CLN/TST: Improve arima_process

Clean and test arima process functions

.coveragerc

@@ -20,6 +20,14 @@ exclude_lines =
     if 0:
     if __name__ == .__main__.:
 
+omit =
+    # Test dirs
+    */tests/*
+    */test_*
+
+    # print_version
+    */print_version.py
+
 ignore_errors = False
 
 [html]

.travis_coveragerc

@@ -1,5 +1,31 @@
-
 [run]
 branch = True
 include =
     */statsmodels/*
+
+omit =
+    # Test dirs
+    */tests/*
+    # Test files
+    */test_*
+
+    # print_version
+    */print_version.py
+
+[report]
+# Regexes for lines to exclude from consideration
+exclude_lines =
+    # Have to re-enable the standard pragma
+    pragma: no cover
+
+    # Don't complain about missing debug-only code:
+    def __repr__
+    if self\.debug
+
+    # Don't complain if tests don't hit defensive assertion code:
+    raise AssertionError
+    raise NotImplementedError
+
+    # Don't complain if non-runnable code isn't run:
+    if 0:
+    if __name__ == .__main__.:

statsmodels/sandbox/tsa/fftarma.py

@@ -239,9 +239,9 @@ def spdroots(self, w):
 
         builds two arrays (number of roots, number of frequencies)
         '''
-        return self.spdroots_(self.arroots, self.maroots, w)
+        return self._spdroots(self.arroots, self.maroots, w)
 
-    def spdroots_(self, arroots, maroots, w):
+    def _spdroots(self, arroots, maroots, w):
         '''spectral density for frequency using polynomial roots
 
         builds two arrays (number of roots, number of frequencies)

statsmodels/tsa/arima_model.py

@@ -1462,7 +1462,7 @@ def predict(self, start=None, end=None, exog=None, dynamic=False):
     def _forecast_error(self, steps):
         sigma2 = self.sigma2
         ma_rep = arma2ma(np.r_[1, -self.arparams],
-                         np.r_[1, self.maparams], nobs=steps)
+                         np.r_[1, self.maparams], lags=steps)
 
         fcasterr = np.sqrt(sigma2 * np.cumsum(ma_rep**2))
         return fcasterr
@@ -1780,7 +1780,7 @@ def predict(self, start=None, end=None, exog=None, typ='linear',
     def _forecast_error(self, steps):
         sigma2 = self.sigma2
         ma_rep = arma2ma(np.r_[1, -self.arparams],
-                         np.r_[1, self.maparams], nobs=steps)
+                         np.r_[1, self.maparams], lags=steps)
 
         fcerr = np.sqrt(np.cumsum(cumsum_n(ma_rep, self.k_diff)**2)*sigma2)
         return fcerr