Non-separable Models with High-dimensional Data
Takuya Ura () and
Yichong Zhang ()
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Takuya Ura: Department of Economics, University of California Davis
Yichong Zhang: School of Economics, Singapore Management University
No 15-2017, Economics and Statistics Working Papers from Singapore Management University, School of Economics
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 in finite 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 (search for similar items in EconPapers)
JEL-codes: C21 I19 (search for similar items in EconPapers)
Pages: 85 pages
New Economics Papers: this item is included in nep-ecm and nep-sea
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Journal Article: Non-separable models with high-dimensional data (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2017_015
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