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Stochastic frontier models with dependent error components

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  • Murray D. Smith
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    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

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    Bibliographic Info

    Article provided by Royal Economic Society in its journal Econometrics Journal.

    Volume (Year): 11 (2008)
    Issue (Month): 1 (03)
    Pages: 172-192

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    Handle: RePEc:ect:emjrnl:v:11:y:2008:i:1:p:172-192

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    Cited by:
    1. 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 Stochast
      ," MPRA Paper 42831, University Library of Munich, Germany.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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