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Bayesian stochastic frontier analysis using WinBUGS

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  • Jim Griffin

    ()

  • Mark Steel

    ()

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. Copyright Springer Science+Business Media, LLC 2007

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Bibliographic Info

Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 27 (2007)
Issue (Month): 3 (June)
Pages: 163-176

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Handle: RePEc:kap:jproda:v:27:y:2007:i:3:p:163-176

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Web page: http://www.springerlink.com/link.asp?id=100296

Related research

Keywords: Efficiency; Markov chain Monte Carlo; Model comparison; Regularity; Software; C11; C23; D24;

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Citations

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Cited by:
  1. A. Tonini, 2012. "A Bayesian stochastic frontier: an application to agricultural productivity growth in European countries," Economic Change and Restructuring, Springer, vol. 45(4), pages 247-269, November.
  2. Jorge E. Galán & Helena Veiga & Michael P. Wiper, 2013. "Bayesian analysis of dynamic effects in inefficiency : evidence from the Colombian banking sector," Statistics and Econometrics Working Papers ws131918, Universidad Carlos III, Departamento de Estadística y Econometría.
  3. Philippe K. Widmer, 2011. "Does prospective payment increase hospital (in)efficiency? Evidence from the Swiss hospital sector," ECON - Working Papers 053, Department of Economics - University of Zurich.
  4. Martín, Juan Carlos & Rodríguez-Déniz, Héctor & Voltes-Dorta, Augusto, 2013. "Determinants of airport cost flexibility in a context of economic recession," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 57(C), pages 70-84.
  5. Philippe K. Widmer & Peter Zweifel & Mehdi Farsi, 2011. "Accounting for heterogeneity in the measurement of hospital performance," ECON - Working Papers 052, Department of Economics - University of Zurich.
  6. Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2012. "Bayesian estimation approaches to first-price auctions," Journal of Econometrics, Elsevier, vol. 168(1), pages 47-59.
  7. 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.
  8. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
  9. Voltes-Dorta, Augusto & Lei, Zheng, 2013. "The impact of airline differentiation on marginal cost pricing at UK airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 72-88.
  10. Juan Martín & Concepción Román & Augusto Voltes-Dorta, 2009. "A stochastic frontier analysis to estimate the relative efficiency of Spanish airports," Journal of Productivity Analysis, Springer, vol. 31(3), pages 163-176, June.
  11. Martín, Juan Carlos & Voltes-Dorta, Augusto, 2011. "The econometric estimation of airports' cost function," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 112-127, January.
  12. Jorge E. Galán & Helena Veiga & Michael P. Wiper, 2012. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Statistics and Econometrics Working Papers ws121007, Universidad Carlos III, Departamento de Estadística y Econometría.
  13. Tecles, Patricia Langsch & Tabak, Benjamin M., 2010. "Determinants of bank efficiency: The case of Brazil," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1587-1598, December.
  14. Hajargasht, Gholamreza & Coelli, Tim & Rao, D.S. Prasada, 2008. "A dual measure of economies of scope," Economics Letters, Elsevier, vol. 100(2), pages 185-188, August.
  15. Jose L. Gallizo & Jordi Moreno & Ioana Iuliana Pop (Grigorescu), 2011. "Banking Efficiency And European Integration. Implications Of The Banking Reform In Romania," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(13), pages 25.
  16. Tonini, Axel & Matus, Silvia Saravia & Gomez y Paloma, Sergio, 2011. "A Bayesian Total Factor Productivity Analysis of Tropical Agricultural Systems in Central-Western Africa And South-East Asia," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 116088, European Association of Agricultural Economists.
  17. Sheng-Kai Chang & Yi-Yi Chen & Hung-Jen Wang, 2012. "A Bayesian estimator for stochastic frontier models with errors in variables," Journal of Productivity Analysis, Springer, vol. 38(1), pages 1-9, August.
  18. 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.
  19. Tabak, Benjamin M. & Langsch Tecles, Patricia, 2010. "Estimating a Bayesian stochastic frontier for the Indian banking system," International Journal of Production Economics, Elsevier, vol. 125(1), pages 96-110, May.

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