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On the Distributional Assumptions in the StoNED model

Author

Listed:
  • Cheng, Xiaomei

    () (Dept. of Business and Management Science, Norwegian School of Economics)

  • Andersson, Jonas

    () (Dept. of Business and Management Science, Norwegian School of Economics)

  • Bjørndal, Endre

    () (Dept. of Business and Management Science, Norwegian School of Economics)

Abstract

In a recent paper Johnson and Kuosmanen (2011) propose a new, semi-parametric, general cost-frontier model, the stochastic nonparametric envelopment of data (StoNED). The model is semi-parametric in the sense that the cost function is estimated nonparametrically, while the functional form of the distribution for the error term is parametrically specified. A common assumption for this distribution is that it is a convolution of a truncated normal distribution, representing inefficiency, and a normal distribution, representing noise. This parametric form has the drawback that a negative skewness implies a negative expected inefficiency. It can thus never capture a negatively skewed distribution with a positive expectation. In this paper we investigate this assumption and its consequences for an analysis of inefficiency. Furthermore, we propose a solution to the problem and investigate its performance by means of a Monte Carlo simulation.

Suggested Citation

  • 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.
  • Handle: RePEc:hhs:nhhfms:2015_024
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    File URL: http://hdl.handle.net/11250/300521
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    References listed on IDEAS

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    1. Kumbhakar, Subal C., 1991. "Estimation of technical inefficiency in panel data models with firm- and time-specific effects," Economics Letters, Elsevier, vol. 36(1), pages 43-48, May.
    2. Green, Alison & Mayes, David, 1991. "Technical Inefficiency in Manufacturing Industries," Economic Journal, Royal Economic Society, vol. 101(406), pages 523-538, May.
    3. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    4. Greene, William H., 1980. "On the estimation of a flexible frontier production model," Journal of Econometrics, Elsevier, vol. 13(1), pages 101-115, May.
    5. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. 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.
    8. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    9. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    10. Cheng, Xiaomei & Bjørndal, Endre & Bjørndal, Mette, 2014. "Cost Efficiency Analysis based on The DEA and StoNED Models: Case of Norwegian Electricity Distribution Companies," Discussion Papers 2014/28, Norwegian School of Economics, Department of Business and Management Science.
    11. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    12. Carree, Martin A., 2002. "Technological inefficiency and the skewness of the error component in stochastic frontier analysis," Economics Letters, Elsevier, vol. 77(1), pages 101-107, September.
    13. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    14. repec:spr:isorms:978-1-4419-7961-2 is not listed on IDEAS
    15. 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.
    16. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 62-98.
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    More about this item

    Keywords

    StoNED model; Composite error; Wrong skewness; Misspecification;

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • Q00 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - General

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