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 +===== Stochastic Production Frontier Models =====
 +
 === Abstract === === Abstract ===
  
-In this project 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 asymetric ​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.+In this project 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.
  
 Key Words: Markov chain Monte Carlo, Gibbs sampler, JAGS, Bayesian model averaging, model comparison. Key Words: Markov chain Monte Carlo, Gibbs sampler, JAGS, Bayesian model averaging, model comparison.
  
-=== Participantes ​===+[[http://​www.leg.ufpr.br/​~ehlers/​SPF|R commands]] 
 + 
 +=== People ​===
   - [[pessoais:​ehlers|Ricardo Ehlers]]   - [[pessoais:​ehlers|Ricardo Ehlers]]
  
-=== Bibliografia ​===+=== Some References ​===
 <​bibtex>​ <​bibtex>​
 @InProceedings{plummer03,​ @InProceedings{plummer03,​

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