Jackknife Model Averaging for Quantile Single-Index Coefficient Model

In the past two decades, model averaging, as a way to solve model uncertainty, has attracted more and more attention. In this paper, the authors propose a jackknife model averaging(JMA)method for the quantile single-index coefficient model, which is widely used in statistics. Under model misspecification, the model averaging estimator is proved to be asymptotically optimal in terms of minimizing out-of-sample quantile loss. Simulation experiments are conducted to compare the JMA method with several model selections and model averaging methods, and the results show that the proposed method has a satisfactory performance. The method is also applied to a real dataset.