Comparison of Bayesian models for production efficiency

Ricardo Ehlers ()

Journal of Applied Statistics, 2011, vol. 38, issue 11, 2433-2443

Abstract: In this paper, we use Markov Chain Monte Carlo (MCMC) methods in order to estimate and compare stochastic production frontier models from a Bayesian perspective. We consider a number of competing models in terms of different production functions and the distribution of the asymmetric error term. All MCMC simulations are done using the package JAGS (Just Another Gibbs Sampler), a clone of the classic BUGS package which works closely with the R package where all the statistical computations and graphics are done.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:38:y:2011:i:11:p:2433-2443

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DOI: 10.1080/02664763.2011.559203

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