did.estimation.event_study

did.estimation.event_study(data, yname, idname, tname, gname, xfml=None, estimator='twfe', att=True, cluster='idname')

Estimate Event Study Model.

This function allows for the estimation of treatment effects using different estimators. Currently, it supports “twfe” for the two-way fixed effects estimator and “did2s” for Gardner’s two-step DID2S estimator. Other estimators are in development.

Parameters

Name	Type	Description	Default
`data`	DataFrame	The DataFrame containing all variables.	required
`yname`	str	The name of the dependent variable.	required
`idname`	str	The name of the id variable.	required
`tname`	str	Variable name for calendar period.	required
`gname`	str	Unit-specific time of initial treatment.	required
`xfml`	str	The formula for the covariates.	`None`
`estimator`	str	The estimator to use. Options are “did2s” and “twfe”.	`'twfe'`
`att`	bool	If True, estimates the average treatment effect on the treated (ATT). If False, estimates the canonical event study design with all leads and lags. Default is True.	`True`

Returns

Type	Description
object	A fitted model object of class [Feols(/reference/Feols.qmd).

Examples

import pandas as pd
from pyfixest.did.estimation import event_study

url = "https://raw.githubusercontent.com/py-econometrics/pyfixest/master/pyfixest/did/data/df_het.csv"
df_het = pd.read_csv(url)

fit_twfe = event_study(
    df_het,
    yname="dep_var",
    idname="unit",
    tname="year",
    gname="g",
    estimator="twfe",
    att=True,
)

fit_twfe.tidy()

	Estimate	Std. Error	t value	Pr(>\|t\|)	2.5%	97.5%
Coefficient
ATT	1.98254	0.019093	103.83491	0.0	1.945118	2.019962