Non-separable models with high-dimensional data

• Main Authors: SU, Liangjun, URA, Takuya, ZHANG, Yichong
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2017
• 主題: Average treatment effect; High dimension; Least absolute shrinkage and selection operator (Lasso); Nonparametric quantile regression; Nonseparable models; Quantile treatment effect; Unconditional average structural derivative.; Econometrics
• 在線閱讀: https://ink.library.smu.edu.sg/soe_research/2105; https://ink.library.smu.edu.sg/context/soe_research/article/3105/viewcontent/suz20170928_.pdf
• 機構: Singapore Management University

This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. In the first...