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Estimating Stochastic Ray Production Frontiers

Author

Listed:
  • Mike Tsionas

    (Department of Economics, Lancaster University Management School)

  • Marwan Izzeldin

    (Department of Economics, Lancaster University Management School)

  • Arne Henningsen

    (Department of Food and Resource Economics, University of Copenhagen)

  • Evaggelos Paravalos

    (Department of Economics, Athens University of Economics and Business (Greece))

Abstract

In this paper, we consider the stochastic ray production function that has been revived recently by Henningsen et al. (2017). We use a profit-maximizing framework to resolve endogeneity problems that are likely to arise, as in all distance functions, and we derive the system of equations after incorporating technical inefficiency. As technical inefficiency enters non-trivially into the system of equations and the Jacobian is highly complicated, we propose Monte Carlo methods of inference. We illustrate the new approach using US banking data and we also address the problems of missing prices and selection of ordering for outputs.

Suggested Citation

  • Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2019. "Estimating Stochastic Ray Production Frontiers," IFRO Working Paper 2019/06, University of Copenhagen, Department of Food and Resource Economics.
    • Handle: RePEc:foi:wpaper:2019_06
    as

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    File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2019/IFRO_WP_2019_06.pdf
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    References listed on IDEAS

    as
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    2. Géraldine Henningsen & Arne Henningsen & Uwe Jensen, 2015. "A Monte Carlo study on multiple output stochastic frontiers: a comparison of two approaches," Journal of Productivity Analysis, Springer, vol. 44(3), pages 309-320, December.
    3. BARTEN, Anton P., 1969. "Maximum likelihood estimation of a complete system of demand equations," LIDAM Reprints CORE 34, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    5. Bernhard Brümmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
    6. Henningsen, Arne & Bělín, Matěj & Henningsen, Géraldine, 2017. "New insights into the stochastic ray production frontier," Economics Letters, Elsevier, vol. 156(C), pages 18-21.
    7. Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
    8. Arne Henningsen & Christian Henning, 2009. "Imposing regional monotonicity on translog stochastic production frontiers with a simple three-step procedure," Journal of Productivity Analysis, Springer, vol. 32(3), pages 217-229, December.
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    10. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
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    More about this item

    Keywords

    Stochastic ray production frontier; Technical inefficiency; Profit maximization; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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