Journal of Productivity Analysis

, Volume 27, Issue 3, pp 163–176

Bayesian stochastic frontier analysis using WinBUGS

Article

DOI: 10.1007/s11123-007-0033-y

Cite this article as:
Griffin, J.E. & Steel, M.F.J. J Prod Anal (2007) 27: 163. doi:10.1007/s11123-007-0033-y

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

EfficiencyMarkov chain Monte CarloModel comparisonRegularitySoftware

JEL Classifications

C11C23D24

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of WarwickCoventryUK