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NUCLEAR NORM REGULARIZED QUANTILE REGRESSION WITH INTERACTIVE FIXED EFFECTS

Published online by Cambridge University Press:  24 April 2023

Junlong Feng*
Affiliation:
Hong Kong University of Science and Technology
*
Address correspondence to Junlong Feng, Department of Economics, Hong Kong University of Science and Technology, Kowloon, Hong Kong; e-mail: jlfeng@ust.hk.

Abstract

This paper studies large N and large T conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of) interactive fixed effects. It also allows the number of covariates to grow slowly with N and T. We derive an error bound on the estimator that holds uniformly in the quantile level. The order of the bound implies uniform consistency of the estimator and is nearly optimal for the low-rank component. Given the error bound, we also propose a consistent estimator of the number of interactive fixed effects at any quantile level. We demonstrate the performance of the estimator via Monte Carlo simulations.

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Type
ARTICLES
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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