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Stochastic frontier models with dependent error components

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  • Murray D. Smith

Abstract

of the stochastic frontier model are assumed to be independent random variables. By employing the copula approach to statistical modelling, the joint behaviour of U and V can be parametrized thereby allowing the data the opportunity to determine the adequacy of the independence assumption. In this context, three examples of the copula approach are given: the first is algebraic (the Logistic-Exponential stochastic frontier model with margins bound by the Farlie--Gumbel--Morgenstern copula), the second uses a cross-section of cost data sampled from the US electrical power industry and the third constructs a model for panel data that is then used to conduct a Monte Carlo exercise in which estimator bias is examined when the dependence structure is incorrectly ignored. Copyright Royal Economic Society 2007

Suggested Citation

  • Murray D. Smith, 2008. "Stochastic frontier models with dependent error components," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 172-192, March.
  • Handle: RePEc:ect:emjrnl:v:11:y:2008:i:1:p:172-192
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    Cited by:

    1. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    2. 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.
    3. Marijn Verschelde & Michel Dumont & Glenn Rayp & Bruno Merlevede, 2016. "Semiparametric stochastic metafrontier efficiency of European manufacturing firms," Journal of Productivity Analysis, Springer, vol. 45(1), pages 53-69, February.
    4. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    5. repec:spr:empeco:v:54:y:2018:i:2:d:10.1007_s00181-016-1216-z is not listed on IDEAS
    6. repec:eee:econom:v:199:y:2017:i:2:p:131-140 is not listed on IDEAS
    7. Bonanno, Graziella, 2012. "L’efficienza del sistema bancario italiano dal 2006 al 2010. Un’applicazione delle frontiere stocastiche
      [The Efficiency of Italian Banking System over 2006-2010. An Application of the Stochastic F
      ," MPRA Paper 42831, University Library of Munich, Germany.
    8. 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.
    9. Hung-pin Lai & Cliff Huang, 2013. "Maximum likelihood estimation of seemingly unrelated stochastic frontier regressions," Journal of Productivity Analysis, Springer, vol. 40(1), pages 1-14, August.
    10. El Mehdi, Rachida & Hafner, Christian M., 2014. "Inference in stochastic frontier analysis with dependent error terms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 102(C), pages 104-116.
    11. Graziella Bonanno, 2014. "The Efficiency of the Italian Banking System over 2006-2011. An Application of the Stochastic Frontier Approach," Rivista italiana degli economisti, Società editrice il Mulino, issue 2, pages 277-306.
    12. Arabinda Das, 2015. "Copula-based Stochastic Frontier Model with Autocorrelated Inefficiency," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 7(2), pages 111-126, June.
    13. Emilio Gómez-Déniz & Jorge Pérez-Rodríguez, 2015. "Closed-form solution for a bivariate distribution in stochastic frontier models with dependent errors," Journal of Productivity Analysis, Springer, vol. 43(2), pages 215-223, April.
    14. Aivazian, Sergei & Afanasiev, Mikhail & Rudenko, Victoria, 2014. "Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 34(2), pages 3-18.
    15. repec:eee:quaeco:v:67:y:2018:i:c:p:51-62 is not listed on IDEAS
    16. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2017. "Endogenous environmental variables in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 199(2), pages 131-140.
    17. repec:eee:touman:v:48:y:2015:i:c:p:268-282 is not listed on IDEAS
    18. repec:eee:quaeco:v:67:y:2018:i:c:p:362-375 is not listed on IDEAS
    19. repec:spr:endesu:v:19:y:2017:i:6:d:10.1007_s10668-016-9853-0 is not listed on IDEAS

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