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Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling

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Author Info

  • KOOP, Gary

    (Department of Economics, University of Toronto)

  • STEEL, Mark F.

    (CentER and Department of Econometrics, Tilburg University)

  • OSIEWALSKI, Jacek

    (Department of Econometrics, Academy of Economics, Krakow and CORE, Universite)

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 Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models. Our findings are illustrated in an empirical example.

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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1994061.

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Date of creation: 01 Dec 1994
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Handle: RePEc:cor:louvco:1994061

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Chris O'Donnell & W.E. Griffiths, 2004. "Estimating State-Contingent Production Frontiers," CEPA Working Papers Series WP022004, School of Economics, University of Queensland, Australia.
  5. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 2000. " A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies," Economic Change and Restructuring, Springer, vol. 33(3), pages 185-202.
  6. Fernández, C. & Osiewalski, J. & Steel, M.F.J., 1996. "On the Use of Panel Data in Bayesian Stochastic Frontier Models," Discussion Paper 1996-17, Tilburg University, Center for Economic Research.
  7. Jim Griffin & Mark Steel, 2005. "Bayesian Stochastic Frontier Analysis Using WinBUGS," Econometrics 0509004, EconWPA.
  8. Koop, G. & Osiewalski, J. & Steel, M.F.J., 1994. "Bayesian efficiency analysis with a flexible form: The aim cost function," Discussion Paper 1994-13, Tilburg University, Center for Economic Research.
  9. Eduardo Ley & Mark F.J. Steel, 1995. "On the Estimation of Demand Systems Through Consumption Efficiency," Econometrics 9503001, EconWPA, revised 22 Feb 1996.
  10. 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.
  11. 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.
  12. 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.
  13. Tim J. Coelli & Chris O'Donnell, 2003. "A Bayesian Approach To Imposing Curvature On Distance Functions," CEPA Working Papers Series WP032003, School of Economics, University of Queensland, Australia.
  14. 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.
  15. 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.

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