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Estimación Bayesiana en modelos de producción con frontera determinista/Bayesian Estimation in Deterministic Frontier Production Models

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  • ORTEGA IRIZO, FCO. JAVIER

    () (Departamento de Economía Aplicada I, Facultad de Ciencias Económicas y Empresariales. UNIVERSIDAD DE SEVILLA)

  • BASULTO SANTOS, JESÚS

    () (Departamento de Economía Aplicada I, Facultad de Ciencias Económicas y Empresariales. UNIVERSIDAD DE SEVILLA.)

Abstract

Como consecuencia de no ser válidos, en los modelos de producción con frontera determinista, las condiciones usuales de regularidad (que justifican la consistencia y normalidad asintótica de los estimadores máximo verosímiles), desconocemos las propiedades generales de estos estimadores. Una alternativa son los métodos de inferencia bayesiana que, gracias al algoritmo de Gibbs, son relativamente fáciles de aplicar. En nuestro trabajo proponemos una distribución a priori no informativa para este modelo y, por simulación, analizamos el comportamiento de los estimadores e intervalos bayesianos. As a consequence of non valid regularity conditions, we unknown the general properties (consistency and asymptotic normality) of the maximun likelihood estimators in the deterministic frontier production models. An alternative to these estimators are the bayesian inference methods. Thanks to Gibbs’ algorithm, these methods are relativity easy to apply. In our work, we propose a prior noninformative distribution for the deterministic frontier models, and, by simulation, we study the Bayesian estimators and intervals behaviour.

Suggested Citation

  • Ortega Irizo, Fco. Javier & Basulto Santos, Jesús, 2009. "Estimación Bayesiana en modelos de producción con frontera determinista/Bayesian Estimation in Deterministic Frontier Production Models," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 27, pages 573(22á)-57, Agosto.
  • Handle: RePEc:lrk:eeaart:27_2_15
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    References listed on IDEAS

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    1. D. S. Prasada Rao & Bart van Ark, 2013. "Introduction," Chapters,in: World Economic Performance, chapter 1, pages 1-6 Edward Elgar Publishing.
    2. Léopold Simar, 2007. "How to improve the performances of DEA/FDH estimators in the presence of noise?," Journal of Productivity Analysis, Springer, vol. 28(3), pages 183-201, December.
    3. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    4. Forsund, Finn R. & Lovell, C. A. Knox & Schmidt, Peter, 1980. "A survey of frontier production functions and of their relationship to efficiency measurement," Journal of Econometrics, Elsevier, vol. 13(1), pages 5-25, May.
    5. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
    6. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
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    More about this item

    Keywords

    Modelos de producción con frontera; Modelo Half-Normal; Estimación Bayesiana; Algoritmo de Gibbs ; Frontier Production Models; Half-Normal Model; Bayesian Estimation; Gibbs Sampling.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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