event_study: Estimate event-study coefficients using TWFE and 5 proposed...

In did2s: Two-Stage Difference-in-Differences Following Gardner (2021)

Estimate event-study coefficients using TWFE and 5 proposed improvements.

Description

Uses the estimation procedures recommended from Borusyak, Jaravel, Spiess (2021); Callaway and Sant'Anna (2020); Gardner (2021); Roth and Sant'Anna (2021); Sun and Abraham (2020)

Usage

event_study(
  data,
  yname,
  idname,
  gname,
  tname,
  xformla = NULL,
  weights = NULL,
  estimator = c("all", "TWFE", "did2s", "did", "impute", "sunab", "staggered")
)

plot_event_study(out, separate = TRUE, horizon = NULL)

Arguments

data	The dataframe containing all the variables
yname	Variable name for outcome variable
idname	Variable name for unique unit id
gname	Variable name for unit-specific date of initial treatment (never-treated should be zero or NA)
tname	Variable name for calendar period
xformla	A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL.
weights	Variable name for estimation weights. This is used in estimating Y(0) and also augments treatment effect weights
estimator	Estimator you would like to use. Use "all" to estimate all. Otherwise see table to know advantages and requirements for each of these.
out	Output from event_study()
separate	Logical. Should the estimators be on separate plots? Default is TRUE.
horizon	Numeric. Vector of length 2. First element is min and second element is max of event_time to plot

Value

event_study returns a data.frame of point estimates for each estimator

plot_event_study returns a ggplot object that can be fully customized

Examples

out = event_study(
  data = did2s::df_het, yname = "dep_var", idname = "unit",
  tname = "year", gname = "g", estimator = "all"
)
plot_event_study(out)

did2s documentation built on April 7, 2023, 5:09 p.m.