Unconditional quantile regression with high-dimensional data

• 成果类型: Article
• 署名作者: Sasaki, Yuya; Ura, Takuya; Zhang, Yichong
• 署名单位: Vanderbilt University; University of California System; University of California Davis; Singapore Management University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1896
• 发表日期: 2022
• 页码: 955-978
• 关键词: Counterfactual analysis; debiased machine learning; doubly; locally robust score; C14; C21

摘要

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.