我复制了一个 例子 从linearmodels PanelOLS的介绍，并包含了稳健的标准误差来学习如何使用该模块。以下是我使用的代码
from linearmodels.datasets import jobtraining
import statsmodels.api as sm2
data = jobtraining.load()
mi_data = data.set_index(['fcode', 'year'])
mi_data.head()
from linearmodels import PanelOLS
mod = PanelOLS(mi_data.lscrap, sm2.add_constant(mi_data.hrsemp), entity_effects=True)
print(mod.fit(cov_type='robust'))
PanelOLS Estimation Summary
================================================================================
Dep. Variable: lscrap R-squared: 0.0528
Estimator: PanelOLS R-squared (Between): -0.0029
No. Observations: 140 R-squared (Within): 0.0528
Date: Tue, May 05 2020 R-squared (Overall): 0.0048
Time: 10:49:58 Log-likelihood -90.459
Cov. Estimator: Robust
F-statistic: 5.0751
Entities: 48 P-value 0.0267
Avg Obs: 2.9167 Distribution: F(1,91)
Min Obs: 1.0000
Max Obs: 3.0000 F-statistic (robust): 8.2299
P-value 0.0051
Time periods: 3 Distribution: F(1,91)
Avg Obs: 46.667
Min Obs: 46.000
Max Obs: 48.000
Parameter Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
const 0.4982 0.0555 8.9714 0.0000 0.3879 0.6085
hrsemp -0.0054 0.0019 -2.8688 0.0051 -0.0092 -0.0017
==============================================================================
F-test for Poolability: 17.094
P-value: 0.0000
Distribution: F(47,91)
Included effects: Entity
当我将结果与我习惯的使用稳健标准误差进行固定效应回归的方法进行比较时，我看到标准误差有很大的不同。
xtset fcode year
xtreg lscrap hrsemp , fe vce(robust)
Fixed-effects (within) regression Number of obs = 140
Group variable: fcode Number of groups = 48
R-sq: within = 0.0528 Obs per group: min = 1
between = 0.0002 avg = 2.9
overall = 0.0055 max = 3
F(1,47) = 7.93
corr(u_i, Xb) = -0.0266 Prob > F = 0.0071
(Std. Err. adjusted for 48 clusters in fcode)
------------------------------------------------------------------------------
| Robust
lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
hrsemp | -.0054186 .0019243 -2.82 0.007 -.0092897 -.0015474
_cons | .4981764 .0295415 16.86 0.000 .4387464 .5576063
-------------+----------------------------------------------------------------
sigma_u | 1.4004191
sigma_e | .57268937
rho | .85672692 (fraction of variance due to u_i)
------------------------------------------------------------------------------
我不明白这种差异从何而来，因为在没有使用稳健SE的情况下，结果几乎是一样的。我怎样才能像在Stata中一样使用Pythons linearmodels.PanelOLS来使用稳健SE呢？
White的稳健协方差，它在Python中使用的是与 cov_type='robust' 选项对固定效应模型不稳健。您应该使用 cov_type='robust',cluster_entity=True 而不是。下面是相应的 手工录入 来自linearmodels。
完整的代码。
from linearmodels.datasets import jobtraining
import statsmodels.api as sm2
data = jobtraining.load()
mi_data = data.set_index(['fcode', 'year'])
mi_data.head()
from linearmodels import PanelOLS
mod = PanelOLS(mi_data.lscrap, sm2.add_constant(mi_data.hrsemp), entity_effects=True)
print(mod.fit(cov_type='robust',cluster_entity=True))
相应的输出结果和Stata的差不多
PanelOLS Estimation Summary
================================================================================
Dep. Variable: lscrap R-squared: 0.0528
Estimator: PanelOLS R-squared (Between): -0.0029
No. Observations: 140 R-squared (Within): 0.0528
Date: Tue, May 05 2020 R-squared (Overall): 0.0048
Time: 18:53:06 Log-likelihood -90.459
Cov. Estimator: Robust
F-statistic: 5.0751
Entities: 48 P-value 0.0267
Avg Obs: 2.9167 Distribution: F(1,91)
Min Obs: 1.0000
Max Obs: 3.0000 F-statistic (robust): 8.2299
P-value 0.0051
Time periods: 3 Distribution: F(1,91)
Avg Obs: 46.667
Min Obs: 46.000
Max Obs: 48.000
Parameter Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
const 0.4982 0.0555 8.9714 0.0000 0.3879 0.6085
hrsemp -0.0054 0.0019 -2.8688 0.0051 -0.0092 -0.0017
==============================================================================
F-test for Poolability: 17.094
P-value: 0.0000
Distribution: F(47,91)
Included effects: Entity