Comparison of Bayesian models for production efficiency
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 38 (2011)
Issue (Month): 11 (January)
|Contact details of provider:|| Web page: http://www.tandfonline.com/CJAS20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/CJAS20|
When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2433-2443. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If references are entirely missing, you can add them using this form.