使用聚类jackknife改进CSDID的推断
Improved Inference for CSDID Using the Cluster Jackknife
AI总结：
本文提出使用聚类jackknife改进CSDID推断，以解决小集群和处理集群数量少时的过度拒绝问题，并提供软件包实现。
摘要：
使用传统差分-in-差分（DiD）方法获得可靠的推断可能很困难。当结果和误差存在序列相关性、集群数量少或处理集群数量少、集群大小差异大，或在各种其他情况下，问题会浮现。近年来，对“阶梯式采用”问题的认识使关注点从推断转向了治疗效应的一致估计。最流行的新型估计器之一是Callaway和Sant'Anna（2021）提出的CSDID程序。我们发现，当集群数量少且/或处理集群数量少时，CSDID的问题至少与传统DiD方法一样严重。我们还提出使用聚类jackknife进行CSDID推断，模拟结果显示这大大提高了推断效果。我们提供了Stata的csdidjack和R的didjack软件包，以便轻松计算聚类-jackknife标准误差。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
