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Technical and allocative inefficiency in production systems: a vine copula approach

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Listed:
  • Zhai Jian
  • James Robert

    (Discipline of Business Analytics, Business School, University of Sydney, Sydney, NSW, Australia)

  • Prokhorov Artem

    (Discipline of Business Analytics, Business School, University of Sydney, Sydney, Australia)

Abstract

Modeling the error terms in stochastic frontier models of production systems requires multivariate distributions with certain characteristics. We argue that canonical vine copulas offer a natural way to model the pairwise dependence between the two main error types that arise in production systems with multiple inputs. We introduce a vine copula construction that permits dependence between the magnitude (but not the sign) of the errors. By using a recently proposed family of copulas, we show how to construct a simulated likelihood based on C-vines. We discuss issues that arise in the estimation of such models and outline why such models better reflect the dependencies that arise in practice. Monte Carlo simulations and a classic empirical application to electricity generation plants illustrate the utility of the proposed approach.

Suggested Citation

  • Zhai Jian & James Robert & Prokhorov Artem, 2022. "Technical and allocative inefficiency in production systems: a vine copula approach," Dependence Modeling, De Gruyter, vol. 10(1), pages 145-158, January.
    • Handle: RePEc:vrs:demode:v:10:y:2022:i:1:p:145-158:n:5
    DOI: 10.1515/demo-2022-0108
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    References listed on IDEAS

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    1. Anil Bera & Subhash Sharma, 1999. "Estimating Production Uncertainty in Stochastic Frontier Production Function Models," Journal of Productivity Analysis, Springer, vol. 12(3), pages 187-210, November.
    2. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    3. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    4. Schmidt, Peter & Lovell, C. A. Knox, 1980. "Estimating stochastic production and cost frontiers when technical and allocative inefficiency are correlated," Journal of Econometrics, Elsevier, vol. 13(1), pages 83-100, May.
    5. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    6. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2021. "A new family of copulas, with application to estimation of a production frontier system," Journal of Productivity Analysis, Springer, vol. 55(1), pages 1-14, February.
    7. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    8. Cowing, Thomas G, 1974. "Technical Change and Scale Economies in an Engineering Production Function: The Case of Steam Electric Power," Journal of Industrial Economics, Wiley Blackwell, vol. 23(2), pages 135-152, December.
    9. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    10. Tran, Kien C. & Tsionas, Efthymios G., 2015. "Endogeneity in stochastic frontier models: Copula approach without external instruments," Economics Letters, Elsevier, vol. 133(C), pages 85-88.
    11. Kumbhakar,Subal C. & Lovell,C. A. Knox, 2003. "Stochastic Frontier Analysis," Cambridge Books, Cambridge University Press, number 9780521666633.
    12. Gourieroux, Christian & Monfort, Alain, 1993. "Simulation-based inference : A survey with special reference to panel data models," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 5-33, September.
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