Generalized Cp Model Averaging for Heteroskedastic Models

This paper proposed a model averaging method, which is called Generalized Mallows’ Cp model averaging (GC). It works well for heteroskedastic models. Under some regularity conditions, we show that our GC has asymptotic optimality as a model averaging method, and also has asymptotic optimality as a model selection method as well for heteroskedastic model. Some Monte-Carlo studies are performed to investigate the small sample properties of GC. The simulation results show that our method works well, gives better performance than other alternative methods.