Improved Average Estimation in Seemingly Unrelated Regressions
Ali Mehrabani () and
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Ali Mehrabani: UCR
No 202013, Working Papers from University of California at Riverside, Department of Economics
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 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.
Keywords: Key Words: Stein-type Shrinkage Estimator; Asymptotic Approximations; SUR; GLS (search for similar items in EconPapers)
Pages: 30 Pages
Date: 2020-01, Revised 2020-06
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https://economics.ucr.edu/repec/ucr/wpaper/202013.pdf First version, 2020 (application/pdf)
https://economics.ucr.edu/repec/ucr/wpaper/202013R.pdf Revised version, 2020 (application/pdf)
Journal Article: Improved Average Estimation in Seemingly Unrelated Regressions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202013
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