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How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

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Author Info

  • Perelman, Sergio
  • Santín, Daniel

Abstract

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb-Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 199 (2009)
Issue (Month): 1 (November)
Pages: 303-310

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Handle: RePEc:eee:ejores:v:199:y:2009:i:1:p:303-310

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Web page: http://www.elsevier.com/locate/eor

Related research

Keywords: Parametric distance function DEA Technical efficiency Scale efficiency Monte Carlo experiments;

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Cited by:
  1. Brissimis, Sophocles & Zervopoulos, Panagiotis, 2011. "Developing a step-by-step effectiveness assessment model for customer-oriented service organizations," MPRA Paper 30765, University Library of Munich, Germany.
  2. Julie Harrison & Paul Rouse & Jamie Armstrong, 2012. "Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models," Journal of Productivity Analysis, Springer, vol. 37(3), pages 261-276, June.
  3. 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, Center of Applied Economic Research Münster (CAWM), University of Münster.
  4. 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.
  5. Kumbhakar, Subal C., 2012. "Specification and estimation of primal production models," European Journal of Operational Research, Elsevier, vol. 217(3), pages 509-518.
  6. Krüger, Jens J., 2012. "A Monte Carlo study of old and new frontier methods for efficiency measurement," European Journal of Operational Research, Elsevier, vol. 222(1), pages 137-148.
  7. Mark Andor & Frederik Hesse, . "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.
  8. Lars-Erik Borge & Marianne Haraldsvik, 2009. "Efficiency potential and determinants of efficiency: An analysis of the care of elderly sector in Norway," Working Paper Series 10109, Department of Economics, Norwegian University of Science and Technology.

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