Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247605 
Year of Publication: 
2021
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 9 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-18
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
double machine learning
instrumental variable
lasso
quantile regression
quantile treatment effect
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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