Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

oleh: Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien

Format: Article
Diterbitkan: MDPI AG 2021-04-01

Deskripsi

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.