statspai.fixest

fixest

High-dimensional fixed effects estimation via pyfixest.

This module provides thin wrappers around pyfixest's estimation functions, converting results into StatsPAI's EconometricResults for seamless integration with outreg2 and the rest of the ecosystem.

Requires: pip install pyfixest

Examples:

>>> from statspai.fixest import feols, fepois
>>>
>>> # Two-way fixed effects with clustered SEs
>>> result = feols("wage ~ experience | firm + year",
...               data=df, vcov={"CRV1": "firm"})
>>> print(result.summary())
>>>
>>> # Poisson regression
>>> result = fepois("patents ~ rd_spending | firm", data=df)
>>>
>>> # Works with outreg2
>>> from statspai import outreg2
>>> outreg2(result, filename="table.xlsx")

feols

feols(fml: str, data: DataFrame, vcov: Optional[Union[str, Dict[str, str]]] = None, *, weights: Optional[str] = None, ssc: Optional[Any] = None, fixef_rm: str = 'none', collin_tol: float = 1e-06, lean: bool = False, **kwargs: Any) -> Union[EconometricResults, List[EconometricResults]]

Estimate OLS / IV with high-dimensional fixed effects via pyfixest.

Uses the Frisch-Waugh-Lovell theorem for fast absorption of high-dimensional fixed effects.

Parameters:

Name	Type	Description	Default
fml	str	A pyfixest-style formula. Examples: "Y ~ X1 + X2" — plain OLS "Y ~ X1 | firm + year" — two-way fixed effects "Y ~ 1 | firm | X1 ~ Z1" — IV with fixed effects "Y ~ X1 | csw0(firm, year)" — multiple estimations	required
data	DataFrame	Input dataset.	required
vcov	str or dict	Variance-covariance estimator. "iid" — classical "HC1", "HC2", "HC3" — heteroskedasticity-robust {"CRV1": "firm"} — cluster-robust {"CRV1": "firm + year"} — two-way clustering	None
weights	str	Column name for regression weights.	None
ssc	optional	Small-sample correction via pyfixest.ssc().	None
fixef_rm	str	How to handle singleton fixed effects: "none" (keep) or "singleton" (drop).	"none"
collin_tol	float	Collinearity tolerance.	1e-6
lean	bool	If True, drop large intermediate arrays to save memory.	False
**kwargs	Any	Additional arguments passed to pyfixest.feols().	{}

Returns:

Type	Description
EconometricResults or list of EconometricResults	Single result for simple formulas, list for multiple estimations (e.g. csw0/sw/sw0 syntax).

Examples:

Two-way fixed effects with clustered SEs:

>>> from statspai.fixest import feols
>>> result = feols("wage ~ experience + tenure | firm + year",
...               data=df, vcov={"CRV1": "firm"})
>>> print(result.summary())

Multiple estimation:

>>> results = feols("Y ~ X1 | csw0(firm, year)", data=df)
>>> for r in results:
...     print(r.summary())

Use with outreg2:

>>> from statspai import outreg2
>>> r1 = feols("Y ~ X1", data=df)
>>> r2 = feols("Y ~ X1 | firm", data=df)
>>> outreg2(r1, r2, filename="table.xlsx")

fepois

fepois(fml: str, data: DataFrame, vcov: Optional[Union[str, Dict[str, str]]] = None, *, weights: Optional[str] = None, ssc: Optional[Any] = None, fixef_rm: str = 'none', collin_tol: float = 1e-06, iwls_tol: float = 1e-08, iwls_maxiter: int = 25, **kwargs: Any) -> Union[EconometricResults, List[EconometricResults]]

Estimate Poisson regression with high-dimensional fixed effects via pyfixest.

Parameters:

Name	Type	Description	Default
fml	str	pyfixest formula. E.g. "Y ~ X1 | firm".	required
data	DataFrame	Input dataset.	required
vcov	str or dict	Variance-covariance estimator.	None
weights	str	Column name for regression weights.	None
ssc	optional	Small-sample correction.	None
fixef_rm	str	Singleton fixed effect handling.	"none"
collin_tol	float	Collinearity tolerance.	1e-6
iwls_tol	float	IWLS convergence tolerance.	1e-8
iwls_maxiter	int	Max IWLS iterations.	25
**kwargs	Any	Additional arguments passed to pyfixest.fepois().	{}

Returns:

Type	Description
EconometricResults or list of EconometricResults

feglm

feglm(fml: str, data: DataFrame, family: str = 'gaussian', vcov: Optional[Union[str, Dict[str, str]]] = None, **kwargs: Any) -> Union[EconometricResults, List[EconometricResults]]

Estimate GLM (logit, probit, Gaussian) with high-dimensional fixed effects.

Parameters:

Name	Type	Description	Default
fml	str	pyfixest formula.	required
data	DataFrame	Input dataset.	required
family	str	GLM family: "gaussian", "logit", "probit".	"gaussian"
vcov	str or dict	Variance-covariance estimator.	None
**kwargs	Any	Additional arguments passed to pyfixest.feglm().	{}

Returns:

Type	Description
EconometricResults or list of EconometricResults

etable

etable(*results: EconometricResults, **kwargs: Any) -> Any

Display a pyfixest-style regression table for StatsPAI results.

If the results carry a _pyfixest_fit reference, uses pyfixest's native etable. Otherwise falls back to a simple pandas summary.

Parameters:

Name	Type	Description	Default
*results	EconometricResults	One or more fitted results.	()
**kwargs	Any	Passed to pyfixest.etable().	{}

Returns:

Type	Description
str or DataFrame