Jackknife Model Averaging for Composite Quantile Regression

In this paper, the authors propose a frequentist model averaging method for composite quantile regression with diverging number of parameters. Different from the traditional model averaging for quantile regression which considers only a single quantile, the proposed model averaging estimator is based on multiple quantiles. The well-known delete-one cross-validation or jackknife approach is applied to estimate the model weights. The resultant jackknife model averaging estimator is shown to be asymptotically optimal in terms of minimizing the out-of-sample composite final prediction error. Simulation studies are conducted to demonstrate the finite sample performance of the new model averaging estimator. The proposed method is also applied to the analysis of the stock returns data and the wage data.