Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models |
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Authors: | Tsionas Efthymios G |
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Institution: | (1) 5 Nikis Street, Office 607, Constitution Square, 101 80 Athens, Greece |
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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. |
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Keywords: | stochastic frontier model Gamma distribution Bayesian analysis Gibbs sampling data augmentation posterior simulation |
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