  We propose a novel conditional quantile prediction method based on 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.
