctypedef complex128_t dcomplex
 ctypedef complex64_t COMPLEX64
+cdef int FORTRAN = 1
 
 cdef extern from "math.h":
     double log(double x)
 
+cdef extern from "capsule.h":
+    void* SMCapsule_AsVoidPtr(object ptr)
+
+from blas_lapack cimport dgemm_t, zgemm_t, ddot_t, dgemv_t, zgemv_t, zdotu_t
+
+cdef dgemm_t *dgemm = <dgemm_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('gemm', dtype=float64)._cpointer)
+cdef zgemm_t *zgemm = <zgemm_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('gemm', dtype=complex128)._cpointer)
+cdef ddot_t *ddot = <ddot_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('dot', dtype=float64)._cpointer)
+cdef dgemv_t *dgemv = <dgemv_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('gemv', dtype=float64)._cpointer)
+cdef zdotu_t *zdotu = <zdotu_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('dotu', dtype=complex128)._cpointer)
+cdef zgemv_t *zgemv = <zgemv_t*>SMCapsule_AsVoidPtr(scipy.linalg.blas.get_blas_funcs('gemv', dtype=complex128)._cpointer)
+
+
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.cdivision(True)
-def kalman_filter_double(ndarray[DOUBLE, ndim=1] y,
+def kalman_filter_double(double[:] y not None,
                    unsigned int k, unsigned int p, unsigned int q,
-                  unsigned int r, unsigned int nobs,
-                   ndarray[DOUBLE, ndim=2] Z_mat,
-                   ndarray[DOUBLE, ndim=2] R_mat,
-                   ndarray[DOUBLE, ndim=2] T_mat):
+                  int r, unsigned int nobs,
+                         double[::1,:] Z_mat,
+                         double[::1,:] R_mat,
+                         double[::1,:] T_mat):
     """
     Cython version of the Kalman filter recursions for an ARMA process.
     """
-    m = Z_mat.shape[1]
-    # store forecast-errors
-    v = zeros((nobs,1))
-    # store variance of forecast errors
-    F = ones((nobs,1))
-    loglikelihood = zeros((1,1))
-    cdef int i = 0
-    # initial state
-#    cdef np.ndarray[DOUBLE, ndim=2] alpha = zeros((m,1))
-    alpha = zeros((m,1))
-    # initial variance
-    P = dot(pinv(identity(m**2)-kron(T_mat, T_mat)),dot(R_mat,
-            R_mat.T).ravel('F')).reshape(r,r, order='F')
-    F_mat = 0
-    while not F_mat == 1 and i < nobs:
+    cdef cnp.npy_intp yshape[2]
+    yshape[0] = <cnp.npy_intp> nobs
+    yshape[1] = <cnp.npy_intp> 1
+    cdef cnp.npy_intp mshape[2]
+    mshape[0] = <cnp.npy_intp> r
+    mshape[1] = <cnp.npy_intp> 1
+    cdef cnp.npy_intp r2shape[2]
+    r2shape[0] = <cnp.npy_intp> r
+    r2shape[1] = <cnp.npy_intp> r
+
+    cdef:
+        int one = 1 # univariate filter
+        int ldz = Z_mat.strides[1]/sizeof(DOUBLE)
+        int ldt = T_mat.strides[1]/sizeof(DOUBLE)
+        int ldr = R_mat.strides[1]/sizeof(DOUBLE)
+        # forecast errors
+        ndarray[DOUBLE, ndim=2] v = PyArray_ZEROS(2, yshape, cnp.NPY_DOUBLE,
+                                                  FORTRAN)
+        # store variance of forecast errors
+        double[::1,:] F = ones((nobs,1), order='F') # variance of forecast errors
+        double loglikelihood = 0
+        int i = 0
+        # initial state
+        double[::1,:] alpha = PyArray_ZEROS(2, mshape, cnp.NPY_DOUBLE, FORTRAN)
+        int lda = alpha.strides[1]/sizeof(DOUBLE)
+        # initial variance
+        double[::1,:] P = asfortranarray(dot(pinv(identity(r**2)-
+                                                  kron(T_mat, T_mat)),
+                                             dot(R_mat,
+                                                 R_mat.T).ravel('F')
+                                             ).reshape(r, r, order='F'))
+        int ldp = P.strides[1]/sizeof(DOUBLE)
+        double F_mat = 0.
+        double Finv = 0.
+        #ndarray[DOUBLE, ndim=2] v_mat = cnp.PyArray_Zeros(2, [1,1], cnp.NPY_FLOAT64,
+        #                                                   0)
+        double v_mat = 0
+        double[::1,:] K = PyArray_ZEROS(2, r2shape, cnp.NPY_DOUBLE, FORTRAN)
+        int ldk = K.strides[1] / sizeof(DOUBLE)
+
+        # pre-allocate some tmp arrays for the dgemm calls
+        # T_mat rows x P cols
+        double[::1,:] tmp1 = PyArray_ZEROS(2, r2shape, cnp.NPY_DOUBLE, FORTRAN)
+        int ldt1 = tmp1.strides[1]/sizeof(DOUBLE)
+        double[::1,:] tmp2 = zeros_like(alpha, order='F') # K rows x v_mat cols
+        int ldt2 = tmp2.strides[1]/sizeof(DOUBLE)
+        double[::1,:] L = zeros_like(T_mat, order='F')
+        int ldl = L.strides[1]/sizeof(DOUBLE)
+        # T_mat rows x P cols
+        double[::1,:] tmp3 = PyArray_ZEROS(2, r2shape, cnp.NPY_DOUBLE, FORTRAN)
+        int ldt3 = tmp3.strides[1]/sizeof(DOUBLE)
+
+        double alph = 1.0
+        double beta = 0.0
+
+    #NOTE: not sure about just checking F_mat[0,0], didn't appear to work
+    while not F_mat == 1. and i < nobs:
+        #print i
         # Predict
-        v_mat = y[i] - dot(Z_mat,alpha) # one-step forecast error
-        v[i] = v_mat
-        F_mat = dot(dot(Z_mat, P), Z_mat.T)
-        F[i] = F_mat
+        #v_mat = ddot(&r, &Z_mat[0,0], &one, &alpha[0,0], &one)
+        #v_mat = y[i] - v_mat # copies?
+        # Z_mat is just a selector matrix
+        v_mat = y[i] - alpha[0,0]
+        v[i, 0] = v_mat
+
+        # one-step forecast error
+        #dgemm("N", "N", &one, &r, &r, &alph, &Z_mat[0,0], &ldz, &P[0,0], &ldp,
+        #      &beta, &tmp1[0,0], &ldt1)
+        #F_mat = ddot(&r, &tmp1[0,0], &one, &Z_mat[0,0], &one)
+        #Z_mat is just a selector matrix
+        F_mat = P[0,0]
+        F[i,0] = F_mat
         Finv = 1./F_mat # always scalar for univariate series
-        K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # Kalman Gain Matrix
+        # compute Kalman Gain, K
+        # K = dot(dot(dot(T_mat,P),Z_mat.T),Finv)
+        #   or K = dot(dot(dot(T_mat, P), Z_mat.T))*Finv
+        # tmp1 = dot(T_mat, P)
+        # tmp3 = dot(tmp1, Z_mat.T)
+        # K = dot(tmp3, Finv) or tmp3*Finv
+
+        #print "Finv: ", asarray(<DOUBLE[:1, :1] *> &Finv[0,0])
+        dgemm("N", "N", &r, &r, &r, &alph, &T_mat[0,0], &ldt, &P[0,0], &ldp,
+              &beta, &tmp1[0,0], &ldt1)
+        #print "tmp1: ", asarray(<DOUBLE[:r, :r] *> &tmp1[0,0])
+        # tmp1 . Z_mat.T
+
+        dgemv("N", &r, &r, &Finv, &tmp1[0,0], &ldt1, &Z_mat[0,0], &one, &beta,
+              &K[0,0], &one)
+
         # update state
-        alpha = dot(T_mat, alpha) + dot(K,v_mat)
-        L = T_mat - dot(K,Z_mat)
-        P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)
+        #alpha = dot(T_mat, alpha) + dot(K, v_mat)
+        #alpha = dot(T_mat, alpha) + K*v_mat
+        #dot(T_mat, alpha)
+        dgemv("N", &r, &r, &alph, &T_mat[0,0], &ldt, &alpha[0,0], &one, &beta,
+              &tmp2[0,0], &one)
+
+        #dot(K, v_mat) + tmp2
+        for ii in range(r):
+            alpha[ii,0] = K[ii,0]*v_mat + tmp2[ii,0]
+
+        #L = T_mat - dot(K,Z_mat)
+        dgemm("N", "N", &r, &r, &one, &alph, &K[0,0], &ldk, &Z_mat[0,0], &ldz,
+              &beta, &L[0,0], &ldl)
+        # L = T_mat - L
+        # L = -(L - T_mat)
+        for jj in range(r):
+            for kk in range(r):
+                L[jj,kk] = T_mat[jj,kk] - L[jj,kk]
+
+        #P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)
+        # tmp5 = dot(R_mat, R_mat.T)
+        # tmp3 = dot(T_mat, P)
+        # P = dot(tmp3, L.T) + tmp5
+        dgemm("N", "N", &r, &r, &r, &alph, &T_mat[0,0], &ldt, &P[0,0],
+              &ldp, &beta, &tmp3[0,0], &ldt3)
+        dgemm("N", "T", &r, &r, &one, &alph, &R_mat[0,0], &ldr, &R_mat[0,0],
+              &ldr, &beta, &P[0,0], &ldp)
+        dgemm("N", "T", &r, &r, &r, &alph, &tmp3[0,0], &ldt3, &L[0,0],
+              &ldl, &alph, &P[0,0], &ldp)
+
+        # 101 = c-order, 122 - lower triangular of R, "N" - no trans (XX')
+        #dsyrk(101, 122, "N", r, 1, 1.0, &R_mat[0,0],
+        #      &ldr, 1.0, &P[0,0], &ldp )
+
         loglikelihood += log(F_mat)
         i+=1
+
     for i in xrange(i,nobs):
-        v_mat = y[i] - dot(Z_mat,alpha)
-        v[i] = v_mat
-        alpha = dot(T_mat, alpha) + dot(K, v_mat)
+        #v_mat = ddot(&r, &Z_mat[0,0], &one, &alpha[0,0], &one)
+        #v_mat = y[i] - v_mat
+        v_mat = y[i] - alpha[0,0]
+        v[i, 0] = v_mat
+        #alpha = dot(T_mat, alpha) + dot(K, v_mat)
+        dgemm("N", "N", &r, &one, &r, &alph, &T_mat[0,0], &ldt, &alpha[0,0],
+              &lda, &beta, &tmp2[0,0], &ldt2)
+        #dot(K, v_mat) + tmp2
+        for ii in range(r):
+            alpha[ii,0] = K[ii,0]*v_mat + tmp2[ii,0]
     return v, F, loglikelihood
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.cdivision(True)
-def kalman_filter_complex(ndarray[COMPLEX128, ndim=1] y,
+def kalman_filter_complex(dcomplex[:] y,
                    unsigned int k, unsigned int p, unsigned int q,
-                  unsigned int r, unsigned int nobs,
-                   ndarray[DOUBLE, ndim=2] Z_mat,
-                   ndarray[COMPLEX128, ndim=2] R_mat,
-                   ndarray[COMPLEX128, ndim=2] T_mat):
+                  int r, unsigned int nobs,
+                          dcomplex[::1,:] Z_mat,
+                          dcomplex[::1,:] R_mat,
+                          dcomplex[::1,:] T_mat):
     """
     Cython version of the Kalman filter recursions for an ARMA process.
     """
-    m = Z_mat.shape[1]
-    # store forecast-errors
-    v = zeros((nobs,1), dtype=complex)
-    # store variance of forecast errors
-    F = ones((nobs,1), dtype=complex)
-    loglikelihood = zeros((1,1), dtype=complex)
-    cdef int i = 0
-    # initial state
-#    cdef np.ndarray[DOUBLE, ndim=2] alpha = zeros((m,1))
-    alpha = zeros((m,1))
-    # initial variance
-    P = dot(pinv(identity(m**2)-kron(T_mat, T_mat)),dot(R_mat,
-            R_mat.T).ravel('F')).reshape(r,r, order='F')
-    F_mat = 0
+    cdef cnp.npy_intp yshape[2]
+    yshape[0] = <cnp.npy_intp> nobs
+    yshape[1] = <cnp.npy_intp> 1
+    cdef cnp.npy_intp mshape[2]
+    mshape[0] = <cnp.npy_intp> r
+    mshape[1] = <cnp.npy_intp> 1
+    cdef cnp.npy_intp r2shape[2]
+    r2shape[0] = <cnp.npy_intp> r
+    r2shape[1] = <cnp.npy_intp> r
+
+
+    cdef:
+        int one = 1
+        int ldz = Z_mat.strides[1]/sizeof(dcomplex)
+        int ldt = T_mat.strides[1]/sizeof(dcomplex)
+        int ldr = R_mat.strides[1]/sizeof(dcomplex)
+        # forecast errors
+        #dcomplex[:,:] v = zeros((nobs,1), dtype=complex)
+        ndarray[complex, ndim=2] v = PyArray_ZEROS(2, yshape, cnp.NPY_CDOUBLE,
+                                                   FORTRAN)
+        # store variance of forecast errors
+        dcomplex[::1,:] F = ones((nobs,1), dtype=complex, order='F')
+        dcomplex loglikelihood = 0 + 0j
+        int i = 0
+        # initial state
+        dcomplex[::1,:] alpha = PyArray_ZEROS(2, mshape, cnp.NPY_CDOUBLE, FORTRAN)
+        int lda = alpha.strides[1]/sizeof(dcomplex)
+        # initial variance
+        dcomplex[::1,:] P = dot(pinv(identity(r**2)-kron(T_mat, T_mat)),
+                            dot(R_mat,R_mat.T).ravel('F')).reshape(r,r, order='F')
+        int ldp = P.strides[1]/sizeof(dcomplex)
+        dcomplex F_mat = 0
+        dcomplex Finv = 0
+        # dcomplex[:,:] v_mat = zeros((1,1), dtype=complex)
+        dcomplex v_mat = 0
+        dcomplex[::1,:] K = PyArray_ZEROS(2, mshape, cnp.NPY_CDOUBLE, FORTRAN)
+        int ldk = K.strides[1]/sizeof(dcomplex)
+
+        # pre-allocate some tmp arrays for the dgemm calls
+        dcomplex[::1,:] tmp1 = PyArray_ZEROS(2, r2shape, cnp.NPY_CDOUBLE, FORTRAN)
+        int ldt1 = tmp1.strides[1]/sizeof(dcomplex)
+        dcomplex[::1,:] tmp2 = zeros_like(alpha, order='F')
+        int ldt2 = tmp2.strides[1]/sizeof(dcomplex)
+        dcomplex[::1,:] L = zeros_like(T_mat, dtype=complex, order='F')
+        int ldl = L.strides[1]/sizeof(dcomplex)
+        # T_mat rows x P cols
+        dcomplex[::1,:] tmp3 = PyArray_ZEROS(2, r2shape, cnp.NPY_CDOUBLE, FORTRAN)
+        int ldt3 = tmp3.strides[1]/sizeof(dcomplex)
+
+        dcomplex alph = 1+0j
+        dcomplex beta = 0
+
     while not F_mat == 1 and i < nobs:
-        # Predict
-        v_mat = y[i] - dot(Z_mat,alpha) # one-step forecast error
-        v[i] = v_mat
-        F_mat = dot(dot(Z_mat, P), Z_mat.T)
-        F[i] = F_mat
+        #v_mat = zdotu(&r, &Z_mat[0,0], &one, &alpha[0,0], &one)
+        # Z_mat is just a selector matrix
+        v_mat = y[i] - alpha[0,0]
+        v[i, 0] = v_mat
+
+        # one-step forecast error
+        #zgemm("N", "N", &one, &r, &r, &alph, &Z_mat[0,0], &ldz, &P[0,0], &ldp,
+        #      &beta, &tmp1[0,0], &ldt1)
+        # F_mat = zdotu(&r, &tmp1[0,0], &one, &Z_mat[0,0], &one)
+        # Z_mat is just a selctor matrix so the below is equivalent
+        F_mat = P[0,0]
+        F[i,0] = F_mat
         Finv = 1./F_mat # always scalar for univariate series
-        K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # Kalman Gain Matrix
+        # compute Kalman Gain, K
+        # K = dot(dot(dot(T_mat,P),Z_mat.T),Finv)
+        # tmp1 = dot(T_mat, P)
+        # tmp3 = dot(tmp1, Z_mat.T)
+        # K = dot(tmp3, Finv)
+
+        # tmp1 = T_mat.dot(P)
+        zgemm("N", "N", &r, &r, &r, &alph, &T_mat[0,0], &ldt, &P[0,0], &ldp,
+              &beta, &tmp1[0,0], &ldt1)
+        # tmp3 = tmp1.dot(Z_mat.T)
+        zgemv("N", &r, &r, &Finv, &tmp1[0,0], &ldt1, &Z_mat[0,0], &one, &beta,
+              &K[0,0], &one)
+
+        # K = tmp3.dot(Finv)
         # update state
-        alpha = dot(T_mat, alpha) + dot(K,v_mat)
-        L = T_mat - dot(K,Z_mat)
-        P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)
+        #alpha = dot(T_mat, alpha) + dot(K, v_mat)
+        #dot(T_mat, alpha)
+        zgemv("N", &r, &r, &alph, &T_mat[0,0], &ldt, &alpha[0,0], &one, &beta,
+              &tmp2[0,0], &one)
+        #dot(K, v_mat) + tmp2
+        # alpha += tmp2
+        #daxpy(r, alph, &tmp2[0,0], 1, &alpha[0,0], 1)
+        for ii in range(r):
+            alpha[ii,0] = K[ii,0]*v_mat + tmp2[ii,0]
+        #print "alpha:", asarray(<dcomplex[:m, :1] *> &alpha[0,0])
+
+        #L = T_mat - dot(K,Z_mat)
+        zgemm("N", "N", &r, &r, &one, &alph, &K[0,0], &ldk, &Z_mat[0,0], &ldz,
+              &beta, &L[0,0], &ldl)
+
+        # L = T_mat - L
+        # L = -(L - T_mat)
+        for jj in range(r):
+            for kk in range(r):
+                L[jj, kk] = T_mat[jj,kk] - L[jj,kk]
+
+        #P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T)
+        # tmp5 = dot(R_mat, R_mat.T)
+        # tmp3 = dot(T_mat, P)
+        # P = dot(tmp3, L.T) + tmp5
+        zgemm("N", "N", &r, &r, &r, &alph, &T_mat[0,0], &ldt, &P[0,0], &ldp,
+              &beta, &tmp3[0,0], &ldt3)
+        zgemm("N", "T", &r, &r, &one, &alph, &R_mat[0,0], &ldr, &R_mat[0,0],
+              &ldr, &beta, &P[0,0], &ldp)
+        zgemm("N", "T", &r, &r, &r, &alph, &tmp3[0,0], &ldt3, &L[0,0], &ldl, &alph,
+              &P[0,0], &ldp)
+
         loglikelihood += nplog(F_mat)
         i+=1
+
     for i in xrange(i,nobs):
-        v_mat = y[i] - dot(Z_mat,alpha)
-        v[i] = v_mat
-        alpha = dot(T_mat, alpha) + dot(K, v_mat)
-    return v,F,loglikelihood
+        #v_mat = zdotu(&r, &Z_mat[0,0], &one, &alpha[0,0], &one)
+        #Z_mat is just a selector
+        v_mat = y[i] - alpha[0,0]
+        v[i, 0] = v_mat
+        #alpha = dot(T_mat, alpha) + dot(K, v_mat)
+        zgemm("N", "N", &r, &one, &r, &alph, &T_mat[0,0], &ldt, &alpha[0,0], &lda,
+              &beta, &tmp2[0,0], &ldt2)
+        #dot(K, v_mat) + tmp2
+        # alpha += tmp2
+        #daxpy(r, alph, &tmp2[0,0], 1, &alpha[0,0], 1)
+        for ii in range(r):
+            alpha[ii,0] = K[ii,0]*v_mat + tmp2[ii,0]
+    return v, F, loglikelihood
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.cdivision(True)
-def kalman_loglike_double(ndarray[DOUBLE, ndim=1] y,
-                   unsigned int k, unsigned int p, unsigned int q,
-                  unsigned int r, unsigned int nobs,
-                   ndarray[DOUBLE, ndim=2] Z_mat,
-                   ndarray[DOUBLE, ndim=2] R_mat,
-                   ndarray[DOUBLE, ndim=2] T_mat):
+def kalman_loglike_double(double[:] y, unsigned int k, unsigned int p,
+                          unsigned int q, int r, unsigned int nobs,
+                          double[::1,:] Z_mat,
+                          double[::1,:] R_mat,
+                          double[::1,:] T_mat):
     """
     Cython version of the Kalman filter recursions for an ARMA process.
     """
@@ -125,16 +348,15 @@ def kalman_loglike_double(ndarray[DOUBLE, ndim=1] y,
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.cdivision(True)
-def kalman_loglike_complex(ndarray[COMPLEX128, ndim=1] y,
-                   unsigned int k, unsigned int p, unsigned int q,
-                  unsigned int r, unsigned int nobs,
-                   ndarray[DOUBLE, ndim=2] Z_mat,
-                   ndarray[COMPLEX128, ndim=2] R_mat,
-                   ndarray[COMPLEX128, ndim=2] T_mat):
+def kalman_loglike_complex(dcomplex[:] y, unsigned int k, unsigned int p,
+                           unsigned int q, int r, unsigned int nobs,
+                           dcomplex[::1,:] Z_mat,
+                           dcomplex[::1,:] R_mat,
+                           dcomplex[::1,:] T_mat):
     """
     Cython version of the Kalman filter recursions for an ARMA process.
     """
-    v,F,loglikelihood = kalman_filter_complex(y,k,p,q,r,nobs,Z_mat,R_mat,T_mat)
+    v, F, loglikelihood = kalman_filter_complex(y,k,p,q,r,nobs,Z_mat,R_mat,T_mat)
     sigma2 = 1./nobs * sum(v**2 / F)
     loglike = -.5 *(loglikelihood + nobs*nplog(sigma2))
     loglike -= nobs/2. * (log(2*pi) + 1)
diff --git a/statsmodels/tsa/kalmanf/kalmanfilter.py b/statsmodels/tsa/kalmanf/kalmanfilter.py
index 45625246cd1..8a2fa0852a5 100644
--- a/statsmodels/tsa/kalmanf/kalmanfilter.py
+++ b/statsmodels/tsa/kalmanf/kalmanfilter.py
@@ -24,7 +24,8 @@
 # http://www.federalreserve.gov/pubs/oss/oss4/aimindex.html
 # Harvey notes that the square root filter will keep P_t pos. def. but
 # is not strictly needed outside of the engineering (long series)
-
+from __future__ import print_function
+from statsmodels.compat.python import lzip, lmap, callable, range
 import numpy as np
 from numpy import dot, identity, kron, log, zeros, pi, exp, eye, issubdtype, ones
 from numpy.linalg import inv, pinv
@@ -357,7 +358,7 @@ def _updateloglike(self, params, xi10, ntrain, penalty, upperbounds, lowerbounds
             H = H(params)
         elif H == None:
             H = 0
-        print callable(Q)
+        print(callable(Q))
         if Q != None and callable(Q):
             Q = Q(params)
         elif Q == None:
@@ -505,9 +506,11 @@ def T(cls, params, r, k, p): # F in Hamilton
         ----------
         Durbin and Koopman Section 3.7.
         """
-        arr = zeros((r,r), dtype=params.dtype) # allows for complex-step
-                                                  # derivative
-        params_padded = zeros(r, dtype=params.dtype) # handle zero coefficients if necessary
+        arr = zeros((r, r), dtype=params.dtype, order="F")
+        # allows for complex-step derivative
+        params_padded = zeros(r, dtype=params.dtype,
+                              order="F")
+                        # handle zero coefficients if necessary
         #NOTE: squeeze added for cg optimizer
         params_padded[:p] = params[k:p+k]
         arr[:,0] = params_padded   # first p params are AR coeffs w/ short params
@@ -538,8 +541,9 @@ def R(cls, params, r, k, q, p): # R is H in Hamilton
         ----------
         Durbin and Koopman Section 3.7.
         """
-        arr = zeros((r,1), dtype=params.dtype) # this allows zero coefficients
-                                                  # dtype allows for compl. der.
+        arr = zeros((r, 1), dtype=params.dtype, order="F")
+                               # this allows zero coefficients
+                               # dtype allows for compl. der.
         arr[1:q+1,:] = params[p+k:p+k+q][:,None]
         arr[0] = 1.0
         return arr
@@ -560,7 +564,7 @@ def Z(cls, r):
         Currently only returns a 1 x r vector [1,0,0,...0].  Will need to
         be generalized when the Kalman Filter becomes more flexible.
         """
-        arr = zeros((1,r))
+        arr = zeros((1,r), order="F")
         arr[:,0] = 1.
         return arr
 
@@ -611,7 +615,7 @@ def _init_kalman_state(cls, params, arma_model):
                newparams, Z_mat, m, R_mat, T_mat, paramsdtype)
 
     @classmethod
-    def loglike(cls, params, arma_model):
+    def loglike(cls, params, arma_model, set_sigma2=True):
         """
         The loglikelihood for an ARMA model using the Kalman Filter recursions.
 
@@ -623,6 +627,10 @@ def loglike(cls, params, arma_model):
             coefficients, then the `q` MA coefficients.
         arma_model : `statsmodels.tsa.arima.ARMA` instance
             A reference to the ARMA model instance.
+        set_sigma2 : bool, optional
+            True if arma_model.sigma2 should be set.
+            Note that sigma2 will be computed in any case,
+            but it will be discarded if set_sigma2 is False.
 
         Notes
         -----
@@ -635,20 +643,22 @@ def loglike(cls, params, arma_model):
         #TODO: this won't work for time-varying parameters
         (y, k, nobs, k_ar, k_ma, k_lags, newparams, Z_mat, m, R_mat, T_mat,
                 paramsdtype) = cls._init_kalman_state(params, arma_model)
-
         if issubdtype(paramsdtype, float):
             loglike, sigma2 =  kalman_loglike.kalman_loglike_double(y, k,
                                     k_ar, k_ma, k_lags, int(nobs), Z_mat,
                                     R_mat, T_mat)
         elif issubdtype(paramsdtype, complex):
             loglike, sigma2 =  kalman_loglike.kalman_loglike_complex(y, k,
-                                    k_ar, k_ma, k_lags, int(nobs), Z_mat,
+                                    k_ar, k_ma, k_lags, int(nobs),
+                                    Z_mat.astype(complex),
                                     R_mat, T_mat)
         else:
             raise TypeError("This dtype %s is not supported "
                             " Please files a bug report." % paramsdtype)
-        arma_model.sigma2 = sigma2
-        return loglike.item() # return a scalar not a 0d array
+        if set_sigma2:
+            arma_model.sigma2 = sigma2
+
+        return loglike
 
 
 if __name__ == "__main__":
@@ -780,7 +790,7 @@ def loglike(cls, params, arma_model):
     lowerbounds = np.array([-.999, .001, .001])
     xi10 = xihistory[:,0]
     ntrain = 1
-    bounds = zip(lowerbounds,upperbounds) # if you use fmin_l_bfgs_b
+    bounds = lzip(lowerbounds,upperbounds) # if you use fmin_l_bfgs_b
 #    results = optimize.fmin_bfgs(updatematrices, params,
 #        args=(y,xi10,ntrain,penalty,upperbounds,lowerbounds),
 #        gtol = 1e-8, epsilon=1e-10)
@@ -843,7 +853,7 @@ def Q(x):
 
 # p. 162 univariate structural time series example
     seatbelt = dk.open('Seatbelt.dat').readlines()
-    seatbelt = [map(float,_.split()) for _ in seatbelt[2:]]
+    seatbelt = [lmap(float,_.split()) for _ in seatbelt[2:]]
     sb_ssm = StateSpaceModel(seatbelt)
     s = 12 # monthly data
 # s p.
diff --git a/statsmodels/tsa/seasonal.py b/statsmodels/tsa/seasonal.py
new file mode 100644
index 00000000000..f2cb37e489a
--- /dev/null
+++ b/statsmodels/tsa/seasonal.py
@@ -0,0 +1,162 @@
+"""
+Seasonal Decomposition by Moving Averages
+"""
+from statsmodels.compat.python import lmap, range, iteritems
+import numpy as np
+from pandas.core.nanops import nanmean as pd_nanmean
+from .filters._utils import _maybe_get_pandas_wrapper_freq
+from .filters.filtertools import convolution_filter
+from statsmodels.tsa.tsatools import freq_to_period
+
+
+def seasonal_mean(x, freq):
+    """
+    Return means for each period in x. freq is an int that gives the
+    number of periods per cycle. E.g., 12 for monthly. NaNs are ignored
+    in the mean.
+    """
+    return np.array([pd_nanmean(x[i::freq]) for i in range(freq)])
+
+
+def seasonal_decompose(x, model="additive", filt=None, freq=None):
+    """
+    Parameters
+    ----------
+    x : array-like
+        Time series
+    model : str {"additive", "multiplicative"}
+        Type of seasonal component. Abbreviations are accepted.
+    filt : array-like
+        The filter coefficients for filtering out the seasonal component.
+        The default is a symmetric moving average.
+    freq : int, optional
+        Frequency of the series. Must be used if x is not a pandas
+        object with a timeseries index.
+
+    Returns
+    -------
+    results : obj
+        A object with seasonal, trend, and resid attributes.
+
+    Notes
+    -----
+    This is a naive decomposition. More sophisticated methods should
+    be preferred.
+
+    The additive model is Y[t] = T[t] + S[t] + e[t]
+
+    The multiplicative model is Y[t] = T[t] * S[t] * e[t]
+
+    The seasonal component is first removed by applying a convolution
+    filter to the data. The average of this smoothed series for each
+    period is the returned seasonal component.
+
+    See Also
+    --------
+    statsmodels.tsa.filters.convolution_filter
+    """
+    _pandas_wrapper, pfreq = _maybe_get_pandas_wrapper_freq(x)
+    x = np.asanyarray(x).squeeze()
+    nobs = len(x)
+
+    if not np.all(np.isfinite(x)):
+        raise ValueError("This function does not handle missing values")
+    if model.startswith('m'):
+        if np.any(x <= 0):
+            raise ValueError("Multiplicative seasonality is not appropriate "
+                             "for zero and negative values")
+
+    if pfreq is not None:
+        pfreq = freq_to_period(pfreq)
+        if freq and pfreq != freq:
+            raise ValueError("Inferred frequency of index and frequency "
+                             "don't match. This function does not re-sample")
+        else:
+            freq = pfreq
+
+    elif freq is None:
+        raise ValueError("You must specify a freq or x must be a "
+                         "pandas object with a timeseries index")
+
+
+    if filt is None:
+        if freq % 2 == 0:  # split weights at ends
+            filt = np.array([.5] + [1] * (freq - 1) + [.5]) / freq
+        else:
+            filt = np.repeat(1./freq, freq)
+
+    trend = convolution_filter(x, filt)
+
+    # nan pad for conformability - convolve doesn't do it
+    if model.startswith('m'):
+        detrended = x / trend
+    else:
+        detrended = x - trend
+
+    period_averages = seasonal_mean(detrended, freq)
+
+    if model.startswith('m'):
+        period_averages /= np.mean(period_averages)
+    else:
+        period_averages -= np.mean(period_averages)
+
+    seasonal = np.tile(period_averages, nobs // freq + 1)[:nobs]
+
+    if model.startswith('m'):
+        resid = x / seasonal / trend
+    else:
+        resid = detrended - seasonal
+
+    results = lmap(_pandas_wrapper, [seasonal, trend, resid, x])
+    return DecomposeResult(seasonal=results[0], trend=results[1],
+                           resid=results[2], observed=results[3])
+
+
+class DecomposeResult(object):
+    def __init__(self, **kwargs):
+        for key, value in iteritems(kwargs):
+            setattr(self, key, value)
+        self.nobs = len(self.observed)
+
+    def plot(self):
+        from statsmodels.graphics.utils import _import_mpl
+        plt = _import_mpl()
+        fig, axes = plt.subplots(4, 1, sharex=True)
+        if hasattr(self.observed, 'plot'):  # got pandas use it
+            self.observed.plot(ax=axes[0], legend=False)
+            axes[0].set_ylabel('Observed')
+            self.trend.plot(ax=axes[1], legend=False)
+            axes[1].set_ylabel('Trend')
+            self.seasonal.plot(ax=axes[2], legend=False)
+            axes[2].set_ylabel('Seasonal')
+            self.resid.plot(ax=axes[3], legend=False)
+            axes[3].set_ylabel('Residual')
+        else:
+            axes[0].plot(self.observed)
+            axes[0].set_ylabel('Observed')
+            axes[1].plot(self.trend)
+            axes[1].set_ylabel('Trend')
+            axes[2].plot(self.seasonal)
+            axes[2].set_ylabel('Seasonal')
+            axes[3].plot(self.resid)
+            axes[3].set_ylabel('Residual')
+            axes[3].set_xlabel('Time')
+            axes[3].set_xlim(0, self.nobs)
+
+        fig.tight_layout()
+        return fig
+
+
+if __name__ == "__main__":
+    x = np.array([-50, 175, 149, 214, 247, 237, 225, 329, 729, 809,
+                  530, 489, 540, 457, 195, 176, 337, 239, 128, 102,
+                  232, 429, 3, 98, 43, -141, -77, -13, 125, 361, -45, 184])
+    results = seasonal_decompose(x, freq=4)
+
+    from pandas import DataFrame, DatetimeIndex
+    data = DataFrame(x, DatetimeIndex(start='1/1/1951',
+                                      periods=len(x),
+                                      freq='Q'))
+
+    res = seasonal_decompose(data)
+
diff --git a/statsmodels/tsa/setup.py b/statsmodels/tsa/setup.py
deleted file mode 100644
index 28be3467cfc..00000000000
--- a/statsmodels/tsa/setup.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#! /usr/bin/env python
-
-import os.path
-
-base_path = os.path.abspath(os.path.dirname(__file__))
-
-def configuration(parent_package='', top_path=None):
-    from numpy.distutils.misc_util import (Configuration,
-                            get_numpy_include_dirs)
-    config = Configuration('tsa', parent_package, top_path)
-
-    config.add_subpackage('kalmanf')
-
-    config.add_data_files('vector_ar/data/*.dat')
-
-    return config
-
-if __name__ == '__main__':
-    from numpy.distutils.core import setup
-    setup(**configuration(top_path='').todict())
diff --git a/statsmodels/tsa/stattools.py b/statsmodels/tsa/stattools.py
index d696fb4febb..715f92168bd 100644
--- a/statsmodels/tsa/stattools.py
+++ b/statsmodels/tsa/stattools.py
@@ -1,15 +1,22 @@
 """
 Statistical tools for time series analysis
 """
-
+from statsmodels.compat.python import (iteritems, range, lrange, string_types, lzip,
+                                zip, map)
 import numpy as np
-from scipy import stats, signal
+from numpy.linalg import LinAlgError
+from scipy import stats
 from statsmodels.regression.linear_model import OLS, yule_walker
-from statsmodels.tools.tools import add_constant
-from tsatools import lagmat, lagmat2ds, add_trend
-#from statsmodels.sandbox.tsa import var
-from adfvalues import mackinnonp, mackinnoncrit
-#from statsmodels.sandbox.rls import RLS
+from statsmodels.tools.tools import add_constant, Bunch
+from .tsatools import lagmat, lagmat2ds, add_trend
+from .adfvalues import mackinnonp, mackinnoncrit
+from statsmodels.tsa.arima_model import ARMA
+from statsmodels.compat.scipy import _next_regular
+
+__all__ = ['acovf', 'acf', 'pacf', 'pacf_yw', 'pacf_ols', 'ccovf', 'ccf',
+           'periodogram', 'q_stat', 'coint', 'arma_order_select_ic',
+           'adfuller']
+
 
 #NOTE: now in two places to avoid circular import
 #TODO: I like the bunch pattern for this too.
@@ -17,8 +24,9 @@ class ResultsStore(object):
     def __str__(self):
         return self._str  # pylint: disable=E1101
 
+
 def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(),
-        fitargs=(), regresults=False):
+             fitargs=(), regresults=False):
     """
     Returns the results for the lag length that maximimizes the info criterion.
 
@@ -60,16 +68,15 @@ def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(),
 
     results = {}
     method = method.lower()
-    for lag in range(startlag, startlag+maxlag+1):
-        mod_instance = mod(endog, exog[:,:lag], *modargs)
+    for lag in range(startlag, startlag + maxlag + 1):
+        mod_instance = mod(endog, exog[:, :lag], *modargs)
         results[lag] = mod_instance.fit()
 
     if method == "aic":
-        icbest, bestlag = min((v.aic,k) for k,v in results.iteritems())
+        icbest, bestlag = min((v.aic, k) for k, v in iteritems(results))
     elif method == "bic":
-        icbest, bestlag = min((v.bic,k) for k,v in results.iteritems())
+        icbest, bestlag = min((v.bic, k) for k, v in iteritems(results))
     elif method == "t-stat":
-        lags = sorted(results.keys())[::-1]
         #stop = stats.norm.ppf(.95)
         stop = 1.6448536269514722
         for lag in range(startlag + maxlag, startlag - 1, -1):
@@ -86,7 +93,9 @@ def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(),
     else:
         return icbest, bestlag, results
 
-#this needs to be converted to a class like HetGoldfeldQuandt, 3 different returns are a mess
+
+#this needs to be converted to a class like HetGoldfeldQuandt,
+# 3 different returns are a mess
 # See:
 #Ng and Perron(2001), Lag length selection and the construction of unit root
 #tests with good size and power, Econometrica, Vol 69 (6) pp 1519-1554
@@ -94,8 +103,9 @@ def _autolag(mod, endog, exog, startlag, maxlag, method, modargs=(),
 # just changes the distribution of the test statistic to a t distribution
 #TODO: autolag is untested
 def adfuller(x, maxlag=None, regression="c", autolag='AIC',
-    store=False, regresults=False):
-    '''Augmented Dickey-Fuller unit root test
+             store=False, regresults=False):
+    '''
+    Augmented Dickey-Fuller unit root test
 
     The Augmented Dickey-Fuller test can be used to test for a unit root in a
     univariate process in the presence of serial correlation.
@@ -108,22 +118,22 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
         Maximum lag which is included in test, default 12*(nobs/100)^{1/4}
     regression : str {'c','ct','ctt','nc'}
         Constant and trend order to include in regression
-        * 'c' : constant only
+        * 'c' : constant only (default)
         * 'ct' : constant and trend
         * 'ctt' : constant, and linear and quadratic trend
         * 'nc' : no constant, no trend
     autolag : {'AIC', 'BIC', 't-stat', None}
         * if None, then maxlag lags are used
-        * if 'AIC' or 'BIC', then the number of lags is chosen to minimize the
-          corresponding information criterium
+        * if 'AIC' (default) or 'BIC', then the number of lags is chosen
+          to minimize the corresponding information criterium
         * 't-stat' based choice of maxlag.  Starts with maxlag and drops a
           lag until the t-statistic on the last lag length is significant at
           the 95 % level.
     store : bool
         If True, then a result instance is returned additionally to
-        the adf statistic
+        the adf statistic (default is False)
     regresults : bool
-        If True, the full regression results are returned.
+        If True, the full regression results are returned (default is False)
 
     Returns
     -------
@@ -137,8 +147,8 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
         Number of observations used for the ADF regression and calculation of
         the critical values.
     critical values : dict
-        Critical values for the test statistic at the 1 %, 5 %, and 10 % levels.
-        Based on MacKinnon (2010)
+        Critical values for the test statistic at the 1 %, 5 %, and 10 %
+        levels. Based on MacKinnon (2010)
     icbest : float
         The maximized information criterion if autolag is not None.
     regresults : RegressionResults instance
@@ -184,24 +194,24 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
     if regresults:
         store = True
 
-    trenddict = {None:'nc', 0:'c', 1:'ct', 2:'ctt'}
+    trenddict = {None: 'nc', 0: 'c', 1: 'ct', 2: 'ctt'}
     if regression is None or isinstance(regression, int):
         regression = trenddict[regression]
     regression = regression.lower()
-    if regression not in ['c','nc','ct','ctt']:
+    if regression not in ['c', 'nc', 'ct', 'ctt']:
         raise ValueError("regression option %s not understood") % regression
     x = np.asarray(x)
     nobs = x.shape[0]
 
     if maxlag is None:
         #from Greene referencing Schwert 1989
-        maxlag = int(np.ceil(12. * np.power(nobs/100., 1/4.)))
+        maxlag = int(np.ceil(12. * np.power(nobs / 100., 1 / 4.)))
 
     xdiff = np.diff(x)
-    xdall = lagmat(xdiff[:,None], maxlag, trim='both', original='in')
+    xdall = lagmat(xdiff[:, None], maxlag, trim='both', original='in')
     nobs = xdall.shape[0]  # pylint: disable=E1103
 
-    xdall[:,0] = x[-nobs-1:-1] # replace 0 xdiff with level of x
+    xdall[:, 0] = x[-nobs - 1:-1]  # replace 0 xdiff with level of x
     xdshort = xdiff[-nobs:]
 
     if store:
@@ -211,7 +221,7 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
             fullRHS = add_trend(xdall, regression, prepend=True)
         else:
             fullRHS = xdall
-        startlag = fullRHS.shape[1] - xdall.shape[1] + 1 # 1 for level  # pylint: disable=E1103
+        startlag = fullRHS.shape[1] - xdall.shape[1] + 1  # 1 for level  # pylint: disable=E1103
         #search for lag length with smallest information criteria
         #Note: use the same number of observations to have comparable IC
         #aic and bic: smaller is better
@@ -221,24 +231,26 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
                                        maxlag, autolag)
         else:
             icbest, bestlag, alres = _autolag(OLS, xdshort, fullRHS, startlag,
-                                        maxlag, autolag, regresults=regresults)
+                                              maxlag, autolag,
+                                              regresults=regresults)
             resstore.autolag_results = alres
 
-        bestlag -= startlag  #convert to lag not column index
+        bestlag -= startlag  # convert to lag not column index
 
         #rerun ols with best autolag
-        xdall = lagmat(xdiff[:,None], bestlag, trim='both', original='in')
+        xdall = lagmat(xdiff[:, None], bestlag, trim='both', original='in')
         nobs = xdall.shape[0]   # pylint: disable=E1103
-        xdall[:,0] = x[-nobs-1:-1] # replace 0 xdiff with level of x
+        xdall[:, 0] = x[-nobs - 1:-1]  # replace 0 xdiff with level of x
         xdshort = xdiff[-nobs:]
         usedlag = bestlag
     else:
         usedlag = maxlag
         icbest = None
     if regression != 'nc':
-        resols = OLS(xdshort, add_trend(xdall[:,:usedlag+1], regression)).fit()
+        resols = OLS(xdshort, add_trend(xdall[:, :usedlag + 1],
+                     regression)).fit()
     else:
-        resols = OLS(xdshort, xdall[:,:usedlag+1]).fit()
+        resols = OLS(xdshort, xdall[:, :usedlag + 1]).fit()
 
     adfstat = resols.tvalues[0]
 #    adfstat = (resols.params[0]-1.0)/resols.bse[0]
@@ -251,7 +263,7 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
     pvalue = mackinnonp(adfstat, regression=regression, N=1)
     critvalues = mackinnoncrit(N=1, regression=regression, nobs=nobs)
     critvalues = {"1%" : critvalues[0], "5%" : critvalues[1],
-            "10%" : critvalues[2]}
+                  "10%" : critvalues[2]}
     if store:
         resstore.resols = resols
         resstore.maxlag = maxlag
@@ -259,7 +271,8 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
         resstore.adfstat = adfstat
         resstore.critvalues = critvalues
         resstore.nobs = nobs
-        resstore.H0 = "The coefficient on the lagged level equals 1 - unit root"
+        resstore.H0 = ("The coefficient on the lagged level equals 1 - "
+                       "unit root")
         resstore.HA = "The coefficient on the lagged level < 1 - stationary"
         resstore.icbest = icbest
         return adfstat, pvalue, critvalues, resstore
@@ -269,6 +282,7 @@ def adfuller(x, maxlag=None, regression="c", autolag='AIC',
         else:
             return adfstat, pvalue, usedlag, nobs, critvalues, icbest
 
+
 def acovf(x, unbiased=False, demean=True, fft=False):
     '''
     Autocovariance for 1D
@@ -300,20 +314,20 @@ def acovf(x, unbiased=False, demean=True, fft=False):
     else:
         xo = x
     if unbiased:
-        xi = np.arange(1, n+1)
+        xi = np.arange(1, n + 1)
         d = np.hstack((xi, xi[:-1][::-1]))
     else:
-        d = n
+        d = n * np.ones(2 * n - 1)
     if fft:
         nobs = len(xo)
-        Frf = np.fft.fft(xo, n=nobs*2)
-        acov = np.fft.ifft(Frf*np.conjugate(Frf))[:nobs]/d
+        Frf = np.fft.fft(xo, n=nobs * 2)
+        acov = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs] / d[n - 1:]
         return acov.real
     else:
-        return (np.correlate(xo, xo, 'full')/d)[n-1:]
+        return (np.correlate(xo, xo, 'full') / d)[n - 1:]
 
 
-def q_stat(x,nobs, type="ljungbox"):
+def q_stat(x, nobs, type="ljungbox"):
     """
     Return's Ljung-Box Q Statistic
 
@@ -335,11 +349,12 @@ def q_stat(x,nobs, type="ljungbox"):
     Written to be used with acf.
     """
     x = np.asarray(x)
-    if type=="ljungbox":
-        ret = nobs*(nobs+2)*np.cumsum((1./(nobs-np.arange(1,
-            len(x)+1)))*x**2)
-    chi2 = stats.chi2.sf(ret,np.arange(1,len(x)+1))
-    return ret,chi2
+    if type == "ljungbox":
+        ret = (nobs * (nobs + 2) *
+               np.cumsum((1. / (nobs - np.arange(1, len(x) + 1))) * x**2))
+    chi2 = stats.chi2.sf(ret, np.arange(1, len(x) + 1))
+    return ret, chi2
+
 
 #NOTE: Changed unbiased to False
 #see for example
@@ -397,51 +412,55 @@ def acf(x, unbiased=False, nlags=40, confint=None, qstat=False, fft=False,
     but the autocorrelation is not an unbiased estimtor.
     '''
     nobs = len(x)
-    d = nobs # changes if unbiased
+    d = nobs  # changes if unbiased
     if not fft:
         avf = acovf(x, unbiased=unbiased, demean=True)
         #acf = np.take(avf/avf[0], range(1,nlags+1))
-        acf = avf[:nlags+1]/avf[0]
+        acf = avf[:nlags + 1] / avf[0]
     else:
         #JP: move to acovf
         x0 = x - x.mean()
-        Frf = np.fft.fft(x0, n=nobs*2) # zero-pad for separability
+        # ensure that we always use a power of 2 or 3 for zero-padding,
+        # this way we'll ensure O(n log n) runtime of the fft.
+        n = _next_regular(2 * nobs + 1)
+        Frf = np.fft.fft(x0, n=n)  # zero-pad for separability
         if unbiased:
             d = nobs - np.arange(nobs)
-        acf = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs]/d
+        acf = np.fft.ifft(Frf * np.conjugate(Frf))[:nobs] / d
         acf /= acf[0]
         #acf = np.take(np.real(acf), range(1,nlags+1))
-        acf = np.real(acf[:nlags+1])   #keep lag 0
+        acf = np.real(acf[:nlags + 1])   # keep lag 0
     if not (confint or qstat or alpha):
         return acf
     if not confint is None:
         import warnings
         warnings.warn("confint is deprecated. Please use the alpha keyword",
                       FutureWarning)
-        varacf = np.ones(nlags+1)/nobs
+        varacf = np.ones(nlags + 1) / nobs
         varacf[0] = 0
-        varacf[1] = 1./nobs
-        varacf[2:] *= 1 + 2*np.cumsum(acf[1:-1]**2)
-        interval = stats.norm.ppf(1-(100-confint)/200.)*np.sqrt(varacf)
-        confint = np.array(zip(acf-interval, acf+interval))
+        varacf[1] = 1. / nobs
+        varacf[2:] *= 1 + 2 * np.cumsum(acf[1:-1]**2)
+        interval = stats.norm.ppf(1 - (100 - confint) / 200.) * np.sqrt(varacf)
+        confint = np.array(lzip(acf - interval, acf + interval))
         if not qstat:
             return acf, confint
     if alpha is not None:
-        varacf = np.ones(nlags+1)/nobs
+        varacf = np.ones(nlags + 1) / nobs
         varacf[0] = 0
-        varacf[1] = 1./nobs
-        varacf[2:] *= 1 + 2*np.cumsum(acf[1:-1]**2)
-        interval = stats.norm.ppf(1-alpha/2.)*np.sqrt(varacf)
-        confint = np.array(zip(acf-interval, acf+interval))
+        varacf[1] = 1. / nobs
+        varacf[2:] *= 1 + 2 * np.cumsum(acf[1:-1]**2)
+        interval = stats.norm.ppf(1 - alpha / 2.) * np.sqrt(varacf)
+        confint = np.array(lzip(acf - interval, acf + interval))
         if not qstat:
             return acf, confint
     if qstat:
-        qstat, pvalue = q_stat(acf[1:], nobs=nobs)  #drop lag 0
+        qstat, pvalue = q_stat(acf[1:], nobs=nobs)  # drop lag 0
         if (confint is not None or alpha is not None):
             return acf, confint, qstat, pvalue
         else:
             return acf, qstat, pvalue
 
+
 def pacf_yw(x, nlags=40, method='unbiased'):
     '''Partial autocorrelation estimated with non-recursive yule_walker
 
@@ -464,12 +483,12 @@ def pacf_yw(x, nlags=40, method='unbiased'):
     This solves yule_walker for each desired lag and contains
     currently duplicate calculations.
     '''
-    xm = x - x.mean()
     pacf = [1.]
-    for k in range(1, nlags+1):
+    for k in range(1, nlags + 1):
         pacf.append(yule_walker(x, k, method=method)[0][-1])
     return np.array(pacf)
 
+
 #NOTE: this is incorrect.
 def pacf_ols(x, nlags=40):
     '''Calculate partial autocorrelations
@@ -499,12 +518,13 @@ def pacf_ols(x, nlags=40):
     xlags = add_constant(xlags)
     pacf = [1.]
     for k in range(1, nlags+1):
-        res = OLS(x0[k:], xlags[k:,:k+1]).fit()
+        res = OLS(x0[k:], xlags[k:, :k+1]).fit()
          #np.take(xlags[k:], range(1,k+1)+[-1],
 
         pacf.append(res.params[-1])
     return np.array(pacf)
 
+
 def pacf(x, nlags=40, method='ywunbiased', alpha=None):
     '''Partial autocorrelation estimated
 
@@ -554,22 +574,22 @@ def pacf(x, nlags=40, method='ywunbiased', alpha=None):
         ld_ = levinson_durbin(acv, nlags=nlags, isacov=True)
         #print 'ld', ld_
         ret = ld_[2]
-    elif method in ['ldb', 'ldbiased', 'ld_biased']: #inconsistent naming with ywmle
+    # inconsistent naming with ywmle
+    elif method in ['ldb', 'ldbiased', 'ld_biased']:
         acv = acovf(x, unbiased=False)
         ld_ = levinson_durbin(acv, nlags=nlags, isacov=True)
         ret = ld_[2]
     else:
         raise ValueError('method not available')
     if alpha is not None:
-        varacf = 1./len(x)
-        interval = stats.norm.ppf(1. - alpha/2.) * np.sqrt(varacf)
-        confint = np.array(zip(ret-interval, ret+interval))
+        varacf = 1. / len(x)
+        interval = stats.norm.ppf(1. - alpha / 2.) * np.sqrt(varacf)
+        confint = np.array(lzip(ret - interval, ret + interval))
         return ret, confint
     else:
         return ret
 
 
-
 def ccovf(x, y, unbiased=True, demean=True):
     ''' crosscovariance for 1D
 
@@ -592,17 +612,18 @@ def ccovf(x, y, unbiased=True, demean=True):
     '''
     n = len(x)
     if demean:
-        xo = x - x.mean();
-        yo = y - y.mean();
+        xo = x - x.mean()
+        yo = y - y.mean()
     else:
         xo = x
         yo = y
     if unbiased:
-        xi = np.ones(n);
+        xi = np.ones(n)
         d = np.correlate(xi, xi, 'full')
     else:
         d = n
-    return (np.correlate(xo,yo,'full') / d)[n-1:]
+    return (np.correlate(xo, yo, 'full') / d)[n - 1:]
+
 
 def ccf(x, y, unbiased=True):
     '''cross-correlation function for 1d
@@ -652,14 +673,15 @@ def periodogram(X):
     Brockwell and Davis.
     """
     X = np.asarray(X)
-#    if kernel == "bartlett":
-#        w = 1 - np.arange(M+1.)/M   #JP removed integer division
+    #if kernel == "bartlett":
+    #    w = 1 - np.arange(M+1.)/M   #JP removed integer division
 
-    pergr = 1./len(X) * np.abs(np.fft.fft(X))**2
-    pergr[0] = 0. # what are the implications of this?
+    pergr = 1. / len(X) * np.abs(np.fft.fft(X))**2
+    pergr[0] = 0.  # what are the implications of this?
     return pergr
 
-#copied from nitime and scikits\statsmodels\sandbox\tsa\examples\try_ld_nitime.py
+
+#copied from nitime and statsmodels\sandbox\tsa\examples\try_ld_nitime.py
 #TODO: check what to return, for testing and trying out returns everything
 def levinson_durbin(s, nlags=10, isacov=False):
     '''Levinson-Durbin recursion for autoregressive processes
@@ -673,8 +695,8 @@ def levinson_durbin(s, nlags=10, isacov=False):
         largest lag to include in recursion or order of the autoregressive
         process
     isacov : boolean
-        flag to indicate whether the first argument, s, contains the autocovariances
-        or the data series.
+        flag to indicate whether the first argument, s, contains the
+        autocovariances or the data series.
 
     Returns
     -------
@@ -695,44 +717,44 @@ def levinson_durbin(s, nlags=10, isacov=False):
     This function returns currently all results, but maybe we drop sigma and
     phi from the returns.
 
-    If this function is called with the time series (isacov=False), then the sample
-    autocovariance function is calculated with the default options (biased, no fft).
-
+    If this function is called with the time series (isacov=False), then the
+    sample autocovariance function is calculated with the default options
+    (biased, no fft).
     '''
     s = np.asarray(s)
-    order = nlags  #rename compared to nitime
+    order = nlags  # rename compared to nitime
     #from nitime
 
-##    if sxx is not None and type(sxx) == np.ndarray:
-##        sxx_m = sxx[:order+1]
-##    else:
-##        sxx_m = ut.autocov(s)[:order+1]
+    ##if sxx is not None and type(sxx) == np.ndarray:
+    ##    sxx_m = sxx[:order+1]
+    ##else:
+    ##    sxx_m = ut.autocov(s)[:order+1]
     if isacov:
         sxx_m = s
     else:
-        sxx_m = acovf(s)[:order+1]  #not tested
+        sxx_m = acovf(s)[:order + 1]  # not tested
 
-    phi = np.zeros((order+1, order+1), 'd')
-    sig = np.zeros(order+1)
+    phi = np.zeros((order + 1, order + 1), 'd')
+    sig = np.zeros(order + 1)
     # initial points for the recursion
-    phi[1,1] = sxx_m[1]/sxx_m[0]
-    sig[1] = sxx_m[0] - phi[1,1]*sxx_m[1]
-    for k in xrange(2,order+1):
-        phi[k,k] = (sxx_m[k] - np.dot(phi[1:k,k-1], sxx_m[1:k][::-1]))/sig[k-1]
-        for j in xrange(1,k):
-            phi[j,k] = phi[j,k-1] - phi[k,k]*phi[k-j,k-1]
-        sig[k] = sig[k-1]*(1 - phi[k,k]**2)
+    phi[1, 1] = sxx_m[1] / sxx_m[0]
+    sig[1] = sxx_m[0] - phi[1, 1] * sxx_m[1]
+    for k in range(2, order + 1):
+        phi[k, k] = (sxx_m[k] - np.dot(phi[1:k, k-1],
+                                       sxx_m[1:k][::-1])) / sig[k-1]
+        for j in range(1, k):
+            phi[j, k] = phi[j, k-1] - phi[k, k] * phi[k-j, k-1]
+        sig[k] = sig[k-1] * (1 - phi[k, k]**2)
 
     sigma_v = sig[-1]
-    arcoefs = phi[1:,-1]
+    arcoefs = phi[1:, -1]
     pacf_ = np.diag(phi).copy()
     pacf_[0] = 1.
-    return sigma_v, arcoefs, pacf_, sig, phi  #return everything
-
+    return sigma_v, arcoefs, pacf_, sig, phi  # return everything
 
 
 def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
-    '''four tests for granger non causality of 2 timeseries
+    """four tests for granger non causality of 2 timeseries
 
     all four tests give similar results
     `params_ftest` and `ssr_ftest` are equivalent based on F test which is
@@ -782,70 +804,82 @@ def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
     http://en.wikipedia.org/wiki/Granger_causality
     Greene: Econometric Analysis
 
-    '''
-    from scipy import stats # lazy import
+    """
+    from scipy import stats
 
     x = np.asarray(x)
 
+    if x.shape[0] <= 3 * maxlag + int(addconst):
+        raise ValueError("Insufficient observations. Maximum allowable "
+                         "lag is {0}".format(int((x.shape[0] - int(addconst)) /
+                                                 3) - 1))
+
     resli = {}
 
-    for mlg in range(1, maxlag+1):
+    for mlg in range(1, maxlag + 1):
         result = {}
         if verbose:
-            print '\nGranger Causality'
-            print 'number of lags (no zero)', mlg
-        mxlg = mlg #+ 1 # Note number of lags starting at zero in lagmat
+            print('\nGranger Causality')
+            print('number of lags (no zero)', mlg)
+        mxlg = mlg
 
         # create lagmat of both time series
         dta = lagmat2ds(x, mxlg, trim='both', dropex=1)
 
         #add constant
         if addconst:
-            dtaown = add_constant(dta[:,1:mxlg+1], prepend=False)
-            dtajoint = add_constant(dta[:,1:], prepend=False)
+            dtaown = add_constant(dta[:, 1:(mxlg + 1)], prepend=False)
+            dtajoint = add_constant(dta[:, 1:], prepend=False)
         else:
-            raise ValueError('Not Implemented')
-            dtaown = dta[:,1:mxlg]
-            dtajoint = dta[:,1:]
+            raise NotImplementedError('Not Implemented')
+            #dtaown = dta[:, 1:mxlg]
+            #dtajoint = dta[:, 1:]
 
-        #run ols on both models without and with lags of second variable
-        res2down = OLS(dta[:,0], dtaown).fit()
-        res2djoint = OLS(dta[:,0], dtajoint).fit()
+        # Run ols on both models without and with lags of second variable
+        res2down = OLS(dta[:, 0], dtaown).fit()
+        res2djoint = OLS(dta[:, 0], dtajoint).fit()
 
         #print results
-        #for ssr based tests see: http://support.sas.com/rnd/app/examples/ets/granger/index.htm
+        #for ssr based tests see:
+        #http://support.sas.com/rnd/app/examples/ets/granger/index.htm
         #the other tests are made-up
 
         # Granger Causality test using ssr (F statistic)
-        fgc1 = (res2down.ssr-res2djoint.ssr)/res2djoint.ssr/(mxlg)*res2djoint.df_resid
+        fgc1 = ((res2down.ssr - res2djoint.ssr) /
+                res2djoint.ssr / mxlg * res2djoint.df_resid)
         if verbose:
-            print 'ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d, df_num=%d' % \
-              (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg)
-        result['ssr_ftest'] = (fgc1, stats.f.sf(fgc1, mxlg, res2djoint.df_resid), res2djoint.df_resid, mxlg)
+            print('ssr based F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
+                   ' df_num=%d' % (fgc1,
+                                    stats.f.sf(fgc1, mxlg,
+                                               res2djoint.df_resid),
+                                    res2djoint.df_resid, mxlg))
+        result['ssr_ftest'] = (fgc1,
+                               stats.f.sf(fgc1, mxlg, res2djoint.df_resid),
+                               res2djoint.df_resid, mxlg)
 
         # Granger Causality test using ssr (ch2 statistic)
-        fgc2 = res2down.nobs*(res2down.ssr-res2djoint.ssr)/res2djoint.ssr
+        fgc2 = res2down.nobs * (res2down.ssr - res2djoint.ssr) / res2djoint.ssr
         if verbose:
-            print 'ssr based chi2 test:   chi2=%-8.4f, p=%-8.4f, df=%d' %  \
-              (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)
+            print('ssr based chi2 test:   chi2=%-8.4f, p=%-8.4f, '
+                   'df=%d' % (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg))
         result['ssr_chi2test'] = (fgc2, stats.chi2.sf(fgc2, mxlg), mxlg)
 
         #likelihood ratio test pvalue:
-        lr = -2*(res2down.llf-res2djoint.llf)
+        lr = -2 * (res2down.llf - res2djoint.llf)
         if verbose:
-            print 'likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %  \
-              (lr, stats.chi2.sf(lr, mxlg), mxlg)
+            print('likelihood ratio test: chi2=%-8.4f, p=%-8.4f, df=%d' %
+                   (lr, stats.chi2.sf(lr, mxlg), mxlg))
         result['lrtest'] = (lr, stats.chi2.sf(lr, mxlg), mxlg)
 
         # F test that all lag coefficients of exog are zero
-        rconstr = np.column_stack((np.zeros((mxlg-1,mxlg-1)), np.eye(mxlg-1, mxlg-1),\
-                                   np.zeros((mxlg-1, 1))))
-        rconstr = np.column_stack((np.zeros((mxlg,mxlg)), np.eye(mxlg, mxlg),\
+        rconstr = np.column_stack((np.zeros((mxlg, mxlg)),
+                                   np.eye(mxlg, mxlg),
                                    np.zeros((mxlg, 1))))
         ftres = res2djoint.f_test(rconstr)
         if verbose:
-            print 'parameter F test:         F=%-8.4f, p=%-8.4f, df_denom=%d, df_num=%d' % \
-              (ftres.fvalue, ftres.pvalue, ftres.df_denom, ftres.df_num)
+            print('parameter F test:         F=%-8.4f, p=%-8.4f, df_denom=%d,'
+                   ' df_num=%d' % (ftres.fvalue, ftres.pvalue, ftres.df_denom,
+                                    ftres.df_num))
         result['params_ftest'] = (np.squeeze(ftres.fvalue)[()],
                                   np.squeeze(ftres.pvalue)[()],
                                   ftres.df_denom, ftres.df_num)
@@ -854,6 +888,7 @@ def grangercausalitytests(x, maxlag, addconst=True, verbose=True):
 
     return resli
 
+
 def coint(y1, y2, regression="c"):
     """
     This is a simple cointegration test. Uses unit-root test on residuals to
@@ -878,7 +913,8 @@ def coint(y1, y2, regression="c"):
     pvalue : float
         MacKinnon's approximate p-value based on MacKinnon (1994)
     crit_value : dict
-        Critical values for the test statistic at the 1 %, 5 %, and 10 % levels.
+        Critical values for the test statistic at the 1 %, 5 %, and 10 %
+        levels.
 
     Notes
     -----
@@ -898,44 +934,165 @@ def coint(y1, y2, regression="c"):
 
     """
     regression = regression.lower()
-    if regression not in ['c','nc','ct','ctt']:
+    if regression not in ['c', 'nc', 'ct', 'ctt']:
         raise ValueError("regression option %s not understood") % regression
     y1 = np.asarray(y1)
     y2 = np.asarray(y2)
     if regression == 'c':
         y2 = add_constant(y2, prepend=False)
-    st1_resid = OLS(y1, y2).fit().resid #stage one residuals
+    st1_resid = OLS(y1, y2).fit().resid  # stage one residuals
     lgresid_cons = add_constant(st1_resid[0:-1], prepend=False)
     uroot_reg = OLS(st1_resid[1:], lgresid_cons).fit()
-    coint_t = (uroot_reg.params[0]-1)/uroot_reg.bse[0]
+    coint_t = (uroot_reg.params[0] - 1) / uroot_reg.bse[0]
     pvalue = mackinnonp(coint_t, regression="c", N=2, lags=None)
     crit_value = mackinnoncrit(N=1, regression="c", nobs=len(y1))
     return coint_t, pvalue, crit_value
 
-__all__ = ['acovf', 'acf', 'pacf', 'pacf_yw', 'pacf_ols', 'ccovf', 'ccf',
-           'periodogram', 'q_stat', 'coint']
 
-if __name__=="__main__":
+def _safe_arma_fit(y, order, model_kw, trend, fit_kw, start_params=None):
+    try:
+        return ARMA(y, order=order, **model_kw).fit(disp=0, trend=trend,
+                                                    start_params=start_params,
+                                                    **fit_kw)
+    except LinAlgError:
+        # SVD convergence failure on badly misspecified models
+        return
+
+    except ValueError as error:
+        if start_params is not None:  # don't recurse again
+            # user supplied start_params only get one chance
+            return
+        # try a little harder, should be handled in fit really
+        elif ((hasattr(error, 'message') and 'initial' not in error.message)
+              or 'initial' in str(error)):  # py2 and py3
+            start_params = [.1] * sum(order)
+            if trend == 'c':
+                start_params = [.1] + start_params
+            return _safe_arma_fit(y, order, model_kw, trend, fit_kw,
+                                  start_params)
+        else:
+            return
+    except:  # no idea what happened
+        return
+
+
+def arma_order_select_ic(y, max_ar=4, max_ma=2, ic='bic', trend='c',
+                         model_kw={}, fit_kw={}):
+    """
+    Returns information criteria for many ARMA models
+
+    Parameters
+    ----------
+    y : array-like
+        Time-series data
+    max_ar : int
+        Maximum number of AR lags to use. Default 4.
+    max_ma : int
+        Maximum number of MA lags to use. Default 2.
+    ic : str, list
+        Information criteria to report. Either a single string or a list
+        of different criteria is possible.
+    trend : str
+        The trend to use when fitting the ARMA models.
+    model_kw : dict
+        Keyword arguments to be passed to the ``ARMA`` model
+    fit_kw : dict
+        Keyword arguments to be passed to ``ARMA.fit``.
+
+    Returns
+    -------
+    obj : Results object
+        Each ic is an attribute with a DataFrame for the results. The AR order
+        used is the row index. The ma order used is the column index. The
+        minimum orders are available as ``ic_min_order``.
+
+    Examples
+    --------
+
+    >>> from statsmodels.tsa.arima_process import arma_generate_sample
+    >>> import statsmodels.api as sm
+    >>> import numpy as np
+
+    >>> arparams = np.array([.75, -.25])
+    >>> maparams = np.array([.65, .35])
+    >>> arparams = np.r_[1, -arparams]
+    >>> maparam = np.r_[1, maparams]
+    >>> nobs = 250
+    >>> np.random.seed(2014)
+    >>> y = arma_generate_sample(arparams, maparams, nobs)
+    >>> res = sm.tsa.arma_order_select_ic(y, ic=['aic', 'bic'], trend='nc')
+    >>> res.aic_min_order
+    >>> res.bic_min_order
+
+    Notes
+    -----
+    This method can be used to tentatively identify the order of an ARMA
+    process, provided that the time series is stationary and invertible. This
+    function computes the full exact MLE estimate of each model and can be,
+    therefore a little slow. An implementation using approximate estimates
+    will be provided in the future. In the meantime, consider passing
+    {method : 'css'} to fit_kw.
+    """
+    from pandas import DataFrame
+
+    ar_range = lrange(0, max_ar + 1)
+    ma_range = lrange(0, max_ma + 1)
+    if isinstance(ic, string_types):
+        ic = [ic]
+    elif not isinstance(ic, (list, tuple)):
+        raise ValueError("Need a list or a tuple for ic if not a string.")
+
+    results = np.zeros((len(ic), max_ar + 1, max_ma + 1))
+
+    for ar in ar_range:
+        for ma in ma_range:
+            if ar == 0 and ma == 0 and trend == 'nc':
+                results[:, ar, ma] = np.nan
+                continue
+
+            mod = _safe_arma_fit(y, (ar, ma), model_kw, trend, fit_kw)
+            if mod is None:
+                results[:, ar, ma] = np.nan
+                continue
+
+            for i, criteria in enumerate(ic):
+                results[i, ar, ma] = getattr(mod, criteria)
+
+    dfs = [DataFrame(res, columns=ma_range, index=ar_range) for res in results]
+
+    res = dict(zip(ic, dfs))
+
+    # add the minimums to the results dict
+    min_res = {}
+    for i, result in iteritems(res):
+        mins = np.where(result.min().min() == result)
+        min_res.update({i + '_min_order' : (mins[0][0], mins[1][0])})
+    res.update(min_res)
+
+    return Bunch(**res)
+
+
+if __name__ == "__main__":
     import statsmodels.api as sm
     data = sm.datasets.macrodata.load().data
     x = data['realgdp']
 # adf is tested now.
-    adf = adfuller(x,4, autolag=None)
+    adf = adfuller(x, 4, autolag=None)
     adfbic = adfuller(x, autolag="bic")
     adfaic = adfuller(x, autolag="aic")
     adftstat = adfuller(x, autolag="t-stat")
 
 # acf is tested now
-    acf1,ci1,Q,pvalue = acf(x, nlags=40, confint=95, qstat=True)
-    acf2, ci2,Q2,pvalue2 = acf(x, nlags=40, confint=95, fft=True, qstat=True)
-    acf3,ci3,Q3,pvalue3 = acf(x, nlags=40, confint=95, qstat=True, unbiased=True)
-    acf4, ci4,Q4,pvalue4 = acf(x, nlags=40, confint=95, fft=True, qstat=True,
-            unbiased=True)
+    acf1, ci1, Q, pvalue = acf(x, nlags=40, confint=95, qstat=True)
+    acf2, ci2, Q2, pvalue2 = acf(x, nlags=40, confint=95, fft=True, qstat=True)
+    acf3, ci3, Q3, pvalue3 = acf(x, nlags=40, confint=95, qstat=True,
+                                 unbiased=True)
+    acf4, ci4, Q4, pvalue4 = acf(x, nlags=40, confint=95, fft=True, qstat=True,
+                                 unbiased=True)
 
 # pacf is tested now
 #    pacf1 = pacorr(x)
 #    pacfols = pacf_ols(x, nlags=40)
 #    pacfyw = pacf_yw(x, nlags=40, method="mle")
-    y = np.random.normal(size=(100,2))
-    grangercausalitytests(y,2)
-
+    y = np.random.normal(size=(100, 2))
+    grangercausalitytests(y, 2)
diff --git a/statsmodels/tsa/tests/results/arma_forecast.inp b/statsmodels/tsa/tests/results/arma_forecast.inp
index 2d0ca17cb00..27dc74ccb9d 100644
--- a/statsmodels/tsa/tests/results/arma_forecast.inp
+++ b/statsmodels/tsa/tests/results/arma_forecast.inp
@@ -1,5 +1,5 @@
 open \
-  /home/skipper/statsmodels/statsmodels-git/scikits/statsmodels/tsa/tests/results/y_arma_data.csv
+  /home/skipper/statsmodels/statsmodels/statsmodels/tsa/tests/results/y_arma_data.csv
 setobs 12 1980:01 --time-series
 dataset addobs 10
 
diff --git a/statsmodels/tsa/tests/results/results_arima.py b/statsmodels/tsa/tests/results/results_arima.py
index 2e43c429621..7d07a4b26c3 100644
--- a/statsmodels/tsa/tests/results/results_arima.py
+++ b/statsmodels/tsa/tests/results/results_arima.py
@@ -23,7 +23,7 @@ def __init__(self, method="mle"):
         self.k_ma = 1
         if method == "mle":
             # from stata
-            from arima111_results import results
+            from .arima111_results import results
 
             # unpack stata results
             self.__dict__.update(results)
@@ -189,7 +189,7 @@ class ARIMA211(object):
     def __init__(self, method="mle"):
         if method == 'mle':
             # from stata
-            from arima111_results import results
+            from .arima111_results import results
 
             self.__dict__.update(results)
             self.resid = self.resid[1:]
@@ -225,7 +225,7 @@ def __init__(self, method="mle"):
             self.forecast_dyn = forecast_results['fc211cdyn'][-25:]
             self.forecasterr_dyn = forecast_results['fc211cdynse'][-25:]
         else:
-            from arima211_css_results import results
+            from .arima211_css_results import results
 
             self.__dict__.update(results)
             self.resid = self.resid[1:]
@@ -269,7 +269,7 @@ def __init__(self, method="mle"):
         self.k_exog = 1
         self.k_diff = 1
         if method == "mle":
-            from arima112_results import results
+            from .arima112_results import results
             # from gretl
             self.arroots = [1.0324 + 0j]
             self.maroots = [1.1447 + 0j, -4.8613+0j]
diff --git a/statsmodels/tsa/tests/test_adfuller_lag.py b/statsmodels/tsa/tests/test_adfuller_lag.py
index 9b77633e1d6..8b5ee8e4a1f 100644
--- a/statsmodels/tsa/tests/test_adfuller_lag.py
+++ b/statsmodels/tsa/tests/test_adfuller_lag.py
@@ -4,7 +4,7 @@
 Created on Wed May 30 21:39:46 2012
 Author: Josef Perktold
 """
-
+from statsmodels.compat.python import iteritems
 import numpy as np
 from numpy.testing import assert_equal, assert_almost_equal
 import statsmodels.tsa.stattools as tsast
@@ -26,7 +26,7 @@ def test_adf_autolag():
         st2 = adf3[-1]
 
         assert_equal(len(st2.autolag_results), 15 + 1)  #+1 for lagged level
-        for l, res in sorted(st2.autolag_results.iteritems())[:5]:
+        for l, res in sorted(iteritems(st2.autolag_results))[:5]:
             lag = l-k_trend
             #assert correct design matrices in _autolag
             assert_equal(res.model.exog[-10:,k_trend], x[-11:-1])
diff --git a/statsmodels/tsa/tests/test_ar.py b/statsmodels/tsa/tests/test_ar.py
index c49fd6be42c..ae43ad39fd8 100644
--- a/statsmodels/tsa/tests/test_ar.py
+++ b/statsmodels/tsa/tests/test_ar.py
@@ -2,10 +2,11 @@
 Test AR Model
 """
 import statsmodels.api as sm
+from statsmodels.compat.python import range
 from statsmodels.tsa.ar_model import AR
 from numpy.testing import (assert_almost_equal, assert_equal, assert_allclose,
                            assert_)
-from results import results_ar
+from .results import results_ar
 import numpy as np
 import numpy.testing as npt
 from pandas import Series, Index
@@ -31,7 +32,7 @@ def test_fpe(self):
         assert_almost_equal(self.res1.fpe, self.res2.fpe, DECIMAL_6)
 
     def test_pickle(self):
-        from statsmodels.compatnp.py3k import BytesIO
+        from statsmodels.compat.python import BytesIO
         fh = BytesIO()
         #test wrapped results load save pickle
         self.res1.save(fh)
@@ -113,6 +114,8 @@ def test_predict(self):
                 self.res2.FVOLSn15start312, DECIMAL_4)
 
         #class TestARMLEConstant(CheckAR):
+
+
 class TestARMLEConstant(object):
     @classmethod
     def setupClass(cls):
@@ -122,7 +125,12 @@ def setupClass(cls):
 
     def test_predict(self):
         model = self.res1.model
-        params = self.res1.params
+        # for some reason convergence is off in 1 out of 10 runs on
+        # some platforms. i've never been able to replicate. see #910
+        params = np.array([ 5.66817602,  1.16071069, -0.39538222,
+                           -0.16634055,  0.15044614, -0.09439266,
+                           0.00906289,  0.05205291, -0.08584362,
+                           0.25239198])
         assert_almost_equal(model.predict(params), self.res2.FVMLEdefault,
                 DECIMAL_4)
         assert_almost_equal(model.predict(params, start=9, end=308),
@@ -148,6 +156,12 @@ def test_predict(self):
                 self.res2.FVMLEstart2end7, DECIMAL_4)
 
     def test_dynamic_predict(self):
+        # for some reason convergence is off in 1 out of 10 runs on
+        # some platforms. i've never been able to replicate. see #910
+        params = np.array([ 5.66817602,  1.16071069, -0.39538222,
+                           -0.16634055,  0.15044614, -0.09439266,
+                           0.00906289,  0.05205291, -0.08584362,
+                           0.25239198])
         res1 = self.res1
         res2 = self.res2
 
@@ -156,52 +170,52 @@ def test_dynamic_predict(self):
 
         # 9, 51
         start, end = 9, 51
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol)
 
         # 9, 308
         start, end = 9, 308
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol)
 
         # 9, 333
         start, end = 9, 333
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn[start:end+1], rtol=rtol)
 
         # 100, 151
         start, end = 100, 151
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol)
 
         # 100, 308
         start, end = 100, 308
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol)
 
         # 100, 333
         start, end = 100, 333
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn2[start:end+1], rtol=rtol)
 
         # 308, 308
         start, end = 308, 308
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn3[start:end+1], rtol=rtol)
 
         # 308, 333
         start, end = 308, 333
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn3[start:end+1], rtol=rtol)
 
         # 309, 333
         start, end = 309, 333
-        fv = res1.predict(start, end, dynamic=True)
+        fv = res1.model.predict(params, start, end, dynamic=True)
         assert_allclose(fv, res2.fcdyn4[start:end+1], rtol=rtol)
 
         # None, None
         start, end = None, None
-        fv = res1.predict(dynamic=True)
+        fv = res1.model.predict(params, dynamic=True)
         assert_allclose(fv, res2.fcdyn[9:309], rtol=rtol)
 
 
@@ -219,7 +233,6 @@ def setupClass(cls):
             k_ar = r.k_ar
             k_trend = r.k_trend
             log_sigma2 = np.log(r.sigma2)
-            #import ipdb; ipdb.set_trace()
             aic = r.aic
             aic = (aic - log_sigma2) * (1 + k_ar)/(1 + k_ar + k_trend)
             aic += log_sigma2
@@ -251,11 +264,9 @@ def test_ar_dates():
     ar_model = sm.tsa.AR(endog, freq='A').fit(maxlag=9, method='mle', disp=-1)
     pred = ar_model.predict(start='2005', end='2015')
     predict_dates = sm.tsa.datetools.dates_from_range('2005', '2015')
-    try:
-        from pandas import DatetimeIndex  # pylint: disable-msg=E0611
-        predict_dates = DatetimeIndex(predict_dates, freq='infer')
-    except ImportError:
-        pass
+    from pandas import DatetimeIndex  # pylint: disable-msg=E0611
+    predict_dates = DatetimeIndex(predict_dates, freq='infer')
+
     assert_equal(ar_model.data.predict_dates, predict_dates)
     assert_equal(pred.index, predict_dates)
 
diff --git a/statsmodels/tsa/tests/test_arima.py b/statsmodels/tsa/tests/test_arima.py
index ceeed2af294..a1a8eb33c10 100644
--- a/statsmodels/tsa/tests/test_arima.py
+++ b/statsmodels/tsa/tests/test_arima.py
@@ -1,16 +1,20 @@
+from statsmodels.compat.python import lrange, BytesIO
 import numpy as np
+from nose.tools import nottest
 from numpy.testing import (assert_almost_equal, assert_equal, assert_,
-                           assert_raises, dec)
+                           assert_raises, dec, TestCase)
 import statsmodels.sandbox.tsa.fftarma as fa
 from statsmodels.tsa.descriptivestats import TsaDescriptive
 from statsmodels.tsa.arma_mle import Arma
 from statsmodels.tsa.arima_model import ARMA, ARIMA
+from statsmodels.regression.linear_model import OLS
 from statsmodels.tsa.base.datetools import dates_from_range
-from results import results_arma, results_arima
+from .results import results_arma, results_arima
 import os
 from statsmodels.tsa.base import datetools
 from statsmodels.tsa.arima_process import arma_generate_sample
 import pandas
+from pandas.util.testing import assert_produces_warning
 try:
     from statsmodels.tsa.kalmanf import kalman_loglike
     fast_kalman = 1
@@ -34,15 +38,11 @@
 sun_dates = dates_from_range('1700', '2008')
 sun_predict_dates = dates_from_range('2008', '2033')
 
-try:
-    from pandas import DatetimeIndex  # pylint: disable-msg=E0611
-    cpi_dates = DatetimeIndex(cpi_dates, freq='infer')
-    sun_dates = DatetimeIndex(sun_dates, freq='infer')
-    cpi_predict_dates = DatetimeIndex(cpi_predict_dates, freq='infer')
-    sun_predict_dates = DatetimeIndex(sun_predict_dates, freq='infer')
-except ImportError:
-    pass
-
+from pandas import DatetimeIndex  # pylint: disable-msg=E0611
+cpi_dates = DatetimeIndex(cpi_dates, freq='infer')
+sun_dates = DatetimeIndex(sun_dates, freq='infer')
+cpi_predict_dates = DatetimeIndex(cpi_predict_dates, freq='infer')
+sun_predict_dates = DatetimeIndex(sun_predict_dates, freq='infer')
 
 
 def test_compare_arma():
@@ -200,7 +200,6 @@ def setupClass(cls):
         cls.res2 = results_arma.Y_arma11()
 
     def test_pickle(self):
-        from statsmodels.compatnp.py3k import BytesIO
         fh = BytesIO()
         #test wrapped results load save pickle
         self.res1.save(fh)
@@ -217,8 +216,7 @@ def setupClass(cls):
         cls.res2 = results_arma.Y_arma14()
 
 #NOTE: Ok
-#can't use class decorators in 2.5....
-#@dec.slow
+@dec.slow
 class Test_Y_ARMA41_NoConst(CheckArmaResultsMixin, CheckForecastMixin):
     @classmethod
     def setupClass(cls):
@@ -265,7 +263,6 @@ def setupClass(cls):
                 confint) = cls.res1.forecast(10)
         cls.res2 = results_arma.Y_arma11c()
 
-#NOTE: OK
 class Test_Y_ARMA14_Const(CheckArmaResultsMixin):
     @classmethod
     def setupClass(cls):
@@ -273,8 +270,7 @@ def setupClass(cls):
         cls.res1 = ARMA(endog, order=(1,4)).fit(trend="c", disp=-1)
         cls.res2 = results_arma.Y_arma14c()
 
-#NOTE: Ok
-#@dec.slow
+@dec.slow
 class Test_Y_ARMA41_Const(CheckArmaResultsMixin, CheckForecastMixin):
     @classmethod
     def setupClass(cls):
@@ -472,7 +468,7 @@ def test_reset_trend():
     res2 = mod.fit(trend="nc", disp=-1)
     assert_equal(len(res1.params), len(res2.params)+1)
 
-#@dec.slow
+@dec.slow
 def test_start_params_bug():
     data = np.array([1368., 1187, 1090, 1439, 2362, 2783, 2869, 2512, 1804,
     1544, 1028, 869, 1737, 2055, 1947, 1618, 1196, 867, 997, 1862, 2525,
@@ -1601,11 +1597,6 @@ def test_arima_predict_q2():
 
 def test_arima_predict_pandas_nofreq():
     # this is issue 712
-    try:
-        from pandas.tseries.api import infer_freq  # pylint: disable-msg=E0611, F0401
-    except ImportError:
-        import nose
-        raise nose.SkipTest
     from pandas import DataFrame
     dates = ["2010-01-04", "2010-01-05", "2010-01-06", "2010-01-07",
              "2010-01-08", "2010-01-11", "2010-01-12", "2010-01-11",
@@ -1635,10 +1626,10 @@ def test_arima_predict_pandas_nofreq():
     assert_(predict.index.equals(data.index[3:10+1]))
 
     predict = arma.predict(start="2010-1-7", end=14)
-    assert_(predict.index.equals(pandas.Index(range(3, 15))))
+    assert_(predict.index.equals(pandas.Index(lrange(3, 15))))
 
     predict = arma.predict(start=3, end=14)
-    assert_(predict.index.equals(pandas.Index(range(3, 15))))
+    assert_(predict.index.equals(pandas.Index(lrange(3, 15))))
 
     # end can be a date if it's in the sample and on the index
     # predict dates is just a slice of the dates index then
@@ -1657,7 +1648,7 @@ def test_arima_predict_exog():
     #y = arma_generate_sample(arparams, maparams, nobs, burnin=100)
 
     ## make an exogenous trend
-    #X = np.array(range(nobs)) / 20.0
+    #X = np.array(lrange(nobs)) / 20.0
     ## add a constant
     #y += 2.5
 
@@ -1714,7 +1705,7 @@ def test_arima_no_diff():
     ma = [1, .25, .9]
     y = arma_generate_sample(ar, ma, 100)
     mod = ARIMA(y, (3, 0, 2))
-    assert_(isinstance(mod, ARMA))
+    assert_(type(mod) is ARMA)
     res = mod.fit(disp=-1)
     # smoke test just to be sure
     res.predict()
@@ -1747,7 +1738,7 @@ def test_arimax():
 
     # 2 exog
     X = dta
-    res = ARIMA(y, (2, 1, 1), X).fit(disp=-1, solver="nm", maxiter=1000,
+    res = ARIMA(y, (2, 1, 1), X).fit(disp=False, solver="nm", maxiter=1000,
                 ftol=1e-12, xtol=1e-12)
 
     # from gretl
@@ -1764,7 +1755,7 @@ def test_arimax():
     assert_almost_equal(res.model.loglike(np.array(params)), stata_llf, 6)
 
     X = dta.diff()
-    res = ARIMA(y, (2, 1, 1), X).fit(disp=-1)
+    res = ARIMA(y, (2, 1, 1), X).fit(disp=False)
 
     # gretl won't estimate this - looks like maybe a bug on their part,
     # but we can just fine, we're close to Stata's answer
@@ -1889,9 +1880,98 @@ def test_small_data():
     # in start params regression.
     res = mod.fit(trend="nc", disp=0, start_params=[.1,.1,.1,.1])
     mod = ARIMA(y, (1, 0, 2))
-    res = mod.fit(disp=0, start_params=[.1, .1, .1, .1])
+    import warnings
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        res = mod.fit(disp=0, start_params=[np.mean(y), .1, .1, .1])
 
 
+class TestARMA00(TestCase):
+
+    @classmethod
+    def setup_class(cls):
+        from statsmodels.datasets.sunspots import load
+
+        sunspots = load().data['SUNACTIVITY']
+        cls.y = y = sunspots
+        cls.arma_00_model = ARMA(y, order=(0, 0))
+        cls.arma_00_res = cls.arma_00_model.fit(disp=-1)
+
+    def test_parameters(self):
+        params = self.arma_00_res.params
+        assert_almost_equal(self.y.mean(), params)
+
+    def test_predictions(self):
+        predictions = self.arma_00_res.predict()
+        assert_almost_equal(self.y.mean() * np.ones_like(predictions), predictions)
+
+    @nottest
+    def test_information_criteria(self):
+        # This test is invalid since the ICs differ due to df_model differences
+        # between OLS and ARIMA
+        res = self.arma_00_res
+        y = self.y
+        ols_res = OLS(y, np.ones_like(y)).fit(disp=-1)
+        ols_ic = np.array([ols_res.aic, ols_res.bic])
+        arma_ic = np.array([res.aic, res.bic])
+        assert_almost_equal(ols_ic, arma_ic, DECIMAL_4)
+
+    def test_arma_00_nc(self):
+        arma_00 = ARMA(self.y, order=(0, 0))
+        assert_raises(ValueError, arma_00.fit, trend='nc', disp=-1)
+
+    def test_css(self):
+        arma = ARMA(self.y, order=(0, 0))
+        fit = arma.fit(method='css', disp=-1)
+        predictions = fit.predict()
+        assert_almost_equal(self.y.mean() * np.ones_like(predictions), predictions)
+
+    def test_arima(self):
+        yi = np.cumsum(self.y)
+        arima = ARIMA(yi, order=(0, 1, 0))
+        fit = arima.fit(disp=-1)
+        assert_almost_equal(np.diff(yi).mean(), fit.params, DECIMAL_4)
+
+    def test_arma_ols(self):
+        y = self.y
+        y_lead = y[1:]
+        y_lag = y[:-1]
+        T = y_lag.shape[0]
+        X = np.hstack((np.ones((T,1)), y_lag[:,None]))
+        ols_res = OLS(y_lead, X).fit()
+        arma_res = ARMA(y_lead,order=(0,0),exog=y_lag).fit(trend='c', disp=-1)
+        assert_almost_equal(ols_res.params, arma_res.params)
+
+    def test_arma_exog_no_constant(self):
+        y = self.y
+        y_lead = y[1:]
+        y_lag = y[:-1]
+        X = y_lag[:,None]
+        ols_res = OLS(y_lead, X).fit()
+        arma_res = ARMA(y_lead,order=(0,0),exog=y_lag).fit(trend='nc', disp=-1)
+        assert_almost_equal(ols_res.params, arma_res.params)
+        pass
+
+
+def test_arima_dates_startatend():
+    # bug
+    np.random.seed(18)
+    x = pandas.TimeSeries(np.random.random(36),
+                          index=pandas.DatetimeIndex(start='1/1/1990',
+                                                     periods=36, freq='M'))
+    res = ARIMA(x, (1, 0, 0)).fit(disp=0)
+    pred = res.predict(start=len(x), end=len(x))
+    assert_(pred.index[0] == x.index.shift(1)[-1])
+    fc = res.forecast()[0]
+    assert_almost_equal(pred.values[0], fc)
+
+def test_arma_missing():
+    from statsmodels.base.data import MissingDataError
+    # bug 1343
+    y = np.random.random(40)
+    y[-1] = np.nan
+    assert_raises(MissingDataError, ARMA, y, (1, 0), missing='raise')
+
 if __name__ == "__main__":
     import nose
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb'], exit=False)
diff --git a/statsmodels/tsa/tests/test_arima_process.py b/statsmodels/tsa/tests/test_arima_process.py
index 4ee28d91e2d..881f1ba7c24 100644
--- a/statsmodels/tsa/tests/test_arima_process.py
+++ b/statsmodels/tsa/tests/test_arima_process.py
@@ -1,4 +1,5 @@
 
+from statsmodels.compat.python import range
 import numpy as np
 from numpy.testing import (assert_array_almost_equal, assert_almost_equal,
                            assert_equal)
@@ -8,7 +9,7 @@
                         arma_acf, arma_impulse_response, lpol_fiar, lpol_fima)
 from statsmodels.sandbox.tsa.fftarma import ArmaFft
 
-from results.results_process import armarep  #benchmarkdata
+from .results.results_process import armarep  #benchmarkdata
 
 arlist = [[1.],
           [1, -0.9],  #ma representation will need many terms to get high precision
diff --git a/statsmodels/tsa/tests/test_seasonal.py b/statsmodels/tsa/tests/test_seasonal.py
new file mode 100644
index 00000000000..bf646ccb30a
--- /dev/null
+++ b/statsmodels/tsa/tests/test_seasonal.py
@@ -0,0 +1,162 @@
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_equal, assert_raises
+from statsmodels.tsa.seasonal import seasonal_decompose
+from pandas import DataFrame, DatetimeIndex
+
+
+class TestDecompose:
+    @classmethod
+    def setupClass(cls):
+        # even
+        data = [-50, 175, 149, 214, 247, 237, 225, 329, 729, 809,
+                530, 489, 540, 457, 195, 176, 337, 239, 128, 102,
+                232, 429, 3, 98, 43, -141, -77, -13, 125, 361, -45, 184]
+        cls.data = DataFrame(data, DatetimeIndex(start='1/1/1951',
+                                                 periods=len(data),
+                                                 freq='Q'))
+
+    def test_ndarray(self):
+        res_add = seasonal_decompose(self.data.values, freq=4)
+        seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17,
+                    -88.38, -60.25, 62.46, 86.17, -88.38, -60.25,
+                     62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25]
+        trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25,
+                 103.00, 131.62, np.nan, np.nan]
+        random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371,
+                  -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299,
+                  -33.246, -27.746, 46.165, -57.924, 28.004, -36.746,
+                  -37.585, 151.826, -75.496, 86.254, -10.210, -194.049,
+                  48.129, 11.004, -40.460, 143.201, np.nan, np.nan]
+        assert_almost_equal(res_add.seasonal, seasonal, 2)
+        assert_almost_equal(res_add.trend, trend, 2)
+        assert_almost_equal(res_add.resid, random, 3)
+
+        res_mult = seasonal_decompose(np.abs(self.data.values), 'm', freq=4)
+
+        seasonal = [1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716,
+                    0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538,
+                    0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815,
+                    1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931,
+                    1.0815, 1.5538, 0.6716, 0.6931]
+        trend = [np.nan, np.nan, 171.62, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 107.25, 80.50, 79.12, 78.75, 116.50,
+                 140.00, 157.38, np.nan, np.nan]
+        random = [np.nan, np.nan, 1.29263, 1.51360, 1.03223, 0.62226,
+                  1.04771, 1.05139, 1.20124, 0.84080, 1.28182, 1.28752,
+                  1.08043, 0.77172, 0.91697, 0.96191, 1.36441, 0.72986,
+                  1.01171, 0.73956, 1.03566, 1.44556, 0.02677, 1.31843,
+                  0.49390, 1.14688, 1.45582, 0.16101, 0.82555, 1.47633,
+                  np.nan, np.nan]
+
+        assert_almost_equal(res_mult.seasonal, seasonal, 4)
+        assert_almost_equal(res_mult.trend, trend, 2)
+        assert_almost_equal(res_mult.resid, random, 4)
+
+        # test odd
+        res_add = seasonal_decompose(self.data.values[:-1], freq=4)
+        seasonal = [68.18, 69.02, -82.66, -54.54, 68.18, 69.02, -82.66,
+                    -54.54, 68.18, 69.02, -82.66, -54.54, 68.18, 69.02,
+                    -82.66, -54.54, 68.18, 69.02, -82.66, -54.54, 68.18,
+                    69.02, -82.66, -54.54, 68.18, 69.02, -82.66, -54.54,
+                    68.18, 69.02, -82.66]
+        trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25,
+                 103.00, np.nan, np.nan]
+        random = [np.nan, np.nan, 72.538, 64.538, -42.426, -77.150,
+                  -12.087, -67.962, 99.699, 120.725, -2.962, -4.462,
+                  9.699, 6.850, -38.962, -33.462, 40.449, -40.775, 22.288,
+                  -42.462, -43.301, 168.975, -81.212, 80.538, -15.926,
+                  -176.900, 42.413, 5.288, -46.176, np.nan, np.nan]
+        assert_almost_equal(res_add.seasonal, seasonal, 2)
+        assert_almost_equal(res_add.trend, trend, 2)
+        assert_almost_equal(res_add.resid, random, 3)
+
+    def test_pandas(self):
+        res_add = seasonal_decompose(self.data, freq=4)
+        seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17,
+                    -88.38, -60.25, 62.46, 86.17, -88.38, -60.25,
+                     62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25]
+        trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25,
+                 103.00, 131.62, np.nan, np.nan]
+        random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371,
+                  -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299,
+                  -33.246, -27.746, 46.165, -57.924, 28.004, -36.746,
+                  -37.585, 151.826, -75.496, 86.254, -10.210, -194.049,
+                  48.129, 11.004, -40.460, 143.201, np.nan, np.nan]
+        assert_almost_equal(res_add.seasonal.values.squeeze(), seasonal, 2)
+        assert_almost_equal(res_add.trend.values.squeeze(), trend, 2)
+        assert_almost_equal(res_add.resid.values.squeeze(), random, 3)
+        assert_equal(res_add.seasonal.index.values.squeeze(),
+                            self.data.index.values)
+
+        res_mult = seasonal_decompose(np.abs(self.data), 'm', freq=4)
+
+        seasonal = [1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716,
+                    0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815, 1.5538,
+                    0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931, 1.0815,
+                    1.5538, 0.6716, 0.6931, 1.0815, 1.5538, 0.6716, 0.6931,
+                    1.0815, 1.5538, 0.6716, 0.6931]
+        trend = [np.nan, np.nan, 171.62, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 107.25, 80.50, 79.12, 78.75, 116.50,
+                 140.00, 157.38, np.nan, np.nan]
+        random = [np.nan, np.nan, 1.29263, 1.51360, 1.03223, 0.62226,
+                  1.04771, 1.05139, 1.20124, 0.84080, 1.28182, 1.28752,
+                  1.08043, 0.77172, 0.91697, 0.96191, 1.36441, 0.72986,
+                  1.01171, 0.73956, 1.03566, 1.44556, 0.02677, 1.31843,
+                  0.49390, 1.14688, 1.45582, 0.16101, 0.82555, 1.47633,
+                  np.nan, np.nan]
+
+        assert_almost_equal(res_mult.seasonal.values.squeeze(), seasonal, 4)
+        assert_almost_equal(res_mult.trend.values.squeeze(), trend, 2)
+        assert_almost_equal(res_mult.resid.values.squeeze(), random, 4)
+        assert_equal(res_mult.seasonal.index.values.squeeze(),
+                            self.data.index.values)
+
+
+    def test_filt(self):
+        filt = np.array([1/8., 1/4., 1./4, 1/4., 1/8.])
+        res_add = seasonal_decompose(self.data.values, filt=filt, freq=4)
+        seasonal = [62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25, 62.46, 86.17,
+                    -88.38, -60.25, 62.46, 86.17, -88.38, -60.25,
+                     62.46, 86.17, -88.38, -60.25, 62.46, 86.17, -88.38,
+                    -60.25, 62.46, 86.17, -88.38, -60.25]
+        trend = [np.nan, np.nan, 159.12, 204.00, 221.25, 245.12, 319.75,
+                 451.50, 561.12, 619.25, 615.62, 548.00, 462.12, 381.12,
+                 316.62, 264.00, 228.38, 210.75, 188.38, 199.00, 207.12,
+                 191.00, 166.88, 72.00, -9.25, -33.12, -36.75, 36.25,
+                 103.00, 131.62, np.nan, np.nan]
+        random = [np.nan, np.nan, 78.254, 70.254, -36.710, -94.299, -6.371,
+                  -62.246, 105.415, 103.576, 2.754, 1.254, 15.415, -10.299,
+                  -33.246, -27.746, 46.165, -57.924, 28.004, -36.746,
+                  -37.585, 151.826, -75.496, 86.254, -10.210, -194.049,
+                  48.129, 11.004, -40.460, 143.201, np.nan, np.nan]
+        assert_almost_equal(res_add.seasonal, seasonal, 2)
+        assert_almost_equal(res_add.trend, trend, 2)
+        assert_almost_equal(res_add.resid, random, 3)
+
+    def test_raises(self):
+        assert_raises(ValueError, seasonal_decompose, self.data.values)
+        assert_raises(ValueError, seasonal_decompose, self.data, 'm',
+                      freq=4)
+        x = self.data.astype(float).copy()
+        x.ix[2] = np.nan
+        assert_raises(ValueError, seasonal_decompose, x)
+
+
diff --git a/statsmodels/tsa/tests/test_stattools.py b/statsmodels/tsa/tests/test_stattools.py
index 6619ca3119e..4fc6e6333fc 100644
--- a/statsmodels/tsa/tests/test_stattools.py
+++ b/statsmodels/tsa/tests/test_stattools.py
@@ -1,12 +1,15 @@
+from statsmodels.compat.python import lrange
 from statsmodels.tsa.stattools import (adfuller, acf, pacf_ols, pacf_yw,
                                                pacf, grangercausalitytests,
-                                               coint, acovf)
+                                               coint, acovf,
+                                               arma_order_select_ic)
 from statsmodels.tsa.base.datetools import dates_from_range
 import numpy as np
-from numpy.testing import assert_almost_equal, assert_equal, assert_raises
+from numpy.testing import (assert_almost_equal, assert_equal, assert_raises,
+                           dec, assert_)
 from numpy import genfromtxt#, concatenate
 from statsmodels.datasets import macrodata, sunspots
-from pandas import Series, Index
+from pandas import Series, Index, DataFrame
 import os
 
 
@@ -214,24 +217,34 @@ def __init__(self):
         self.teststat = -1.8208817
 
 
-def test_grangercausality():
-    # some example data
-    mdata = macrodata.load().data
-    mdata = mdata[['realgdp','realcons']]
-    data = mdata.view((float,2))
-    data = np.diff(np.log(data), axis=0)
+class TestGrangerCausality(object):
+
+    def test_grangercausality(self):
+        # some example data
+        mdata = macrodata.load().data
+        mdata = mdata[['realgdp', 'realcons']]
+        data = mdata.view((float, 2))
+        data = np.diff(np.log(data), axis=0)
+
+        #R: lmtest:grangertest
+        r_result = [0.243097, 0.7844328, 195, 2]  # f_test
+        gr = grangercausalitytests(data[:, 1::-1], 2, verbose=False)
+        assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7)
+        assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'], decimal=7)
+
+    def test_granger_fails_on_nobs_check(self):
+        # Test that if maxlag is too large, Granger Test raises a clear error.
+        X = np.random.rand(10, 2)
+        grangercausalitytests(X, 2, verbose=False)  # This should pass.
+        assert_raises(ValueError, grangercausalitytests, X, 3, verbose=False)
+
 
-    #R: lmtest:grangertest
-    r_result = [0.243097, 0.7844328, 195, 2]  #f_test
-    gr = grangercausalitytests(data[:,1::-1], 2, verbose=False)
-    assert_almost_equal(r_result, gr[2][0]['ssr_ftest'], decimal=7)
-    assert_almost_equal(gr[2][0]['params_ftest'], gr[2][0]['ssr_ftest'],
-                        decimal=7)
 
 def test_pandasacovf():
-    s = Series(range(1, 11))
+    s = Series(lrange(1, 11))
     assert_almost_equal(acovf(s), acovf(s.values))
 
+
 def test_acovf2d():
     dta = sunspots.load_pandas().data
     dta.index = Index(dates_from_range('1700', '2008'))
@@ -241,6 +254,74 @@ def test_acovf2d():
     X = np.random.random((10,2))
     assert_raises(ValueError, acovf, X)
 
+def test_acovf_fft_vs_convolution():
+    np.random.seed(1)
+    q = np.random.normal(size=100)
+
+    for demean in [True, False]:
+        for unbiased in [True, False]:
+            F1 = acovf(q, demean=demean, unbiased=unbiased, fft=True)
+            F2 = acovf(q, demean=demean, unbiased=unbiased, fft=False)
+            assert_almost_equal(F1, F2, decimal=7)
+
+@dec.slow
+def test_arma_order_select_ic():
+    # smoke test, assumes info-criteria are right
+    from statsmodels.tsa.arima_process import arma_generate_sample
+    import statsmodels.api as sm
+
+    arparams = np.array([.75, -.25])
+    maparams = np.array([.65, .35])
+    arparams = np.r_[1, -arparams]
+    maparam = np.r_[1, maparams]
+    nobs = 250
+    np.random.seed(2014)
+    y = arma_generate_sample(arparams, maparams, nobs)
+    res = arma_order_select_ic(y, ic=['aic', 'bic'], trend='nc')
+    # regression tests in case we change algorithm to minic in sas
+    aic_x = np.array([[       np.nan,  552.7342255 ,  484.29687843],
+                      [ 562.10924262,  485.5197969 ,  480.32858497],
+                      [ 507.04581344,  482.91065829,  481.91926034],
+                      [ 484.03995962,  482.14868032,  483.86378955],
+                      [ 481.8849479 ,  483.8377379 ,  485.83756612]])
+    bic_x = np.array([[       np.nan,  559.77714733,  494.86126118],
+                      [ 569.15216446,  496.08417966,  494.41442864],
+                      [ 517.61019619,  496.99650196,  499.52656493],
+                      [ 498.12580329,  499.75598491,  504.99255506],
+                      [ 499.49225249,  504.96650341,  510.48779255]])
+    aic = DataFrame(aic_x , index=lrange(5), columns=lrange(3))
+    bic = DataFrame(bic_x , index=lrange(5), columns=lrange(3))
+    assert_almost_equal(res.aic.values, aic.values, 5)
+    assert_almost_equal(res.bic.values, bic.values, 5)
+    assert_equal(res.aic_min_order, (1, 2))
+    assert_equal(res.bic_min_order, (1, 2))
+    assert_(res.aic.index.equals(aic.index))
+    assert_(res.aic.columns.equals(aic.columns))
+    assert_(res.bic.index.equals(bic.index))
+    assert_(res.bic.columns.equals(bic.columns))
+
+    res = arma_order_select_ic(y, ic='aic', trend='nc')
+    assert_almost_equal(res.aic.values, aic.values, 5)
+    assert_(res.aic.index.equals(aic.index))
+    assert_(res.aic.columns.equals(aic.columns))
+    assert_equal(res.aic_min_order, (1, 2))
+
+def test_arma_order_select_ic_failure():
+    # this should trigger an SVD convergence failure, smoke test that it
+    # returns, likely platform dependent failure...
+    y = np.array([ 0.86074377817203640006,  0.85316549067906921611,
+        0.87104653774363305363,  0.60692382068987393851,
+        0.69225941967301307667,  0.73336177248909339976,
+        0.03661329261479619179,  0.15693067239962379955,
+        0.12777403512447857437, -0.27531446294481976   ,
+       -0.24198139631653581283, -0.23903317951236391359,
+       -0.26000241325906497947, -0.21282920015519238288,
+       -0.15943768324388354896,  0.25169301564268781179,
+        0.1762305709151877342 ,  0.12678133368791388857,
+        0.89755829086753169399,  0.82667068795350151511])
+    res = arma_order_select_ic(y)
+
+
 if __name__=="__main__":
     import nose
 #    nose.runmodule(argv=[__file__, '-vvs','-x','-pdb'], exit=False)
diff --git a/statsmodels/tsa/tests/test_tsa_tools.py b/statsmodels/tsa/tests/test_tsa_tools.py
index 71d072d995c..dc09425dabb 100644
--- a/statsmodels/tsa/tests/test_tsa_tools.py
+++ b/statsmodels/tsa/tests/test_tsa_tools.py
@@ -2,6 +2,7 @@
 
 '''
 
+from statsmodels.compat.python import zip
 import numpy as np
 from numpy.testing import assert_array_almost_equal, assert_equal
 import statsmodels.api as sm
@@ -9,8 +10,8 @@
 import statsmodels.tsa.tsatools as tools
 from statsmodels.tsa.tsatools import vec, vech
 
-from results import savedrvs
-from results.datamlw_tls import mlacf, mlccf, mlpacf, mlywar
+from .results import savedrvs
+from .results.datamlw_tls import mlacf, mlccf, mlpacf, mlywar
 
 xo = savedrvs.rvsdata.xar2
 x100 = xo[-100:]/1000.
@@ -196,6 +197,13 @@ def test_add_lag_drop_noinsert():
     lag_data = sm.tsa.add_lag(data, 'realgdp', 3, insert=False, drop=True)
     assert_equal(lag_data.view((float,len(lag_data.dtype.names))), results)
 
+def test_freq_to_period():
+    from pandas.tseries.frequencies import to_offset
+    freqs = ['A', 'AS-MAR', 'Q', 'QS', 'QS-APR', 'W', 'W-MON', 'B']
+    expected = [1, 1, 4, 4, 4, 52, 52, 52]
+    for i, j in zip(freqs, expected):
+        assert_equal(tools.freq_to_period(i), j)
+        assert_equal(tools.freq_to_period(to_offset(i)), j)
 
 if __name__ == '__main__':
     #running them directly
diff --git a/statsmodels/tsa/tsatools.py b/statsmodels/tsa/tsatools.py
index 29ff417d929..590faded766 100644
--- a/statsmodels/tsa/tsatools.py
+++ b/statsmodels/tsa/tsatools.py
@@ -1,6 +1,10 @@
+from statsmodels.compat.python import range, lrange, lzip
 import numpy as np
 import numpy.lib.recfunctions as nprf
 from statsmodels.tools.tools import add_constant
+from pandas.tseries import offsets
+from pandas.tseries.frequencies import to_offset
+
 
 def add_trend(X, trend="c", prepend=False):
     """
@@ -111,7 +115,7 @@ def add_lag(x, col=None, lags=1, drop=False, insert=True):
     if x.dtype.names:
         names = x.dtype.names
         if not col and np.squeeze(x).ndim > 1:
-            raise IndexError, "col is None and the input array is not 1d"
+            raise IndexError("col is None and the input array is not 1d")
         elif len(names) == 1:
             col = names[0]
         if isinstance(col, int):
@@ -129,8 +133,10 @@ def add_lag(x, col=None, lags=1, drop=False, insert=True):
             ins_idx = len(names) + 1
         else: # insert is an int
             if insert > len(names):
-                raise Warning("insert > number of variables, inserting at the"+
-                              " last position")
+                import warnings
+                warnings.warn("insert > number of variables, inserting at the"
+                              " last position",
+                              UserWarning)
             ins_idx = insert
 
         first_names = list(names[:ins_idx])
@@ -146,7 +152,7 @@ def add_lag(x, col=None, lags=1, drop=False, insert=True):
             first_arr = nprf.append_fields(x[first_names][lags:],tmp_names,
                         ndlags.T, usemask=False)
         else:
-            first_arr = np.zeros(len(x)-lags, dtype=zip(tmp_names,
+            first_arr = np.zeros(len(x)-lags, dtype=lzip(tmp_names,
                 (x[col].dtype,)*lags))
             for i,name in enumerate(tmp_names):
                 first_arr[name] = ndlags[:,i]
@@ -178,13 +184,15 @@ def add_lag(x, col=None, lags=1, drop=False, insert=True):
                 insert = x.shape[1] + insert + 1
             if insert > x.shape[1]:
                 insert = x.shape[1]
-                raise Warning("insert > number of variables, inserting at the"+
-                              " last position")
+                import warnings
+                warnings.warn("insert > number of variables, inserting at the"
+                              " last position",
+                              UserWarning)
             ins_idx = insert
 
         ndlags = lagmat(contemp, lags, trim='Both')
-        first_cols = range(ins_idx)
-        last_cols = range(ins_idx,x.shape[1])
+        first_cols = lrange(ins_idx)
+        last_cols = lrange(ins_idx,x.shape[1])
         if drop:
             if col in first_cols:
                 first_cols.pop(first_cols.index(col))
@@ -221,7 +229,7 @@ def detrend(x, order=1, axis=0):
     if order == 0:
         return x - np.expand_dims(x.mean(ax), x)
     else:
-        if x.ndim == 2 and range(2)[axis]==1:
+        if x.ndim == 2 and lrange(2)[axis]==1:
             x = x.T
         elif x.ndim > 2:
             raise NotImplementedError('x.ndim>2 is not implemented until it is needed')
@@ -229,7 +237,7 @@ def detrend(x, order=1, axis=0):
         trends = np.vander(np.arange(nobs).astype(float), N=order+1)
         beta = np.linalg.lstsq(trends, x)[0]
         resid = x - np.dot(trends, beta)
-        if x.ndim == 2 and range(2)[axis]==1:
+        if x.ndim == 2 and lrange(2)[axis]==1:
             resid = resid.T
         return resid
 
@@ -570,6 +578,42 @@ def unintegrate(x, levels):
     x0 = levels[0]
     return np.cumsum(np.r_[x0, x])
 
+
+def freq_to_period(freq):
+    """
+    Convert a pandas frequency to a periodicity
+
+    Parameters
+    ----------
+    freq : str or offset
+        Frequency to convert
+
+    Returns
+    -------
+    period : int
+        Periodicity of freq
+
+    Notes
+    -----
+    Annual maps to 1, quarterly maps to 4, monthly to 12, weekly to 52.
+    """
+    if not isinstance(freq, offsets.DateOffset):
+        freq = to_offset(freq)  # go ahead and standardize
+    freq = freq.rule_code.upper()
+
+    if freq == 'A' or freq.startswith(('A-', 'AS-')):
+        return 1
+    elif freq == 'Q' or freq.startswith(('Q-', 'QS-')):
+        return 4
+    elif freq == 'M' or freq.startswith(('M-', 'MS')):
+        return 12
+    elif freq == 'B' or freq == 'W' or freq.startswith('W-'):
+        return 52
+    else:  # pragma : no cover
+        raise ValueError("freq {} not understood. Please report if you "
+                         "think this in error.".format(freq))
+
+
 __all__ = ['lagmat', 'lagmat2ds','add_trend', 'duplication_matrix',
            'elimination_matrix', 'commutation_matrix',
            'vec', 'vech', 'unvec', 'unvech']
diff --git a/statsmodels/tsa/varma_process.py b/statsmodels/tsa/varma_process.py
index d8fb9e6affd..7bc5b2e8878 100644
--- a/statsmodels/tsa/varma_process.py
+++ b/statsmodels/tsa/varma_process.py
@@ -28,7 +28,7 @@
 see also VAR section in Notes.txt
 
 """
-
+from __future__ import print_function
 import numpy as np
 from numpy.testing import assert_equal
 from scipy import signal
@@ -165,7 +165,7 @@ def varinversefilter(ar, nobs, version=1):
     '''
     nlags, nvars, nvarsex = ar.shape
     if nvars != nvarsex:
-        print 'exogenous variables not implemented not tested'
+        print('exogenous variables not implemented not tested')
     arinv = np.zeros((nobs+1, nvarsex, nvars))
     arinv[0,:,:] = ar[0]
     arinv[1:nlags,:,:] = -ar[1:]
@@ -177,9 +177,9 @@ def varinversefilter(ar, nobs, version=1):
             arinv[i,:,:] = tmp
     if version == 0:
         for i in range(nlags+1,nobs+1):
-            print ar[1:].shape, arinv[i-1:i-nlags:-1,:,:].shape
+            print(ar[1:].shape, arinv[i-1:i-nlags:-1,:,:].shape)
             #arinv[i,:,:] = np.dot(-ar[1:],arinv[i-1:i-nlags:-1,:,:])
-            #print np.tensordot(-ar[1:],arinv[i-1:i-nlags:-1,:,:],axes=([2],[1])).shape
+            #print(np.tensordot(-ar[1:],arinv[i-1:i-nlags:-1,:,:],axes=([2],[1])).shape
             #arinv[i,:,:] = np.tensordot(-ar[1:],arinv[i-1:i-nlags:-1,:,:],axes=([2],[1]))
             raise NotImplementedError('waiting for generalized ufuncs or something')
 
@@ -226,7 +226,7 @@ def vargenerate(ar, u, initvalues=None):
     nlagsm1 = nlags - 1
     nobs = u.shape[0]
     if nvars != nvarsex:
-        print 'exogenous variables not implemented not tested'
+        print('exogenous variables not implemented not tested')
     if u.shape[1] != nvars:
         raise ValueError('u needs to have nvars columns')
     if initvalues is None:
@@ -274,9 +274,9 @@ def padone(x, front=0, back=0, axis=0, fillvalue=0):
     startind[axis] = front
     endind = startind + shapearr
     myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
-    #print myslice
-    #print out.shape
-    #print out[tuple(myslice)].shape
+    #print(myslice
+    #print(out.shape
+    #print(out[tuple(myslice)].shape
     out[tuple(myslice)] = x
     return out
 
@@ -296,15 +296,15 @@ def trimone(x, front=0, back=0, axis=0):
     '''
     shape = np.array(x.shape)
     shape[axis] -= (front + back)
-    #print shape, front, back
+    #print(shape, front, back
     shapearr = np.array(x.shape)
     startind = np.zeros(x.ndim)
     startind[axis] = front
     endind = startind + shape
     myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
-    #print myslice
-    #print shape, endind
-    #print x[tuple(myslice)].shape
+    #print(myslice
+    #print(shape, endind
+    #print(x[tuple(myslice)].shape
     return x[tuple(myslice)]
 
 
@@ -680,7 +680,7 @@ def reduceform(self, apoly):
     res = np.linalg.lstsq(lagmat(ar2s,1), ar2s)
     bhat = res[0].reshape(1,2,2)
     arhat = ar2full(bhat)
-    #print maxabs(arhat - a22)
+    #print(maxabs(arhat - a22)
 
 
     v = _Var(ar2s)
@@ -714,13 +714,13 @@ def reduceform(self, apoly):
                      [ 0.1, -0.1]]])
 
     vp = VarmaPoly(ar23, ma22)
-    print vars(vp)
-    print vp.vstack()
-    print vp.vstack(a24)
-    print vp.hstackarma_minus1()
-    print vp.getisstationary()
-    print vp.getisinvertible()
+    print(vars(vp))
+    print(vp.vstack())
+    print(vp.vstack(a24))
+    print(vp.hstackarma_minus1())
+    print(vp.getisstationary())
+    print(vp.getisinvertible())
 
     vp2 = VarmaPoly(ar23ns)
-    print vp2.getisstationary()
-    print vp2.getisinvertible()  # no ma lags
+    print(vp2.getisstationary())
+    print(vp2.getisinvertible()) # no ma lags
diff --git a/statsmodels/tsa/vector_ar/dynamic.py b/statsmodels/tsa/vector_ar/dynamic.py
index ffe4b44ebb0..d2ccad37a3e 100644
--- a/statsmodels/tsa/vector_ar/dynamic.py
+++ b/statsmodels/tsa/vector_ar/dynamic.py
@@ -1,26 +1,24 @@
 # pylint: disable=W0201
 
+from statsmodels.compat.python import iteritems, string_types, range
 import numpy as np
-
 from statsmodels.tools.decorators import cache_readonly
+import pandas as pd
 
-import var_model as _model
-import util
-import plotting
+from . import var_model as _model
+from . import util
+from . import plotting
 
 FULL_SAMPLE = 0
 ROLLING = 1
 EXPANDING = 2
 
-try:
-    import pandas as pn
-except ImportError:
-    pass
+
 
 def _get_window_type(window_type):
     if window_type in (FULL_SAMPLE, ROLLING, EXPANDING):
         return window_type
-    elif isinstance(window_type, basestring):
+    elif isinstance(window_type, string_types):
         window_type_up = window_type.upper()
 
         if window_type_up in ('FULL SAMPLE', 'FULL_SAMPLE'):
@@ -32,12 +30,6 @@ def _get_window_type(window_type):
 
     raise Exception('Unrecognized window type: %s' % window_type)
 
-def require_pandas():
-    try:
-        import pandas as pn
-    except ImportError:
-        raise ImportError('pandas is required to use this code (for now)')
-
 class DynamicVAR(object):
     """
     Estimates time-varying vector autoregression (VAR(p)) using
@@ -66,8 +58,6 @@ class DynamicVAR(object):
     """
     def __init__(self, data, lag_order=1, window=None, window_type='expanding',
                  trend='c', min_periods=None):
-        require_pandas()
-
         self.lag_order = lag_order
 
         self.names = list(data.columns)
@@ -115,14 +105,14 @@ def T(self):
     @property
     def nobs(self):
         # Stub, do I need this?
-        data = dict((eq, r.nobs) for eq, r in self.equations.iteritems())
-        return pn.DataFrame(data)
+        data = dict((eq, r.nobs) for eq, r in iteritems(self.equations))
+        return pd.DataFrame(data)
 
     @cache_readonly
     def equations(self):
         eqs = {}
-        for col, ts in self.y.iteritems():
-            model = pn.ols(y=ts, x=self.x, window=self._window,
+        for col, ts in iteritems(self.y):
+            model = pd.ols(y=ts, x=self.x, window=self._window,
                            window_type=self._window_type,
                            min_periods=self._min_periods)
 
@@ -136,10 +126,10 @@ def coefs(self):
         Return dynamic regression coefficients as WidePanel
         """
         data = {}
-        for eq, result in self.equations.iteritems():
+        for eq, result in iteritems(self.equations):
             data[eq] = result.beta
 
-        panel = pn.WidePanel.fromDict(data)
+        panel = pd.WidePanel.fromDict(data)
 
         # Coefficient names become items
         return panel.swapaxes('items', 'minor')
@@ -182,10 +172,10 @@ def _intercepts_raw(self):
     @cache_readonly
     def resid(self):
         data = {}
-        for eq, result in self.equations.iteritems():
+        for eq, result in iteritems(self.equations):
             data[eq] = result.resid
 
-        return pn.DataFrame(data)
+        return pd.DataFrame(data)
 
     def forecast(self, steps=1):
         """
@@ -222,7 +212,7 @@ def forecast(self, steps=1):
 
             output[i] = forcs[-1]
 
-        return pn.DataFrame(output, index=forc_index, columns=self.names)
+        return pd.DataFrame(output, index=forc_index, columns=self.names)
 
     def plot_forecast(self, steps=1, figsize=(10, 10)):
         """
@@ -269,8 +259,8 @@ def _is_rolling(self):
     @cache_readonly
     def r2(self):
         """Returns the r-squared values."""
-        data = dict((eq, r.r2) for eq, r in self.equations.iteritems())
-        return pn.DataFrame(data)
+        data = dict((eq, r.r2) for eq, r in iteritems(self.equations))
+        return pd.DataFrame(data)
 
 class DynamicPanelVAR(DynamicVAR):
     """
@@ -350,7 +340,7 @@ def _make_lag_matrix(x, lags):
         data.update(lag._series)
         columns.extend(lag.columns)
 
-    return pn.DataFrame(data, columns=columns)
+    return pd.DataFrame(data, columns=columns)
 
 class Equation(object):
     """
diff --git a/statsmodels/tsa/vector_ar/irf.py b/statsmodels/tsa/vector_ar/irf.py
index f7588218086..ed602595c87 100644
--- a/statsmodels/tsa/vector_ar/irf.py
+++ b/statsmodels/tsa/vector_ar/irf.py
@@ -13,7 +13,7 @@
 from statsmodels.tools.decorators import cache_readonly
 from statsmodels.tools.tools import chain_dot
 #from statsmodels.tsa.api import VAR
-
+from statsmodels.compat.python import range
 import statsmodels.tsa.tsatools as tsa
 import statsmodels.tsa.vector_ar.plotting as plotting
 import statsmodels.tsa.vector_ar.util as util
@@ -335,8 +335,8 @@ def err_band_sz1(self, orth=False, svar=False, repl=1000,
         # here take the kth column of W, which we determine by finding the largest eigenvalue of the covaraince matrix
         lower = np.copy(irfs)
         upper = np.copy(irfs)
-        for i in xrange(neqs):
-            for j in xrange(neqs):
+        for i in range(neqs):
+            for j in range(neqs):
                 lower[1:,i,j] = irfs[1:,i,j] + W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]])
                 upper[1:,i,j] = irfs[1:,i,j] - W[i,j,:,k[i,j]]*q*np.sqrt(eigva[i,j,k[i,j]])
 
@@ -395,9 +395,9 @@ def err_band_sz2(self, orth=False, repl=1000, signif=0.05,
                 k = component
 
         gamma = np.zeros((repl, periods+1, neqs, neqs))
-        for p in xrange(repl):
-            for i in xrange(neqs):
-                for j in xrange(neqs):
+        for p in range(repl):
+            for i in range(neqs):
+                for j in range(neqs):
                     gamma[p,1:,i,j] = W[i,j,k[i,j],:] * irf_resim[p,1:,i,j]
 
         gamma_sort = np.sort(gamma, axis=0) #sort to get quantiles
@@ -405,8 +405,8 @@ def err_band_sz2(self, orth=False, repl=1000, signif=0.05,
 
         lower = np.copy(irfs)
         upper = np.copy(irfs)
-        for i in xrange(neqs):
-            for j in xrange(neqs):
+        for i in range(neqs):
+            for j in range(neqs):
                 lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j]
                 upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j]
 
@@ -455,8 +455,8 @@ def err_band_sz3(self, orth=False, repl=1000, signif=0.05,
 
         #stack left to right, up and down
 
-        for p in xrange(repl):
-            for i in xrange(neqs):
+        for p in range(repl):
+            for i in range(neqs):
                 stack[i, p,:] = np.ravel(irf_resim[p,1:,:,i].T)
 
         stack_cov=np.zeros((neqs, periods*neqs, periods*neqs))
@@ -473,15 +473,15 @@ def err_band_sz3(self, orth=False, repl=1000, signif=0.05,
                 k = component
 
         #compute for eigen decomp for each stack
-        for i in xrange(neqs):
+        for i in range(neqs):
             stack_cov[i] = np.cov(stack[i],rowvar=0)
             W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i])
 
         gamma = np.zeros((repl, periods+1, neqs, neqs))
-        for p in xrange(repl):
+        for p in range(repl):
             c=0
-            for j in xrange(neqs):
-                for i in xrange(neqs):
+            for j in range(neqs):
+                for i in range(neqs):
                         gamma[p,1:,i,j] = W[j,k[j],i*periods:(i+1)*periods] * irf_resim[p,1:,i,j]
                         if i == neqs-1:
                             gamma[p,1:,i,j] = W[j,k[j],i*periods:] * irf_resim[p,1:,i,j]
@@ -491,8 +491,8 @@ def err_band_sz3(self, orth=False, repl=1000, signif=0.05,
 
         lower = np.copy(irfs)
         upper = np.copy(irfs)
-        for i in xrange(neqs):
-            for j in xrange(neqs):
+        for i in range(neqs):
+            for j in range(neqs):
                 lower[:,i,j] = irfs[:,i,j] + gamma_sort[indx[0],:,i,j]
                 upper[:,i,j] = irfs[:,i,j] + gamma_sort[indx[1],:,i,j]
 
@@ -511,16 +511,16 @@ def _eigval_decomp_SZ(self, irf_resim):
         periods = self.periods
 
         cov_hold = np.zeros((neqs, neqs, periods, periods))
-        for i in xrange(neqs):
-            for j in xrange(neqs):
+        for i in range(neqs):
+            for j in range(neqs):
                 cov_hold[i,j,:,:] = np.cov(irf_resim[:,1:,i,j],rowvar=0)
 
         W = np.zeros((neqs, neqs, periods, periods))
         eigva = np.zeros((neqs, neqs, periods, 1))
         k = np.zeros((neqs, neqs))
 
-        for i in xrange(neqs):
-            for j in xrange(neqs):
+        for i in range(neqs):
+            for j in range(neqs):
                 W[i,j,:,:], eigva[i,j,:,0], k[i,j] = util.eigval_decomp(cov_hold[i,j,:,:])
         return W, eigva, k
 
diff --git a/statsmodels/tsa/vector_ar/output.py b/statsmodels/tsa/vector_ar/output.py
index 141529482f2..083bfcfb397 100644
--- a/statsmodels/tsa/vector_ar/output.py
+++ b/statsmodels/tsa/vector_ar/output.py
@@ -1,4 +1,5 @@
-from cStringIO import StringIO
+from __future__ import print_function
+from statsmodels.compat.python import cStringIO, lzip, lrange, StringIO, range
 import numpy as np
 
 from statsmodels.iolib import SimpleTable
@@ -74,10 +75,10 @@ def make(self, endog_names=None, exog_names=None):
         """
         buf = StringIO()
 
-        print >> buf, self._header_table()
-        print >> buf, self._stats_table()
-        print >> buf, self._coef_table()
-        print >> buf, self._resid_info()
+        buf.write(self._header_table() + '\n')
+        buf.write(self._stats_table() + '\n')
+        buf.write(self._coef_table() + '\n')
+        buf.write(self._resid_info() + '\n')
 
         return buf.getvalue()
 
@@ -140,7 +141,7 @@ def _coef_table(self):
 
         Xnames = self.model.exog_names
 
-        data = zip(model.params.T.ravel(),
+        data = lzip(model.params.T.ravel(),
                    model.stderr.T.ravel(),
                    model.tvalues.T.ravel(),
                    model.pvalues.T.ravel())
@@ -151,14 +152,15 @@ def _coef_table(self):
         dim = k * model.k_ar + model.k_trend
         for i in range(k):
             section = "Results for equation %s" % model.names[i]
-            print >> buf, section
+            buf.write(section + '\n')
+            #print >> buf, section
 
             table = SimpleTable(data[dim * i : dim * (i + 1)], header,
                                 Xnames, title=None, txt_fmt = self.default_fmt)
+            buf.write(str(table) + '\n')
 
-            print >> buf, str(table)
-
-            if i < k - 1: buf.write('\n')
+            if i < k - 1:
+                buf.write('\n')
 
         return buf.getvalue()
 
@@ -166,8 +168,8 @@ def _resid_info(self):
         buf = StringIO()
         names = self.model.names
 
-        print >> buf, "Correlation matrix of residuals"
-        print >> buf, pprint_matrix(self.model.resid_corr, names, names)
+        buf.write("Correlation matrix of residuals" + '\n')
+        buf.write(pprint_matrix(self.model.resid_corr, names, names) + '\n')
 
         return buf.getvalue()
 
@@ -193,10 +195,10 @@ def hypothesis_test_table(results, title, null_hyp):
                         ['Test statistic', 'Critical Value', 'p-value',
                          'df'], [''], title=None, txt_fmt=fmt)
 
-    print >> buf, title
-    print >> buf, table
+    buf.write(title + '\n')
+    buf.write(str(table) + '\n')
 
-    print >> buf, null_hyp
+    buf.write(null_hyp + '\n')
 
     buf.write("Conclusion: %s H_0" % results['conclusion'])
     buf.write(" at %.2f%% significance level" % (results['signif'] * 100))
@@ -226,12 +228,12 @@ def print_ic_table(ics, selected_orders):
                data_fmts=("%s",) * len(cols))
 
     buf = StringIO()
-    table = SimpleTable(data, cols, range(len(data)),
+    table = SimpleTable(data, cols, lrange(len(data)),
                         title='VAR Order Selection', txt_fmt=fmt)
-    print >> buf, table
-    print >> buf, '* Minimum'
+    buf.write(str(table) + '\n')
+    buf.write('* Minimum' + '\n')
 
-    print buf.getvalue()
+    print(buf.getvalue())
 
 
 def pprint_matrix(values, rlabels, clabels, col_space=None):
@@ -252,7 +254,7 @@ def pprint_matrix(values, rlabels, clabels, col_space=None):
     for j, h in enumerate(clabels):
         head += _pfixed(h, col_space[j])
 
-    print >> buf, head
+    buf.write(head + '\n')
 
     for i, rlab in enumerate(rlabels):
         line = ('%s' % rlab).ljust(row_space)
@@ -260,7 +262,7 @@ def pprint_matrix(values, rlabels, clabels, col_space=None):
         for j in range(K):
             line += _pfixed(values[i,j], col_space[j])
 
-        print >> buf, line
+        buf.write(line + '\n')
 
     return buf.getvalue()
 
diff --git a/statsmodels/tsa/vector_ar/plotting.py b/statsmodels/tsa/vector_ar/plotting.py
index f6350c0bbe1..71ebe2c7a6a 100644
--- a/statsmodels/tsa/vector_ar/plotting.py
+++ b/statsmodels/tsa/vector_ar/plotting.py
@@ -1,3 +1,4 @@
+from statsmodels.compat.python import lrange, range
 import numpy as np
 import statsmodels.tsa.vector_ar.util as util
 
@@ -92,7 +93,7 @@ def plot_with_error(y, error, x=None, axes=None, value_fmt='k',
     if axes is None:
         axes = plt.gca()
 
-    x = x if x is not None else range(len(y))
+    x = x if x is not None else lrange(len(y))
     plot_action = lambda y, fmt: axes.plot(x, y, fmt)
     plot_action(y, value_fmt)
 
@@ -203,7 +204,7 @@ def irf_grid_plot(values, stderr, impcol, rescol, names, title,
 
     k = len(names)
 
-    rng = range(len(values))
+    rng = lrange(len(values))
     for (j, i, ai, aj) in to_plot:
         ax = axes[ai][aj]
 
diff --git a/statsmodels/tsa/vector_ar/svar_model.py b/statsmodels/tsa/vector_ar/svar_model.py
index 93e1d60396f..fa8aed32753 100644
--- a/statsmodels/tsa/vector_ar/svar_model.py
+++ b/statsmodels/tsa/vector_ar/svar_model.py
@@ -5,17 +5,12 @@
 ----------
 Lutkepohl (2005) New Introduction to Multiple Time Series Analysis
 """
-
-from __future__ import division
+from __future__ import print_function, division
+from statsmodels.compat.python import range
 
 import numpy as np
 import numpy.linalg as npl
-
-try:
-    from numpy.linalg import slogdet as np_slogdet
-except:
-    def np_slogdet(x):
-        return 1, np.log(np.linalg.det(x))
+from numpy.linalg import slogdet
 
 from statsmodels.tools.numdiff import (approx_hess, approx_fprime)
 from statsmodels.tools.decorators import cache_readonly
@@ -25,8 +20,7 @@ def np_slogdet(x):
 
 import statsmodels.tsa.vector_ar.util as util
 import statsmodels.tsa.base.tsa_model as tsbase
-
-from statsmodels.tools.tools import rank as smrank
+from statsmodels.compat.numpy import np_matrix_rank
 
 mat = np.array
 
@@ -184,7 +178,7 @@ def fit(self, A_guess=None, B_guess=None, maxlags=None, method='ols',
                                 % (ic, sorted(selections)))
             lags = selections[ic]
             if verbose:
-                print 'Using %d based on %s criterion' %  (lags, ic)
+                print('Using %d based on %s criterion' %  (lags, ic))
         else:
             if lags is None:
                 lags = 1
@@ -305,7 +299,7 @@ def loglike(self, params):
 
         W = np.dot(npl.inv(B),A)
         trc_in = np.dot(np.dot(W.T,W),sigma_u)
-        sign, b_logdet = np_slogdet(B**2) #numpy 1.4 compat
+        sign, b_logdet = slogdet(B**2) #numpy 1.4 compat
         b_slogdet = sign * b_logdet
 
         likl = -nobs/2. * (neqs * np.log(2 * np.pi) - \
@@ -378,7 +372,7 @@ def _solve_AB(self, start_params, maxiter, maxfun, override=False,
             self.check_order(J)
             self.check_rank(J)
         else: #TODO: change to a warning?
-            print "Order/rank conditions have not been checked"
+            print("Order/rank conditions have not been checked")
 
         retvals = super(SVAR, self).fit(start_params=start_params,
                     method=solver, maxiter=maxiter,
@@ -408,7 +402,7 @@ def _compute_J(self, A_solve, B_solve):
 
         D_nT=np.zeros([(1.0/2)*(neqs)*(neqs+1),neqs**2])
 
-        for j in xrange(neqs):
+        for j in range(neqs):
             i=j
             while j <= i < neqs:
                 u=np.zeros([(1.0/2)*neqs*(neqs+1),1])
@@ -430,13 +424,13 @@ def _compute_J(self, A_solve, B_solve):
         j_d = 0
         if len(A_solve[A_mask]) is not 0:
             A_vec = np.ravel(A_mask, order='F')
-            for k in xrange(neqs**2):
+            for k in range(neqs**2):
                 if A_vec[k] == True:
                     S_B[k,j] = -1
                     j += 1
         if len(B_solve[B_mask]) is not 0:
             B_vec = np.ravel(B_mask, order='F')
-            for k in xrange(neqs**2):
+            for k in range(neqs**2):
                 if B_vec[k] == True:
                     S_D[k,j_d] = 1
                     j_d +=1
@@ -457,7 +451,7 @@ def check_order(self, J):
                              "solution may not be unique")
 
     def check_rank(self, J):
-        rank = smrank(J)
+        rank = np_matrix_rank(J)
         if rank < np.size(J, axis=1):
             raise ValueError("Rank condition not met: "
                              "solution may not be unique.")
diff --git a/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py b/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py
new file mode 100644
index 00000000000..1b3339614e3
--- /dev/null
+++ b/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py
@@ -0,0 +1,741 @@
+import numpy as np
+
+class Bunch(dict):
+    def __init__(self, **kw):
+        dict.__init__(self, kw)
+        self.__dict__  = self
+
+        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
+            self[att] = self.params_table[:,i]
+
+
+est = dict(
+           mlag_var = 3,
+           fpe_var = 7.47593408072e-13,
+           aic_var = -19.4085401106207,
+           hqic_var = -19.20760258859687,
+           sbic_var = -18.91206199634062,
+           tparms_var = 30,
+           k_var = 30,
+           df_eq_var = 10,
+           k_aux = 18,
+           k_eq = 18,
+           k_eq_var = 3,
+           k_dv_var = 3,
+           neqs_var = 3,
+           k_dv = 3,
+           neqs = 3,
+           N_cns = 12,
+           ic_ml = 9,
+           rc_ml = 0,
+           oid_df = 0,
+           N = 199,
+           rank = 6,
+           F_3_var = 9.018236399703298,
+           df_r3_var = 189,
+           df_m3_var = 9,
+           ll_3_var = 364.1156201942387,
+           r2_3_var = .3004252574875596,
+           rmse_3_var = .0398398997327694,
+           k_3_var = 10,
+           obs_3_var = 199,
+           F_2_var = 5.002566602091567,
+           df_r2_var = 189,
+           df_m2_var = 9,
+           ll_2_var = 728.0001662442413,
+           r2_2_var = .1923874161594955,
+           rmse_2_var = .0064000343524738,
+           k_2_var = 10,
+           obs_2_var = 199,
+           F_1_var = 8.356742395485949,
+           df_r1_var = 189,
+           df_m1_var = 9,
+           ll_1_var = 694.4411801251371,
+           r2_1_var = .2846617748967589,
+           rmse_1_var = .0075756675969815,
+           k_1_var = 10,
+           obs_1_var = 199,
+           df_r = 193,
+           df_r_var = 189,
+           ll = 1945.759734821802,
+           ll_var = 1961.149741006759,
+           detsig_ml_var = 5.52855987611e-13,
+           detsig_var = 6.45335912865e-13,
+           T_var = 199,
+           N_gaps_var = 0,
+           tmin = 0,
+           tmax = 198,
+           cmd = "svar",
+           cmdline = "svar gdp cons inv, aeq(A) beq(B) lags(1/3) var dfk small",
+           predict = "svar_p",
+           dfk_var = "dfk",
+           vcetype = "EIM",
+           lags_var = "1 2 3",
+           depvar_var = "gdp cons inv",
+           eqnames_var = "gdp cons inv",
+           endog_var = "gdp cons inv",
+           timevar = "qtrdate",
+           tsfmt = "%tq",
+           small = "small",
+           title = "Structural vector autoregression",
+           cns_b = "[b_1_2]_cons = 0:[b_1_3]_cons = 0:[b_2_1]_cons = 0:[b_2_3]_cons = 0:[b_3_1]_cons = 0:[b_3_2]_cons = 0",
+           cns_a = "[a_1_1]_cons = 1:[a_1_2]_cons = 0:[a_1_3]_cons = 0:[a_2_2]_cons = 1:[a_2_3]_cons = 0:[a_3_3]_cons = 1",
+           properties = "b V",
+          )
+
+params_table = np.array([
+                   1, np.nan, np.nan, np.nan,
+    np.nan, np.nan,              193,  1.9723316757957,
+                   0, -.50680224519119,  .04791445158754, -10.577231469827,
+     6.466439125e-21, -.60130543578568, -.41229905459671,              193,
+     1.9723316757957,                0, -5.5360565201616,  .24220266982262,
+    -22.857124259679,  8.232580974e-57,  -6.013760517815, -5.0583525225081,
+                 193,  1.9723316757957,                0,                0,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,              193,  1.9723316757957,                0,
+                   1, np.nan, np.nan, np.nan,
+    np.nan, np.nan,              193,  1.9723316757957,
+                   0,  3.0411768648574,  .28669329203947,  10.607771263929,
+     5.260805180e-21,  2.4757226037298,   3.606631125985,              193,
+     1.9723316757957,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+                 193,  1.9723316757957,                0,                0,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,              193,  1.9723316757957,                0,
+                   1, np.nan, np.nan, np.nan,
+    np.nan, np.nan,              193,  1.9723316757957,
+                   0,  .00757566759698,  .00037973390425,   19.94993734326,
+     8.739086225e-49,  .00682670638925,  .00832462880472,              193,
+     1.9723316757957,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+                 193,  1.9723316757957,                0,                0,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,              193,  1.9723316757957,                0,
+                   0, np.nan, np.nan, np.nan,
+    np.nan, np.nan,              193,  1.9723316757957,
+                   0,  .00512051886486,  .00025666841839,   19.94993734326,
+     8.739086225e-49,  .00461428361309,  .00562675411662,              193,
+     1.9723316757957,                0,                0, np.nan,
+    np.nan, np.nan, np.nan, np.nan,
+                 193,  1.9723316757957,                0,                0,
+    np.nan, np.nan, np.nan, np.nan,
+    np.nan,              193,  1.9723316757957,                0,
+                   0, np.nan, np.nan, np.nan,
+    np.nan, np.nan,              193,  1.9723316757957,
+                   0,  .02070894812762,  .00103804577284,   19.94993734326,
+     8.739086225e-49,  .01866157756892,  .02275631868632,              193,
+     1.9723316757957,                0]).reshape(18,9)
+
+params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()
+
+params_table_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+b = np.array([
+                   1, -.50680224519119, -5.5360565201616,                0,
+                   1,  3.0411768648574,                0,                0,
+                   1,  .00757566759698,                0,                0,
+                   0,  .00512051886486,                0,                0,
+                   0,  .02070894812762])
+
+b_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+b_rownames = 'y1'.split()
+
+cov = np.array([
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,  .00229579467093,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,   .0586621332692,                0,
+                   0, -.04165561908647,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+    -.04165561908647,                0,                0,  .08219304370043,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,  1.441978380e-07,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,  6.587867700e-08,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,  1.077539027e-06
+    ]).reshape(18,18)
+
+cov_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+cov_rownames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons'.split()
+
+constraints = np.array([
+                   1,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                1,                0,
+                   0,                0,                1,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   1,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                1,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                1,
+                   0,                0,                0,                0,
+                   0,                0,                0,                1,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                1,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                1,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                1,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   1,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                1,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                1,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                1,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                0,
+                   0,                0,                0,                1,
+                   0,                0,                0,                0
+    ]).reshape(12,19)
+
+constraints_colnames = '_cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _r'.split()
+
+constraints_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12'.split()
+
+Sigma = np.array([
+     .00005739073954,  .00002908575565,  .00022926345064,  .00002908575565,
+     .00004096043971,   .0000364524456,  .00022926345064,   .0000364524456,
+     .00158721761072]).reshape(3,3)
+
+Sigma_colnames = 'gdp cons inv'.split()
+
+Sigma_rownames = 'gdp cons inv'.split()
+
+G_var = np.array([
+                   1,  .00772119249309,  .00832845872247,  .00812414768988,
+     .00772450051084,  .00839168407728,  .00810118500591,  .00793331513676,
+     .00846090823295,    .009386666817,  .00772119249309,  .00013590038218,
+     .00010436399127,  .00039355021854,  .00008395547668,  .00009296949447,
+      .0001468047815,  .00007985818625,  .00008622263703,  .00012491817455,
+     .00832845872247,  .00010436399127,   .0001177915254,   .0001572415776,
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+     .00033983014403,  .00038874930061,  .00016022586575,  .00210416228523
+    ]).reshape(10,10)
+
+G_var_colnames = '_cons L.gdp L.cons L.inv L2.gdp L2.cons L2.inv L3.gdp L3.cons L3.inv'.split()
+
+G_var_rownames = '_cons L.gdp L.cons L.inv L2.gdp L2.cons L2.inv L3.gdp L3.cons L3.inv'.split()
+
+bf_var = np.array([
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+
+bf_var_colnames = 'L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons'.split()
+
+bf_var_rownames = 'r1'.split()
+
+B = np.array([
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+     .00512051886486,                0,                0,                0,
+     .02070894812762]).reshape(3,3)
+
+B_colnames = 'gdp cons inv'.split()
+
+B_rownames = 'gdp cons inv'.split()
+
+A = np.array([
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+                   1,                0, -5.5360565201616,  3.0411768648574,
+                   1]).reshape(3,3)
+
+A_colnames = 'gdp cons inv'.split()
+
+A_rownames = 'gdp cons inv'.split()
+
+beq = np.array([
+    np.nan,                0,                0,                0,
+    np.nan,                0,                0,                0,
+    np.nan]).reshape(3,3)
+
+beq_colnames = 'c1 c2 c3'.split()
+
+beq_rownames = 'r1 r2 r3'.split()
+
+aeq = np.array([
+                   1,                0,                0, np.nan,
+                   1,                0, np.nan, np.nan,
+                   1]).reshape(3,3)
+
+aeq_colnames = 'c1 c2 c3'.split()
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+aeq_rownames = 'r1 r2 r3'.split()
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+     -.0111150130859,  .00086015960527,  .00102392389806,  .01110812116296,
+    -.00160422865342, -.00045468997555,  .00135982972124, -.00025264500554,
+    -.00020816755381, -.00001119725114, -.48397149277921,  .03745319280989,
+      .0445838411169,  .48367140368251, -.06985155394389, -.01979817608096,
+     .05920990061166, -.01100070503994, -.00906406146236, -.00048755231406,
+     .00807329290157, -.07904268481996,  .00493625386149,   -.010089611016,
+     .08749015273475, -.00711016720733, -.00440612996585,  .00990765535257,
+    -.00143760405483, -.00011190477537,  .00128363796975, -.01256763413681,
+     .00078485482469, -.00160422865342,  .01391076521058, -.00113050284561,
+    -.00070056614975,  .00157529805457, -.00022857626656, -.00001779264303,
+      .0558923484488, -.54722172676604,  .03417426126141, -.06985155394389,
+     .60570453247617, -.04922451692591, -.03050415169677,  .06859185366219,
+    -.00995270055771, -.00077472981275, -.00551569453385,  .00566196061062,
+    -.07906195726942, -.00285972013799, -.00711016720733,  .08755796400357,
+     .00022974971529,  -.0040192367482,  .00957217286629, -.00009041795403,
+    -.00087698477183,  .00090024079538, -.01257069841903, -.00045468997555,
+    -.00113050284561,  .01392154707127,  .00003652976074, -.00063905087581,
+     .00152195698757, -.00001437627996, -.03818579662369,  .03919841373406,
+    -.54735515218165, -.01979817608096, -.04922451692591,  .60617399779988,
+     .00159058407757, -.02782564482269,  .06626926927807, -.00062597404237,
+    -.01538693895515,  .00231167441725,  .00023983274364,  .00855249213135,
+    -.00440612996585,  .00022974971529,  .00279002957959, -.00038179815311,
+    -.00001876611391,   .0000434507567, -.00244649355869,  .00036755176504,
+     .00003813294276,  .00135982972124, -.00070056614975,  .00003652976074,
+     .00044360931144, -.00006070516852, -2.983775846e-06,  6.908586346e-06,
+     -.1065255731616,  .01600399163071,  .00166039005894,  .05920990061166,
+    -.03050415169677,  .00159058407757,  .01931570021627, -.00264323314799,
+    -.00012991999552,  .00030081465683,  .00154722391453, -.01617041813247,
+       .002107855628, -.00158898161155,  .00990765535257,  -.0040192367482,
+    -.00038179815311,  .00290422640037, -.00026690557379,  .00004241741679,
+     .00024600561241, -.00257106523382,  .00033514497143, -.00025264500554,
+     .00157529805457, -.00063905087581, -.00006070516852,  .00046176638526,
+    -.00004243747044,  6.744287298e-06,  .01071161163275,  -.1119496908934,
+     .01459293037875, -.01100070503994,  .06859185366219, -.02782564482269,
+    -.00264323314799,  .02010629812676, -.00184781841992,  .00029366072412,
+     .00081231717487,  .00062958109943, -.01624344032441, -.00130924581082,
+    -.00143760405483,  .00957217286629, -.00001876611391, -.00026690557379,
+     .00279462471985,  .00003579021573,  .00012915686101,  .00010010217815,
+    -.00258267562122, -.00020816755381, -.00022857626656,  .00152195698757,
+    -2.983775846e-06, -.00004243747044,  .00044433992986,  5.690575137e-06,
+     .00562376655253,  .00435866338746, -.11245523204569, -.00906406146236,
+    -.00995270055771,  .06626926927807, -.00012991999552, -.00184781841992,
+     .01934751290831,  .00024777983816, -.00016553827919,  -.0001874289971,
+    -.00019740945784,  -.0000704238191, -.00011190477537, -.00009041795403,
+      .0000434507567,  .00004241741679,  .00003579021573,  6.702164252e-06,
+    -.00002632026649, -.00002980084833,  -.0000313877223, -.00001119725114,
+    -.00001779264303, -.00001437627996,  6.908586346e-06,  6.744287298e-06,
+     5.690575137e-06,  1.065631164e-06, -.00114604081571, -.00129759280914,
+    -.00136668870299, -.00048755231406, -.00077472981275, -.00062597404237,
+     .00030081465683,  .00029366072412,  .00024777983816,  .00004639986487
+    ]).reshape(30,30)
+
+V_var_colnames = 'L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons'.split()
+
+V_var_rownames = 'L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons'.split()
+
+b_var = np.array([
+    -.28614799058891,  .02569110476595, -.18003096181942,   .6738689560015,
+     .29544106895159,  .18370240194258,  .03057777928182, -.01444291994803,
+     .01263245201514,  .00128149319157, -.12715587337617, -.08663431448056,
+    -.35906668730993,  .25639388994688,  .20570668527827,  .41845237867104,
+     .02404284475263,  .00384555072972,  .04190581088286,  .00483719365525,
+    -1.8625374877103,  .33142498594011, -.48831009148236,  4.4033743272466,
+     .87819807698004, -.12378698529172,  .22371717935155, -.09655522236577,
+     .03345298758638, -.02059735685585])
+
+b_var_colnames = 'L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons L.gdp L2.gdp L3.gdp L.cons L2.cons L3.cons L.inv L2.inv L3.inv _cons'.split()
+
+b_var_rownames = 'y1'.split()
+
+
+
+results_svar1_small = Bunch(
+                params_table=params_table,
+                params_table_colnames=params_table_colnames,
+                params_table_rownames=params_table_rownames,
+                b=b,
+                b_colnames=b_colnames,
+                b_rownames=b_rownames,
+                cov=cov,
+                cov_colnames=cov_colnames,
+                cov_rownames=cov_rownames,
+                constraints=constraints,
+                constraints_colnames=constraints_colnames,
+                constraints_rownames=constraints_rownames,
+                Sigma=Sigma,
+                Sigma_colnames=Sigma_colnames,
+                Sigma_rownames=Sigma_rownames,
+                G_var=G_var,
+                G_var_colnames=G_var_colnames,
+                G_var_rownames=G_var_rownames,
+                bf_var=bf_var,
+                bf_var_colnames=bf_var_colnames,
+                bf_var_rownames=bf_var_rownames,
+                B=B,
+                B_colnames=B_colnames,
+                B_rownames=B_rownames,
+                A=A,
+                A_colnames=A_colnames,
+                A_rownames=A_rownames,
+                beq=beq,
+                beq_colnames=beq_colnames,
+                beq_rownames=beq_rownames,
+                aeq=aeq,
+                aeq_colnames=aeq_colnames,
+                aeq_rownames=aeq_rownames,
+                V_var=V_var,
+                V_var_colnames=V_var_colnames,
+                V_var_rownames=V_var_rownames,
+                b_var=b_var,
+                b_var_colnames=b_var_colnames,
+                b_var_rownames=b_var_rownames,
+                **est
+                )
+
+
+results_svar1_small.__doc__ = """
+    Scalars
+      e(N)                number of observations
+      e(N_cns)            number of constraints
+      e(k_eq)             number of equations in e(b)
+      e(k_dv)             number of dependent variables
+      e(k_aux)            number of auxiliary parameters
+      e(ll)               log likelihood from svar
+      e(ll_#)             log likelihood for equation #
+      e(N_gaps_var)       number of gaps in the sample
+      e(k_var)            number of coefficients in VAR
+      e(k_eq_var)         number of equations in underlying VAR
+      e(k_dv_var)         number of dependent variables in underlying VAR
+      e(df_eq_var)        average number of parameters in an equation
+      e(df_m_var)         model degrees of freedom
+      e(df_r_var)         if small, residual degrees of freedom
+      e(obs_#_var)        number of observations on equation #
+      e(k_#_var)          number of coefficients in equation #
+      e(df_m#_var)        model degrees of freedom for equation #
+      e(df_r#_var)        residual degrees of freedom for equation # (small only)
+      e(r2_#_var)         R-squared for equation #
+      e(ll_#_var)         log likelihood for equation # VAR
+      e(chi2_#_var)       chi-squared statistic for equation #
+      e(F_#_var)          F statistic for equation # (small only)
+      e(rmse_#_var)       root mean squared error for equation #
+      e(mlag_var)         highest lag in VAR
+      e(tparms_var)       number of parameters in all equations
+      e(aic_var)          Akaike information criterion
+      e(hqic_var)         Hannan-Quinn information criterion
+      e(sbic_var)         Schwarz-Bayesian information criterion
+      e(fpe_var)          final prediction error
+      e(ll_var)           log likelihood from var
+      e(detsig_var)       determinant of e(Sigma)
+      e(detsig_ml_var)    determinant of Sigma_ml hat
+      e(tmin)             first time period in the sample
+      e(tmax)             maximum time
+      e(chi2_oid)         overidentification test
+      e(oid_df)           number of overidentifying restrictions
+      e(rank)             rank of e(V)
+      e(ic_ml)            number of iterations
+      e(rc_ml)            return code from ml
+
+    Matrices
+      e(b)                coefficient vector
+      e(Cns)              constraints matrix
+      e(Sigma)            Sigma hat matrix
+      e(V)                variance-covariance matrix of the estimators
+      e(b_var)            coefficient vector of underlying VAR model
+      e(V_var)            VCE of underlying VAR model
+      e(bf_var)           full coefficient vector with zeros in dropped lags
+      e(G_var)            Gamma matrix saved by var; see Methods and formulas in [TS] var svar
+      e(aeq)              aeq(matrix), if specified
+      e(acns)             acns(matrix), if specified
+      e(beq)              beq(matrix), if specified
+      e(bcns)             bcns(matrix), if specified
+      e(lreq)             lreq(matrix), if specified
+      e(lrcns)            lrcns(matrix), if specified
+      e(Cns_var)          constraint matrix from var, if varconstraints() is specified
+      e(A)                estimated A matrix, if a short-run model
+      e(B)                estimated B matrix
+      e(C)                estimated C matrix, if a long-run model
+      e(A1)               estimated A bar matrix, if a long-run model
+"""
diff --git a/statsmodels/tsa/vector_ar/tests/test_svar.py b/statsmodels/tsa/vector_ar/tests/test_svar.py
index 305fd61f6aa..65fcbd0da0f 100644
--- a/statsmodels/tsa/vector_ar/tests/test_svar.py
+++ b/statsmodels/tsa/vector_ar/tests/test_svar.py
@@ -4,8 +4,8 @@
 
 import statsmodels.api as sm
 from statsmodels.tsa.vector_ar.svar_model import SVAR
-from numpy.testing import assert_almost_equal, assert_equal
-from results import results_svar
+from numpy.testing import assert_almost_equal, assert_equal, assert_allclose
+from .results import results_svar
 import numpy as np
 import numpy.testing as npt
 
@@ -25,8 +25,38 @@ def setupClass(cls):
         B = np.asarray([['E', 0, 0], [0, 'E', 0], [0, 0, 'E']])
         results = SVAR(data, svar_type='AB', A=A, B=B).fit(maxlags=3)
         cls.res1 = results
-        cls.res2 = results_svar.SVARdataResults()
+        #cls.res2 = results_svar.SVARdataResults()
+        from .results import results_svar_st
+        cls.res2 = results_svar_st.results_svar1_small
+
+
+    def _reformat(self, x):
+        return x[[1, 4, 7, 2, 5, 8, 3, 6, 9, 0], :].ravel("F")
+
+
     def test_A(self):
         assert_almost_equal(self.res1.A, self.res2.A, DECIMAL_4)
+
+
     def test_B(self):
         assert_almost_equal(self.res1.B, self.res2.B, DECIMAL_4)
+
+
+    def test_basic(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_allclose(self._reformat(res1.params), res2.b_var, atol=1e-12)
+        bse_st = np.sqrt(np.diag(res2.V_var))
+        assert_allclose(self._reformat(res1.bse), bse_st, atol=1e-12)
+
+
+    def test_llf_ic(self):
+        res1 = self.res1
+        res2 = self.res2
+        assert_allclose(res1.llf, res2.ll_var, atol=1e-12)
+        # different definition, missing constant term ?
+        corr_const = -8.51363119922803
+        assert_allclose(res1.fpe, res2.fpe_var, atol=1e-12)
+        assert_allclose(res1.aic - corr_const, res2.aic_var, atol=1e-12)
+        assert_allclose(res1.bic - corr_const, res2.sbic_var, atol=1e-12)
+        assert_allclose(res1.hqic - corr_const, res2.hqic_var, atol=1e-12)
diff --git a/statsmodels/tsa/vector_ar/tests/test_var.py b/statsmodels/tsa/vector_ar/tests/test_var.py
index c7478350f09..a735d717050 100644
--- a/statsmodels/tsa/vector_ar/tests/test_var.py
+++ b/statsmodels/tsa/vector_ar/tests/test_var.py
@@ -1,11 +1,9 @@
 """
 Test VAR Model
 """
-from __future__ import with_statement
-
+from __future__ import print_function
 # pylint: disable=W0612,W0231
-
-from cStringIO import StringIO
+from statsmodels.compat.python import iteritems, StringIO, lrange, BytesIO, range
 from nose.tools import assert_raises
 import nose
 import os
@@ -18,7 +16,7 @@
 import statsmodels.tsa.vector_ar.util as util
 import statsmodels.tools.data as data_util
 from statsmodels.tsa.vector_ar.var_model import VAR
-from statsmodels.compatnp.py3k import BytesIO
+
 
 from numpy.testing import assert_almost_equal, assert_equal, assert_
 
@@ -107,7 +105,7 @@ class RResults(object):
 
     def __init__(self):
         #data = np.load(resultspath + 'vars_results.npz')
-        from results.results_var_data import var_results
+        from .results.results_var_data import var_results
         data = var_results.__dict__
 
         self.names = data['coefs'].dtype.names
@@ -157,7 +155,7 @@ def have_matplotlib():
     try:
         import matplotlib
         if matplotlib.__version__ < '1':
-            raise
+            raise ImportError("matplotlib not is too old.  Please update.")
         return True
     except:
         return False
@@ -222,7 +220,7 @@ def test_fevd_plot(self):
         close_plots()
 
     def test_fevd_repr(self):
-        print self.fevd
+        self.fevd
 
     def test_fevd_summary(self):
         self.fevd.summary()
@@ -266,7 +264,7 @@ def test_names(self):
         assert_equal(model2.names, self.ref.names)
 
     def test_get_eq_index(self):
-        assert(isinstance(self.res.names, list))
+        assert(type(self.res.names) is list)
 
         for i, name in enumerate(self.names):
             idx = self.res.get_eq_index(i)
@@ -300,7 +298,7 @@ def test_pvalues(self):
 
     def test_summary(self):
         summ = self.res.summary()
-        print summ
+
 
     def test_detsig(self):
         assert_almost_equal(self.res.detomega, self.ref.detomega)
@@ -349,7 +347,7 @@ def test_causality(self):
             result = self.res.test_causality(name, variables, kind='f')
             assert_almost_equal(result['pvalue'], causedby[i], DECIMAL_4)
 
-            rng = range(self.k)
+            rng = lrange(self.k)
             rng.remove(i)
             result2 = self.res.test_causality(i, rng, kind='f')
             assert_almost_equal(result['pvalue'], result2['pvalue'], DECIMAL_12)
@@ -442,7 +440,6 @@ def test_reorder(self):
         assert_almost_equal(res2.stderr, res3.stderr)
 
     def test_pickle(self):
-        from statsmodels.compatnp.py3k import BytesIO
         fh = BytesIO()
         #test wrapped results load save pickle
         self.res.save(fh)
@@ -559,7 +556,7 @@ def test_get_trendorder():
         'ctt' : 3
     }
 
-    for t, trendorder in results.iteritems():
+    for t, trendorder in iteritems(results):
         assert(util.get_trendorder(t) == trendorder)
 
 if __name__ == '__main__':
diff --git a/statsmodels/tsa/vector_ar/util.py b/statsmodels/tsa/vector_ar/util.py
index 450a84bcf44..98fd72dbe92 100644
--- a/statsmodels/tsa/vector_ar/util.py
+++ b/statsmodels/tsa/vector_ar/util.py
@@ -1,7 +1,7 @@
 """
 Miscellaneous utility code for VAR estimation
 """
-
+from statsmodels.compat.python import range, string_types, asbytes
 import numpy as np
 import scipy.stats as stats
 import scipy.linalg as L
@@ -23,7 +23,7 @@ def get_var_endog(y, lags, trend='c'):
     """
     nobs = len(y)
     # Ravel C order, need to put in descending order
-    Z = np.array([y[t-lags : t][::-1].ravel() for t in xrange(lags, nobs)])
+    Z = np.array([y[t-lags : t][::-1].ravel() for t in range(lags, nobs)])
 
     # Add constant, trend, etc.
     if trend != 'nc':
@@ -54,13 +54,13 @@ def make_lag_names(names, lag_order, trendorder=1):
 
     """
     lag_names = []
-    if isinstance(names, basestring): # python 3?
+    if isinstance(names, string_types):     
         names = [names]
 
     # take care of lagged endogenous names
     for i in range(1, lag_order + 1):
         for name in names:
-            if not isinstance(name, basestring):
+            if not isinstance(name, string_types):
                 name = str(name) # will need consistent unicode handling
             lag_names.append('L'+str(i)+'.'+name)
 
@@ -113,7 +113,6 @@ def parse_lutkepohl_data(path): # pragma: no cover
     import pandas
     import pandas.core.datetools as dt
     import re
-    from statsmodels.compatnp.py3k import asbytes
 
     regex = re.compile(asbytes('<(.*) (\w)([\d]+)>.*'))
     lines = deque(open(path, 'rb'))
@@ -152,18 +151,14 @@ def parse_lutkepohl_data(path): # pragma: no cover
     start_date = offset.rollforward(datetime(year, 1, 1)) + inc
 
     offset = offsets[freq]
-    try:
-        from pandas import DatetimeIndex   # pylint: disable=E0611
-        date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
-    except ImportError:
-        from pandas import DateRange
-        date_range = DateRange(start_date, offset=offset, periods=n)
+    from pandas import DatetimeIndex   # pylint: disable=E0611
+    date_range = DatetimeIndex(start=start_date, freq=offset, periods=n)
 
     return data, date_range
 
 def get_logdet(m):
-    from statsmodels.tools.compatibility import np_slogdet
-    logdet = np_slogdet(m)
+    from numpy.linalg import slogdet
+    logdet = slogdet(m)
 
     if logdet[0] == -1: # pragma: no cover
         raise ValueError("Matrix is not positive definite")
@@ -198,9 +193,9 @@ def varsim(coefs, intercept, sig_u, steps=100, initvalues=None, seed=None):
     result[p:] = intercept + ugen[p:]
 
     # add in AR terms
-    for t in xrange(p, steps):
+    for t in range(p, steps):
         ygen = result[t]
-        for j in xrange(p):
+        for j in range(p):
             ygen += np.dot(coefs[j], result[t-j-1])
 
     return result
@@ -237,7 +232,7 @@ def vech(A):
 
     length=A.shape[1]
     vechvec=[]
-    for i in xrange(length):
+    for i in range(length):
         b=i
         while b < length:
             vechvec.append(A[b,i])
diff --git a/statsmodels/tsa/vector_ar/var_model.py b/statsmodels/tsa/vector_ar/var_model.py
index 1a98a699e7a..e1d0b15e467 100644
--- a/statsmodels/tsa/vector_ar/var_model.py
+++ b/statsmodels/tsa/vector_ar/var_model.py
@@ -6,10 +6,11 @@
 Lutkepohl (2005) New Introduction to Multiple Time Series Analysis
 """
 
-from __future__ import division
+from __future__ import division, print_function
+from statsmodels.compat.python import (range, lrange, string_types, StringIO, iteritems,
+                                cStringIO)
 
 from collections import defaultdict
-from cStringIO import StringIO
 
 import numpy as np
 import numpy.linalg as npl
@@ -67,8 +68,8 @@ def ma_rep(coefs, maxn=10):
     phis[0] = np.eye(k)
 
     # recursively compute Phi matrices
-    for i in xrange(1, maxn + 1):
-        for j in xrange(1, i+1):
+    for i in range(1, maxn + 1):
+        for j in range(1, i+1):
             if j > p:
                 break
 
@@ -93,9 +94,9 @@ def is_stable(coefs, verbose=False):
     eigs = np.linalg.eigvals(A_var1)
 
     if verbose:
-        print 'Eigenvalues of VAR(1) rep'
+        print('Eigenvalues of VAR(1) rep')
         for val in np.abs(eigs):
-            print val
+            print(val)
 
     return (np.abs(eigs) <= 1).all()
 
@@ -130,11 +131,11 @@ def var_acf(coefs, sig_u, nlags=None):
     result[:p] = _var_acf(coefs, sig_u)
 
     # yule-walker equations
-    for h in xrange(p, nlags + 1):
+    for h in range(p, nlags + 1):
         # compute ACF for lag=h
         # G(h) = A_1 G(h-1) + ... + A_p G(h-p)
 
-        for j in xrange(p):
+        for j in range(p):
             result[h] += np.dot(coefs[j], result[h-j-1])
 
     return result
@@ -193,10 +194,10 @@ def forecast(y, coefs, intercept, steps):
     # forcs[0] = y[-1]
 
     # make indices easier to think about
-    for h in xrange(1, steps + 1):
+    for h in range(1, steps + 1):
         # y_t(h) = intercept + sum_1^p A_i y_t_(h-i)
         f = forcs[h - 1]
-        for i in xrange(1, p + 1):
+        for i in range(1, p + 1):
             # slightly hackish
             if h - i <= 0:
                 # e.g. when h=1, h-1 = 0, which is y[-1]
@@ -226,7 +227,7 @@ def forecast_cov(ma_coefs, sig_u, steps):
     forc_covs = np.zeros((steps, k, k))
 
     prior = np.zeros((k, k))
-    for h in xrange(steps):
+    for h in range(steps):
         # Sigma(h) = Sigma(h-1) + Phi Sig_u Phi'
         phi = ma_coefs[h]
         var = chain_dot(phi, sig_u, phi.T)
@@ -431,7 +432,7 @@ def fit(self, maxlags=None, method='ols', ic=None, trend='c',
                                 % (ic, sorted(selections)))
             lags = selections[ic]
             if verbose:
-                print 'Using %d based on %s criterion' %  (lags, ic)
+                print('Using %d based on %s criterion' %  (lags, ic))
         else:
             if lags is None:
                 lags = 1
@@ -510,11 +511,11 @@ def select_order(self, maxlags=None, verbose=True):
             # order
             result = self._estimate_var(p, offset=maxlags-p)
 
-            for k, v in result.info_criteria.iteritems():
+            for k, v in iteritems(result.info_criteria):
                 ics[k].append(v)
 
         selected_orders = dict((k, mat(v).argmin())
-                               for k, v in ics.iteritems())
+                               for k, v in iteritems(ics))
 
         if verbose:
             output.print_ic_table(ics, selected_orders)
@@ -706,7 +707,7 @@ def mse(self, steps):
         forc_covs = np.zeros((steps, k, k))
 
         prior = np.zeros((k, k))
-        for h in xrange(steps):
+        for h in range(steps):
             # Sigma(h) = Sigma(h-1) + Phi Sig_u Phi'
             phi = ma_coefs[h]
             var = chain_dot(phi, self.sigma_u, phi.T)
@@ -1291,7 +1292,7 @@ def reorder(self, order):
         if len(order) != len(self.params[0,:]):
             raise ValueError("Reorder specification length should match number of endogenous variables")
        #This convert order to list of integers if given as strings
-        if type(order[0]) is str:
+        if isinstance(order[0], string_types):
             order_new = []
             for i, nam in enumerate(order):
                 order_new.append(self.names.index(order[i]))
@@ -1330,7 +1331,7 @@ def test_causality(self, equation, variables, kind='f', signif=0.05,
         -------
         results : dict
         """
-        if isinstance(variables, (basestring, int, np.integer)):
+        if isinstance(variables, (string_types, int, np.integer)):
             variables = [variables]
 
         k, p = self.neqs, self.k_ar
@@ -1382,7 +1383,7 @@ def test_causality(self, equation, variables, kind='f', signif=0.05,
         if verbose:
             summ = output.causality_summary(results, variables, equation, kind)
 
-            print summ
+            print(summ)
 
         return results
 
@@ -1402,10 +1403,10 @@ def test_whiteness(self, nlags=10, plot=True, linewidth=8):
         # TODO: this probably needs some UI work
 
         if (np.abs(acorrs) > bound).any():
-            print ('FAIL: Some autocorrelations exceed %.4f bound. '
+            print('FAIL: Some autocorrelations exceed %.4f bound. '
                    'See plot' % bound)
         else:
-            print 'PASS: No autocorrelations exceed %.4f bound' % bound
+            print('PASS: No autocorrelations exceed %.4f bound' % bound)
 
         if plot:
             fig = plotting.plot_full_acorr(acorrs[1:],
@@ -1458,7 +1459,7 @@ def test_normality(self, signif=0.05, verbose=True):
 
         if verbose:
             summ = output.normality_summary(results)
-            print summ
+            print(summ)
 
         return results
 
@@ -1565,7 +1566,7 @@ def __init__(self, model, P=None, periods=None):
         # cumulative impulse responses
         irfs = (self.orth_irfs[:periods] ** 2).cumsum(axis=0)
 
-        rng = range(self.neqs)
+        rng = lrange(self.neqs)
         mse = self.model.mse(periods)[:, rng, rng]
 
         # lag x equation x component
@@ -1580,14 +1581,14 @@ def __init__(self, model, P=None, periods=None):
     def summary(self):
         buf = StringIO()
 
-        rng = range(self.periods)
+        rng = lrange(self.periods)
         for i in range(self.neqs):
             ppm = output.pprint_matrix(self.decomp[i], rng, self.names)
 
-            print >> buf, 'FEVD for %s' % self.names[i]
-            print >> buf, ppm
+            buf.write('FEVD for %s\n' % self.names[i])
+            buf.write(ppm + '\n')
 
-        print buf.getvalue()
+        print(buf.getvalue())
 
     def cov(self):
         """Compute asymptotic standard errors
@@ -1648,7 +1649,7 @@ def _compute_acov(x, nlags=1):
     x = x - x.mean(0)
 
     result = []
-    for lag in xrange(nlags + 1):
+    for lag in range(nlags + 1):
         if lag > 0:
             r = np.dot(x[lag:].T, x[:-lag])
         else:
diff --git a/tools/R2nparray/NAMESPACE b/tools/R2nparray/NAMESPACE
new file mode 100644
index 00000000000..072671fd813
--- /dev/null
+++ b/tools/R2nparray/NAMESPACE
@@ -0,0 +1 @@
+exportPattern( "." )
diff --git a/tools/backport_pr.py b/tools/backport_pr.py
new file mode 100644
index 00000000000..fdc3c812882
--- /dev/null
+++ b/tools/backport_pr.py
@@ -0,0 +1,171 @@
+#!/usr/bin/env python
+"""
+Backport pull requests to a particular branch.
+
+Usage: backport_pr.py branch [PR]
+
+e.g.:
+
+    python tools/backport_pr.py 0.13.1 123
+
+to backport PR #123 onto branch 0.13.1
+
+or
+
+    python tools/backport_pr.py 1.x
+
+to see what PRs are marked for backport that have yet to be applied.
+
+Copied from IPython 9e82bc5
+https://github.com/ipython/ipython/blob/master/tools/backport_pr.py
+"""
+
+from __future__ import print_function
+
+import os
+import re
+import sys
+
+from subprocess import Popen, PIPE, check_call, check_output
+from urllib import urlopen
+
+from gh_api import (
+    get_issues_list,
+    get_pull_request,
+    get_pull_request_files,
+    is_pull_request,
+    get_milestone_id,
+)
+
+from pandas import Series
+
+def find_rejects(root='.'):
+    for dirname, dirs, files in os.walk(root):
+        for fname in files:
+            if fname.endswith('.rej'):
+                yield os.path.join(dirname, fname)
+
+def get_current_branch():
+    branches = check_output(['git', 'branch'])
+    for branch in branches.splitlines():
+        if branch.startswith('*'):
+            return branch[1:].strip()
+
+def backport_pr(branch, num, project='statsmodels/statsmodels'):
+    current_branch = get_current_branch()
+    if branch != current_branch:
+        check_call(['git', 'checkout', branch])
+    check_call(['git', 'pull'])
+    pr = get_pull_request(project, num, auth=True)
+    files = get_pull_request_files(project, num, auth=True)
+    patch_url = pr['patch_url']
+    title = pr['title']
+    description = pr['body']
+    fname = "PR%i.patch" % num
+    if os.path.exists(fname):
+        print("using patch from {fname}".format(**locals()))
+        with open(fname) as f:
+            patch = f.read()
+    else:
+        req = urlopen(patch_url)
+        patch = req.read()
+
+    msg = "Backport PR #%i: %s" % (num, title) + '\n\n' + description
+    check = Popen(['git', 'apply', '--check', '--verbose'], stdin=PIPE)
+    a,b = check.communicate(patch)
+
+    if check.returncode:
+        print("patch did not apply, saving to {fname}".format(**locals()))
+        print("edit {fname} until `cat {fname} | git apply --check` succeeds".format(**locals()))
+        print("then run tools/backport_pr.py {num} again".format(**locals()))
+        if not os.path.exists(fname):
+            with open(fname, 'wb') as f:
+                f.write(patch)
+        return 1
+
+    p = Popen(['git', 'apply'], stdin=PIPE)
+    a,b = p.communicate(patch)
+
+    filenames = [ f['filename'] for f in files ]
+
+    check_call(['git', 'add'] + filenames)
+
+    check_call(['git', 'commit', '-m', msg])
+
+    print("PR #%i applied, with msg:" % num)
+    print()
+    print(msg)
+    print()
+
+    if branch != current_branch:
+        check_call(['git', 'checkout', current_branch])
+
+    return 0
+
+backport_re = re.compile(r"[Bb]ackport.*?(\d+)")
+
+def already_backported(branch, since_tag=None):
+    """return set of PRs that have been backported already"""
+    if since_tag is None:
+        since_tag = check_output(['git','describe', branch, '--abbrev=0']).decode('utf8').strip()
+    cmd = ['git', 'log', '%s..%s' % (since_tag, branch), '--oneline']
+    lines = check_output(cmd).decode('utf8')
+    return set(int(num) for num in backport_re.findall(lines))
+
+def should_backport(labels=None, milestone=None):
+    """return set of PRs marked for backport"""
+    if labels is None and milestone is None:
+        raise ValueError("Specify one of labels or milestone.")
+    elif labels is not None and milestone is not None:
+        raise ValueError("Specify only one of labels or milestone.")
+    if labels is not None:
+        issues = get_issues_list("statsmodels/statsmodels",
+                labels=labels,
+                state='closed',
+                auth=True,
+        )
+    else:
+        milestone_id = get_milestone_id("statsmodels/statsmodels", milestone,
+                auth=True)
+        issues = get_issues_list("statsmodels/statsmodels",
+                milestone=milestone_id,
+                state='closed',
+                auth=True,
+        )
+
+    should_backport = []
+    merged_dates = []
+    for issue in issues:
+        if not is_pull_request(issue):
+            continue
+        pr = get_pull_request("statsmodels/statsmodels", issue['number'],
+                auth=True)
+        if not pr['merged']:
+            print ("Marked PR closed without merge: %i" % pr['number'])
+            continue
+        if pr['number'] not in should_backport:
+            merged_dates.append(pr['merged_at'])
+            should_backport.append(pr['number'])
+    return Series(merged_dates, index=should_backport)
+
+if __name__ == '__main__':
+
+    if len(sys.argv) < 2:
+        print(__doc__)
+        sys.exit(1)
+
+    if len(sys.argv) < 3:
+        branch = sys.argv[1]
+        already = already_backported(branch)
+        #NOTE: change this to the label you've used for marking a backport
+        should = should_backport(milestone="0.5.1")
+        print ("The following PRs should be backported:")
+        to_backport = []
+        if already:
+            should = should.ix[set(should.index).difference(already)]
+        should.sort()
+        for pr, date in should.iteritems():
+            print (pr)
+        sys.exit(0)
+
+    sys.exit(backport_pr(sys.argv[1], int(sys.argv[2])))
diff --git a/tools/build_win_bdist32-py33.bat b/tools/build_win_bdist32-py33.bat
new file mode 100644
index 00000000000..d5d22c9cfc4
--- /dev/null
+++ b/tools/build_win_bdist32-py33.bat
@@ -0,0 +1,5 @@
+setlocal EnableDelayedExpansion
+CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x86 /release
+set DISTUTILS_USE_SDK=1
+rem C:\Python27_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi
+C:\Python33_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst
diff --git a/tools/build_win_bdist32-py34.bat b/tools/build_win_bdist32-py34.bat
new file mode 100644
index 00000000000..fef35435111
--- /dev/null
+++ b/tools/build_win_bdist32-py34.bat
@@ -0,0 +1,5 @@
+setlocal EnableDelayedExpansion
+CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x86 /release
+set DISTUTILS_USE_SDK=1
+rem C:\Python27_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi
+C:\Python34_32bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst
diff --git a/tools/build_win_bdist64-py33.bat b/tools/build_win_bdist64-py33.bat
new file mode 100644
index 00000000000..4259064be10
--- /dev/null
+++ b/tools/build_win_bdist64-py33.bat
@@ -0,0 +1,5 @@
+setlocal EnableDelayedExpansion
+CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64 /release
+set DISTUTILS_USE_SDK=1
+rem C:\Python27\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi
+C:\Python33_64bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst
diff --git a/tools/build_win_bdist64-py34.bat b/tools/build_win_bdist64-py34.bat
new file mode 100644
index 00000000000..c6e43f1244b
--- /dev/null
+++ b/tools/build_win_bdist64-py34.bat
@@ -0,0 +1,5 @@
+setlocal EnableDelayedExpansion
+CALL "C:\Program Files\Microsoft SDKs\Windows\v7.1\Bin\SetEnv.cmd" /x64 /release
+set DISTUTILS_USE_SDK=1
+rem C:\Python27\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_msi
+C:\Python34_64bit\python.exe C:\Users\skipper\statsmodels\statsmodels\setup.py bdist_wininst
diff --git a/tools/cythonize.py b/tools/cythonize.py
new file mode 100755
index 00000000000..a8be5506db6
--- /dev/null
+++ b/tools/cythonize.py
@@ -0,0 +1,198 @@
+#!/usr/bin/env python
+""" cythonize
+
+Cythonize pyx files into C files as needed.
+
+Usage: cythonize [root_dir]
+
+Default [root_dir] is 'statsmodels'.
+
+Checks pyx files to see if they have been changed relative to their
+corresponding C files.  If they have, then runs cython on these files to
+recreate the C files.
+
+The script thinks that the pyx files have changed relative to the C files
+by comparing hashes stored in a database file.
+
+Simple script to invoke Cython (and Tempita) on all .pyx (.pyx.in)
+files; while waiting for a proper build system. Uses file hashes to
+figure out if rebuild is needed.
+
+For now, this script should be run by developers when changing Cython files
+only, and the resulting C files checked in, so that end-users (and Python-only
+developers) do not get the Cython/Tempita dependencies.
+
+Originally written by Dag Sverre Seljebotn, and copied here from:
+
+https://raw.github.com/dagss/private-scipy-refactor/cythonize/cythonize.py
+
+Note: this script does not check any of the dependent C libraries; it only
+operates on the Cython .pyx files.
+"""
+
+from __future__ import division, print_function, absolute_import
+
+import os
+import re
+import sys
+import hashlib
+import subprocess
+
+HASH_FILE = 'cythonize.dat'
+DEFAULT_ROOT = 'statsmodels'
+
+# WindowsError is not defined on unix systems
+try:
+    WindowsError
+except NameError:
+    WindowsError = None
+
+#
+# Rules
+#
+def process_pyx(fromfile, tofile):
+    try:
+        from Cython.Compiler.Version import version as cython_version
+        from distutils.version import LooseVersion
+        if LooseVersion(cython_version) < LooseVersion('0.19'):
+            raise Exception('Building Statsmodels requires Cython >= 0.19')
+
+    except ImportError:
+        pass
+
+    flags = ['--fast-fail']
+    if tofile.endswith('.cxx'):
+        flags += ['--cplus']
+
+    try:
+        try:
+            r = subprocess.call(['cython'] + flags + ["-o", tofile, fromfile])
+            if r != 0:
+                raise Exception('Cython failed')
+        except OSError:
+            # There are ways of installing Cython that don't result in a cython
+            # executable on the path, see gh-2397.
+            r = subprocess.call([sys.executable, '-c',
+                                 'import sys; from Cython.Compiler.Main import '
+                                 'setuptools_main as main; sys.exit(main())'] + flags +
+                                 ["-o", tofile, fromfile])
+            if r != 0:
+                raise Exception('Cython failed')
+    except OSError:
+        raise OSError('Cython needs to be installed')
+
+def process_tempita_pyx(fromfile, tofile):
+    try:
+        try:
+            from Cython import Tempita as tempita
+        except ImportError:
+            import tempita
+    except ImportError:
+        raise Exception('Building Statsmodels requires Tempita: '
+                        'pip install --user Tempita')
+    with open(fromfile, "r") as f:
+        tmpl = f.read()
+    pyxcontent = tempita.sub(tmpl)
+    assert fromfile.endswith('.pyx.in')
+    pyxfile = fromfile[:-len('.pyx.in')] + '.pyx'
+    with open(pyxfile, "w") as f:
+        f.write(pyxcontent)
+    process_pyx(pyxfile, tofile)
+
+rules = {
+    # fromext : function
+    '.pyx' : process_pyx,
+    '.pyx.in' : process_tempita_pyx
+    }
+#
+# Hash db
+#
+def load_hashes(filename):
+    # Return { filename : (sha1 of input, sha1 of output) }
+    if os.path.isfile(filename):
+        hashes = {}
+        with open(filename, 'r') as f:
+            for line in f:
+                filename, inhash, outhash = line.split()
+                hashes[filename] = (inhash, outhash)
+    else:
+        hashes = {}
+    return hashes
+
+def save_hashes(hash_db, filename):
+    with open(filename, 'w') as f:
+        for key, value in sorted(hash_db.items()):
+            f.write("%s %s %s\n" % (key, value[0], value[1]))
+
+def sha1_of_file(filename):
+    h = hashlib.sha1()
+    with open(filename, "rb") as f:
+        h.update(f.read())
+    return h.hexdigest()
+
+#
+# Main program
+#
+
+def normpath(path):
+    path = path.replace(os.sep, '/')
+    if path.startswith('./'):
+        path = path[2:]
+    return path
+
+def get_hash(frompath, topath):
+    from_hash = sha1_of_file(frompath)
+    to_hash = sha1_of_file(topath) if os.path.exists(topath) else None
+    return (from_hash, to_hash)
+
+def process(path, fromfile, tofile, processor_function, hash_db):
+    fullfrompath = os.path.join(path, fromfile)
+    fulltopath = os.path.join(path, tofile)
+    current_hash = get_hash(fullfrompath, fulltopath)
+    if current_hash == hash_db.get(normpath(fullfrompath), None):
+        print('%s has not changed' % fullfrompath)
+        return
+
+    orig_cwd = os.getcwd()
+    try:
+        os.chdir(path)
+        print('Processing %s' % fullfrompath)
+        processor_function(fromfile, tofile)
+    finally:
+        os.chdir(orig_cwd)
+    # changed target file, recompute hash
+    current_hash = get_hash(fullfrompath, fulltopath)
+    # store hash in db
+    hash_db[normpath(fullfrompath)] = current_hash
+
+
+def find_process_files(root_dir):
+    hash_db = load_hashes(HASH_FILE)
+    for cur_dir, dirs, files in os.walk(root_dir):
+        for filename in files:
+            in_file = os.path.join(cur_dir, filename + ".in")
+            if filename.endswith('.pyx') and os.path.isfile(in_file):
+                continue
+            for fromext, function in rules.items():
+                if filename.endswith(fromext):
+                    toext = ".c"
+                    with open(os.path.join(cur_dir, filename), 'rb') as f:
+                        data = f.read()
+                        m = re.search(br"^\s*#\s*distutils:\s*language\s*=\s*c\+\+\s*$", data, re.I|re.M)
+                        if m:
+                            toext = ".cxx"
+                    fromfile = filename
+                    tofile = filename[:-len(fromext)] + toext
+                    process(cur_dir, fromfile, tofile, function, hash_db)
+                    save_hashes(hash_db, HASH_FILE)
+
+def main():
+    try:
+        root_dir = sys.argv[1]
+    except IndexError:
+        root_dir = DEFAULT_ROOT
+    find_process_files(root_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/dataset_rst.py b/tools/dataset_rst.py
index a0af194c54b..477d4e42939 100755
--- a/tools/dataset_rst.py
+++ b/tools/dataset_rst.py
@@ -7,13 +7,18 @@
 
 import statsmodels.api as sm
 import os
-from os.path import join
+from os.path import join, realpath, dirname
 import inspect
 from string import Template
 
+import hash_funcs
+
+file_path = dirname(__file__)
+dest_dir = realpath(join(file_path, '..', 'docs', 'source', 'datasets',
+                         'generated'))
+
 datasets = dict(inspect.getmembers(sm.datasets, inspect.ismodule))
 datasets.pop('utils')
-datasets.pop('nile') #TODO: fix docstring in nile
 
 doc_template = Template(u"""$TITLE
 $title_
@@ -37,12 +42,15 @@
 $COPYRIGHT
 """)
 
-for dataset in datasets:
-    write_pth = join('../docs/source/datasets/generated',
-                             dataset+'.rst')
-    data_mod = datasets[dataset]
-    with open(os.path.realpath(write_pth), 'w') as rst_file:
-        title = getattr(data_mod,'TITLE')
+if __name__ == "__main__":
+
+    if not os.path.exists(dest_dir):
+        os.makedirs(dest_dir)
+
+    for dataset in datasets:
+        write_pth = join(dest_dir, dataset + '.rst')
+        data_mod = datasets[dataset]
+        title = getattr(data_mod, 'TITLE')
         descr = getattr(data_mod, 'DESCRLONG')
         copyr = getattr(data_mod, 'COPYRIGHT')
         notes = getattr(data_mod, 'NOTE')
@@ -51,4 +59,13 @@
                                              title_='='*len(title),
                                              DESCRIPTION=descr, NOTES=notes,
                                              SOURCE=source, COPYRIGHT=copyr)
-        rst_file.write(write_file)
+        to_write, filehash = hash_funcs.check_hash(write_file,
+                                                   data_mod.__name__)
+        if not to_write:
+            print("Hash has not changed for docstring of dataset "
+                  "{}".format(dataset))
+            continue
+        with open(os.path.realpath(write_pth), 'w') as rst_file:
+            rst_file.write(write_file)
+        if filehash is not None:
+            hash_funcs.update_hash_dict(filehash, data_mod.__name__)
diff --git a/tools/gh_api.py b/tools/gh_api.py
index acc26bf052a..bcbe3110c4b 100644
--- a/tools/gh_api.py
+++ b/tools/gh_api.py
@@ -1,7 +1,8 @@
 """Functions for Github API requests.
 
-Copied from IPython 732be29
-https://github.com/ipython/ipython/blob/master/tools/gh_api.py"""
+Copied from IPython 9e82bc5
+https://github.com/ipython/ipython/blob/master/tools/gh_api.py
+"""
 from __future__ import print_function
 
 try:
@@ -110,15 +111,25 @@ def get_pull_request(project, num, auth=False):
     response.raise_for_status()
     return json.loads(response.text, object_hook=Obj)
 
+def get_pull_request_files(project, num, auth=False):
+    """get list of files in a pull request"""
+    url = "https://api.github.com/repos/{project}/pulls/{num}/files".format(project=project, num=num)
+    if auth:
+        header = make_auth_header()
+    else:
+        header = None
+    return get_paged_request(url, headers=header)
+
 element_pat = re.compile(r'<(.+?)>')
 rel_pat = re.compile(r'rel=[\'"](\w+)[\'"]')
 
-def get_paged_request(url, headers=None):
+def get_paged_request(url, headers=None, **params):
     """get a full list, handling APIv3's paging"""
     results = []
+    params.setdefault("per_page", 100)
     while True:
-        print("fetching %s" % url, file=sys.stderr)
-        response = requests.get(url, headers=headers)
+        print("fetching %s with %s" % (url, params), file=sys.stderr)
+        response = requests.get(url, headers=headers, params=params)
         response.raise_for_status()
         results.extend(response.json())
         if 'next' in response.links:
@@ -127,28 +138,49 @@ def get_paged_request(url, headers=None):
             break
     return results
 
-def get_pulls_list(project, state="closed", auth=False):
-    """get pull request list
-    """
-    url = "https://api.github.com/repos/{project}/pulls?state={state}&per_page=100".format(project=project, state=state)
+def get_pulls_list(project, auth=False, **params):
+    """get pull request list"""
+    params.setdefault("state", "closed")
+    url = "https://api.github.com/repos/{project}/pulls".format(project=project)
     if auth:
         headers = make_auth_header()
     else:
         headers = None
-    pages = get_paged_request(url, headers=headers)
+    pages = get_paged_request(url, headers=headers, params=params)
     return pages
 
-def get_issues_list(project, state="closed", auth=False):
-    """get pull request list
-    """
-    url = "https://api.github.com/repos/{project}/pulls?state={state}&per_page=100".format(project=project, state=state)
+def get_issues_list(project, auth=False, **params):
+    """get issues list"""
+    params.setdefault("state", "closed")
+    url = "https://api.github.com/repos/{project}/issues".format(project=project)
+    if auth:
+        headers = make_auth_header()
+    else:
+        headers = None
+    pages = get_paged_request(url, headers=headers, **params)
+    return pages
+
+def get_milestones(project, auth=False, **params):
+    url = "https://api.github.com/repos/{project}/milestones".format(project=project)
     if auth:
         headers = make_auth_header()
     else:
         headers = None
-    pages = get_paged_request(url, headers=headers)
+    pages = get_paged_request(url, headers=headers, **params)
     return pages
 
+def get_milestone_id(project, milestone, auth=False, **params):
+    pages = get_milestones(project, auth=auth, **params)
+    for page in pages:
+        if page['title'] == milestone:
+            return page['number']
+    else:
+        raise ValueError("milestone %s not found" % milestone)
+
+def is_pull_request(issue):
+    """Return True if the given issue is a pull request."""
+    return bool(issue.get('pull_request', {}).get('html_url', None))
+
 # encode_multipart_formdata is from urllib3.filepost
 # The only change is to iter_fields, to enforce S3's required key ordering
 
diff --git a/tools/hash_funcs.py b/tools/hash_funcs.py
index 601a24c801a..31e872f3abe 100644
--- a/tools/hash_funcs.py
+++ b/tools/hash_funcs.py
@@ -3,7 +3,7 @@
 generated from certain files in the project (examples, datasets).
 """
 import os
-import pickle
+from statsmodels.compat import cPickle
 
 file_path = os.path.dirname(__file__)
 
@@ -22,12 +22,12 @@ def update_hash_dict(filehash, filename):
     """
     try:
         with open(file_path+'/hash_dict.pickle','r') as f:
-            hash_dict = pickle.load(f)
+            hash_dict = cPickle.load(f)
     except IOError as err:
         hash_dict = {}
     hash_dict.update({filename : filehash})
     with open(os.path.join(file_path,'hash_dict.pickle'),'w') as f:
-        pickle.dump(hash_dict, f)
+        cPickle.dump(hash_dict, f)
 
 def check_hash(rawfile, filename):
     """
@@ -35,7 +35,7 @@ def check_hash(rawfile, filename):
     """
     try:
         with open(file_path+'/hash_dict.pickle','r') as f:
-            hash_dict = pickle.load(f)
+            hash_dict = cPickle.load(f)
     except IOError as err:
         hash_dict = {}
     try:
diff --git a/tools/matplotlibrc b/tools/matplotlibrc
index 7022415ee69..fa12a90c14f 100644
--- a/tools/matplotlibrc
+++ b/tools/matplotlibrc
@@ -28,7 +28,7 @@
 # You can also deploy your own backend outside of matplotlib by
 # referring to the module name (which must be in the PYTHONPATH) as
 # 'module://my_backend'
-backend      : Qt4Agg
+backend      : Agg
 
 # if you are runing pyplot inside a GUI and your backend choice
 # conflicts, we will automatically try and find a compatible one for
@@ -118,7 +118,7 @@ font.family         : monospace
 #font.sans-serif     : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
 #font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, cursive
 #font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy
-font.monospace      : Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+font.monospace       : Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, Bitstream Vera Sans Mono, Ubuntu Mono, monospace
 ### TEXT
 # text properties used by text.Text.  See
 # http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.text for more
diff --git a/tools/nbgenerate.py b/tools/nbgenerate.py
index f2b27e9c766..9ceadfe68f8 100755
--- a/tools/nbgenerate.py
+++ b/tools/nbgenerate.py
@@ -6,8 +6,7 @@
 
 # prefer HTML over rST for now until nbconvert changes drop
 OUTPUT = "html"
-SOURCE_DIR = ("/home/skipper/statsmodels/statsmodels-skipper/examples/"
-              "notebooks")
+SOURCE_DIR = "/home/skipper/statsmodels/statsmodels/examples/notebooks"
 
 import os
 import io
@@ -31,20 +30,13 @@
 
 from IPython.config import Config
 try:
-    from nbconvert.converters.template import ConverterTemplate
-    from nbconvert.converters.rst import ConverterRST
+    from IPython.nbconvert.exporters import HTMLExporter
 except ImportError:
-    if os.path.exists("/home/skipper/src/nbconvert"):
-        if not os.path.exists(os.path.join(cur_dir, "nbconvert")):
-            os.symlink("/home/skipper/src/nbconvert",
-               os.path.join(cur_dir, "nbconvert"))
-        from nbconvert.converters.template import ConverterTemplate
-        from nbconvert.converters.rst import ConverterRST
-    else:
-        from warnings import warn
-        warn("Notebook examples not built. Add nbconvert to path or update "
-             "paths in tools/nbgenerate.py")
-        sys.exit(0)
+    from warnings import warn
+    from statsmodels.tools.sm_exceptions import ModuleUnavailableWarning
+    warn("Notebook examples not built. You need IPython 1.0.",
+         ModuleUnavailableWarning)
+    sys.exit(0)
 
 import hash_funcs
 
@@ -110,7 +102,7 @@ def run_cell(self, shell, iopub, cell, exec_count):
         outs = []
         shell.execute(cell.input)
         # hard-coded timeout, problem?
-        shell.get_msg(timeout=20)
+        shell.get_msg(timeout=90)
         cell.prompt_number = exec_count # msg["content"]["execution_count"]
 
         while True:
@@ -159,6 +151,7 @@ def run_notebook(self, nb):
         iopub = self.iopub
         cells = 0
         errors = 0
+        cell_errors = 0
         exec_count = 1
 
         #TODO: What are the worksheets? -ss
@@ -171,6 +164,8 @@ def run_notebook(self, nb):
                 try:
                     # attaches the output to cell inplace
                     outs = self.run_cell(shell, iopub, cell, exec_count)
+                    if outs and outs[-1]['output_type'] == 'pyerr':
+                        cell_errors += 1
                     exec_count += 1
                 except Exception as e:
                     print "failed to run cell:", repr(e)
@@ -184,7 +179,11 @@ def run_notebook(self, nb):
         if errors:
             print "    %3i cells raised exceptions" % errors
         else:
-            print "    there were no errors"
+            print "    there were no errors in run_cell"
+        if cell_errors:
+            print "    %3i cells have exceptions in their output" % cell_errors
+        else:
+            print "    all code executed in the notebook as expected"
 
     def __del__(self):
         self.kc.stop_channels()
@@ -212,17 +211,18 @@ def nb2html(nb):
     Cribbed from nbviewer
     """
     config = Config()
-    config.ConverterTemplate.template_file = 'basichtml'
+    config.HTMLExporter.template_file = 'basic'
     config.NbconvertApp.fileext = "html"
     config.CSSHtmlHeaderTransformer.enabled = False
 
-    C = ConverterTemplate(config=config)
-    return C.convert(nb)[0]
+    C = HTMLExporter(config=config)
+    return C.from_notebook_node(nb)[0]
 
 def nb2rst(nb, files_dir):
     """
     nb should be a NotebookNode
     """
+    #NOTE: This does not currently work. Needs to be update to IPython 1.0.
     config = Config()
     C = ConverterRST(config=config)
     # bastardize how this is supposed to be called
diff --git a/tools/notebook2python.tpl b/tools/notebook2python.tpl
new file mode 100644
index 00000000000..2e7887ac895
--- /dev/null
+++ b/tools/notebook2python.tpl
@@ -0,0 +1,46 @@
+{%- extends 'null.tpl' -%}
+
+{% block input %}
+{{ cell.input | ipython2python }}
+{% endblock input %}
+
+{# Those Two are for error displaying
+even if the first one seem to do nothing, 
+it introduces a new line
+#}
+{% block pyerr %}
+{{ super() }}
+{% endblock pyerr %}
+
+{% block traceback_line %}
+{{ line | indent | strip_ansi }}
+{% endblock traceback_line %}
+{# .... #}
+
+{% block pyout %}
+{{ output.text | indent | comment_lines }}
+{% endblock pyout %}
+
+{% block stream %}
+{{ output.text | indent | comment_lines }}
+{% endblock stream %}
+
+{% block display_data scoped %}
+# image file:
+{% endblock display_data %}
+
+{% block markdowncell scoped %}
+{{ cell.source | comment_lines }}
+{% endblock markdowncell %}
+
+{% block headingcell scoped %}
+{{ '#' * cell.level }}{{ cell.source | replace('\n', ' ') | comment_lines }}
+{% endblock headingcell %}
+
+{% block rawcell scoped %}
+{{ cell.source | comment_lines }}
+{% endblock rawcell %}
+
+{% block unknowncell scoped %}
+unknown type  {{ cell.type }}
+{% endblock unknowncell %}
diff --git a/tools/notebook_output_template.py b/tools/notebook_output_template.py
index 799dd2d6091..79604b2a7f8 100644
--- a/tools/notebook_output_template.py
+++ b/tools/notebook_output_template.py
@@ -3,7 +3,7 @@
 notebook_template = Template("""
 .. _${name}_notebook:
 
-`Link to Notebook GitHub <https://github.com/statsmodels/statsmodels/tree/master/examples/notebooks/$name.ipynb>`_
+`Link to Notebook GitHub <https://github.com/statsmodels/statsmodels/blob/master/examples/notebooks/$name.ipynb>`_
 
 .. raw:: html
 
@@ -44,5 +44,5 @@
    // loadcssfile({{pathto("_static/nbviewer.pygments.css", 1) }})
    // loadcssfile({{pathto("_static/nbviewer.min.css", 1) }})
    loadcssfile("../../../_static/nbviewer.pygments.css")
-   loadcssfile("../../../_static/nbviewer.min.css")
+   loadcssfile("../../../_static/ipython.min.css")
    </script>""")
diff --git a/tools/update_web.py b/tools/update_web.py
index 48275eb3d50..c52c2436b9f 100755
--- a/tools/update_web.py
+++ b/tools/update_web.py
@@ -9,30 +9,61 @@
 Depends
 -------
 virtualenv
+
+Notes
+-----
+If you set it up as an anacron job, you should do it as a user, so that it
+has access to your ssh keys.
 """
+import traceback
 import base64
 import subprocess
 import os
-import shutil
 import re
+import shutil
 import smtplib
 import sys
+from urllib2 import urlopen
 from email.MIMEText import MIMEText
+import logging
+logging.basicConfig(filename='/home/skipper/statsmodels/statsmodels/tools/'
+                             'docs_build_config.log', level=logging.DEBUG,
+                    format="%(asctime)s %(message)s")
+sys.stdout = open('/home/skipper/statsmodels/statsmodels/tools/crontab.out',
+                  'w')
+sys.stderr = open('/home/skipper/statsmodels/statsmodels/tools/crontab.err',
+                  'w')
+
+# Environment for subprocess calls. Needed for cron execution
+env = {'MATPLOTLIBRC' : ('/home/skipper/statsmodels/statsmodels/tools/'),
+       'HOME' : '/home/skipper',
+       'PATH' : ':'.join((os.getenv('PATH', ''), '/home/skipper/.local/bin')),
+       # Need this for my openblas setup on my laptop
+       # maybe no longer necessary with newer numpy
+       'LD_LIBRARY_PATH' : os.getenv('LD_LIBRARY_PATH', '')}
+
 
 ######### INITIAL SETUP ##########
 
 #hard-coded "current working directory" ie., you will need file permissions
 #for this folder
-script = os.path.abspath(sys.argv[0])
+# follow symbolic links
+script = os.path.realpath(sys.argv[0])
 dname = os.path.abspath(os.path.dirname(script))
 gname = 'statsmodels'
 gitdname = os.path.join(dname, gname)
 os.chdir(dname)
 
+logging.debug('script: {}'.format(script))
+logging.debug('dname: {}'.format(dname))
+
+# sourceforge account for rsync
+sf_account = 'jseabold,statsmodels@web.sourceforge.net'
+
 # hard-coded git branch names
 repo = 'git://github.com/statsmodels/statsmodels.git'
 stable_trunk = 'master'
-last_release = 'v0.4.3'
+last_release = 'v0.5.0'
 branches = [stable_trunk]
 # change last_release above and uncomment the below to update for a release
 #branches = [stable_trunk, last_release]
@@ -41,8 +72,7 @@
 virtual_dir = 'BUILDENV'
 virtual_dir = os.path.join(dname, virtual_dir)
 # this points to the newly installed python in the virtualenv
-virtual_python = os.path.join(virtual_dir,'bin','python')
-
+virtual_python = os.path.join(virtual_dir, 'bin', 'python')
 
 # my security holes
 with open('/home/skipper/statsmodels/gmail.txt') as f:
@@ -50,11 +80,11 @@
 gmail_pwd = base64.b64decode(pwd)
 
 ########### EMAIL #############
-email_name ='statsmodels.dev' + 'AT' + 'gmail' +'.com'
-email_name = email_name.replace('AT','@')
-gmail_pwd= gmail_pwd
-to_email = [email_name, ('josef.pktd' + 'AT' + 'gmail' + '.com').replace('AT',
-    '@')]
+email_name = 'statsmodels.dev' + 'AT' + 'gmail' + '.com'
+email_name = email_name.replace('AT', '@')
+gmail_pwd = gmail_pwd
+to_email = [email_name,
+            ('josef.pktd' + 'AT' + 'gmail' + '.com').replace('AT', '@')]
 #to_email = [email_name]
 
 
@@ -64,8 +94,9 @@ def create_virtualenv():
     # make a virtualenv for installation if it doesn't exist
     # and easy_install sphinx
     if not os.path.exists(virtual_dir):
-        retcode = subprocess.call(['/usr/local/bin/virtualenv',
-                                   "--system-site-packages", virtual_dir])
+        retcode = subprocess.call(['/home/skipper/.local/bin/virtualenv',
+                                   "--system-site-packages", virtual_dir],
+                                   stderr=sys.stderr, stdout=sys.stdout)
         if retcode != 0:
             msg = """There was a problem creating the virtualenv"""
             raise Exception(msg)
@@ -74,28 +105,64 @@ def create_virtualenv():
             msg = """There was a problem installing sphinx"""
             raise Exception(msg)
 
+
 def create_update_gitdir():
     """
-    Creates a directory for local repo if it doesn't exist, updates repo otherwise.
+    Creates a directory for local repo if it doesn't exist,
+    updates repo otherwise.
     """
     if not os.path.exists(gitdname):
-        retcode = subprocess.call('git clone '+repo, shell=True)
+        retcode = subprocess.call('git clone '+repo, shell=True,
+                                  stdout=sys.stdout, stderr=sys.stderr)
         if retcode != 0:
             msg = """There was a problem cloning the repo"""
             raise Exception(msg)
-    else: # directory exists, can't pull if you're not on a branch
-          # just delete it and clone again. Lazy but clean solution.
+    else:  # directory exists, can't pull if you're not on a branch
+           # just delete it and clone again. Lazy but clean solution.
         shutil.rmtree(gitdname)
         create_update_gitdir()
 
+
+def check_version(branch, latest_hash=None):
+    if branch == 'master':
+        remote_dir = 'devel'
+        regex = ("(?<=This documentation is for version <b>\d{1}\.\d{1}\."
+                 "\d{1}\.dev-)(\w{7})")
+    else:
+        remote_dir = 'stable'
+        regex = ("(?<=This documentation is for the <b>)(\d{1}\.\d{1}\.\d{1})"
+                 "(?=</b> release.)")
+    base_url = 'http://statsmodels.sourceforge.net/{}'
+    page = urlopen(base_url.format(remote_dir)).read()
+
+    try:
+        version = re.search(regex, page).group()
+    except AttributeError:
+        return True
+
+    if remote_dir == 'stable':
+        if last_release[1:] == version:
+            return False
+        else:
+            return True
+
+    # get the lastest hash
+    if latest_hash == version:
+        return False
+    else:
+        return True
+
+
 def getdirs():
     """
     Get current directories of cwd in order to restore to this
     """
-    dirs = [i for i in os.listdir(dname) if not \
-            os.path.isfile(os.path.join(dname, i))]
+    dirs = [i for i in os.listdir(dname)]
+    dirs = filter(lambda x : not os.path.isfile(os.path.join(dname, x)),
+                  dirs)
     return dirs
 
+
 def newdir(dirs):
     """
     Returns difference in directories between dirs and current directories
@@ -103,23 +170,24 @@ def newdir(dirs):
     If the difference is greater than one directory it raises an error.
     """
     dirs = set(dirs)
-    newdirs = set([i for i in os.listdir(dname) if not \
-            os.path.isfile(os.path.join(dname,i))])
+    newdirs = set([i for i in os.listdir(dname) if not
+                   os.path.isfile(os.path.join(dname, i))])
     newdir = newdirs.difference(dirs)
     if len(newdir) != 1:
-        msg = """There was more than one directory created.  Don't know what to delete."""
+        msg = ("There was more than one directory created. Don't know what "
+               "to delete.")
         raise Exception(msg)
     newdir = newdir.pop()
     return newdir
 
+
 def install_branch(branch):
     """
     Installs the branch in a virtualenv.
     """
-
     # if it's already in the virtualenv, remove it
-    ver = '.'.join(map(str,(sys.version_info.major,sys.version_info.minor)))
-    sitepack = os.path.join(virtual_dir, 'lib','python'+ver, 'site-packages')
+    ver = '.'.join(map(str, (sys.version_info.major, sys.version_info.minor)))
+    sitepack = os.path.join(virtual_dir, 'lib', 'python'+ver, 'site-packages')
     if os.path.exists(sitepack):
         dir_list = os.listdir(sitepack)
     else:
@@ -130,40 +198,57 @@ def install_branch(branch):
 
     # checkout the branch
     os.chdir(gitdname)
-    retcode = subprocess.call('git checkout ' + branch, shell=True)
+    retcode = subprocess.call('git checkout ' + branch, shell=True,
+                              stdout=sys.stdout, stderr=sys.stderr)
     if retcode != 0:
         msg = """Could not checkout out branch %s""" % branch
         raise Exception(msg)
 
+    p = subprocess.Popen('git rev-parse HEAD ', shell=True,
+                              stdout=subprocess.PIPE, stderr=sys.stderr)
+    version = p.communicate()[0][:7]
+
     # build and install
     retcode = subprocess.call(" ".join([virtual_python, 'setup.py', 'build']),
-                                shell=True)
+                              shell=True, stdout=sys.stdout, stderr=sys.stderr)
     if retcode != 0:
-        msg = """ Could not build branch %s""" % branch
+        msg = """Could not build branch %s""" % branch
         raise Exception(msg)
     retcode = subprocess.call(" ".join([virtual_python, os.path.join(gitdname,
-        'setup.py'), 'install']), shell=True)
+                                        'setup.py'), 'install']), shell=True,
+                              stdout=sys.stdout, stderr=sys.stderr)
     if retcode != 0:
         os.chdir(dname)
         msg = """Could not install branch %s""" % branch
         raise Exception(msg)
     os.chdir(dname)
+    return version
+
+
+def print_info():
+    subprocess.Popen([virtual_python, os.path.join(gitdname,
+                                                   "statsmodels",
+                                                   "tools",
+                                                   "print_version.py"
+                                                   )],
+                     stdout=sys.stdout, stderr=sys.stderr)
+
 
 def build_docs(branch):
     """
     Changes into gitdname and builds the docs using BUILDENV virtualenv
     """
     os.chdir(os.path.join(gitdname, 'docs'))
-    sphinx_dir = os.path.join(virtual_dir,'bin')
-    retcode = subprocess.call("make clean", shell=True)
+    retcode = subprocess.call("make clean", shell=True,
+                              stdout=sys.stdout, stderr=sys.stderr)
     if retcode != 0:
         os.chdir(dname)
         msg = """Could not clean the html docs for branch %s""" % branch
         raise Exception(msg)
     #NOTE: The python call in the below makes sure that it uses the Python
     # that is referenced after entering the virtualenv
-    sphinx_call = " ".join(['make','html',
-                        "SPHINXBUILD=' python /usr/local/bin/sphinx-build'"])
+    sphinx_call = " ".join(['make', 'html', "SPHINXBUILD=' python "
+                            "/usr/local/bin/sphinx-build'"])
     activate = os.path.join(virtual_dir, "bin", "activate")
     activate_virtualenv = ". " + activate
     #NOTE: You have to enter virtualenv in the same call. As soon as the
@@ -171,45 +256,49 @@ def build_docs(branch):
     # getting the correct env from bin/activate and passing to env is
     # annoying
     retcode = subprocess.call(" && ".join([activate_virtualenv, sphinx_call]),
-                                shell=True,
-        env = {'MATPLOTLIBRC' : # put this in the environment to use local rc
-               '/home/skipper/statsmodels/statsmodels/tools/'})
+                              shell=True, env=env, stdout=sys.stdout,
+                              stderr=sys.stderr)
+
     if retcode != 0:
         os.chdir(dname)
         msg = """Could not build the html docs for branch %s""" % branch
         raise Exception(msg)
     os.chdir(dname)
 
+
 def build_pdf(branch):
     """
     Changes into new_branch_dir and builds the docs using sphinx in the
     BUILDENV virtualenv
     """
-    os.chdir(os.path.join(gitdname,'statsmodels','docs'))
-    sphinx_dir = os.path.join(virtual_dir,'bin')
-    retcode = subprocess.call(" ".join(['make','latexpdf',
-        'SPHINXBUILD='+sphinx_dir+'/sphinx-build']), shell=True)
+    os.chdir(os.path.join(gitdname, 'statsmodels', 'docs'))
+    sphinx_dir = os.path.join(virtual_dir, 'bin')
+    retcode = subprocess.call(" ".join(['make', 'latexpdf',
+                              'SPHINXBUILD='+sphinx_dir+'/sphinx-build']),
+                              shell=True)
     if retcode != 0:
-        os.chdir(old_cwd)
         msg = """Could not build the pdf docs for branch %s""" % branch
         raise Exception(msg)
     os.chdir(dname)
 
+
 def upload_docs(branch):
     if branch == 'master':
         remote_dir = 'devel'
     else:
         remote_dir = 'stable'
-    #    old_cwd = os.getcwd()
     os.chdir(os.path.join(gitdname, 'docs'))
-    retcode = subprocess.call(['rsync', '-avPr' ,'-e ssh', 'build/html/',
-        'jseabold,statsmodels@web.sourceforge.net:htdocs/'+remote_dir])
+    retcode = subprocess.call(['rsync', '-avPrzh', '--inplace', '-e ssh',
+                               'build/html/', sf_account + ':htdocs/' +
+                               remote_dir],
+                              stderr=sys.stderr, stdout=sys.stdout)
     if retcode != 0:
-        os.chdir(old_cwd)
-        msg = """Could not upload html to %s for branch %s""" % (remote_dir, branch)
+        msg = """Could not upload html to %s for branch %s""" % (remote_dir,
+                                                                 branch)
         raise Exception(msg)
     os.chdir(dname)
 
+
 #TODO: upload pdf is not tested
 def upload_pdf(branch):
     if branch == 'master':
@@ -218,15 +307,15 @@ def upload_pdf(branch):
         remote_dir = 'stable'
     os.chdir(os.path.join(dname, new_branch_dir, 'statsmodels','docs'))
     retcode = subprocess.call(['rsync', '-avPr', '-e ssh',
-        'build/latex/statsmodels.pdf',
-        'jseabold,statsmodels@web.sourceforge.net:htdocs/'+remote_dir+'pdf/'])
+                               'build/latex/statsmodels.pdf',
+                               sf_account + ':htdocs/' + remote_dir + 'pdf/'])
     if retcode != 0:
-        os.chdir(old_cwd)
-        msg = """Could not upload pdf to %s for branch %s""" % (remote_dir+'/pdf',
-                branch)
+        msg = ("Could not upload pdf to %s for branch %s" %
+               (remote_dir+'/pdf', branch))
         raise Exception(msg)
     os.chdir(dname)
 
+
 def email_me(status='ok'):
     if status == 'ok':
         message = """
@@ -242,7 +331,7 @@ def email_me(status='ok'):
     msg['From'] = email_name
     msg['To'] = email_name
 
-    server = smtplib.SMTP('smtp.gmail.com',587)
+    server = smtplib.SMTP('smtp.gmail.com', 587)
     server.ehlo()
     server.starttls()
     server.ehlo()
@@ -260,15 +349,20 @@ def main():
         try:
             create_virtualenv()
             create_update_gitdir()
-            install_branch(branch)
-            build_docs(branch)
-            upload_docs(branch)
+            version = install_branch(branch)
+            if check_version(branch, version):
+                print_info()
+                build_docs(branch)
+                upload_docs(branch)
+            else:
+                msg += ('Latest version already available for branch '
+                        '{}.\n'.format(branch))
     #        build_pdf(new_branch_dir)
     #        upload_pdf(branch, new_branch_dir)
-        except Exception as status:
-            msg += str(status) + '\n'
+        except:
+            msg += traceback.format_exc()
 
-    if msg == '': # if it doesn't something went wrong and was caught above
+    if msg == '':  # if it doesn't something went wrong and was caught above
         email_me()
     else:
         email_me(msg)