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A Bayesian approach to statistical inference in stochastic DEA

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  • Tsionas, Efthymios G.
  • Papadakis, Emmanuel N.

Abstract

Stochastic DEA can deal effectively with noise in the non-parametric measurement of efficiency but unfortunately formal statistical inference on efficiency measures in not possible. In this paper, we provide a Bayesian approach to the problem organized around simulation techniques that allow for finite-sample inferences on efficiency scores. The new methods are applied to efficiency analysis of the Greek banking system for the period 1993-1999. The results show that the majority of the Greek banks operate close to best market practices.

Suggested Citation

  • Tsionas, Efthymios G. & Papadakis, Emmanuel N., 2010. "A Bayesian approach to statistical inference in stochastic DEA," Omega, Elsevier, vol. 38(5), pages 309-314, October.
    • Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:309-314
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    7. Robin C. Sickles & Wonho Song & Valentin Zelenyuk, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," CEPA Working Papers Series WP082018, School of Economics, University of Queensland, Australia.
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    10. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    11. Ali Ebrahimnejad & Madjid Tavana & Seyed Hadi Nasseri & Omid Gholami, 2019. "A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 147-170, January.
    12. Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
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    15. Zaiwu Gong & Xiaoqing Chen, 2017. "Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    16. Tsionas, Mike G., 2020. "A coherent approach to Bayesian Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 281(2), pages 439-448.
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    18. Abdullah Üstün & Gülay Barbarosoğlu, 2015. "Performance evaluation of Turkish disaster relief management system in 1999 earthquakes using data envelopment analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1977-1996, January.
    19. Leme, Rafael C. & Paiva, Anderson P. & Steele Santos, Paulo E. & Balestrassi, Pedro P. & Galvão, Leandro de Lima, 2014. "Design of experiments applied to environmental variables analysis in electricity utilities efficiency: The Brazilian case," Energy Economics, Elsevier, vol. 45(C), pages 111-119.
    20. Rashed Khanjani Shiraz & Adel Hatami-Marbini & Ali Emrouznejad & Hirofumi Fukuyama, 2020. "Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs," Operational Research, Springer, vol. 20(3), pages 1863-1898, September.
    21. Friesner, Daniel & Mittelhammer, Ron & Rosenman, Robert, 2013. "Inferring the incidence of industry inefficiency from DEA estimates," European Journal of Operational Research, Elsevier, vol. 224(2), pages 414-424.

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