有一个名为linearmodels(https://pypi.org/project/linearmodels/)的包，它具有相当完整的固定效果和随机效果实现，包括集群标准错误。它不使用高维OLS来消除影响，因此可以用于大型数据集。# Outer is entity, inner is time

entity = list(map(chr,range(65,91)))

time = list(pd.date_range('1-1-2014',freq='A', periods=4))

index = pd.MultiIndex.from_product([entity, time])

df = pd.DataFrame(np.random.randn(26*4, 2),index=index, columns=['y','x'])

from linearmodels.panel import PanelOLS

mod = PanelOLS(df.y, df.x, entity_effects=True)

res = mod.fit(cov_type='clustered', cluster_entity=True)

print(res)

这将产生以下输出：PanelOLS Estimation Summary

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

Dep. Variable: y R-squared: 0.0029

Estimator: PanelOLS R-squared (Between): -0.0109

No. Observations: 104 R-squared (Within): 0.0029

Date: Thu, Jun 29 2017 R-squared (Overall): -0.0007

Time: 23:52:28 Log-likelihood -125.69

Cov. Estimator: Clustered

F-statistic: 0.2256

Entities: 26 P-value 0.6362

Avg Obs: 4.0000 Distribution: F(1,77)

Min Obs: 4.0000

Max Obs: 4.0000 F-statistic (robust): 0.1784

P-value 0.6739

Time periods: 4 Distribution: F(1,77)

Avg Obs: 26.000

Min Obs: 26.000

Max Obs: 26.000

Parameter Estimates

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

Parameter Std. Err. T-stat P-value Lower CI Upper CI

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

x 0.0573 0.1356 0.4224 0.6739 -0.2127 0.3273

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

F-test for Poolability: 1.0903

P-value: 0.3739

Distribution: F(25,77)

Included effects: Entity

它还有一个类似于statsmodels的公式接口mod = PanelOLS.from_formula('y ~ x + EntityEffects', df)