2025/01/22 by Bruce E. Hansen · 1 voice · 20 citations
Mathematics · #Advanced Statistical Methods and Models #Confidence interval #Difference in differences #Econometrics #Linear regression #Mathematics #Mean difference #Regression #Regression analysis #Significant difference #Standard error #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics
paper · pdf · doi:10.1002/jae.3110
published in Journal of Applied Econometrics 40(3), 291-309 (Wiley)
openalex publication_date 2025/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
ABSTRACT This paper makes a case for the use of jackknife methods for standard error, 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.