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A Monte Carlo Simulation comparing DEA, SFA and two simple approaches to combine efficiency estimates

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  • Mark Andor
  • Frederik Hesse
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    Abstract

    In certain circumstances, both researchers and policy makers are faced with the challenge of determining individual eciency scores for each decision making unit (DMU) under consideration. In this study, we use a Monte Carlo experimentation to analyze the optimal approach to determining individual eciency scores. Our rst research objective is a systematic comparison of the two most popular estimation methods, data envelopment (DEA) and stochastic frontier analysis (SFA). Accordingly we extend the existing comparisons in several ways. We are thus able to identify the factors which in uence the performance of the methods and give additional information about the reasons for performance variation. Furthermore, we indicate speci c situations in which an estimation technique proves superior. As none of the methods is in all respects superior, in real word applications, such as energy incentive regulation systems, it is regarded as \best-practice" to combine the estimates obtained from DEA and SFA. Hence in a second step, we compare the approaches to transforming the estimates into eciency scores, with the elementary estimates of the two methods. Our results demonstrate that combination approaches can actually constitute \best-practice" for estimating precise e- ciency scores.

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    Bibliographic Info

    Paper provided by Institute of Spatial and Housing Economics, Munster Universitary in its series Working Papers with number 201177.

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    Handle: RePEc:muc:wpaper:201177

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    Related research

    Keywords: eciency; data envelopment analysis; stochastic frontier analysis; simulation; regulation;

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    1. Haney, Aoife Brophy & Pollitt, Michael G., 2009. "Efficiency analysis of energy networks: An international survey of regulators," Energy Policy, Elsevier, vol. 37(12), pages 5814-5830, December.
    2. 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.
    3. R. Banker & W. Cooper & E. Grifell-Tajté & Jesús Pastor & Paul Wilson & Eduardo Ley & C. Lovell, 1994. "Validation and generalization of DEA and its uses," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 2(2), pages 249-314, December.
    4. 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.
    5. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284.
    6. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    7. Banker, Rajiv D. & Gadh, Vandana M. & Gorr, Wilpen L., 1993. "A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 67(3), pages 332-343, June.
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    Cited by:
    1. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.

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