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Analytical Derivations of New Specifications for Stochastic Frontiers with Applications

Author

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  • Graziella Bonanno

    (Department of Economics and Statistics, University of Salerno, 84084 Fisciano, Italy
    These authors contributed equally to this work.)

  • Filippo Domma

    (Department of Economics, Statistics and Finance ‘Giovanni Anania’, University of Calabria, 87036 Arcavacata, Italy
    These authors contributed equally to this work.)

Abstract

In this paper, we propose the analytical derivations of new specifications for the stochastic frontier (SF) approach. In order to avoid some limitations of the traditional SF method, we introduce dependence between the two error components through copula functions and the asymmetry of the random error assigning a generalized logistic distribution. We report the density functions of the overall error term, some important summary measures and the derivation of the efficiency scores for both cost and production frontiers. Finally, we propose two empirical applications in order to test our methodological approach: the first one refers to the estimation of production frontiers for the Italian airport system; the second one investigates the cost efficiency of the Italian banking sector.

Suggested Citation

  • Graziella Bonanno & Filippo Domma, 2022. "Analytical Derivations of New Specifications for Stochastic Frontiers with Applications," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3876-:d:946626
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    References listed on IDEAS

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    Cited by:

    1. Rouven E. Haschka, 2024. "Endogeneity in stochastic frontier models with 'wrong' skewness: copula approach without external instruments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 807-826, July.
    2. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    3. Alecos Papadopoulos & Christopher F. Parmeter, 2024. "The wrong skewness problem in stochastic frontier analysis: a review," Journal of Productivity Analysis, Springer, vol. 61(2), pages 121-134, April.

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