Robust Inference in Difference-in-Differences and Event Study Designs
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Creates a standard basis vector.
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $Delta = Delta^...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Computes conditional and hybridized confidence set for $\Delta = \Delt...
Constructs original confidence interval for parameter of interest, the...
Constructs event study plot
Constructs sensitivity plot for , $\Delta^...
Constructs sensitivity plot for , $\Delta^{SD...
Constructs robust confidence intervals for $\Delta = \Delta^{RM}(Mbar)...
Constructs robust confidence intervals for , ...
Construct lower bound for M for based on obs...
Construct upper bound for M for based on obs...
Constructs optimal fixed length confidence interval for $\Delta = \Del...
Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.