Title: Debiased machine learning for instrumental variable quantile regressions
Authors: Jau-er Chen - Tokyo International University (Japan) [presenting]
Abstract: The aim is to investigate estimation and inference on a low-dimensional (causal) parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. The estimation and inference are based on the Neyman-type orthogonal moment conditions, that are relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the econometric procedure performs well. We also apply the procedure to empirically investigate the effect of 401(k) eligibility and participation on net financial assets.