论文标题
差异差异模型中的错误分类
Misclassification in Difference-in-differences Models
论文作者
论文摘要
差异差异（DID）设计是经验经济学研究中使用的最流行的方法之一。但是，在存在错误分类的治疗变量的情况下，几乎没有工作来研究DID方法所识别的方法。本文研究了处理治疗错误分类时DID设计中治疗效果的鉴定。错误分类以各种方式出现，包括政策干预的时间模棱两可或研究人员需要从辅助数据中推断治疗时。我们表明，DID估计是有偏见的，并在两个亚群中恢复了对所处理（ATT）的平均治疗效果的加权平均值 - 正确分类和错误分类的组。在某些情况下，DID估计可能会产生错误的标志，否则会减弱。当研究人员可以访问数据中错误分类程度时，我们就会提供有关ATT的界限。我们使用模拟证明了我们的理论结果，并提供了两种经验应用，以指导研究人员使用我们建议的方法进行灵敏度分析。
The difference-in-differences (DID) design is one of the most popular methods used in empirical economics research. However, there is almost no work examining what the DID method identifies in the presence of a misclassified treatment variable. This paper studies the identification of treatment effects in DID designs when the treatment is misclassified. Misclassification arises in various ways, including when the timing of a policy intervention is ambiguous or when researchers need to infer treatment from auxiliary data. We show that the DID estimand is biased and recovers a weighted average of the average treatment effects on the treated (ATT) in two subpopulations -- the correctly classified and misclassified groups. In some cases, the DID estimand may yield the wrong sign and is otherwise attenuated. We provide bounds on the ATT when the researcher has access to information on the extent of misclassification in the data. We demonstrate our theoretical results using simulations and provide two empirical applications to guide researchers in performing sensitivity analysis using our proposed methods.
