Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

Jacek Osiewalski and Mark Steel

Journal of Productivity Analysis, 1998, vol. 10, issue 1, 103-117

Abstract: 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. Copyright Kluwer Academic Publishers 1998

Keywords: Efficiency analysis; composed error models; posterior inference; Monte Carlo-importance sampling; Gibbs sampling (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (25)

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Related works:
Working Paper: Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models (1996)
Working Paper: Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models (1996)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:10:y:1998:i:1:p:103-117

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DOI: 10.1023/A:1018302600587

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