潜在指数选择模型中的尖锐边界
Sharp Bounds in the Latent Index Selection Model
AI总结：
本文在潜在指数选择模型中，利用随机阶理论和边际分布的全部内容，推导出边际处理效应及其线性泛函的尖锐解析边界，并实证表明结合辅助假设利用模型全部实证内容具有重要价值。
摘要：
部分识别文献背后的一个基本问题是：对于与政策相关但不一定能被我们观察到的外生变化所点识别的参数，我们能了解什么？本文针对一类重要的政策相关处理效应，即边际处理效应及其线性泛函，在Vytlacil (2002)形式化的潜在指数选择模型中，以尖锐的解析表征和边界的形式给出了答案。这些尖锐边界利用了已识别边际分布的全部内容，解析推导依赖于随机阶理论。所提出的方法还使得能够将关于分布的新辅助假设尖锐地纳入潜在指数选择框架。在实证中，我将这些方法应用于Oregon Health Insurance Experiment中研究Medicaid对急诊室利用的影响，表明基于分布假设（秩相似性）的外推预测与基于参数均值假设（线性）的现有外推在实质上且一致地存在差异。这凸显了结合辅助假设利用模型全部实证内容的价值。
英文摘要：
A fundamental question underlying the literature on partial identification is: what can we learn about parameters that are relevant for policy but not necessarily point-identified by the exogenous variation we observe? This paper provides an answer in terms of sharp, analytic characterizations and bounds for an important class of policy-relevant treatment effects, consisting of marginal treatment effects and linear functionals thereof, in the latent index selection model as formalized in Vytlacil (2002). The sharp bounds use the full content of identified marginal distributions, and analytic derivations rely on the theory of stochastic orders. The proposed methods also make it possible to sharply incorporate new auxiliary assumptions on distributions into the latent index selection framework. Empirically, I apply the methods to study the effects of Medicaid on emergency room utilization in the Oregon Health Insurance Experiment, showing that the predictions from extrapolations based on a distribution assumption (rank similarity) differ substantively and consistently from existing extrapolations based on a parametric mean assumption (linearity). This underscores the value of utilizing the model's full empirical content in combination with auxiliary assumptions.
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。
