event_study function

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

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

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)

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

Returns

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)