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fastdid

fastdid implements the Difference-in-Differences (DiD) estimators in Callaway and Sant’Anna’s (2021). fastdid is

• fast, reducing the computation time with millions of units from hours to seconds,
• flexible, allowing extensions such as time-varying covariates and multiple events.

Getting Started

fastdid can be installed from CRAN,

install.packages("fastdid")

or the latest developmental version can be installed via GitHub,

# install.packages("devtools")
devtools::install_github("TsaiLintung/fastdid")

To use fastdid, you need to provide the dataset data, the column name of time timevar, cohort cohortvar, unit unitvar, and outcome(s) outcomevar. Here is a simple call:

library(fastdid) #loading the package
did_sim <- sim_did(1e+03, 10) #simulate some data
did_estimate <- fastdid(data = did_sim$dt, timevar = "time",
                  cohortvar = "G", unitvar = "unit", outcomevar = "y")

The function returns a data.table that includes the estimates. Column att is the point estimate, se the standard error of the estimate, att_ciub and att_cilb the confidence interval. The other columns indexes the estimated parameter.

To create event study plots, use plot_did_dynamics(did_estimate).

More

• did: staggered Difference in Difference by Callaway and Sant’Anna
• fastdid: full list of arguments and features.
• double: introduction to DiD with multiple events.
• misc: comparison with did, benchmark, tests, and experimental features.

Acknowledgments

fastdid is created and maintained by Lin-Tung Tsai. Many thanks to Maxwell Kellogg and Kuan-Ju Tseng for their contribution.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.