Non-separable models with high-dimensional data

• Main Authors: SU, Liangjun, URA, T, ZHANG, YC
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: 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; Economic Theory
• Online Access: https://ink.library.smu.edu.sg/soe_research/2623; https://ink.library.smu.edu.sg/context/soe_research/article/3622/viewcontent/Non_separable_models_with_high_dimensional_data.pdf
• Institution: 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...