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Stochastic Production Frontier Models

Stochastic Production Frontier Models


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.

R commands


Some References

[2003, inproceedings]
Plummer, M. (2003). JAGS: A program for analysis Bayesian graphical models using Gibbs sampling. Paper presented at the Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC, 2003).
[2004, techreport]
Griffin, J., & Steel, M. (2004). Flexible mixture modelling of stochastic frontiers (No. 418). University of Warwick.
[2004, techreport]
Migon, H. S., & Medrano, L. A. T. (2004). Deviance-based Criteria for comparing Bayesian stochastic production frontier models (in Portuguese) (No. 176). Universidade Federal do Rio de Janeiro.

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