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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

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Optimal combinations of stochastic frontier and data envelopment analysis models

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Optimal combinations of stochastic frontier and data envelopment analysis models. / Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 294, No. 2, 2, 16.10.2021, p. 790-800.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Tsionas MG. Optimal combinations of stochastic frontier and data envelopment analysis models. European Journal of Operational Research. 2021 Oct 16;294(2):790-800. 2. Epub 2021 Feb 11. doi: 10.1016/j.ejor.2021.02.003

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Tsionas, Mike G. / Optimal combinations of stochastic frontier and data envelopment analysis models. In: European Journal of Operational Research. 2021 ; Vol. 294, No. 2. pp. 790-800.

Bibtex

@article{aab0e64b1634428f8e2c0610dde6ec41,
title = "Optimal combinations of stochastic frontier and data envelopment analysis models",
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.",
keywords = "Productivity and competitiveness, Data envelopment analysis, Stochastic frontier analysis, Efficiency analysis, Predictive distributions",
author = "Tsionas, {Mike G.}",
note = "This is the author{\textquoteright}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",
year = "2021",
month = oct,
day = "16",
doi = "10.1016/j.ejor.2021.02.003",
language = "English",
volume = "294",
pages = "790--800",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "2",

}

RIS

TY - JOUR

T1 - Optimal combinations of stochastic frontier and data envelopment analysis models

AU - Tsionas, Mike G.

N1 - 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

PY - 2021/10/16

Y1 - 2021/10/16

N2 - 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.

AB - 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.

KW - Productivity and competitiveness

KW - Data envelopment analysis

KW - Stochastic frontier analysis

KW - Efficiency analysis

KW - Predictive distributions

U2 - 10.1016/j.ejor.2021.02.003

DO - 10.1016/j.ejor.2021.02.003

M3 - Journal article

VL - 294

SP - 790

EP - 800

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

M1 - 2

ER -