def test_exog(self):
# check that trend and exog are equivalent for basics and varsim
data = self.res0.model.endog
res_lin_trend = VAR(data).fit(maxlags=2, trend="ct")
ex = np.arange(len(data))
res_lin_trend1 = VAR(data, exog=ex).fit(maxlags=2)
ex2 = np.arange(len(data))[:, None]**[0, 1]
res_lin_trend2 = VAR(data, exog=ex2).fit(maxlags=2, trend="n")
# TODO: intercept differs by 4e-3, others are < 1e-12
assert_allclose(res_lin_trend.params, res_lin_trend1.params, rtol=5e-3)
assert_allclose(res_lin_trend.params, res_lin_trend2.params, rtol=5e-3)
assert_allclose(res_lin_trend1.params,
res_lin_trend2.params,
rtol=1e-10)
y1 = res_lin_trend.simulate_var(seed=987128)
y2 = res_lin_trend1.simulate_var(seed=987128)
y3 = res_lin_trend2.simulate_var(seed=987128)
assert_allclose(y2.mean(0), y1.mean(0), rtol=1e-12)
assert_allclose(y3.mean(0), y1.mean(0), rtol=1e-12)
assert_allclose(y3.mean(0), y2.mean(0), rtol=1e-12)
h = 10
fc1 = res_lin_trend.forecast(res_lin_trend.endog[-2:], h)
exf = np.arange(len(data), len(data) + h)
fc2 = res_lin_trend1.forecast(res_lin_trend1.endog[-2:],
h,
exog_future=exf)
with pytest.raises(ValueError, match="exog_future only has"):
wrong_exf = np.arange(len(data), len(data) + h // 2)
res_lin_trend1.forecast(res_lin_trend1.endog[-2:],
h,
exog_future=wrong_exf)
exf2 = exf[:, None]**[0, 1]
fc3 = res_lin_trend2.forecast(res_lin_trend2.endog[-2:],
h,
exog_future=exf2)
assert_allclose(fc2, fc1, rtol=1e-12, atol=1e-12)
assert_allclose(fc3, fc1, rtol=1e-12, atol=1e-12)
assert_allclose(fc3, fc2, rtol=1e-12, atol=1e-12)
fci1 = res_lin_trend.forecast_interval(res_lin_trend.endog[-2:], h)
exf = np.arange(len(data), len(data) + h)
fci2 = res_lin_trend1.forecast_interval(res_lin_trend1.endog[-2:],
h,
exog_future=exf)
exf2 = exf[:, None]**[0, 1]
fci3 = res_lin_trend2.forecast_interval(res_lin_trend2.endog[-2:],
h,
exog_future=exf2)
assert_allclose(fci2, fci1, rtol=1e-12, atol=1e-12)
assert_allclose(fci3, fci1, rtol=1e-12, atol=1e-12)
assert_allclose(fci3, fci2, rtol=1e-12, atol=1e-12)
