Local Composite Quantile Regression Smoothing: A Flexible Data Structure and Cross-Validation
Department
Economics, Finance and Quantitative Analysis
Document Type
Article
Publication Date
6-1-2021
Abstract
In this paper, we study the local composite quantile regression estimator for mixed categorical and continuous data. The local composite quantile estimator is an efficient and safe alternative to the local polynomial method and has been well-studied for continuous covariates. Generalization of the local composite quantile regression estimator to a flexible data structure is appealing to practitioners as empirical studies often encounter categorical data. Furthermore, we study the theoretical properties of the cross-validated bandwidth selection for the local composite quantile estimator. Under mild conditions, we derive the rates of convergence of the cross-validated smoothing parameters to their optimal benchmark values for both categorical and continuous covariates. Monte Carlo experiments show that the proposed estimator may have large efficiency gains compared with the local linear estimator. Furthermore, we illustrate the robustness of the local composite quantile estimator using the Boston housing dataset.
Journal Title
Econometric Theory
Journal ISSN
02664666
Volume
37
Issue
3
First Page
613
Last Page
631
Digital Object Identifier (DOI)
10.1017/S0266466620000146