.png){kind=icon}
Return
Non-separable models with high-dimensional data
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2019.06.004.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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
Journal
IF:
4
Papers:
5.3K
Citations:
3.0W
Organization
Cited Papers
ShareSaveShareSavePost-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments.png)
AMERICAN ECONOMIC REVIEWOAAI
AMERICAN ECONOMIC REVIEWIF11.6
OAAIShareSaveShareSaveShareSaveMACHINE LEARNING IN ECONOMETRICS Double/Debiased/Neyman Machine Learning of Treatment EffectsAMERICAN ECONOMIC REVIEWOAAI
AMERICAN ECONOMIC REVIEWIF11.6
OAAIShareSave
View more