Complete Subset Averaging for Quantile Regressions
McMaster University Department of Economics Working Paper Series 2020–03
33 Pages Posted: 2 Apr 2020
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: Suggested Citation