A combination method for averaging OLS and GLS estimators

To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information crit...

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Bibliographic Details
Published in:Econometrics 2019-09, Vol.7 (3), p.1-12
Main Authors: Liu, Qingfeng, Vasnev, Andrey L
Format: Article
Language:English
Subjects:
Approximation
combination method
Econometrics
GLS
Methods
model averaging
OLS
Random variables
Regression analysis
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Summary:To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information criteria and propose feasible versions that work even when the variance-covariance matrix is unknown. The optimality of the method is proven under some regularity conditions. The results of a Monte Carlo simulation demonstrate that the method is adaptive in the sense that it achieves almost the same estimation accuracy as if the homoscedasticity or heteroscedasticity of the error term were known.
ISSN:2225-1146
2225-1146
DOI:10.3390/econometrics7030038