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Posterior analysis of stochastic frontier models using Gibbs sampling

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URI: http://hdl.handle.net/10016/3677
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ws924531.pdf (496.37 KB)
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1992-12
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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.
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Composed error models, Bayesian econometrics, Gibbs sampling, Rejection sampling
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DES - Working Papers. Statistics and Econometrics. WS