Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

Jau-er Chen, Chien-Hsun Huang and Jia-Jyun Tien
Additional contact information
Chien-Hsun Huang: The Office of the Chief Economist, Microsoft Research, Redmond, WA 98052, USA
Jia-Jyun Tien: Department of Economics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan

Econometrics, 2021, vol. 9, issue 2, 1-18

Abstract: In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et al. 2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.

Keywords: quantile treatment effect; instrumental variable; quantile regression; double machine learning; lasso (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:9:y:2021:i:2:p:15-:d:529537

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