Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models
作者： Jacek Osiewalski , Mark F. J. Steel
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摘要: In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models panel data or individual cross- sections. Two Monte Carlo have been used practical applications. We survey these two some detail and argue that Gibbs sampling can greatly reduce computational difficulties involved analyzing such models.
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