Griffin, Jim E. and Steel, Mark F. J. (2007) Bayesian stochastic frontier analysis using WinBUGS. Journal of Productivity Analysis, Vol.27 (No.3). pp. 163-176. doi:10.1007/s11123-007-0033-y

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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.

Item Type:	Journal Article
Subjects:	H Social Sciences > HF Commerce H Social Sciences > HC Economic History and Conditions H Social Sciences
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Journal of Productivity Analysis
Publisher:	Springer New York LLC
ISSN:	0895-562X
Official Date:	June 2007
Dates:	Date Event June 2007 Published
Volume:	Vol.27
Number:	No.3
Number of Pages:	14
Page Range:	pp. 163-176
DOI:	10.1007/s11123-007-0033-y
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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