Is there a way to add fixed effects in statsmodels.api.ols or statsmodels.formula.api.ols without creating dummy variables manually?
statsmodels does not support fixed effect regression. However, the package linearmodels does.
from linearmodels.panel import PanelOLS
import pandas as pd
from linearmodels.datasets import wage_panel
import statsmodels.api as sm
data = wage_panel.load()
year = pd.Categorical(data.year)
data = data.set_index(["nr", "year"])
data["year"] = year
exog_vars = ["expersq", "union", "married", "year"]
exog = sm.add_constant(data[exog_vars])
mod = PanelOLS(data.lwage, exog, entity_effects=True)
fe_res = mod.fit()
print(fe_res)
This prints
PanelOLS Estimation Summary
================================================================================
Dep. Variable: lwage R-squared: 0.1806
Estimator: PanelOLS R-squared (Between): -0.0052
No. Observations: 4360 R-squared (Within): 0.1806
Date: Wed, Feb 16 2022 R-squared (Overall): 0.0807
Time: 12:49:00 Log-likelihood -1324.8
Cov. Estimator: Unadjusted
F-statistic: 83.851
Entities: 545 P-value 0.0000
Avg Obs: 8.0000 Distribution: F(10,3805)
Min Obs: 8.0000
Max Obs: 8.0000 F-statistic (robust): 83.851
P-value 0.0000
Time periods: 8 Distribution: F(10,3805)
Avg Obs: 545.00
Min Obs: 545.00
Max Obs: 545.00
Parameter Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
const 1.4260 0.0183 77.748 0.0000 1.3901 1.4620
expersq -0.0052 0.0007 -7.3612 0.0000 -0.0066 -0.0038
union 0.0800 0.0193 4.1430 0.0000 0.0421 0.1179
married 0.0467 0.0183 2.5494 0.0108 0.0108 0.0826
year.1981 0.1512 0.0219 6.8883 0.0000 0.1082 0.1942
year.1982 0.2530 0.0244 10.360 0.0000 0.2051 0.3008
year.1983 0.3544 0.0292 12.121 0.0000 0.2971 0.4118
year.1984 0.4901 0.0362 13.529 0.0000 0.4191 0.5611
year.1985 0.6175 0.0452 13.648 0.0000 0.5288 0.7062
year.1986 0.7655 0.0561 13.638 0.0000 0.6555 0.8755
year.1987 0.9250 0.0688 13.450 0.0000 0.7902 1.0599
==============================================================================
F-test for Poolability: 9.1568
P-value: 0.0000
Distribution: F(544,3805)
Included effects: Entity
The line Included effects: Entity indicates that entity, aka fixed, effects have been included.