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Bayesian Analysis of Stochastic Frontier Models

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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.

Suggested Citation

  • Gary Koop & Mark F J Steel, 1999. "Bayesian Analysis of Stochastic Frontier Models," Edinburgh School of Economics Discussion Paper Series 19, Edinburgh School of Economics, University of Edinburgh.
    • Handle: RePEc:edn:esedps:19
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    Cited by:

    1. Feng, Guohua & Serletis, Apostolos, 2010. "Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 127-138, January.
    2. 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.
    3. 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.
    4. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    5. 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.
    6. Lavado, Rouselle F. & Barrios, Erniel B., 2010. "Spatial Stochastic Frontier Models," Discussion Papers DP 2010-08, Philippine Institute for Development Studies.
    7. 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.
    8. Kelvin_Balcombe & Dirk_Bezemer & Junior_Davis & Iain_Fraser, 2005. "Livelihoods and Farm Efficiency in Rural Georgia," Development and Comp Systems 0502005, University Library of Munich, Germany.
    9. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
    10. 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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