Standard Errors for Difference-in-Difference Regression

This paper makes a case for the use of jackknife methods for standard error, $p$ value, and confidence interval construction for difference-in-difference (DiD) regression. We review cluster-robust, bootstrap, and jackknife standard error methods and show that standard methods can substantially underperform in conventional settings. In contrast, our proposed jackknife inference methods work well in broad contexts. We illustrate the relevance by replicating several influential DiD applications and showing how inferential results can change if jackknife standard error and inference methods are used.