Debiased/double machine learning for instrumental variable quantile regressions

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-18 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: double machine learning ; instrumental variable ; lasso ; quantile regression ; quantile treatment effect

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Chen, Jau-er ; Huang, Chien-Hsun ; Tien, Jia-Jyun
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2021

Further information

• DOI: doi:10.3390/econometrics9020015
• Handle: http://hdl.handle.net/10419/247605
• Last update: 10.03.2025, 11:44 AM CET