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Efficiency Measurement with the Weibull Stochastic Frontier

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  • Efthymios G. Tsionas

Abstract

In this paper we consider the Weibull distribution as a model for technical efficiency. The distribution has a shape and scale parameter like the gamma distribution and can be a reasonable competitor in practice. The techniques are illustrated using artificial data as well as a panel of Spanish dairy farms.

Suggested Citation

  • Efthymios G. Tsionas, 2007. "Efficiency Measurement with the Weibull Stochastic Frontier," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(5), pages 693-706, October.
  • Handle: RePEc:bla:obuest:v:69:y:2007:i:5:p:693-706
    DOI: 10.1111/j.1468-0084.2007.00475.x
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    File URL: https://doi.org/10.1111/j.1468-0084.2007.00475.x
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    1. Álvarez, Antonio & Arias, Carlos & Kumbhakar, Subal, 2003. "Empirical Consequences of Direction Choice in Technical Efficiency Analysis," Efficiency Series Papers 2003/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
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    1. Graziella Bonanno & Domenico De Giovanni & Filippo Domma, 2017. "The ‘wrong skewness’ problem: a re-specification of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 47(1), pages 49-64, February.
    2. Carta, Alessandro & Steel, Mark F.J., 2012. "Modelling multi-output stochastic frontiers using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3757-3773.
    3. Sheriff, Glenn, 2009. "Implementing second-best environmental policy under adverse selection," Journal of Environmental Economics and Management, Elsevier, vol. 57(3), pages 253-268, May.
    4. William C. Horrace & Christopher F. Parmeter, 2018. "A Laplace stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 260-280, March.
    5. Meena Badade & T. V. Ramanathan, 2020. "Probabilistic frontier regression model for multinomial ordinal type output data," Journal of Productivity Analysis, Springer, vol. 53(3), pages 339-354, June.
    6. Gholamreza Hajargasht & William E. Griffiths, 2018. "Estimation and testing of stochastic frontier models using variational Bayes," Journal of Productivity Analysis, Springer, vol. 50(1), pages 1-24, October.
    7. Phill Wheat & Alexander D. Stead & William H. Greene, 2019. "Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England," Journal of Productivity Analysis, Springer, vol. 51(1), pages 21-38, February.
    8. William C. Horrace & Yulong Wang, 2020. "Nonparametric Tests of Tail Behavior in Stochastic Frontier Models," Center for Policy Research Working Papers 230, Center for Policy Research, Maxwell School, Syracuse University.
    9. 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.
    10. 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.
    11. Gholamreza Hajargasht, 2015. "Stochastic frontiers with a Rayleigh distribution," Journal of Productivity Analysis, Springer, vol. 44(2), pages 199-208, October.

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    1. Antonio Alvarez & Carlos Arias, 2014. "A selection of relevant issues in applied stochastic frontier analysis," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 3-11.
    2. Subal C. Kumbhakar & Efthymios G. Tsionas, 2008. "Estimation of input‐oriented technical efficiency using a nonhomogeneous stochastic production frontier model," Agricultural Economics, International Association of Agricultural Economists, vol. 38(1), pages 99-108, January.
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