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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, 282, 3, 2020 DOI: 10.1016/10.1016/j.ejor.2019.10.012

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Quantile Stochastic Frontiers

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

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Quantile Stochastic Frontiers. / Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 282, No. 3, 01.05.2020, p. 1177-1184.

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

Harvard

Tsionas, MG 2020, 'Quantile Stochastic Frontiers', European Journal of Operational Research, vol. 282, no. 3, pp. 1177-1184. https://doi.org/10.1016/j.ejor.2019.10.012

APA

Tsionas, M. G. (2020). Quantile Stochastic Frontiers. European Journal of Operational Research, 282(3), 1177-1184. https://doi.org/10.1016/j.ejor.2019.10.012

Vancouver

Tsionas MG. Quantile Stochastic Frontiers. European Journal of Operational Research. 2020 May 1;282(3):1177-1184. Epub 2019 Nov 22. doi: 10.1016/j.ejor.2019.10.012

Author

Tsionas, Mike G. / Quantile Stochastic Frontiers. In: European Journal of Operational Research. 2020 ; Vol. 282, No. 3. pp. 1177-1184.

Bibtex

@article{aca730265fca4c1086dcbf22e5d2b40d,
title = "Quantile Stochastic Frontiers",
abstract = "In this paper, based on Jradi and Ruggiero (2019). Stochastic Data Envelopment Analysis: A Quantile Regression Approach to Estimate the Production Frontier. European Journal of Operational Research, 278 (2), 385–393] we propose a novel quantile Stochastic Frontier Model (SFM) and develop Markov Chain Monte Carlo techniques for numerical Bayesian inference. In an empirical application to US large banks we document important differences between the Quantile and the traditional SFM, in terms of several aspects of the data. We also document considerable heterogeneity among different quantiles in terms of returns to scale, technical change, efficiency change, technical efficiency, as well as productivity growth.",
keywords = "Productivity and competitiveness, Efficiency, Quantile Stochastic Frontier model, Bayesian Inference",
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, 282, 3, 2020 DOI: 10.1016/10.1016/j.ejor.2019.10.012",
year = "2020",
month = may,
day = "1",
doi = "10.1016/j.ejor.2019.10.012",
language = "English",
volume = "282",
pages = "1177--1184",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - Quantile Stochastic Frontiers

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, 282, 3, 2020 DOI: 10.1016/10.1016/j.ejor.2019.10.012

PY - 2020/5/1

Y1 - 2020/5/1

N2 - In this paper, based on Jradi and Ruggiero (2019). Stochastic Data Envelopment Analysis: A Quantile Regression Approach to Estimate the Production Frontier. European Journal of Operational Research, 278 (2), 385–393] we propose a novel quantile Stochastic Frontier Model (SFM) and develop Markov Chain Monte Carlo techniques for numerical Bayesian inference. In an empirical application to US large banks we document important differences between the Quantile and the traditional SFM, in terms of several aspects of the data. We also document considerable heterogeneity among different quantiles in terms of returns to scale, technical change, efficiency change, technical efficiency, as well as productivity growth.

AB - In this paper, based on Jradi and Ruggiero (2019). Stochastic Data Envelopment Analysis: A Quantile Regression Approach to Estimate the Production Frontier. European Journal of Operational Research, 278 (2), 385–393] we propose a novel quantile Stochastic Frontier Model (SFM) and develop Markov Chain Monte Carlo techniques for numerical Bayesian inference. In an empirical application to US large banks we document important differences between the Quantile and the traditional SFM, in terms of several aspects of the data. We also document considerable heterogeneity among different quantiles in terms of returns to scale, technical change, efficiency change, technical efficiency, as well as productivity growth.

KW - Productivity and competitiveness

KW - Efficiency

KW - Quantile Stochastic Frontier model

KW - Bayesian Inference

U2 - 10.1016/j.ejor.2019.10.012

DO - 10.1016/j.ejor.2019.10.012

M3 - Journal article

VL - 282

SP - 1177

EP - 1184

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -