论文标题
高维数据的无条件分位数回归
Unconditional Quantile Regression with High Dimensional Data
论文作者
论文摘要
本文考虑了具有高维数据的异构反事实效应的估计和推断。我们提出了一个新颖的鲁棒分数，以对无条件分位回归的估计（Firpo，Fortin和Lemieux，2009年），以衡量异构反事实边缘效应的量度。我们提出了乘数自举推断，并发展渐近理论，以确保大型样本中的尺寸控制。模拟研究支持我们的理论。将提出的方法应用于工作兵团调查数据，我们发现一项反作用的政策会将暴露时间扩展到工作兵团培训计划将是有效的，特别是对于较低的潜在工资收入者的目标亚群。
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy which counterfactually extends the duration of exposures to the Job Corps training program will be effective especially for the targeted subpopulations of lower potential wage earners.
