发表机构
Florida State University; Columbia University（佛罗里达州立大学; 哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对预趋势检验可能引入偏差的问题，提出模型平均双重差分（MADID）估计量，通过加权平均候选DID估计量，无需规范检验，在至少一个预处理期满足平行趋势假设时一致，并可用子抽样或模拟进行推断。
AI 中文摘要
我们研究了当研究者不确定哪些预处理期满足平行趋势假设时，对受处理者的平均处理效应的双重差分（DID）估计。Roth（2022）表明，预趋势检验可能引入偏差，这补充了关于选择后推断的统计文献。我们提出了模型平均双重差分（MADID）估计量，它是候选的$2\times2$ DID估计量的加权平均。权重是候选残差平方和的归一化指数函数，因此实现无需规范检验。我们在使得无效比较比至少一个有效比较更具可检测变异性的条件下推导了MADID的渐近性质。在这些条件下，当至少一个预处理期满足平行趋势假设时，MADID是一致的。尽管其在多个后处理期的联合极限分布通常是非高斯的，但可以通过子抽样或模拟进行推断。
英文摘要
We study difference-in-differences (DID) estimation of average treatment effects on the treated when the researcher is uncertain about which pre-treatment periods satisfy the parallel trends assumption. Roth (2022) shows that pre-trend testing can induce bias, complementing the statistical literature on post-selection inference. We propose the model averaged difference-in-differences (MADID) estimator, a weighted average of the candidate $2\times2$ DID estimators. The weights are normalized exponential functions of the candidate residual sums of squares, so implementation requires no specification test. We derive MADID's asymptotic properties under conditions that make invalid comparisons detectably more variable than at least one valid comparison. Under these conditions, MADID is consistent when the parallel trends assumption holds for at least one pre-treatment period. Although its joint limiting distribution across post-treatment periods is generally non-Gaussian, inference can be conducted by subsampling or simulation.
