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

  • Andor, Mark
  • Hesse, Frederik
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    In certain circumstances, both researchers and policy makers are faced with the challenge of determining individual efficiency 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 efficiency scores. Our first 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 influence the performance of the methods and give additional information about the reasons for performance variation. Furthermore, we indicate specific 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 efficiency scores, with the elementary estimates of the two methods. Our results demonstrate that combination approaches can actually constitute best-practice for estimating precise efficiency scores.

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    File URL: http://econstor.eu/bitstream/10419/51383/1/672474034.pdf
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    Paper provided by Center of Applied Economic Research Münster (CAWM), University of Münster in its series CAWM Discussion Papers with number 51.

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    Date of creation: 2011
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    Handle: RePEc:zbw:cawmdp:51
    Contact details of provider: Postal: 02 51 / 83-2 29 10
    Phone: 02 51 / 83-2 29 10
    Fax: 02 51 / 83-2 83 99
    Web page: http://www.wiwi.uni-muenster.de/cawm/

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    1. 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.
    2. Brophy Haney, A. & Pollitt, M.G., 2009. "Efficiency Analysis of Energy Networks : An International Survey of Regulators," Cambridge Working Papers in Economics 0926, Faculty of Economics, University of Cambridge.
    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. 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.
    5. Gong, Byeong-Ho & Sickles, Robin C., 1991. "Finite Sample Evidence on the Performance of Stochastic Frontiers and Data Envelopment Analysis Using Panel Data," Working Papers 91-12, C.V. Starr Center for Applied Economics, New York University.
    6. 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.
    7. 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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