did.estimation.event_study

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

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
cluster	str | None	The name of the cluster variable. If None, defaults to idname.	None
xfml	str	The formula for the covariates.	None
estimator	str	The estimator to use. Options are “did2s”, “twfe”, and “saturated”.	'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

Name	Type	Description
	object	A fitted model object of class Feols.

Examples

import pandas as pd
import pyfixest as pf

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

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

fit_twfe.tidy()

# run saturated event study
fit_twfe_saturated = pf.event_study(
    df_het,
    yname="dep_var",
    idname="unit",
    tname="year",
    gname="g",
    estimator="saturated",
)

fit_twfe_saturated.aggregate()
fit_twfe_saturated.iplot_aggregate()