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Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

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  • Jacek Osiewalski
  • Mark Steel

Abstract

In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross- sections. Two Monte Carlo methods have been used in practical applications. We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models. Copyright Kluwer Academic Publishers 1998

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  • Jacek Osiewalski & Mark Steel, 1998. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 10(1), pages 103-117, July.
    • Handle: RePEc:kap:jproda:v:10:y:1998:i:1:p:103-117
    DOI: 10.1023/A:1018302600587
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    3. Fernández, C. & Osiewalski, J. & Steel, M.F.J., 1996. "On the Use of Panel Data in Bayesian Stochastic Frontier Models," Other publications TiSEM d27e7bcf-bb16-457a-934a-a, Tilburg University, School of Economics and Management.
    4. Shabbir Ahmad, 2020. "Estimating input-mix efficiency in a parametric framework: application to state-level agricultural data for the United States," Applied Economics, Taylor & Francis Journals, vol. 52(36), pages 3976-3997, July.
    5. Jerzy Marzec & Andrzej Pisulewski, 2015. "Analysis of the Economic Activity of Dairy Farms in Poland: Results obtained from the Short-run Cost Function," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 39, pages 167-182.
    6. Junrong Liu & Robin C. Sickles & E. G. Tsionas, 2017. "Bayesian Treatments for Panel Data Stochastic Frontier Models with Time Varying Heterogeneity," Econometrics, MDPI, Open Access Journal, vol. 5(3), pages 1-21, July.
    7. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    8. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, Open Access Journal, vol. 8(2), pages 1-22, April.
    9. Makieła, Kamil & Marzec, Jerzy & Pisulewski, Andrzej, 2016. "Productivity Change Analysis of Polish Dairy Farms After Poland’s Accession to the EU – An Output Growth Decomposition Approach," MPRA Paper 80295, University Library of Munich, Germany.
    10. Henderson, Heath & Follett, Lendie, 2020. "A Bayesian framework for estimating human capabilities," World Development, Elsevier, vol. 129(C).
    11. Arabinda Das, 2015. "Copula-based Stochastic Frontier Model with Autocorrelated Inefficiency," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 7(2), pages 111-126, June.
    12. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    13. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    14. Lambarraa, Fatima, 2011. "Dynamic Efficiency Analysis of Spanish Outdoor and Greenhouse Horticulture Sector," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114408, European Association of Agricultural Economists.
    15. Jerzy Marzec & Andrzej Pisulewski, 2017. "The Effect of CAP Subsidies on the Technical Efficiency of Polish Dairy Farms," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(3), pages 243-273, September.

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