Publication Type
Working Paper
Version
publishedVersion
Publication Date
10-2022
Abstract
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope matrix equals one, and the other for the null hypothesis that the slope coefficient exhibits an additive structure under the case that the true rank of slope matrix equals two. We illustrate the finite sample performance of estimation and inference via Monte Carlo simulations and real datasets.
Keywords
Debiasing, heterogeneity, nuclear norm regularization, panel quantile regression, sample splitting, specification test
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
134
Citation
WANG, Yiren; ZHANG, Yichong; and ZHANG, Yichong.
Low-rank panel quantile regression: Estimation and inference. (2022). 1-134.
Available at: https://ink.library.smu.edu.sg/soe_research/2634
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://arxiv.org/abs/2210.11062