BUG: Allow pandas to wrap VAR covariance

Extend PandasData to allow cov_names to be set separately from param_names

closes statsmodels#5305
closes statsmodels#2270

Author: bashtage

statsmodels/base/data.py

@@ -53,6 +53,7 @@ class ModelData(object):
     appropriate form
     """
     _param_names = None
+    _cov_names = None
 
     def __init__(self, endog, exog=None, missing='none', hasconst=None,
                  **kwargs):
@@ -350,6 +351,24 @@ def param_names(self):
     def param_names(self, values):
         self._param_names = values
 
+    @property
+    def cov_names(self):
+        """
+        Labels for covariance matrices
+
+        In multidimensional models, each dimension of a covariance matrix
+        differs from the number of param_names.
+
+        If not set, returns param_names
+        """
+        # for handling names of covariance names in multidimensional models
+        return self._cov_names or self.param_names
+
+    @cov_names.setter
+    def cov_names(self, value):
+        # for handling names of covariance names in multidimensional models
+        self._cov_names = value
+
     @cache_readonly
     def row_labels(self):
         exog = self.orig_exog
@@ -531,8 +550,7 @@ def attach_columns_eq(self, result):
         return DataFrame(result, index=self.xnames, columns=self.ynames)
 
     def attach_cov(self, result):
-        return DataFrame(result, index=self.param_names,
-                         columns=self.param_names)
+        return DataFrame(result, index=self.cov_names, columns=self.cov_names)
 
     def attach_cov_eq(self, result):
         return DataFrame(result, index=self.ynames, columns=self.ynames)

statsmodels/tsa/vector_ar/var_model.py

@@ -6,33 +6,32 @@
 ----------
 Lütkepohl (2005) New Introduction to Multiple Time Series Analysis
 """
-
+from statsmodels.compat.pandas import deprecate_kwarg
 from statsmodels.compat.python import (range, lrange, string_types,
                                        StringIO, iteritems)
-from statsmodels.compat.pandas import deprecate_kwarg
+
 from collections import defaultdict
 
 import numpy as np
-import scipy.stats as stats
 import scipy.linalg
+import scipy.stats as stats
 
+import statsmodels.base.wrapper as wrap
+from statsmodels.tsa.base.tsa_model import (TimeSeriesModel,
+                                            TimeSeriesResultsWrapper)
+import statsmodels.tsa.tsatools as tsa
 from statsmodels.iolib.table import SimpleTable
 from statsmodels.tools.decorators import cache_readonly, deprecated_alias
+from statsmodels.tools.linalg import logdet_symm
 from statsmodels.tools.sm_exceptions import OutputWarning
 from statsmodels.tools.tools import chain_dot
-from statsmodels.tools.linalg import logdet_symm
 from statsmodels.tsa.tsatools import vec, unvec, duplication_matrix
+from statsmodels.tsa.vector_ar import output, plotting, util
 from statsmodels.tsa.vector_ar.hypothesis_test_results import \
     CausalityTestResults, NormalityTestResults, WhitenessTestResults
-
 from statsmodels.tsa.vector_ar.irf import IRAnalysis
 from statsmodels.tsa.vector_ar.output import VARSummary
 
-import statsmodels.tsa.tsatools as tsa
-from statsmodels.tsa.vector_ar import output, plotting, util
-import statsmodels.tsa.base.tsa_model as tsbase
-import statsmodels.base.wrapper as wrap
-
 
 # -------------------------------------------------------------------------------
 # VAR process routines
@@ -495,7 +494,7 @@ def __str__(self):
 # VARProcess class: for known or unknown VAR process
 
 
-class VAR(tsbase.TimeSeriesModel):
+class VAR(TimeSeriesModel):
     r"""
     Fit VAR(p) process and do lag order selection
 
@@ -641,7 +640,9 @@ def fit(self, maxlags=None, method='ols', ic=None, trend='c',
             self.data.xnames = (self.data.xnames[:k_trend] +
                                 x_names_to_add +
                                 self.data.xnames[k_trend:])
-
+        self.data.cov_names = ['.'.join((str(yn), str(xn)))
+                               for xn in self.data.xnames
+                               for yn in self.data.ynames]
         return self._estimate_var(lags, trend=trend)
 
     def _estimate_var(self, lags, offset=0, trend='c'):
@@ -1347,7 +1348,6 @@ def sigma_u_mle(self):
         """
         return self.sigma_u * self.df_resid / self.nobs
 
-    @cache_readonly
     def cov_params(self):
         """Estimated variance-covariance of model coefficients
 
@@ -1359,20 +1359,9 @@ def cov_params(self):
         Adjusted to be an unbiased estimator
         Ref: Lütkepohl p.74-75
         """
-        import warnings
-        warnings.warn("For consistency with other statmsodels models, "
-                      "starting in version 0.11.0 `VARResults.cov_params` "
-                      "will be a method instead of a property.",
-                      category=FutureWarning)
         z = self.endog_lagged
         return np.kron(scipy.linalg.inv(np.dot(z.T, z)), self.sigma_u)
 
-    def _cov_params(self):
-        """Wrapper to avoid FutureWarning.  Remove after 0.11"""
-        import warnings
-        with warnings.catch_warnings(record=True):
-            return self.cov_params
-
     def cov_ybar(self):
         r"""Asymptotically consistent estimate of covariance of the sample mean
 
@@ -1405,7 +1394,8 @@ def _cov_alpha(self):
         Estimated covariance matrix of model coefficients w/o exog
         """
         # drop exog
-        return self._cov_params()[self.k_exog*self.neqs:, self.k_exog*self.neqs:]
+        kn = self.k_exog * self.neqs
+        return self.cov_params()[kn:, kn:]
 
     @cache_readonly
     def _cov_sigma(self):
@@ -1427,7 +1417,7 @@ def llf(self):
     def stderr(self):
         """Standard errors of coefficients, reshaped to match in size
         """
-        stderr = np.sqrt(np.diag(self._cov_params()))
+        stderr = np.sqrt(np.diag(self.cov_params()))
         return stderr.reshape((self.df_model, self.neqs), order='C')
 
     bse = stderr  # statsmodels interface?
@@ -1829,7 +1819,7 @@ def test_causality(self, caused, causing=None, kind='f', signif=0.05):
 
         # Lütkepohl 3.6.5
         Cb = np.dot(C, vec(self.params.T))
-        middle = scipy.linalg.inv(chain_dot(C, self._cov_params(), C.T))
+        middle = scipy.linalg.inv(chain_dot(C, self.cov_params(), C.T))
 
         # wald statistic
         lam_wald = statistic = chain_dot(Cb, middle, Cb)
@@ -2145,12 +2135,11 @@ class VARResultsWrapper(wrap.ResultsWrapper):
               'tvalues': 'columns_eq', 'sigma_u': 'cov_eq',
               'sigma_u_mle': 'cov_eq',
               'stderr': 'columns_eq'}
-    _wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
+    _wrap_attrs = wrap.union_dicts(TimeSeriesResultsWrapper._wrap_attrs,
                                    _attrs)
     _methods = {}
-    _wrap_methods = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_methods,
+    _wrap_methods = wrap.union_dicts(TimeSeriesResultsWrapper._wrap_methods,
                                      _methods)
-    _wrap_methods.pop('cov_params')  # not yet a method in VARResults
 wrap.populate_wrapper(VARResultsWrapper, VARResults)  # noqa:E305