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A Survey of the Use of Copulas in Stochastic Frontier Models

In: Advances in Efficiency and Productivity Analysis

Author

Listed:
  • Christine Amsler

    (Michigan State University)

  • Peter Schmidt

    (Michigan State University)

Abstract

Copulas are used to create joint distributions with specified marginal distributions. The copula models the dependence between the corresponding marginal random variables. In the normal case, the multivariate normal distribution is a natural choice of joint distribution with normal marginals and its covariance matrix parameterizes the dependence between the individual marginal normals. But how would we specify a joint distribution for a normal and a half-normal, where these two random variables are allowed to be dependent? We can do this using copulas.

Suggested Citation

  • Christine Amsler & Peter Schmidt, 2021. "A Survey of the Use of Copulas in Stochastic Frontier Models," Springer Proceedings in Business and Economics, in: Christopher F. Parmeter & Robin C. Sickles (ed.), Advances in Efficiency and Productivity Analysis, pages 125-138, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-47106-4_6
    DOI: 10.1007/978-3-030-47106-4_6
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    Cited by:

    1. Hung-pin Lai, 2021. "Maximum simulated likelihood estimation of the seemingly unrelated stochastic frontier regressions," Empirical Economics, Springer, vol. 60(6), pages 2943-2968, June.
    2. Graziella Bonanno & Filippo Domma, 2022. "Analytical Derivations of New Specifications for Stochastic Frontiers with Applications," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    3. Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.

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