Feols.fixef

fixef(atol=1e-06, btol=1e-06)

Compute the coefficients of (swept out) fixed effects for a regression model.

This method creates the following attributes: - _alpha (pd.DataFrame): A DataFrame with the estimated fixed effects. - _sumFE (np.array): An array with the sum of fixed effects for each observation (i = 1, …, N).

Parameters

Name	Type	Description	Default
atol	Float	Stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html	1e-6
btol	Float	Another stopping tolerance for scipy.sparse.linalg.lsqr(). See https://docs.scipy.org/doc/ scipy/reference/generated/scipy.sparse.linalg.lsqr.html	1e-6

Returns

Name	Type	Description
	pd.DataFrame	A tidy DataFrame with columns variable, code, level, and coefficient containing the estimated fixed effects.

Examples

import pyfixest as pf

fit = pf.feols("Y ~ X1 + X2 | f1", pf.get_data())
fixed_effects = fit.fixef()
fixed_effects.head()

	variable	code	level	coefficient
0	f1	15	15.0	1.887085
1	f1	6	6.0	-0.254456
2	f1	1	1.0	3.066142
3	f1	19	19.0	1.123039
4	f1	13	13.0	2.013726