Python ARIMA(1,0,0)和内生回归对外生变量的不同结果
我试图在statsmodels中运行异方差的White测试。但是,其中一个参数需要一个外生变量数组。所以,我创建了一个带有常量、趋势和外生项的数据框架(见下文) 上述应等同于ARIMA(1,0,0),以“dlog_biz_machine_investment”作为结束语,以一个滞后和一个趋势项log_biz_machine_investment(一个滞后)表示。然而,当我运行ARIMA(1,0,0)模型时,我得到了不同的结果(从技术上讲,我应该得到相同的答案)。这里有我遗漏的东西吗? 此外,我想知道是否有可能从ARIMAResults中获取外生值,这将省去我创建另一个数组用于White测试的麻烦。谢谢Python ARIMA(1,0,0)和内生回归对外生变量的不同结果,python,time-series,statsmodels,arima,economics,Python,Time Series,Statsmodels,Arima,Economics,我试图在statsmodels中运行异方差的White测试。但是,其中一个参数需要一个外生变量数组。所以,我创建了一个带有常量、趋势和外生项的数据框架(见下文) 上述应等同于ARIMA(1,0,0),以“dlog_biz_machine_investment”作为结束语,以一个滞后和一个趋势项log_biz_machine_investment(一个滞后)表示。然而,当我运行ARIMA(1,0,0)模型时,我得到了不同的结果(从技术上讲,我应该得到相同的答案)。这里有我遗漏的东西吗? 此外,我想
mod = sm.tsa.arima.ARIMA(df.dlog_biz_machine_investment.dropna(), exog=df.log_biz_machine_investment_L1.dropna(), order=(1,0,0), freq='QS', trend='ct')
results = mod.fit()
print(results.summary())
>>> SARIMAX Results
=======================================================================================
Dep. Variable: dlog_biz_machine_investment No. Observations: 129
Model: ARIMA(1, 0, 0) Log Likelihood 122.067
Date: Tue, 05 May 2020 AIC -234.133
Time: 16:22:10 BIC -219.834
Sample: 04-01-1971 HQIC -228.323
- 04-01-2003
Covariance Type: opg
=================================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------------
const 0.5641 0.171 3.307 0.001 0.230 0.898
drift 0.0017 0.001 3.318 0.001 0.001 0.003
log_biz_machine_investment_L1 -0.1596 0.049 -3.277 0.001 -0.255 -0.064
ar.L1 -0.1696 0.096 -1.769 0.077 -0.357 0.018
sigma2 0.0088 0.001 11.728 0.000 0.007 0.010
===================================================================================
Ljung-Box (Q): 54.55 Jarque-Bera (JB): 51.61
Prob(Q): 0.06 Prob(JB): 0.00
Heteroskedasticity (H): 0.38 Skew: -0.31
Prob(H) (two-sided): 0.00 Kurtosis: 6.03
===================================================================================
在本节中,您的答案已解决:在本节中,您的答案已解决:
mod = sm.tsa.arima.ARIMA(df.dlog_biz_machine_investment.dropna(), exog=df.log_biz_machine_investment_L1.dropna(), order=(1,0,0), freq='QS', trend='ct')
results = mod.fit()
print(results.summary())
>>> SARIMAX Results
=======================================================================================
Dep. Variable: dlog_biz_machine_investment No. Observations: 129
Model: ARIMA(1, 0, 0) Log Likelihood 122.067
Date: Tue, 05 May 2020 AIC -234.133
Time: 16:22:10 BIC -219.834
Sample: 04-01-1971 HQIC -228.323
- 04-01-2003
Covariance Type: opg
=================================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------------
const 0.5641 0.171 3.307 0.001 0.230 0.898
drift 0.0017 0.001 3.318 0.001 0.001 0.003
log_biz_machine_investment_L1 -0.1596 0.049 -3.277 0.001 -0.255 -0.064
ar.L1 -0.1696 0.096 -1.769 0.077 -0.357 0.018
sigma2 0.0088 0.001 11.728 0.000 0.007 0.010
===================================================================================
Ljung-Box (Q): 54.55 Jarque-Bera (JB): 51.61
Prob(Q): 0.06 Prob(JB): 0.00
Heteroskedasticity (H): 0.38 Skew: -0.31
Prob(H) (two-sided): 0.00 Kurtosis: 6.03
===================================================================================