Optimal combinations of stochastic frontier and data envelopment analysis models

Tsionas, Mike G. (2021) Optimal combinations of stochastic frontier and data envelopment analysis models. European Journal of Operational Research, 294 (2). pp. 790-800. ISSN 0377-2217

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Abstract

Recent research has shown that combination approaches, such as taking the maximum or the mean over different methods of estimating efficiency scores, have practical merits and offer a useful alternative to adopting only one technique. This recent research shows that taking the maximum minimizes the risk of underestimation, and improves the precision of efficiency estimation. In this paper, we propose and implement a formal criterion of weighting based on maximizing proper criteria of model fit (viz. log predictive scoring) and show how it can be applied in Stochastic Frontier as well as in Data Envelopment Analysis models, where the problem is more difficult. Monte Carlo simulations show that the new techniques perform very well and a substantive application to large U.S. banks shows some important differences with traditional models. The Monte Carlo simulations are also substantive as it is for the first time that proper and coherent optimal model pools are subjected to extensive testing in finite samples.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Additional Information:
This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 294, 2, 2021 DOI: 10.1016/j.ejor.2021.02.003
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code:
160649
Deposited By:
Deposited On:
07 Oct 2021 13:50
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Oct 2021 05:00