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|
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