estimation.fepois

estimation.fepois(fml, data, vcov=None, ssc=ssc(), fixef_rm='none', fixef_tol=1e-08, iwls_tol=1e-08, iwls_maxiter=25, collin_tol=1e-10, drop_intercept=False, i_ref1=None, copy_data=True, store_data=True)

Estimate Poisson regression model with fixed effects using the ppmlhdfe algorithm.

Parameters

Name	Type	Description	Default
`fml`	str	A two-sided formula string using fixest formula syntax. Syntax: “Y ~ X1 + X2 \| FE1 + FE2”. “\|” separates left-hand side and fixed effects. Special syntax includes: - Stepwise regressions (sw, sw0) - Cumulative stepwise regression (csw, csw0) - Multiple dependent variables (Y1 + Y2 ~ X) - Interaction of variables (i(X1,X2)) - Interacted fixed effects (fe1^fe2) Compatible with formula parsing via the formulaic module.	required
`data`	DataFrameType	A pandas or polars dataframe containing the variables in the formula.	required
`vcov`	Union[str, dict[str, str]]	Type of variance-covariance matrix for inference. Options include “iid”, “hetero”, “HC1”, “HC2”, “HC3”, or a dictionary for CRV1/CRV3 inference.	`None`
`ssc`	str	A ssc object specifying the small sample correction for inference.	`ssc()`
`fixef_rm`	str	Specifies whether to drop singleton fixed effects. Options: “none” (default), “singleton”.	`'none'`
`fixef_tol`	float	Tolerance for the fixed effects demeaning algorithm. Defaults to 1e-08.	`1e-08`
`iwls_tol`	Optional[float]	Tolerance for IWLS convergence, by default 1e-08.	`1e-08`
`iwls_maxiter`	Optional[float]	Maximum number of iterations for IWLS convergence, by default 25.	`25`
`collin_tol`	float	Tolerance for collinearity check, by default 1e-10.	`1e-10`
`drop_intercept`	bool	Whether to drop the intercept from the model, by default False.	`False`
`i_ref1`		Deprecated with pyfixest version 0.18.0. Please use i-syntax instead, i.e. fepois(‘Y~ i(f1, ref=1)’, data = data) instead of the former fepois(‘Y~ i(f1)’, data = data, i_ref=1).	`None`
`copy_data`	bool	Whether to copy the data before estimation, by default True. If set to False, the data is not copied, which can save memory but may lead to unintended changes in the input data outside of `fepois`. For example, the input data set is re-index within the function. As far as I know, the only other relevant case is when using interacted fixed effects, in which case you’ll find a column with interacted fixed effects in the data set.	`True`
`store_data`	bool	Whether to store the data in the model object, by default True. If set to False, the data is not stored in the model object, which can improve performance and save memory. However, it will no longer be possible to access the data via the `data` attribute of the model object. This has impact on post-estimation capabilities that rely on the data, e.g. `predict()` or `vcov()`.	`True`

Returns

Type	Description
object	An instance of the `Fepois` class or an instance of class `FixestMulti` for multiple models specified via `fml`.

Examples

The fepois() function can be used to estimate a simple Poisson regression model with fixed effects. The following example regresses Y on X1 and X2 with fixed effects for f1 and f2: fixed effects are specified after the | symbol.

import pyfixest as pf

data = pf.get_data(model = "Fepois")
fit = pf.fepois("Y ~ X1 + X2 | f1 + f2", data)
fit.summary()

###

Estimation:  Poisson
Dep. var.: Y, Fixed effects: f1+f2
Inference:  CRV1
Observations:  997

| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5% |   97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| X1            |     -0.007 |        0.035 |    -0.190 |      0.850 | -0.075 |   0.062 |
| X2            |     -0.015 |        0.010 |    -1.449 |      0.147 | -0.035 |   0.005 |
---
Deviance: 1068.169

For more examples, please take a look at the documentation of the feols() function.