Debiased/double machine learning for instrumental variable quantile regressions - Jau-er Chen; Chien-Hsun Huang; Jia-Jyun Tien (Econometrics, 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.
Keywords: 330; double machine learning; instrumental variable; lasso; quantile regression; quantile treatment effect