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A General Framework for Frontier Estimation with Panel Data


  • KNEIP, Alois

    (CORE and Institut de Statistique, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • SIMAR, Léopold

    () (CORE and Institut de Statistique, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)


The main objective of the paper is to present a general framework for estimating production frontier models with panel data: a sample of firms i = 1, ... ,N is observed on several time periods t = 1. . .. , T. In this framework , nonparametric stochastic models for the frontier will be analysed. The usual parametric formulations of the literature are viewed as particular cases and the convergence of the obtained estimators in this general framework are investigated. Special attention is devoted to the role of N and of T on the speeds of convergence of the obtained estimators. First, a very general model is investigated, in this model almost no restriction is imposed on the structure of the model or of the inefficiencies. This model is estimable from a nonpruametric point of view but needs large values of T and of N to obtain reliable estimates of the individual production functions and estimates of the frontier function. Then more specific nonparametric firm effect models are presented. In these cases, only NT must be large to estimate the common production function; but again both large :N and T are needed for estimating individual efficiencies and for estimating the frontier. The methods are illustrated through a numerical example with real data.

Suggested Citation

  • KNEIP, Alois & SIMAR, Léopold, 1995. "A General Framework for Frontier Estimation with Panel Data," CORE Discussion Papers 1995060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:1995060

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    Cited by:

    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Léopold Simar, 2007. "How to improve the performances of DEA/FDH estimators in the presence of noise?," Journal of Productivity Analysis, Springer, vol. 28(3), pages 183-201, December.
    3. Tran, Kien C. & Tsionas, Efthymios G., 2009. "Estimation of nonparametric inefficiency effects stochastic frontier models with an application to British manufacturing," Economic Modelling, Elsevier, vol. 26(5), pages 904-909, September.
    4. Qian, Junhui & Wang, Le, 2012. "Estimating semiparametric panel data models by marginal integration," Journal of Econometrics, Elsevier, vol. 167(2), pages 483-493.
    5. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    6. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    7. Oleg Badunenko & Daniel J. Henderson & Subal C. Kumbhakar, 2012. "When, where and how to perform efficiency estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 863-892, October.
    8. Daniel J. Henderson, 2009. "A Non-parametric Examination of Capital-Skill Complementarity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 519-538, August.
    9. Cheng, Ming-Yen & Hall, Peter, 2006. "Methods for tracking support boundaries with corners," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1870-1893, September.
    10. Ferjani, Ali, 2005. "Does Switzerland Have a Productivity Problem?," Agrarwirtschaft und Agrarsoziologie/ Economie et Sociologie Rurales, Swiss Society for Agricultural Economics and Rural Sociology, issue 1.
    11. Zhou, Xianbo & Li, Kui-Wai & Li, Qin, 2011. "An analysis on technical efficiency in post-reform China," China Economic Review, Elsevier, vol. 22(3), pages 357-372, September.
    12. Kortelainen, Mika, 2008. "Estimation of semiparametric stochastic frontiers under shape constraints with application to pollution generating technologies," MPRA Paper 9257, University Library of Munich, Germany.
    13. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    14. Dieter Gstach, 1996. "A new approach to stochastic frontier estimation: DEA+," Department of Economics Working Papers wuwp039, Vienna University of Economics and Business, Department of Economics.
    15. Morteza Haghiri & James Nolan & Kien Tran, 2004. "Assessing the impact of economic liberalization across countries: a comparison of dairy industry efficiency in Canada and the USA," Applied Economics, Taylor & Francis Journals, vol. 36(11), pages 1233-1243.
    16. Andor, Mark & Hesse, Frederik, 2012. "The StoNED age: The departure into a new era of efficiency analysis? An MC study comparing StoNED and the "oldies" (SFA and DEA)," CAWM Discussion Papers 60, University of Münster, Center of Applied Economic Research Münster (CAWM).
    17. William C. Horrace & Peter Schmidt, 2002. "Confidence Statements for Efficiency Estimates from Stochastic Frontier Models," Econometrics 0206006, EconWPA.
    18. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.

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