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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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -