Merge pull request #6867 from bashtage/limit-var-order

BUG: Limit maxlags in VAR

docs/source/names_wordlist.txt

@@ -174,3 +174,13 @@ Piet
 Pelletier
 Gentonb
 Nychkac
+Bańbura
+Domenico
+Elsevier
+Escanciano
+Giannone
+Jacoby
+Lobato
+Lucrezia
+Modugno
+Reichlin

docs/source/spelling_wordlist.txt

@@ -2352,3 +2352,14 @@ lamda
 parallelized
 Jupyter
 Reseasonalize
+blobbogram
+capstyle
+dotplots
+ldadjusted
+linestyle
+marginnames
+nonsymmetric
+Nowcasting
+Seasonalities
+substrings
+ywadjusted

statsmodels/graphics/dotplots.py

@@ -64,7 +64,7 @@ def dot_plot(points, intervals=None, lines=None, sections=None,
     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
+        is True) or from left to right (if horizontal is False).  If
         None (default), the order is lexical.
     striped : bool
         If True, every other line is enclosed in a shaded box.
@@ -101,6 +101,13 @@ def dot_plot(points, intervals=None, lines=None, sections=None,
     `points`, `intervals`, `lines`, `sections`, `styles` must all have
     the same length whenever present.
 
+    References
+    ----------
+    .. [1] Cleveland, William S. (1993). "Visualizing Data". Hobart Press.
+    .. [2] Jacoby, William G. (2006) "The Dot Plot: A Graphical Display
+       for Labeled Quantitative Values." The Political Methodologist
+       14(1): 6-14.
+
     Examples
     --------
     This is a simple dotplot with one point per line:
@@ -112,13 +119,6 @@ def dot_plot(points, intervals=None, lines=None, sections=None,
     the same line):
 
     >>> dot_plot(points=point_values, lines=label_values)
-
-    References
-    ----------
-    .. [1] Cleveland, William S. (1993). "Visualizing Data". Hobart Press.
-    .. [2] 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

statsmodels/imputation/mice.py

@@ -166,6 +166,17 @@ class MICEData(object):
         MICEData object is passed as the sole argument to
         `history_callback`.
 
+    Notes
+    -----
+    Allowed perturbation methods are 'gaussian' (the model parameters
+    are set to a draw from the Gaussian approximation to the posterior
+    distribution), and 'boot' (the model parameters are set to the
+    estimated values obtained when fitting a bootstrapped version of
+    the data set).
+
+    `history_callback` can be implemented to have side effects such as
+    saving the current imputed data set to disk.
+
     Examples
     --------
     Draw 20 imputations from a data set called `data` and save them in
@@ -179,17 +190,6 @@ class MICEData(object):
     Impute using default models, using the MICEData object as an
     iterator.
     %(_mice_data_example_2)s
-
-    Notes
-    -----
-    Allowed perturbation methods are 'gaussian' (the model parameters
-    are set to a draw from the Gaussian approximation to the posterior
-    distribution), and 'boot' (the model parameters are set to the
-    estimated values obtained when fitting a bootstrapped version of
-    the data set).
-
-    `history_callback` can be implemented to have side effects such as
-    saving the current imputed data set to disk.
     """ % {'_mice_data_example_1': _mice_data_example_1,
            '_mice_data_example_2': _mice_data_example_2}
 

statsmodels/tsa/mlemodel.py

@@ -45,6 +45,11 @@ def loglike(self, params):
         """
         Loglikelihood for timeseries model
 
+        Parameters
+        ----------
+        params : array_like
+            The model parameters
+
         Notes
         -----
         needs to be overwritten by subclass

statsmodels/tsa/statespace/kalman_filter.py

@@ -88,6 +88,11 @@ class KalmanFilter(Representation):
         Keyword arguments may be used to provide default values for state space
         matrices. See `Representation` for more details.
 
+    See Also
+    --------
+    FilterResults
+    statsmodels.tsa.statespace.representation.Representation
+
     Notes
     -----
     There are several types of options available for controlling the Kalman
@@ -130,11 +135,6 @@ class KalmanFilter(Representation):
     than explicit matrix inversion) is used. If only SOLVE_CHOLESKY had been
     set, then the Cholesky decomposition method would *always* be used, even in
     the case of 1-dimensional data.
-
-    See Also
-    --------
-    FilterResults
-    statsmodels.tsa.statespace.representation.Representation
     """
 
     filter_methods = [

statsmodels/tsa/statespace/kalman_smoother.py

@@ -1025,7 +1025,7 @@ def news(self, previous, t=None, start=None, end=None,
             argument.
         design : array, optional
             Design matrix for the period `t` in time-varying models. If this
-            model has a time-varying design matrix, and the arugment `t` is out
+            model has a time-varying design matrix, and the argument `t` is out
             of this model's sample, then a new design matrix for period `t`
             must be provided. Unused otherwise.
 

statsmodels/tsa/statespace/mlemodel.py

@@ -110,6 +110,12 @@ class MLEModel(tsbase.TimeSeriesModel):
     ssm : statsmodels.tsa.statespace.kalman_filter.KalmanFilter
         Underlying state space representation.
 
+    See Also
+    --------
+    statsmodels.tsa.statespace.mlemodel.MLEResults
+    statsmodels.tsa.statespace.kalman_filter.KalmanFilter
+    statsmodels.tsa.statespace.representation.Representation
+
     Notes
     -----
     This class wraps the state space model with Kalman filtering to add in
@@ -122,12 +128,6 @@ class MLEModel(tsbase.TimeSeriesModel):
 
     The `start_params` `update` method must be overridden in the
     child class (and the `transform` and `untransform` methods, if needed).
-
-    See Also
-    --------
-    statsmodels.tsa.statespace.mlemodel.MLEResults
-    statsmodels.tsa.statespace.kalman_filter.KalmanFilter
-    statsmodels.tsa.statespace.representation.Representation
     """
 
     def __init__(self, endog, k_states, exog=None, dates=None, freq=None,
@@ -893,6 +893,11 @@ def loglike(self, params, *args, **kwargs):
             Additional keyword arguments to pass to the Kalman filter. See
             `KalmanFilter.filter` for more details.
 
+        See Also
+        --------
+        update : modifies the internal state of the state space model to
+                 reflect new params
+
         Notes
         -----
         [1]_ recommend maximizing the average likelihood to avoid scale issues;
@@ -903,11 +908,6 @@ def loglike(self, params, *args, **kwargs):
         .. [1] Koopman, Siem Jan, Neil Shephard, and Jurgen A. Doornik. 1999.
            Statistical Algorithms for Models in State Space Using SsfPack 2.2.
            Econometrics Journal 2 (1): 107-60. doi:10.1111/1368-423X.00023.
-
-        See Also
-        --------
-        update : modifies the internal state of the state space model to
-                 reflect new params
         """
         transformed, includes_fixed, complex_step, kwargs = _handle_args(
             MLEModel._loglike_param_names, MLEModel._loglike_param_defaults,
@@ -944,6 +944,10 @@ def loglikeobs(self, params, transformed=True, includes_fixed=False,
             Additional keyword arguments to pass to the Kalman filter. See
             `KalmanFilter.filter` for more details.
 
+        See Also
+        --------
+        update : modifies the internal state of the Model to reflect new params
+
         Notes
         -----
         [1]_ recommend maximizing the average likelihood to avoid scale issues;
@@ -954,10 +958,6 @@ def loglikeobs(self, params, transformed=True, includes_fixed=False,
         .. [1] Koopman, Siem Jan, Neil Shephard, and Jurgen A. Doornik. 1999.
            Statistical Algorithms for Models in State Space Using SsfPack 2.2.
            Econometrics Journal 2 (1): 107-60. doi:10.1111/1368-423X.00023.
-
-        See Also
-        --------
-        update : modifies the internal state of the Model to reflect new params
         """
         params = self.handle_params(params, transformed=transformed,
                                     includes_fixed=includes_fixed)
@@ -1608,16 +1608,16 @@ def transform_jacobian(self, unconstrained, approx_centered=False):
         jacobian : ndarray
             Jacobian matrix of the transformation, evaluated at `unconstrained`
 
+        See Also
+        --------
+        transform_params
+
         Notes
         -----
         This is a numerical approximation using finite differences. Note that
         in general complex step methods cannot be used because it is not
         guaranteed that the `transform_params` method is a real function (e.g.
         if Cholesky decomposition is used).
-
-        See Also
-        --------
-        transform_params
         """
         return approx_fprime(unconstrained, self.transform_params,
                              centered=approx_centered)
@@ -2943,6 +2943,11 @@ def test_normality(self, method):
             Jarque-Bera normality test. If None, an attempt is made to select
             an appropriate test.
 
+        See Also
+        --------
+        statsmodels.stats.stattools.jarque_bera
+            The Jarque-Bera test of normality.
+
         Notes
         -----
         Let `d` = max(loglikelihood_burn, nobs_diffuse); this test is
@@ -2952,11 +2957,6 @@ def test_normality(self, method):
         data are missing completely at random. This test is then run on the
         standardized residuals excluding those corresponding to missing
         observations.
-
-        See Also
-        --------
-        statsmodels.stats.stattools.jarque_bera
-            The Jarque-Bera test of normality.
         """
         if method is None:
             method = 'jarquebera'
@@ -3171,17 +3171,17 @@ def test_serial_correlation(self, method, lags=None):
             `statsmodels.stats.diagnostic.acorr_ljungbox`) for the `i` th
             endogenous variable.
 
+        See Also
+        --------
+        statsmodels.stats.diagnostic.acorr_ljungbox
+            Ljung-Box test for serial correlation.
+
         Notes
         -----
         Let `d` = max(loglikelihood_burn, nobs_diffuse); this test is
         calculated ignoring the first `d` residuals.
 
         Output is nan for any endogenous variable which has missing values.
-
-        See Also
-        --------
-        statsmodels.stats.diagnostic.acorr_ljungbox
-            Ljung-Box test for serial correlation.
         """
         if method is None:
             method = 'ljungbox'
@@ -3860,6 +3860,11 @@ def append(self, endog, exog=None, refit=False, fit_kwargs=None, **kwargs):
         can be much faster if the original dataset is large), see the `extend`
         method.
 
+        See Also
+        --------
+        statsmodels.tsa.statespace.mlemodel.MLEResults.extend
+        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
+
         Examples
         --------
         >>> index = pd.period_range(start='2000', periods=2, freq='A')
@@ -3893,11 +3898,6 @@ def append(self, endog, exog=None, refit=False, fit_kwargs=None, **kwargs):
         >>> print(updated_res.forecast(1))
         2003    0.878
         Freq: A-DEC, dtype: float64
-
-        See Also
-        --------
-        statsmodels.tsa.statespace.mlemodel.MLEResults.extend
-        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
         """
         start = self.nobs
         end = self.nobs + len(endog) - 1
@@ -3970,6 +3970,11 @@ def extend(self, endog, exog=None, fit_kwargs=None, **kwargs):
             Updated Results object, that includes results only for the new
             dataset.
 
+        See Also
+        --------
+        statsmodels.tsa.statespace.mlemodel.MLEResults.append
+        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
+
         Notes
         -----
         The `endog` argument to this method should consist of new observations
@@ -4013,11 +4018,6 @@ def extend(self, endog, exog=None, fit_kwargs=None, **kwargs):
         >>> print(updated_res.forecast(1))
         2003    0.878
         Freq: A-DEC, dtype: float64
-
-        See Also
-        --------
-        statsmodels.tsa.statespace.mlemodel.MLEResults.append
-        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
         """
         start = self.nobs
         end = self.nobs + len(endog) - 1
@@ -4076,6 +4076,11 @@ def apply(self, endog, exog=None, refit=False, fit_kwargs=None,
             Updated Results object, that includes results only for the new
             dataset.
 
+        See Also
+        --------
+        statsmodels.tsa.statespace.mlemodel.MLEResults.append
+        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
+
         Notes
         -----
         The `endog` argument to this method should consist of new observations
@@ -4117,11 +4122,6 @@ def apply(self, endog, exog=None, refit=False, fit_kwargs=None,
         >>> print(new_res.forecast(1))
         1983    1.1707
         Freq: A-DEC, dtype: float64
-
-        See Also
-        --------
-        statsmodels.tsa.statespace.mlemodel.MLEResults.append
-        statsmodels.tsa.statespace.mlemodel.MLEResults.apply
         """
         mod = self.model.clone(endog, exog=exog, **kwargs)
         if copy_initialization:
@@ -4149,6 +4149,16 @@ def plot_diagnostics(self, variable=0, lags=10, fig=None, figsize=None):
             If a figure is created, this argument allows specifying a size.
             The tuple is (width, height).
 
+        Returns
+        -------
+        Figure
+            Figure instance with diagnostic plots
+
+        See Also
+        --------
+        statsmodels.graphics.gofplots.qqplot
+        statsmodels.graphics.tsaplots.plot_acf
+
         Notes
         -----
         Produces a 2x2 plot grid with the following plots (ordered clockwise
@@ -4159,11 +4169,6 @@ def plot_diagnostics(self, variable=0, lags=10, fig=None, figsize=None):
            with a Normal(0,1) density plotted for reference.
         3. Normal Q-Q plot, with Normal reference line.
         4. Correlogram
-
-        See Also
-        --------
-        statsmodels.graphics.gofplots.qqplot
-        statsmodels.graphics.tsaplots.plot_acf
         """
         from statsmodels.graphics.utils import _import_mpl, create_mpl_fig
         _import_mpl()