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Generalized Least Squares Model Averaging.
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- Author(s): Liu, Qingfeng1 (AUTHOR); Okui, Ryo2 (AUTHOR) ; Yoshimura, Arihiro3 (AUTHOR)
- Source:
Econometric Reviews. 2016, Vol. 35 Issue 8-10, p1692-1752. 61p.- Subject Terms:
- Source:
- Additional Information
- Abstract: 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’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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Econometric Reviews is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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