Generalized Least Squares Model Averaging

In this article, 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' C p -like criterion. We show that the weight vector selected by our method is optimal. It is als...

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Bibliographic Details
Published in:Econometric reviews 2016-11, Vol.35 (8-10), p.1692-1752
Main Authors: Liu, Qingfeng, Okui, Ryo, Yoshimura, Arihiro
Format: Article
Language:English
Subjects:
Computer simulation
Econometrics
Economic models
Errors
Estimators
FGLS
GLS
Least squares method
Mallows' C
Mathematical analysis
Model averaging
Monte Carlo simulation
Optimization
Regression
Regression analysis
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Summary:In this article, 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' C p -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.
ISSN:0747-4938
1532-4168
DOI:10.1080/07474938.2015.1092817