Publication Type
Working Paper
Version
publishedVersion
Publication Date
9-2017
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 penalized conditional density estimation, respectively, where control variables are selected via a localized method of L1-penalization at each value of the continuous treatment. In the second stage we estimate the average and the 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 the proposed estimators perform well infinite samples.
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
Research Areas
Econometrics
First Page
1
Last Page
85
Publisher
SMU Economics and Statistics Working Paper Series, No. 15-2017
City or Country
Singapore
Citation
SU, Liangjun; URA, Takuya; and ZHANG, Yichong.
Non-separable models with high-dimensional data. (2017). 1-85.
Available at: https://ink.library.smu.edu.sg/soe_research/2105
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.