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Journal of Productivity Analysis

, Volume 27, Issue 3, pp 163–176 | Cite as

Bayesian stochastic frontier analysis using WinBUGS

  • Jim E. Griffin
  • Mark F. J. Steel
Article

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. Although WinBUGS may not be that efficient for more complicated models, it does make Bayesian inference with stochastic frontier models easily accessible for applied researchers and its generic structure allows for a lot of flexibility in model specification.

Keywords

Efficiency Markov chain Monte Carlo Model comparison Regularity Software 

JEL Classifications

C11 C23 D24 

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References

  1. Aigner D, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Economet 6:21–37CrossRefGoogle Scholar
  2. Arickx F, Broeckhove J, Dejonghe M, van den Broeck J (1997) BSFM: a computer program for Bayesian stochastic frontier models. Comput Stat 12:403–421Google Scholar
  3. Azzalini A (1985) A class of distributions which includes the normal ones. Scand J Stat 12:171–178Google Scholar
  4. Battese GE, Coelli TJ (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J Prod Anal 3:153–169CrossRefGoogle Scholar
  5. Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Econ 20:325–332CrossRefGoogle Scholar
  6. Christensen LR, Greene WH (1976) Economies of scale in U.S. electric power generation. J Polit Econ 84:655–676CrossRefGoogle Scholar
  7. Dorfman JH, Koop G (2005). Current developments in productivity and efficiency measurement. Special issue of J Economet 126:233–570Google Scholar
  8. Ennsfellner KC, Lewis D, Anderson RI (2004) Production efficiency in the Austrian insurance industry: a Bayesian examination. J␣Risk Insur 71:135–159CrossRefGoogle Scholar
  9. Fernández C, Koop G, Steel MFJ (2002) Multiple output production with undesirable outputs: an application to nitrogen surplus in agriculture. J Am Stat Assoc 97:432–442CrossRefGoogle Scholar
  10. Greene WH (1990) A gamma-distributed stochastic frontier model. J␣Economet 46:141–163CrossRefGoogle Scholar
  11. Griffin JE, Steel MFJ (2004a) Semiparametric Bayesian inference for stochastic frontier models. J Economet 123:121–152CrossRefGoogle Scholar
  12. Griffin JE, Steel MFJ (2004b) Flexible mixture modelling of stochastic frontiers. Technical Report, University of WarwickGoogle Scholar
  13. Huang HC (2004) Estimation of technical inefficiencies with heterogeneous technologies. J Prod Anal 21:277–296CrossRefGoogle Scholar
  14. Kim Y, Schmidt P (2000) A review and empirical comparison of Bayesian and classical approaches to inference on efficiency levels in stochastic frontier models with panel data. J Prod Anal 14:91–118CrossRefGoogle Scholar
  15. Koop G (1999) Bayesian analysis, computation and communication. J␣Appl Economet 14:677–689CrossRefGoogle Scholar
  16. Koop G, Steel MFJ, Osiewalski J (1995) Posterior analysis of stochastic frontier models using Gibbs sampling. Comput Stat 10:353–373Google Scholar
  17. Koop G, Osiewalski J, Steel MFJ (1997) Bayesian efficiency analysis through individual effects: hospital cost frontier. J Economet 76:77–105CrossRefGoogle Scholar
  18. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, New YorkGoogle Scholar
  19. Kumbhakar SC, Tsionas EG (2005). Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach. J Economet 126:355–384CrossRefGoogle Scholar
  20. Kurkalova LA, Carriquiry A (2002) An analysis of grain production decline during the early transition in Ukraine: a Bayesian inference. Am J Agric Econ 84:1256–1263CrossRefGoogle Scholar
  21. Lee YH, Schmidt P (1993) A production frontier model with flexible temporal variation in technical efficiency. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, New YorkGoogle Scholar
  22. Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed errors. Int Econ Rev 8:435–444CrossRefGoogle Scholar
  23. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 64:583–640CrossRefGoogle Scholar
  24. Stevenson RE (1980) Likelihood functions for generalized stochastic frontier estimation. J Economet 13:57–66CrossRefGoogle Scholar
  25. Terrell D (1996) Incorporating monotonicity and concavity conditions in flexible functional forms. J Appl Economet 11:179–194CrossRefGoogle Scholar
  26. Tsionas EG (2000) Full likelihood inference in Normal-gamma stochastic frontier models. J Prod Anal 13:183–205CrossRefGoogle Scholar
  27. Tsionas EG (2002) Stochastic frontier models with random coefficients. J Appl Economet 17:127–147CrossRefGoogle Scholar
  28. van den Broeck J, Koop G, Osiewalski J, Steel MFJ (1994) Stochastic frontier models: a Bayesian perspective. J Economet 61:273–303CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of WarwickCoventryUK

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