论文标题
潜在指数选择模型中的尖锐界限
Sharp Bounds in the Latent Index Selection Model
论文作者
论文摘要
关于部分识别的文献基础的一个基本问题是：我们可以了解与政策相关但不一定会被我们观察到的外源性变异所确定的参数吗？在Vytlacil（2002）中正式化的潜在指数选择模型中，本文提供了一类重要的政策相关治疗效果的尖锐，分析特征和界限的答案。尖锐的边界使用已确定的边际分布的全部内容，分析推导依赖于随机顺序的理论。所提出的方法还可以将有关分布的新辅助假设彻底融合到潜在索引选择框架中。从经验上讲，我采用了方法来研究俄勒冈州健康保险实验中医疗补助对急诊室利用的影响，表明基于分布假设（等级相似性）的外推的预测与基于参数平均假设（线性）（线性）的现有外推有实质性差异（等级相似性）。这强调了将模型完整的经验内容与辅助假设结合使用的价值。
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.
