Linear Models

Linear (regression) models for Python. Extends statsmodels to include Panel regression and instrumental variable estimators:

• Panel regression with fixed effects (maximum two-way)
• First difference regression
• Between estimator for panel data
• Pooled regression for panel data
• Two-stage Least Squares
• Limited Information Maximum Likelihood
• k-class Estimators
• Generalized Method of Moments, also with continuously updating

Designed to work equally well with NumPy, Pandas or xarray data.

Panel models

Like statsmodels to include, supports patsy formulas for specifying models. For example, the classic Grunfeld regression can be specified

import numpy as np
from statsmodels.datasets import grunfeld
data = grunfeld.load_pandas().data
data.year = data.year.astype(np.int64)
# MultiIndex, entity - time
data = data.set_index(['firm','year'])
from linearmodels import PanelOLS
mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True)
res = mod.fit(cov_type='clustered', cluster_entity=True)

Models can also be specified using the formula interface.

from linearmodels import PanelOLS
mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffect', data)
res = mod.fit(cov_type='clustered', cluster_entity=True)

The formula interface for PanelOLS supports the special values EntityEffects and TimeEffects which add entity (fixed) and time effects, respectively.

Instrumental Variable Models

IV regression models can be similarly specified.

import numpy as np
from linearmodels.iv import IV2SLS
from linearmodels.datasets import mroz
data = mroz.load()
mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)

The expressions in the [ ] indicate endogenous regressors (before ~) and the instruments.

Installing

The latest release can be installed using pip

pip install linearmodels

The master branch can be installed by cloning the repo and running setup

git clone https://github.com/bashtage/linearmodels
cd linearmodels
python setup.py install

Documentation

Documentation is automatically built using doctr on every successful build of master. The documentation is still rough but should improve quickly.

Plan and status

Should eventually add some useful linear model estimators such as panel regression. Currently only the single variable IV estimators are polished.

• Linear Instrumental variable estimation - complete
• Linear Panel model estimation - complete
• Fama-MacBeth regression - not started
• Linear IV Panel model estimation - not started
• System regression - not started

Requirements

Running

With the exception of Python 3.5+, which is a hard requirement, the others are the version that are being used in the test environment. It is possible that older versions work.

• Python 3.5+: extensive use of @ operator
• NumPy (1.11+)
• SciPy (0.17+)
• Pandas (0.19+)
• xarray (0.9+)
• Statsmodels (0.8+)

Testing

• py.test

Documentation

• sphinx
• sphinx_rtd_theme
• nbsphinx
• nbconvert
• nbformat
• ipython
• jupyter