Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling
AbstractIn 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.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1994061.
Date of creation: 01 Dec 1994
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