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Bayesian Analysis of Stochastic Frontier Models

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  • Gary Koop

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  • M. F. J. Steel

Abstract

In this chapter, we described a Bayesian approach to efficiency analysis using stochastic frontier models. With cross-sectional data and a log-linear frontier, a simple Gibbs sampler can be used to carry out Bayesian inference. In the case of a nonlinear frontier, more complicated posterior simulation methods are necessary. Bayesian efficiency measurement with panel data is then discussed. We show how a Bayesian analogue of the classical fixed effects panel data model can be used to calculate the efficiency of each firm relative to the most efficient firm. However, absolute efficiency calculations are precluded in this model and inference on efficiencies can be quite sensitive to prior assumptions. Accordingly, we describe a Bayesian analogue of the classical random effects panel data model which can be used for robust inference on absolute efficiencies. Throughout, we emphasize the computational methods necessary to carry out Bayesian inference. We show how random number generation from well-known distributions is sufficient to develop posterior simulators for a wide variety of models.

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Bibliographic Info

Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 19.

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Date of creation: Sep 2004
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Handle: RePEc:edn:esedps:19

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Cited by:
  1. Kelvin_Balcombe & Dirk_Bezemer & Junior_Davis & Iain_Fraser, 2005. "Livelihoods and Farm Efficiency in Rural Georgia," Development and Comp Systems 0502005, EconWPA.
  2. William Griffiths, 2002. "A Gibbs’ Sampler for the Parameters of a Truncated Multivariate Normal Distribution," Department of Economics - Working Papers Series 856, The University of Melbourne.
  3. 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.
  4. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2006. "On the Accuracy of Bootstrap Confidence Intervals for Efficiency Levels in Stochastic Frontier Models with Panel Data," Working Papers 0704, University of Crete, Department of Economics.
  5. 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.
  6. Guohua Feng & Apostolos Serletis, 2009. "Efficiency, Technical Change, and Returns to Scale in Large U.S. Banks: Panel Data Evidence from an Output Distance Function Satisfying Theoretical Regularity," Monash Econometrics and Business Statistics Working Papers 5/09, Monash University, Department of Econometrics and Business Statistics.
  7. Erniel B. Barrios & Rouselle F. Lavado, 2010. "Spatial Stochastic Frontier Models," Microeconomics Working Papers 23091, East Asian Bureau of Economic Research.
  8. 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.
  9. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Department of Economics - Working Papers Series 1092, The University of Melbourne.
  10. William Greene, 2008. "A Stochastic Frontier Model with Correction for Sample Selection," Working Papers 08-9, New York University, Leonard N. Stern School of Business, Department of Economics.
  11. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 7, University of Verona, Department of Economics.
  12. C. Charles Okeahalam, 2006. "Production Efficiency in the South African Banking Sector: A Stochastic Analysis," International Review of Applied Economics, Taylor & Francis Journals, vol. 20(1), pages 103-123.

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