Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2019
Abstract
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 stage we estimate the conditional mean, distribution, and density objects by penalized local least squares, penalized local maximum likelihood estimation, and numerical differentiation, respectively, where control variables are selected via a localized method of L-1-penalization at each value of the continuous treatment. In the second stage we estimate the average and marginal distribution of the potential outcome via the plug-in principle. In the third stage, we estimate the quantile and marginal treatment effects by inverting the estimated distribution function and using the local linear regression, respectively. We study the asymptotic properties of these estimators and propose a weighted-bootstrap method for inference. Using simulated and real datasets, we demonstrate that the proposed estimators perform well in finite samples. (C) 2019 Elsevier B.V. All rights reserved.
Keywords
Average treatment effect;High dimension;Least absolute shrinkage and selection operator (Lasso);Nonparametric quantile regression;Nonseparable models;Quantile treatment effect;Unconditional average structural derivative
Discipline
Econometrics | Economic Theory
Research Areas
Econometrics; Economic Theory
Publication
Journal of Econometrics
Volume
212
Issue
2
First Page
646
Last Page
677
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2019.06.004
Publisher
Elsevier: 24 months
Citation
SU, Liangjun; URA, T; and ZHANG, YC.
Non-separable models with high-dimensional data. (2019). Journal of Econometrics. 212, (2), 646-677.
Available at: https://ink.library.smu.edu.sg/soe_research/2623
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.