This package is a simple extension of the synthdid package. It implements the Synthetic Difference-in-Differences (DiD) estimator by Arkhangelsky et al. (2021) for staggered adoption settings. As proposed in the Appendix of Arkhangelsky et al. (2021), the estimator is applied repeatedly, once for every adoption date, and a weighted average is calculated.
The current development version can be installed from Github:
devtools::install_github("gregorsteiner/StaggeredSynthDiD")# simulate simple example dataset with staggered treatment
set.seed(1)
N <- 10 # number of units
TT <- 10 # number of time periods
tau <- 0.5 # actual treatment effect
data <- data.frame("Unit" = rep(1:N, each = TT),
"Time" = rep(1:TT, N),
"W" = 0)
data <- within(data, {
# add staggered treatment (for units 1, 2, and 3 with different start timing)
W[Unit == 1 & Time >= 3] <- 1
W[Unit == 2 & Time >= 4] <- 1
W[Unit == 3 & Time >= 5] <- 1
# dependent variable
Y <- tau * W + rnorm(N * TT)
})
# use function
StaggeredSynthDiD(data)
Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. 2021. "Synthetic Difference-in-Differences." American Economic Review, 111 (12): 4088-4118.