def test_predict_missing(self):
ex = self.data[:5].copy()
ex.iloc[0, 1] = np.nan
predicted1 = self.res.predict(ex)
predicted2 = self.res.predict(ex[1:])
assert_index_equal(predicted1.index, ex.index)
assert_series_equal(predicted1[1:], predicted2)
assert_equal(predicted1.values[0], np.nan)
def test_predict_missing(self):
ex = self.data[:5].copy()
ex.iloc[0, 1] = np.nan
predicted1 = self.res.predict(ex)
predicted2 = self.res.predict(ex[1:])
assert_index_equal(predicted1.index, ex.index)
assert_series_equal(predicted1[1:], predicted2)
assert_equal(predicted1.values[0], np.nan)
def test_innovations_filter_pandas(reset_randomstate):
ma = np.array([-0.9, 0.5])
acovf = np.array([1 + (ma**2).sum(), ma[0] + ma[1] * ma[0], ma[1]])
theta, _ = innovations_algo(acovf, nobs=10)
endog = np.random.randn(10)
endog_pd = pd.Series(endog, index=pd.date_range('2000-01-01', periods=10))
resid = innovations_filter(endog, theta)
resid_pd = innovations_filter(endog_pd, theta)
assert_allclose(resid, resid_pd.values)
assert_index_equal(endog_pd.index, resid_pd.index)
def test_var_cov_params_pandas(bivariate_var_data):
df = pd.DataFrame(bivariate_var_data, columns=['x', 'y'])
mod = VAR(df)
res = mod.fit(2)
cov = res.cov_params()
assert isinstance(cov, pd.DataFrame)
exog_names = ('const', 'L1.x', 'L1.y', 'L2.x', 'L2.y')
index = pd.MultiIndex.from_product((exog_names, ('x', 'y')))
assert_index_equal(cov.index, cov.columns)
assert_index_equal(cov.index, index)
def test_innovations_filter_pandas(reset_randomstate):
ma = np.array([-0.9, 0.5])
acovf = np.array([1 + (ma ** 2).sum(), ma[0] + ma[1] * ma[0], ma[1]])
theta, _ = innovations_algo(acovf, nobs=10)
endog = np.random.randn(10)
endog_pd = pd.Series(endog,
index=pd.date_range('2000-01-01', periods=10))
resid = innovations_filter(endog, theta)
resid_pd = innovations_filter(endog_pd, theta)
assert_allclose(resid, resid_pd.values)
assert_index_equal(endog_pd.index, resid_pd.index)
