didimpute

An R port of Georgii Marinichev's Python did_imputation package, implementing the imputation estimator of Borusyak, Jaravel and Spiess (2024, Review of Economic Studies) for staggered difference-in-differences designs. (A separate, independent R implementation is also available as didimputation by Kyle Butts.)

Full documentation: https://xiangao.github.io/didimpute

Installation

# Install from GitHub
remotes::install_github("xiangao/didimpute")

Quick start

The example below constructs a minimal synthetic staggered panel and estimates event-study effects at horizons 0, 1, and 2, with two pre-trend placebo coefficients.

library(didimpute)

# Synthetic staggered panel: 20 units, 10 periods
set.seed(42)
n_units <- 20; n_t <- 10
panel <- expand.grid(i = seq_len(n_units), t = seq_len(n_t))

# Units 1–10 treated at t = 6; units 11–20 are never treated
panel$Ei <- ifelse(panel$i <= 10, 6L, NA_integer_)

# DGP: treatment effect of 1.0 in post periods
panel$y <- 1.0 * (!is.na(panel$Ei) & panel$t >= panel$Ei) +
             rnorm(nrow(panel), sd = 0.5)

# Estimate event study: horizons 0–2, two pre-trend placebo tests
res <- did_impute(
  panel,
  y        = "y",
  i        = "i",
  t        = "t",
  Ei       = "Ei",
  horizons = 0:2,
  pretrends = 2
)

# Tidy summary table
summary(res)

# Event-study plot (requires ggplot2)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  event_plot(res)
}

Reference

Borusyak, K., Jaravel, X., and Spiess, J. (2024). Revisiting Event-Study Designs: Robust and Efficient Estimation. Review of Economic Studies, 91(6), 3253–3285.