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Article

Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

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Institute for International Strategy, Tokyo International University, 1-13-1 Matobakita Kawagoe, Saitama 350-1197, Japan
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Center for Research in Econometric Theory and Applications, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
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The Office of the Chief Economist, Microsoft Research, Redmond, WA 98052, USA
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Department of Economics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: In Choi
Received: 29 December 2020 / Revised: 14 March 2021 / Accepted: 1 April 2021 / Published: 2 April 2021
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. View Full-Text
Keywords: quantile treatment effect; instrumental variable; quantile regression; double machine learning; lasso quantile treatment effect; instrumental variable; quantile regression; double machine learning; lasso
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MDPI and ACS Style

Chen, J.-e.; Huang, C.-H.; Tien, J.-J. Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions. Econometrics 2021, 9, 15. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9020015

AMA Style

Chen J-e, Huang C-H, Tien J-J. Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions. Econometrics. 2021; 9(2):15. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9020015

Chicago/Turabian Style

Chen, Jau-er, Chien-Hsun Huang, and Jia-Jyun Tien. 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions" Econometrics 9, no. 2: 15. https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9020015

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