Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling
In this paper we describe the use of Gibbs sampling methods for making posterior inferences.in stochastic frontier models with composed error. We show how Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models. Our findings are illustrated in an empirical example.
|Date of creation:||01 Dec 1994|
|Date of revision:|
|Contact details of provider:|| Postal: |
Fax: +32 10474304
Web page: http://www.uclouvain.be/core
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:cor:louvco:1994061. 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: (Alain GILLIS)
If references are entirely missing, you can add them using this form.