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Open AccessArticle

Improved Average Estimation in Seemingly Unrelated Regressions

Department of Economics, University of California, Riverside, CA 92521, USA
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Econometrics 2020, 8(2), 15; https://doi.org/10.3390/econometrics8020015
Received: 24 February 2020 / Revised: 16 April 2020 / Accepted: 17 April 2020 / Published: 27 April 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
In this paper, we propose an efficient weighted average estimator in Seemingly Unrelated Regressions. This average estimator shrinks a generalized least squares (GLS) estimator towards a restricted GLS estimator, where the restrictions represent possible parameter homogeneity specifications. The shrinkage weight is inversely proportional to a weighted quadratic loss function. The approximate bias and second moment matrix of the average estimator using the large-sample approximations are provided. We give the conditions under which the average estimator dominates the GLS estimator on the basis of their mean squared errors. We illustrate our estimator by applying it to a cost system for United States (U.S.) Commercial banks, over the period from 2000 to 2018. Our results indicate that on average most of the banks have been operating under increasing returns to scale. We find that over the recent years, scale economies are a plausible reason for the growth in average size of banks and the tendency toward increasing scale is likely to continue View Full-Text
Keywords: Stein-type shrinkage estimator; asymptotic approximations; SUR; GLS Stein-type shrinkage estimator; asymptotic approximations; SUR; GLS
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Mehrabani, A.; Ullah, A. Improved Average Estimation in Seemingly Unrelated Regressions. Econometrics 2020, 8, 15.

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