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Bayesian Estimation of Technical Efficiency of a Single Input

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  • Kurkalova, Lyubov A.
  • Carriquiry, Alicia L.

Abstract

We propose estimation of a stochastic production frontier model within a Bayesian framework to obtain the posterior distribution of single-input-oriented technical efficiency at the firm level. The proposed method is applicable to the estimation of environmental efficiency of agricultural production when the technology interaction with the environment is modeled via public inputs such as soil quality and environmental conditions. All computations are carried out using Markov chain Monte Carlo methods. We illustrate the approach by applying it to production data from Ukrainian collective farms.

Suggested Citation

  • Kurkalova, Lyubov A. & Carriquiry, Alicia L., 2000. "Bayesian Estimation of Technical Efficiency of a Single Input," Hebrew University of Jerusalem Archive 18545, Hebrew University of Jerusalem.
    • Handle: RePEc:ags:hebarc:18545
    DOI: 10.22004/ag.econ.18545
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    References listed on IDEAS

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    1. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    2. Kurkalova, Lyubov A. & Jensen, Helen H., 1996. "Production Efficiency in Ukrainian Agriculture and the Process of Economic Reform," Hebrew University of Jerusalem Archive 18581, Hebrew University of Jerusalem.
    3. S.R. Johnson & Aziz Bouzaher & Alicia Carriquiry & Helen Jensen & P.G. Lakshminarayan, 1994. "Production Efficiency and Agricultural Reform in Ukraine," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(3), pages 629-635.
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    7. Sotnikov, Sergey, 1998. "Evaluating the Effects of Price and Trade Liberalisation on the Technical Efficiency of Agricultural Production in a Transition Economy: The Case of Russia," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 25(3), pages 412-431.
    8. Robert D. Weaver, 1998. "Measuring Productivity of Environmentally Interactive Technologies: The Case of Agriculture and the Environment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(3), pages 595-599.
    9. Steel, M.F.J., 1995. "Posterior analysis of stochastic volatility models with flexible tails," Discussion Paper 1995-68, Tilburg University, Center for Economic Research.
    10. Jacek Osiewalski & Mark Steel, 1998. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 10(1), pages 103-117, July.
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    13. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    14. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
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    2. Gstach, Dieter, 2005. "Estimating output targets to evaluate output-specific efficiencies: A statistical framework," European Journal of Operational Research, Elsevier, vol. 161(2), pages 564-578, March.

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