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A copula-based approach to the simultaneous estimation of group and meta-frontiers by constrained maximum likelihood

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  • Repkine, Alexandre

Abstract

Existing approaches to the meta-frontier estimation consist of two stages where the estimates of the local frontier parameters obtained in the first step are used to estimate meta-frontier parameters by means of a linear or quadratic minimisation procedure in the second. Since it was shown by Schmidt (Review of Economics and Statistics 58: 238) that the second step is equivalent to constrained maximisation of a likelihood function, we extend this idea and offer a copula-based approach to the estimation of the parameters of both meta- and group frontiers in a one-step setting. In this way, we ensure a single data-generating mechanism for the estimated parameters, expand the set of potential meta-frontiers and account for the fact that shocks to the individual production units may be correlated with shocks to the local technological environment as a whole. We apply our estimation methodology to a data set on the world agriculture and find that the deviations from the group frontiers are positively correlated with deviations from the meta-frontier, which is a conclusion that is impossible to reach without accounting for stochastic dependence between the two deviation types represented by a copula.

Suggested Citation

  • Repkine, Alexandre, 2014. "A copula-based approach to the simultaneous estimation of group and meta-frontiers by constrained maximum likelihood," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(1), January.
    • Handle: RePEc:ags:aareaj:260074
    DOI: 10.22004/ag.econ.260074
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    References listed on IDEAS

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    1. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    2. Schmidt, Peter, 1976. "On the Statistical Estimation of Parametric Frontier Production Functions," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 238-239, May.
    3. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    4. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    5. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Huang, Tai-Hsin & Chen, Kuan-Chen & Lin, Chung-I, 2018. "An extension from network DEA to copula-based network SFA: Evidence from the U.S. commercial banks in 2009," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 51-62.

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