Abstract
The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 1799-1826 |
| Number of pages | 28 |
| Journal | Journal of Econometrics |
| Volume | 235 |
| Issue number | 2 |
| DOIs | |
| State | Published - Aug 2023 |
Bibliographical note
Publisher Copyright:© 2023 Elsevier B.V.
Funding
We would like to thank the Co-editor Xiaohong Chen, an Associate Editor, and two referees for their detailed and constructive comments which have improved the paper considerably. We are also grateful to Antonio Galvao and Matt Harding for helpful comments and suggestions as well as seminar participants at the University of Kentucky, the 2019 CFE/CMStatistics conference, and the 2021 New York Camp Econometrics meeting.
| Funders | Funder number |
|---|---|
| University of Kentucky |
Keywords
- Bootstrap inference
- Panel data
- Penalized estimator
- Quantile regression
ASJC Scopus subject areas
- Economics and Econometrics
- Applied Mathematics