Obtaining reliable inferences with traditional difference-in-differences (DiD) methods can be difficult. Problems can arise when both outcomes and errors are serially correlated, when there are few clusters or few treated clusters, when cluster sizes vary greatly, and in various other cases. In recent years, recognition of the ``staggered adoption'' problem has shifted the focus away from inference towards consistent estimation of treatment effects. One of the most popular new estimators is the CSDID procedure of Callaway and Sant'Anna (2021). We find that the issues of over-rejection with few clusters and/or few treated clusters are at least as severe for CSDID as for traditional DiD methods. We also propose using a cluster jackknife for inference with CSDID, which simulations suggest greatly improves inference. We provide software packages in Stata csdidjack and R didjack to calculate cluster-jackknife standard errors easily.

翻译：使用传统的双重差分法（DiD）获得可靠的统计推断可能较为困难。当结果变量和误差项均存在序列相关性、聚类数量或处理组聚类数量较少、聚类规模差异较大以及其他多种情况下，都可能出现问题。近年来，对“交错采用”问题的认识已将研究重点从统计推断转向处理效应的一致性估计。Callaway和Sant'Anna（2021）提出的CSDID程序是目前最受欢迎的新型估计量之一。我们发现，对于CSDID方法而言，在聚类数量较少和/或处理组聚类数量较少情况下的过度拒绝问题，至少与传统DiD方法一样严重。我们还提出使用聚类刀切法进行CSDID推断，模拟研究表明该方法能显著改善推断效果。我们提供了Stata软件包csdidjack和R语言软件包didjack，以便轻松计算聚类刀切标准误。