def test_forecast():
# Numpy
endog = dta['infl'].iloc[:100].values
mod = ARIMA(endog[:50], order=(1, 1, 0), trend='t')
res = mod.filter([0.2, 0.3, 1.0])
endog2 = endog.copy()
endog2[50:] = np.nan
mod2 = mod.clone(endog2)
res2 = mod2.filter(res.params)
assert_allclose(res.forecast(50), res2.fittedvalues[-50:])
def test_forecast_with_exog():
# Numpy
endog = dta['infl'].iloc[:100].values
exog = np.arange(len(endog))**2
mod = ARIMA(endog[:50], order=(1, 1, 0), exog=exog[:50], trend='t')
res = mod.filter([0.2, 0.05, 0.3, 1.0])
endog2 = endog.copy()
endog2[50:] = np.nan
mod2 = mod.clone(endog2, exog=exog)
print(mod.param_names)
print(mod2.param_names)
res2 = mod2.filter(res.params)
assert_allclose(res.forecast(50, exog=exog[50:]), res2.fittedvalues[-50:])
