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A Combination Method for Averaging OLS and GLS Estimators

Department of Economics, Otaru University of Commerce, Otaru 047-8501, Japan
Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Sydney, NSW 2006, Australia
Author to whom correspondence should be addressed.
The authors thank Okui Ryo, Mototsugu Shintani, Arihiro Yoshimura and the conference participants at the 2014 European Meeting of the Econometric Society and the 2013 Kansai Keiryo Keizaigaku Kenkyukai for their helpful comments. Liu acknowledges financial support from the JSPS Grant-in-Aid for Young Scientists (B) No. 25780148 and JSPS KAKENHI Grant (C) No. JP16K03590 and No. JP19K01582. The authors are grateful to the Editor and two reviewers for their constructive comments.
Econometrics 2019, 7(3), 38;
Received: 6 June 2019 / Revised: 3 September 2019 / Accepted: 4 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
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. View Full-Text
Keywords: model averaging; OLS; GLS; combination method model averaging; OLS; GLS; combination method
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Liu, Q.; Vasnev, A.L. A Combination Method for Averaging OLS and GLS Estimators. Econometrics 2019, 7, 38.

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