Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

Jau-er Chen; Chien-Hsun Huang; Jia-Jyun Tien

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arXiv · 1909.12592

Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

Jau-er Chen·Chien-Hsun Huang·Jia-Jyun Tien

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Abstract

In this study, we investigate 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, Hansen and Wuthrich (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.

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BibTeX RIS

Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien. 2019-09-27. Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions. https://arxiv.org/abs/1909.12592

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