Heteroscedasticity-robust Cp model averaging

This paper proposes a new model-averaging method, called the hetero-scedasticity-robust Cp (HRCp) method, for linear regression models with heteroscedastic errors. We provide a feasible form of the Mallows' Cp-like criterion for choosing the weight vector for averaging. Under some regularity conditions, we show that the HRCp method has asymptotic optimality. The simulation results show that our method works well and performs better than alternative methods in finite samples when the number of candidate models is large and/or the population coefficient of determination is not small.