Bayesian stochastic frontier analysis using WinBUGS
AbstractMarkov 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. Copyright Springer Science+Business Media, LLC 2007
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Bibliographic InfoArticle provided by Springer in its journal Journal of Productivity Analysis.
Volume (Year): 27 (2007)
Issue (Month): 3 (June)
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Web page: http://www.springerlink.com/link.asp?id=100296
Efficiency; Markov chain Monte Carlo; Model comparison; Regularity; Software; C11; C23; D24;
Other versions of this item:
- Jim Griffin & Mark Steel, 2005. "Bayesian Stochastic Frontier Analysis Using WinBUGS," Econometrics 0509004, EconWPA.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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