Posterior analysis of stochastic frontier models using Gibbs sampling
e-Archivo (Carlos III University of Madrid)Published 1 December 1994Open access
Gary Koop, Mark F. J. Steel, Jacek Osiewalski
Citations155
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TL;DR
It is shown how Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing stochastic frontier models with composed error.
Abstract
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 the Gibbs sampler can greatly reduce the computational difficulties involved in analyzing such models. Our fidings are illustrated in an empirical example.
Keywords
MathematicsEconomics, Econometrics and Finance
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