Heteroskedasticity-Robust Cp Model Averaging

33 Pages Posted: 23 Sep 2011 Last revised: 7 Apr 2020

See all articles by Qingfeng Liu

Qingfeng Liu

Otaru University of Commerce - Department of Economics

Ryo Okui

Seoul National University

Date Written: September 19, 2012

Abstract

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

Liu, Qingfeng and Okui, Ryo, Heteroskedasticity-Robust Cp Model Averaging (September 19, 2012). Available at SSRN: https://ssrn.com/abstract=1932232 or http://dx.doi.org/10.2139/ssrn.1932232

Qingfeng Liu (Contact Author)

Otaru University of Commerce - Department of Economics ( email )

3-5-21 Midori
Otaru City, Hokkaido 047-8501
Japan

Ryo Okui

Seoul National University ( email )

Seoul
Korea, Republic of (South Korea)

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