面板数据中基于Copula等式假设的分布处理效应的部分识别
Partial Identification of Distributional Treatment Effects in Panel Data using Copula Equality Assumptions
AI总结：
本文提出利用面板数据和两种组内Copula等式假设部分识别分布处理效应，并通过锻炼对体重影响的实证展示其增强识别能力。
摘要：
本文旨在部分识别依赖于处理组和未处理组潜在结果未知联合分布的分布处理效应（DTEs）。我们利用面板数据构建DTE界，并允许个体在时间上多次在处理与未处理状态之间切换。个体根据其过去的处理历史进行分组，且DTEs允许在不同组间存在异质性。我们提供了两种替代的组内Copula等式假设来界定未知联合分布及DTEs，这两种假设均利用了过往观测信息。我们还讨论了这两种假设的可检验性，并给出了检验结果。我们将该方法应用于研究锻炼对成年人身体体重的处理效应异质性。结果表明，与现有方法相比，我们的方法提高了DTE界的识别能力。
英文摘要：
This paper aims to partially identify the distributional treatment effects (DTEs) that depend on the unknown joint distribution of treated and untreated potential outcomes. We construct the DTE bounds using panel data and allow individuals to switch between the treated and untreated states more than once over time. Individuals are grouped based on their past treatment history, and DTEs are allowed to be heterogeneous across different groups. We provide two alternative group-wise copula equality assumptions to bound the unknown joint and the DTEs, both of which leverage information from the past observations. Testability of these two assumptions are also discussed, and test results are presented. We apply this method to study the treatment effect heterogeneity of exercising on the adults' body weight. These results demonstrate that our method improves the identification power of the DTE bounds compared to the existing methods.
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。
