Generalized Least Squares Model Averaging
54 Pages Posted: 1 Jul 2013 Last revised: 15 Apr 2015
Date Written: December 21, 2014
In this paper, we propose a method of averaging generalized least squares estimators for linear regression models with heteroskedastic errors. The averaging weights are chosen to minimize Mallows’ Cp-like criterion. We show that the weight vector selected by our method is optimal. It is also shown that this optimality holds even when the variances of the error terms are estimated and the feasible generalized least squares estimators are averaged. The variances can be estimated parametrically or nonparametrically. Monte Carlo simulation results are encouraging. An empirical example illustrates that the proposed method is useful for predicting a measure of firms’ performance.
Keywords: model averaging, GLS, FGLS, asymptotic optimality, Mallows’ Cp
JEL Classification: C51, C52
Suggested Citation: Suggested Citation