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Bayesian Stochastic Frontier Analysis Using WinBUGS

Author

Listed:
  • Jim Griffin

    (University of Warwick)

  • Mark Steel

    (University of Warwick)

Abstract

Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of stochastic frontier models using the WinBUGS package, a freely available software. General code for cross-sectional and panel data are presented and various ways of summarizing posterior inference are discussed. Several examples illustrate that analyses with models of genuine practical interest can be performed straightforwardly and model changes are easily implemented.

Suggested Citation

  • Jim Griffin & Mark Steel, 2005. "Bayesian Stochastic Frontier Analysis Using WinBUGS," Econometrics 0509004, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpem:0509004
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    References listed on IDEAS

    as
    1. Fernandez C. & Koop G. & Steel M.F.J., 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 432-442, June.
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    13. Osiewalski, Jacek & Steel, Mark F.J. & Koop, Gary, 1992. "Posterior analysis of stochastic frontier models using Gibbs sampling," DES - Working Papers. Statistics and Econometrics. WS 3677, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    15. J. Griffin & M. Steel, 2008. "Flexible mixture modelling of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 29(1), pages 33-50, February.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Efficiency; Markov chain Monte Carlo; Model comparison; Regularity; Software;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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