发表机构
CERGE-EI; University of California, Berkeley（捷克经济与管理研究中心; 加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合准则函数与 Neyman 正交矩的两步去偏推断方法，用于估计部分识别参数（如工资不平等界），并证明其收敛速率与覆盖性质，实证分析性别工资差距。
AI 中文摘要
我们研究部分识别参数的估计与推断问题，该参数的识别集依赖于一个本身需要估计的第一阶段 nuisance 参数。将准则函数方法与支撑双重/去偏机器学习的 Neyman 正交矩理论相结合，我们提出一个两步程序：点识别的 nuisance 参数通过灵活机器学习方法估计，而集合识别的目标参数则作为基于交叉拟合的正交矩不等式构建的样本准则的水平集来恢复。当轮廓水平有界时，我们证明所得集合估计器在 Hausdorff 距离下以不可行准则（基于真实 nuisance 构建）的参数速率收敛。我们进一步开发了一个子抽样程序，只要第一阶段估计误差的乘积为 $o(N^{-1/2})$，该程序就能提供渐近有效的覆盖。我们通过就业选择下的工资分布和分位距的界以及区间删失工资下的性别工资差距来展示该方法。实证应用使用 2015 年当前人口调查的三月增补研究性别工资差距。
英文摘要
We study estimation and inference for a partially identified parameter whose identified set depends on a first-stage nuisance parameter that must itself be estimated. Combining the criterion-function approach with the theory of Neyman-orthogonal moments that underlies double/debiased machine learning, we propose a two-step procedure: the point-identified nuisance is estimated by flexible machine-learning methods, and the set-identified target is recovered as a level set of a sample criterion built from orthogonal moment inequalities with cross-fitting. When the contour level is bounded, we show that the resulting set estimator converges in Hausdorff distance at the parametric rate of the infeasible criterion built on the true nuisance. We further develop a subsampling procedure that delivers asymptotically valid coverage, provided the product of the first-stage estimation errors is $o(N^{-1/2})$. We illustrate the method on bounds for the wage distribution and the interquantile range under selection into employment and on the gender wage gap with an interval-censored wage. The empirical application studies the gender wage gap using the March supplement of the 2015 Current Population Survey.
