发表机构
UC Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对三重差分回归中协变量调整的缺陷，提出匹配三重差分框架，构建一致估计量，实证显示结果异于传统3WFE回归。
AI 中文摘要
三重差分（DDD）中的一种常见实证策略是在三向固定效应（3WFE）回归中加入协变量趋势或协变量水平。该策略通常基于条件平行缺口假设，该假设假定在具有可比观测协变量的单元之间，偏离平行趋势的情况是相似的。我们正式研究了两种3WFE设定，并表明，在一般情况下，两者都不能一致地估计处理组的平均处理效应（ATT）。我们的诊断分为两部分。首先，我们表明，两种设定的OLS估计量都不满足足以使其等于ATT的协变量平衡条件。其次，我们刻画了每个OLS估计量等于ATT所需的额外假设。为解决这些局限性，我们提出了一个匹配三重差分框架，该框架容纳了一类通用的匹配程序，包括最近邻匹配和核匹配。在此框架内，我们构建了一类一致的匹配估计量，建立了其渐近正态性，并提供了一个一致的方差估计量，该估计量考虑了匹配步骤引入的变异性。我们的实证应用表明，所提出的匹配三重差分估计量可以产生与使用3WFE回归所获得的估计值和定性结论不同的结果。
英文摘要
A common empirical strategy in triple-differences (DDD) is to include either covariate trends or covariate levels to a three-way fixed effects (3WFE) regression. This strategy is typically motivated by the conditional parallel gaps assumption which assumes that deviations from parallel trends are similar among units with comparable observed covariates. We formally study both 3WFE specifications and show that, in general, neither consistently estimates the average treatment effect on the treated (ATT). Our diagnosis has two parts. First, we show that the OLS estimands of both specifications fail to satisfy the covariate balancing condition that is sufficient for them to equal the ATT. Second, we characterize the additional assumptions for each OLS estimand to equal the ATT. To address these limitations, we propose a matched triple-differences framework that accommodates a general class of matching procedures, including nearest-neighbor matching and kernel matching. Within this framework, we construct a class of consistent matching estimators, establish their asymptotic normality, and provide a consistent variance estimator that accounts for the variability introduced by the matching step. Our empirical application shows that the proposed matched triple-differences estimator can yield estimates and qualitative conclusions that differ from those obtained using the 3WFE regression.
