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Technological Inefficiency and the Skewness of the Error Component in Stochastic Frontier Analysis

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  • Martin A. Carree

    () (Department of General Economics, Faculty of Economics, Erasmus University Rotterdam, Tinbergen Institute, and Maastricht University)

Abstract

This paper concentrates on negatively skewed one-sided distributions as an explanation ofthe occurence of positive (negative) skewness in the case of stochastic production (cost) frontieranalysis. It takes as example the binomial distribution that can have negative or positive skewand derives the method-of-moments estimators.

Suggested Citation

  • Martin A. Carree, 2002. "Technological Inefficiency and the Skewness of the Error Component in Stochastic Frontier Analysis," Tinbergen Institute Discussion Papers 02-012/2, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20020012
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    References listed on IDEAS

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    1. Mester, Loretta J., 1997. "Measuring efficiency at U.S. banks: Accounting for heterogeneity is important," European Journal of Operational Research, Elsevier, vol. 98(2), pages 230-242, April.
    2. Waldman, Donald M., 1982. "A stationary point for the stochastic frontier likelihood," Journal of Econometrics, Elsevier, vol. 18(2), pages 275-279, February.
    3. Green, Alison & Mayes, David, 1991. "Technical Inefficiency in Manufacturing Industries," Economic Journal, Royal Economic Society, vol. 101(406), pages 523-538, May.
    4. Li, Qi, 1996. "Estimating a stochastic production frontier when the adjusted error is symmetric," Economics Letters, Elsevier, vol. 52(3), pages 221-228, September.
    5. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
    6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    7. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    8. 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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    Citations

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    Cited by:

    1. Christian M. Hafner & Hans Manner & Léopold Simar, 2018. "The “wrong skewness” problem in stochastic frontier models: A new approach," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 380-400, April.
    2. Michael Fritsch & Andreas Stephan, 2007. "Die Heterogenität der Effizienz innerhalb von Branchen: eine Auswertung von Unternehmensdaten der Kostenstrukturerhebung im Verarbeitenden Gewerbe," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 76(3), pages 59-75.
    3. Cheng, Xiaomei & Bjørndal, Endre & Bjørndal, Mette, 2015. "Malmquist Productivity Analysis based on StoNED," Discussion Papers 2015/25, Norwegian School of Economics, Department of Business and Management Science.
    4. Graziella Bonanno & Domenico De Giovanni & Filippo Domma, 2017. "The ‘wrong skewness’ problem: a re-specification of stochastic frontiers," Journal of Productivity Analysis, Springer, vol. 47(1), pages 49-64, February.
    5. Qu Feng & William Horrace & Guiying Laura Wu, 2013. "Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models," Center for Policy Research Working Papers 154, Center for Policy Research, Maxwell School, Syracuse University.
    6. William Horrace, 2015. "Moments of the truncated normal distribution," Journal of Productivity Analysis, Springer, vol. 43(2), pages 133-138, April.
    7. Cheng, Xiaomei & Andersson, Jonas & Bjørndal, Endre, 2015. "On the Distributional Assumptions in the StoNED model," Discussion Papers 2015/24, Norwegian School of Economics, Department of Business and Management Science.
    8. Sheriff, Glenn, 2009. "Implementing second-best environmental policy under adverse selection," Journal of Environmental Economics and Management, Elsevier, vol. 57(3), pages 253-268, May.
    9. William C. Horrace & Christopher F. Parmeter, 2018. "A Laplace stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 260-280, March.
    10. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.
    11. Ricardo S. Ehlers, 2011. "Comparison of Bayesian models for production efficiency," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2433-2443, January.
    12. repec:kap:jproda:v:47:y:2017:i:3:d:10.1007_s11123-016-0474-2 is not listed on IDEAS
    13. Arnab Bhattacharjee & Eduardo de Castro & (Late) Chris Jensen-Butler, 2007. "Evaluating Economic Theories of Growth and Inequality: A Study of the Danish Economy," CDMA Working Paper Series 200723, Centre for Dynamic Macroeconomic Analysis.
    14. Bielecki, Andre & Albers, Sönke, 2012. "Eine Analyse der Forschungseffizienz deutscher betriebswirtschaftlicher Fachbereiche basierend auf den Daten des Centrums für Hochschulentwicklung (CHE)," EconStor Preprints 57429, ZBW - Leibniz Information Centre for Economics.
    15. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    16. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.

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