编辑（10/31/2015）以反映statsmodels
2015年秋季的首选编码样式。
在
statsmodels0.6.1版中，您可以执行以下操作：
import pandas as pdimport numpy as npimport statsmodels.formula.api as smfdf = pd.Dataframe({'a':[1,3,5,7,4,5,6,4,7,8,9], 'b':[3,5,6,2,4,6,7,8,7,8,9]})reg = smf.ols('a ~ 1 + b',data=df).fit(cov_type='HAC',cov_kwds={'maxlags':1})print reg.summary() OLS Regression Results==============================================================================Dep. Variable:a R-squared: 0.281Model: OLS Adj. R-squared: 0.201Method: Least Squares F-statistic: 1.949Date: Sat, 31 Oct 2015 Prob (F-statistic): 0.196Time: 03:15:46 Log-Likelihood: -22.603No. Observations: 11 AIC: 49.21Df Residuals: 9 BIC: 50.00Df Model: 1Covariance Type: HAC============================================================================== coef std err z P>|z| [95.0% Conf. Int.]------------------------------------------------------------------------------Intercept 2.0576 2.661 0.773 0.439 -3.157 7.272b 0.5595 0.401 1.396 0.163 -0.226 1.345==============================================================================Omnibus: 0.361 Durbin-Watson: 1.468Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.331Skew: 0.321 Prob(JB): 0.847Kurtosis: 2.442 Cond. No. 19.1==============================================================================Warnings:[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 1 lags and without small sample correction或者您可以
get_robustcov_results在拟合模型后使用该方法：
reg = smf.ols('a ~ 1 + b',data=df).fit()new = reg.get_robustcov_results(cov_type='HAC',maxlags=1)print new.summary() OLS Regression Results==============================================================================Dep. Variable:a R-squared: 0.281Model: OLS Adj. R-squared: 0.201Method: Least Squares F-statistic: 1.949Date: Sat, 31 Oct 2015 Prob (F-statistic): 0.196Time: 03:15:46 Log-Likelihood: -22.603No. Observations: 11 AIC: 49.21Df Residuals: 9 BIC: 50.00Df Model: 1Covariance Type: HAC============================================================================== coef std err z P>|z| [95.0% Conf. Int.]------------------------------------------------------------------------------Intercept 2.0576 2.661 0.773 0.439 -3.157 7.272b 0.5595 0.401 1.396 0.163 -0.226 1.345==============================================================================Omnibus: 0.361 Durbin-Watson: 1.468Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.331Skew: 0.321 Prob(JB): 0.847Kurtosis: 2.442 Cond. No. 19.1==============================================================================Warnings:[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 1 lags and without small sample correction的默认
statsmodels值与中等效方法的默认值略有不同
R。通过将调用更改为以下内容，
R可以使该方法等效于
statsmodels默认方法（如上所述）
vcov,：
temp.summ$coefficients <- unclass(coeftest(temp.lm, vcov. = NeweyWest(temp.lm,lag=1,prewhite=FALSE)))print (temp.summ$coefficients) Estimate Std. Error t value Pr(>|t|)(Intercept) 2.0576208 2.6605060 0.7733945 0.4591196b0.5594796 0.4007965 1.3959193 0.1962142
您仍然可以在熊猫（0.17）中执行Newey-West，尽管我认为该计划将在熊猫中弃用OLS：
print pd.stats.ols.OLS(df.a,df.b,nw_lags=1)-------------------------Summary of Regression Analysis-------------------------Formula: Y ~ <x> + <intercept>Number of Observations: 11Number of Degrees of Freedom: 2R-squared: 0.2807Adj R-squared: 0.2007Rmse: 2.0880F-stat (1, 9): 1.5943, p-value: 0.2384Degrees of Freedom: model 1, resid 9-----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- x 0.5595 0.4431 1.26 0.2384 -0.3090 1.4280 intercept 2.0576 2.9413 0.70 0.5019 -3.7073 7.8226*** The calculations are Newey-West adjusted with lags 1---------------------------------End of Summary---------------------------------
