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A hierarchical panel data stochastic frontier model for the estimation of stochastic metafrontiers

Author

Listed:
  • Christine Amsler

    (Michigan State University)

  • Yi Yi Chen

    (Tamkang University)

  • Peter Schmidt

    (Michigan State University)

  • Hung Jen Wang

    (National Taiwan University)

Abstract

This paper proposes a stochastic frontier model with three composed errors, and therefore six error components. As in the metafrontier literature, firms belong to groups with a group-specific frontier. A firm has a level of short-run and long-run inefficiency relative to its group-specific frontier, as in existing models with two composed errors and four error components. But now there is also a group-specific inefficiency, that is, a shortfall of the group-specific frontier from the best practice metafrontier. The paper shows how to estimate this model and how to extract predictions of the various inefficiencies.

Suggested Citation

  • Christine Amsler & Yi Yi Chen & Peter Schmidt & Hung Jen Wang, 2021. "A hierarchical panel data stochastic frontier model for the estimation of stochastic metafrontiers," Empirical Economics, Springer, vol. 60(1), pages 353-363, January.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:1:d:10.1007_s00181-020-01929-w
    DOI: 10.1007/s00181-020-01929-w
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    References listed on IDEAS

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    1. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    2. Laszlo Matyas (ed.), 2017. "The Econometrics of Multi-dimensional Panels," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-319-60783-2.
    3. Roberto Colombi & Gianmaria Martini & Giorgio Vittadini, 2011. "A Stochastic Frontier Model with short-run and long-run inefficiency random effects," Working Papers 1101, Department of Management, Information and Production Engineering, University of Bergamo.
    4. Lau, Lawrence J. & Yotopoulos, Pan A., 1989. "The meta-production function approach to technological change in world agriculture," Journal of Development Economics, Elsevier, vol. 31(2), pages 241-269, October.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    6. Qi Li & Jeffrey Scott Racine, 2006. "Density Estimation, from Nonparametric Econometrics: Theory and Practice," Introductory Chapters, in: Nonparametric Econometrics: Theory and Practice, Princeton University Press.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    8. Mark M. Pitt, 1983. "Farm-Level Fertilizer Demand in Java: A Meta-Production Function Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 65(3), pages 502-508.
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    Cited by:

    1. Qi Li & Vasilis Sarafidis & Joakim Westerlund, 2021. "Essays in honor of Professor Badi H Baltagi," Empirical Economics, Springer, vol. 60(1), pages 1-11, January.
    2. Li, Qi & Sarafidis, Vasilis & Westerlund, Joakim, 2020. "Essays in Honor of Professor Badi H Baltagi: Editorial," MPRA Paper 104751, University Library of Munich, Germany.

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    More about this item

    Keywords

    Stochastic frontier; Panel data; Hierarchical model; Metafrontier; Inefficiency;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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