Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models

Mike Tsionas

Journal of Productivity Analysis, 2000, vol. 13, issue 3, 183-205

Abstract: The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a computationally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest related to technical inefficiency. Copyright Kluwer Academic Publishers 2000

Keywords: stochastic frontier model; Gamma distribution; Bayesian analysis; Gibbs sampling; data augmentation; posterior simulation (search for similar items in EconPapers)
Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:13:y:2000:i:3:p:183-205

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DOI: 10.1023/A:1007845424552

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