Heteroskedasticity-Robust Cp Model Averaging
33 Pages Posted: 23 Sep 2011 Last revised: 7 Apr 2020
Date Written: September 19, 2012
This paper proposes a new model-averaging method, called the Heteroskedasticity-Robust Cp (HRCp) method, for linear regression models with heteroskedastic errors. We provide a feasible form of the Mallows’ Cp-like criterion for choosing the weighting 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.
Keywords: Model Averaging, Model Selection, Asymptotic Optimality, Mallows' Cp, Heteroskedastic errors
JEL Classification: C51, C52
Suggested Citation: Suggested Citation
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By Chu-an Liu