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Posterior analysis of stochastic frontier models using Gibbs sampling

Author

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  • Osiewalski, Jacek
  • Steel, Mark F.J.
  • Koop, Gary

Abstract

In this paper we describe the use of Gibbs sampling methods for making posterior inferences in stochastic frontier models with composed error. We show how the Gibbs sampler can greatly reduce the computational difficulties involved in analyzing such models. Our fidings are illustrated in an empirical example.

Suggested Citation

  • 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.
  • Handle: RePEc:cte:wsrepe:3677
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    Cited by:

    1. Seongho Song & David Yi, 2011. "The fundraising efficiency in U.S. non-profit art organizations: an application of a Bayesian estimation approach using the stochastic frontier production model," Journal of Productivity Analysis, Springer, vol. 35(2), pages 171-180, April.
    2. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Hospital efficiency analysis through individual effects : A Bayesian approach," Discussion Paper 1994-47, Tilburg University, Center for Economic Research.
    3. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    4. Ley, Eduardo & Steel, Mark F J, 1996. "On the Estimation of Demand Systems through Consumption Efficiency," The Review of Economics and Statistics, MIT Press, vol. 78(3), pages 539-543, August.
    5. Areal, Francisco J & Tiffin, Richard & Balcombe, Kelvin, 2010. "Provision of an environmental output within a multi-output distance function approach," MPRA Paper 25051, University Library of Munich, Germany.
    6. Supawat Rungsuriyawiboon & Chris O'Donnell, 2004. "Curvature-Constrained Estimates of Technical Efficiency and Returns to Scale for U.S. Electric Utilities," CEPA Working Papers Series WP072004, School of Economics, University of Queensland, Australia.
    7. Danielle Lewis & Randy Anderson, 1999. "Residential Real Estate Brokerage Efficiency and the Implications of Franchising: A Bayesian Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(3), pages 543-560.
    8. Lambarraa, Fatima, 2011. "Dynamic Efficiency Analysis of Spanish Outdoor and Greenhouse Horticulture Sector," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114408, European Association of Agricultural Economists.
    9. Kamil Makieła, 2009. "Economic Growth Decomposition. An Empirical Analysis Using Bayesian Frontier Approach," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(4), pages 333-369, December.
    10. Koop G., 2002. "Comparing the Performance of Baseball Players: A Multiple-Output Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 710-720, September.
    11. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    12. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    13. Griffiths, William E. & O'Donnell, Christopher J., 2005. "Estimating variable returns to scale production frontiers with alternative stochastic assumptions," Journal of Econometrics, Elsevier, vol. 126(2), pages 385-409, June.
    14. Goto, Mika & Makhija, Anil K., 2007. "The Impact of Competition and Corporate Structure on Productive Efficiency: The Case of the U.S. Electric Utility Industry, 1990-2004," Working Paper Series 2007-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.

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    Keywords

    Composed error models;

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