  Model averaging considers the model uncertainty and is an alternative to
model selection. In this paper, we propose a frequentist model averaging
estimator for composite quantile regressions. In recent years, research on
these topics has been added as a separate method, but no study has investigated
them in combination. We apply a delete-one cross-validation method to estimate
the model weights, and prove that the jackknife model averaging estimator is
asymptotically optimal in terms of minimizing out-of-sample composite final
prediction error. Simulations are conducted to demonstrate the good finite
sample properties of our estimator and compare it with commonly used model
selection and averaging methods. The proposed method is applied to the analysis
of the stock returns data and the wage data and performs well.
