BUG: Fix ARMA cov_params

Fix cov_params so that it works with standard methods

closes statsmodels#947

statsmodels/tsa/ar_model.py

@@ -1,4 +1,6 @@
 # -*- coding: utf-8 -*-
+import copy
+
 import numpy as np
 from numpy.linalg import inv, slogdet
 from scipy.stats import norm
@@ -593,7 +595,7 @@ 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)
+        arfit = ARResults(copy.copy(self), params, normalized_cov_params)
         if method == 'mle' and full_output:
             arfit.mle_retvals = mlefit.mle_retvals
             arfit.mle_settings = mlefit.mle_settings

statsmodels/tsa/arima_model.py

@@ -4,6 +4,7 @@
 #       This code does not allow this, but it adds consistency with other
 #       packages such as gretl and X12-ARIMA
 
+import copy
 from datetime import datetime
 
 import numpy as np
@@ -12,7 +13,7 @@
 from numpy.linalg import inv
 from scipy import optimize
 from scipy.signal import lfilter
-from scipy.stats import t, norm
+from scipy.stats import norm
 
 import statsmodels.base.wrapper as wrap
 from statsmodels.regression.linear_model import yule_walker, OLS
@@ -1008,7 +1009,7 @@ def fit(self, start_params=None, trend='c', method="css-mle",
         self.transparams = False  # so methods do not expect transf.
 
         normalized_cov_params = None  # TODO: fix this
-        armafit = ARMAResults(self, params, normalized_cov_params)
+        armafit = ARMAResults(copy.copy(self), params, normalized_cov_params)
         armafit.mle_retvals = mlefit.mle_retvals
         armafit.mle_settings = mlefit.mle_settings
         # Save core fit parameters for future checks
@@ -1494,9 +1495,9 @@ def bse(self):
             return np.sqrt(-1. / hess[0])
         return np.sqrt(np.diag(-inv(hess)))
 
-    def cov_params(self):  # add scale argument?
-        params = self.params
-        hess = self.model.hessian(params)
+    @cache_readonly
+    def cov_params_default(self):
+        hess = self.model.hessian(self.params)
         return -inv(hess)
 
     @cache_readonly
@@ -1532,12 +1533,6 @@ def fittedvalues(self):
     def resid(self):
         return self.model.geterrors(self.params)
 
-    @cache_readonly
-    def pvalues(self):
-        # TODO: same for conditional and unconditional?
-        df_resid = self.df_resid
-        return t.sf(np.abs(self.tvalues), df_resid) * 2
-
     def predict(self, start=None, end=None, exog=None, dynamic=False,
                 **kwargs):
         return self.model.predict(self.params, start, end, exog, dynamic,

statsmodels/tsa/tests/test_arima.py

@@ -2699,3 +2699,22 @@ def test_arima_repeated_fit():
     assert_allclose(res.params, repeat.params)
     assert isinstance(res.summary().as_text(), str)
     assert isinstance(repeat.summary().as_text(), str)
+
+
+def test_arma_ttest_ftest():
+    # GH947
+    ar, ma = [1, -0.5], [1., 0.4]
+    x = 2 + fa.ArmaFft(ar, ma, 40).generate_sample(nsample=1000, burnin=100)
+    arma = ARMA(x, (1, 1))
+    res = arma.fit(disp=-1)
+    r = np.array([0, 1, 1])
+    t_test = res.t_test(r)
+    c = res.cov_params()
+    v = r @ c @ r
+    numerator = res.params @ r
+    denom = np.sqrt(v)
+    stat = numerator / denom
+    assert_allclose(stat, t_test.statistic)
+
+    f_test = res.f_test(r)
+    assert_allclose(stat**2, f_test.statistic)