Standard Errors for Difference‐in‐Difference Regression
论证在双重差分回归中使用刀切法构建标准误、p值和置信区间，通过复制多个经典应用展示其优于传统聚类稳健和自助法，对实证研究者选择推断方法有参考价值。
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.