Censored Quantile Regression with Many Controls ...
This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I prov...
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
arXiv
2023
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| Online Access: | https://dx.doi.org/10.48550/arxiv.2303.02784 https://arxiv.org/abs/2303.02784 |
| Summary: | This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I provide valid inference for low-dimensional parameters of interest in the presence of high-dimensional nuisance parameters when implementing machine learning estimators. The proposed estimator is shown to be consistent and asymptotically normal. The performance of the estimator with high-dimensional controls is illustrated with numerical simulation and an empirical application that examines the effect of 401(k) eligibility on savings. ... |
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