双样本无条件分位数效应
Two Sample Unconditional Quantile Effect
摘要：
本文提出了一个新的框架来评估数据组合模型中的无条件分位数效应（UQE）。UQE测量协变量的无条件分布中的边际反事实变化对目标结果的无条件分布的分位数的影响。在秩相似性和条件独立性假设下，我们分别提供了一组目标协变量连续分布和离散分布时的UQE识别结果。基于这些识别结果，我们提出了半参数估计，并建立了原始条件下的大样本性质。将我们的方法应用到明瑟收入函数的一个变体上，我们研究了实际工作经验对收入的反事实分位数效应。
英文摘要：
This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model. The UQE measures the effect of a marginal counterfactual change in the unconditional distribution of a covariate on quantiles of the unconditional distribution of a target outcome. Under rank similarity and conditional independence assumptions, we provide a set of identification results for UQEs when the target covariate is continuously distributed and when it is discrete, respectively. Based on these identification results, we propose semiparametric estimators and establish their large sample properties under primitive conditions. Applying our method to a variant of Mincer's earnings function, we study the counterfactual quantile effect of actual work experience on income.
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