python时间序列滞后命令_如何在Python回归模型中合并和预测滞后的时间序列变量...

>>>fit.summary()"""

OLS Regression Results

==============================================================================

Dep. Variable: y R-squared: 0.696

Model: OLS Adj. R-squared: 0.691

Method: Least Squares F-statistic: 150.8

Date: Tue, 08 Oct 2019 Prob (F-statistic): 6.93e-51

Time: 17:51:20 Log-Likelihood: -53.357

No. Observations: 201 AIC: 112.7

Df Residuals: 198 BIC: 122.6

Df Model: 3

Covariance Type: nonrobust

==============================================================================

coef std err t P>|t| [0.025 0.975]

------------------------------------------------------------------------------

y AR(1) 0.2972 0.072 4.142 0.000 0.156 0.439

x1 0.0211 0.003 6.261 0.000 0.014 0.028

x2 AR(3) 0.0161 0.007 2.264 0.025 0.002 0.030

==============================================================================

Omnibus: 2.115 Durbin-Watson: 2.277

Prob(Omnibus): 0.347 Jarque-Bera (JB): 1.712

Skew: 0.064 Prob(JB): 0.425

Kurtosis: 2.567 Cond. No. 41.5

==============================================================================

Warnings:

[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

"""