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An introduction to efficiency measurement using Bayesian stochastic frontier models

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  • Efthymios G. Tsionas

Abstract

The purpose of this paper is to provide an introduction to stochastic frontier models as seen from the point of view of Bayesian analysis. Stochastic frontier models are central in efficiency measurement, and recent advances in Bayesian computation allow us to explore significant extensions of the basic model in a coherent way. In this paper, we describe the fundamentals of efficiency measurement using stochastic frontier models, and describe in reasonable detail the computational aspects of posterior inference and posterior efficiency measurement using the basic model and its extensions.

Suggested Citation

  • Efthymios G. Tsionas, 2001. "An introduction to efficiency measurement using Bayesian stochastic frontier models," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 3(2), pages 287-311.
    • Handle: RePEc:ids:gbusec:v:3:y:2001:i:2:p:287-311
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    Citations

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    Cited by:

    1. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    2. 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.
    3. Henderson, Heath & Follett, Lendie, 2020. "A Bayesian framework for estimating human capabilities," World Development, Elsevier, vol. 129(C).
    4. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    5. Hampf, Benjamin, 2015. "Estimating the materials balance condition: A stochastic frontier approach," Darmstadt Discussion Papers in Economics 226, Darmstadt University of Technology, Department of Law and Economics.

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