Complete Subset Averaging for Quantile Regressions

McMaster University Department of Economics Working Paper Series 2020–03

33 Pages Posted: 2 Apr 2020

See all articles by Ji Hyung Lee

Ji Hyung Lee

University of Illinois at Urbana-Champaign - Department of Economics

Youngki Shin

Department of Economics

Date Written: March 6, 2020

Abstract

We propose a novel conditional quantile prediction method based on the complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987). We check the finite sample performance via Monte Carlo simulations and empirical applications.

Keywords: complete subset averaging, quantile regression, prediction, equal-weight, model averaging

JEL Classification: C21, C52, C53

Suggested Citation

Lee, Ji Hyung and Shin, Youngki, Complete Subset Averaging for Quantile Regressions (March 6, 2020). McMaster University Department of Economics Working Paper Series 2020–03, Available at SSRN: https://ssrn.com/abstract=3551560 or http://dx.doi.org/10.2139/ssrn.3551560

Ji Hyung Lee

University of Illinois at Urbana-Champaign - Department of Economics ( email )

214 David Kinley Hall
1407 W. Gregory
Urbana, IL 61801
United States

Youngki Shin (Contact Author)

Department of Economics ( email )

1280 Main Street West
Hamilton, Ontario L8S 4M4
Canada

HOME PAGE: http://sites.google.com/site/yshin12

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