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Comparison of Bayesian models for production efficiency


  • Ricardo S. Ehlers


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.

Suggested Citation

  • Ricardo S. Ehlers, 2011. "Comparison of Bayesian models for production efficiency," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2433-2443, January.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2433-2443
    DOI: 10.1080/02664763.2011.559203

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    Cited by:

    1. Gholamreza Hajargasht & William E. Griffiths, 2016. "Estimation and Testing of Stochastic Frontier Models using Variational Bayes," Department of Economics - Working Papers Series 2024, The University of Melbourne.
    2. repec:eee:infome:v:11:y:2017:i:3:p:613-628 is not listed on IDEAS

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