Bayesian Stochastic Frontier Analysis Using WinBUGS

Griffin, J.E. and Steel, Mark F.J. (2007) Bayesian Stochastic Frontier Analysis Using WinBUGS. Journal of Productivity Analysis, 27 (3). pp. 163-176. ISSN 0895-562X. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/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.

Item Type: Article
Uncontrolled keywords: efficiency; Markov chain Monte Carlo; model comparison; regularity; software
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 03 Jun 2008 13:56
Last Modified: 08 Jun 2012 11:08
Resource URI: http://kar.kent.ac.uk/id/eprint/3149 (The current URI for this page, for reference purposes)
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