Unconditional quantile regression with high-dimensional data

• Main Authors: SASAKI, Yuya, URA, Takuya, ZHANG, Yichong
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2020
• 主題: counterfactual analysis; double/debiased machine learning; doubly robust score; Econometrics
• 在線閱讀: https://ink.library.smu.edu.sg/soe_research/2460; https://ink.library.smu.edu.sg/context/soe_research/article/3459/viewcontent/Unconditional_Quantile_Regression_High_D_wp__1_.pdf

Credible counterfactual analysis requires high-dimensional controls. This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel doubly robust score for double/debiased estimation and inference for the unconditional quantile r...