论文标题
差异的两个阶段差异
Two-stage differences in differences
论文作者
论文摘要
最近的文献表明，当采用治疗时，平均治疗效果在各组中都会有所不同，随着时间的流逝，差异差异回归并不能识别出对治疗典型效应的易于解释的度量。在本文中，我以两种方式扩展了这一文献。首先，我提供了一些简单的基础直觉，说明为什么差异差异回归不能识别$ \ tims $ times $ apies $ aide平均治疗效果。其次，我提出了一个由这种直觉促进的替代性两阶段估计框架。在此框架中，从未经处理的观察结果样本中的第一阶段中确定了组和周期效应，在删除这些组和周期效应后，通过比较治疗和未经治疗的结果来在第二阶段通过比较未经治疗的结果来确定平均治疗效果。在交错采用下，两阶段的方法对治疗效应的异质性是可靠的，可用于识别一系列不同的平均治疗效果指标。它也是简单，直观且易于实现的。我建立了两阶段方法的理论特性，并使用蒙特卡洛证据证明了其有效性和适用性，也证明了文献的示例。
A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable measure of the typical effect of the treatment. In this paper, I extend this literature in two ways. First, I provide some simple underlying intuition for why difference-in-differences regression does not identify a group$\times$period average treatment effect. Second, I propose an alternative two-stage estimation framework, motivated by this intuition. In this framework, group and period effects are identified in a first stage from the sample of untreated observations, and average treatment effects are identified in a second stage by comparing treated and untreated outcomes, after removing these group and period effects. The two-stage approach is robust to treatment-effect heterogeneity under staggered adoption, and can be used to identify a host of different average treatment effect measures. It is also simple, intuitive, and easy to implement. I establish the theoretical properties of the two-stage approach and demonstrate its effectiveness and applicability using Monte-Carlo evidence and an example from the literature.
