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    Rights statement: 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

    Accepted author manuscript, 640 KB, PDF document

    Embargo ends: 11/02/23

    Available under license: CC BY-NC-ND

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Optimal combinations of stochastic frontier and data envelopment analysis models

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>16/10/2021
<mark>Journal</mark>European Journal of Operational Research
Issue number2
Volume294
Number of pages11
Pages (from-to)790-800
Publication StatusPublished
Early online date11/02/21
<mark>Original language</mark>English

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.

Bibliographic note

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