Journal of Productivity Analysis

, Volume 27, Issue 3, pp 163-176

First online:

Bayesian stochastic frontier analysis using WinBUGS

  • Jim E. GriffinAffiliated withDepartment of Statistics, University of Warwick Email author 
  • , Mark F. J. SteelAffiliated withDepartment of Statistics, University of Warwick

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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.


Efficiency Markov chain Monte Carlo Model comparison Regularity Software

JEL Classifications

C11 C23 D24