Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

J. Osiewalski, M.F.J. Steel

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In 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.
Original languageEnglish
Place of PublicationTilburg
Number of pages17
Publication statusPublished - 1996

Publication series

NameCentER Discussion Paper


  • Monte Carlo Technique
  • Numerical Integration


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