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Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models

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  • Efthymios Tsionas

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

Suggested Citation

  • Efthymios Tsionas, 2000. "Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 13(3), pages 183-205, May.
    • Handle: RePEc:kap:jproda:v:13:y:2000:i:3:p:183-205
    DOI: 10.1023/A:1007845424552
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    References listed on IDEAS

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    1. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
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    5. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
    6. Roberts, G. O. & Smith, A. F. M., 1994. "Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms," Stochastic Processes and their Applications, Elsevier, vol. 49(2), pages 207-216, February.
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