Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models
AbstractIn this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross-sections.Two Monte Carlo methods have been used in practical applications.We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1996-03.
Date of creation: 1996
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Web page: http://center.uvt.nl
Monte Carlo Technique; Numerical Integration;
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