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Testing over-representation of observations in subsets of a DEA technology

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Author Info

  • Asmild, Mette

    ()
    (ORMS Group)

  • Hougaard, Jens Leth

    (Department of Operations Management)

  • Olesen, Ole B.

    ()
    (Department of Business and Economics)

Abstract

This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and suggests good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space.

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Bibliographic Info

Paper provided by Department of Business and Economics, University of Southern Denmark in its series Discussion Papers of Business and Economics with number 2/2010.

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Length: 27 pages
Date of creation: 01 May 2010
Date of revision:
Handle: RePEc:hhs:sdueko:2010_002

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Postal: Department of Business and Economics, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
Phone: 65 50 32 33
Fax: 65 50 32 37
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Web page: http://www.sdu.dk/ivoe
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Related research

Keywords: Data Envelopment Analysis (DEA); Over-representation; Data density; Binomial test; Convex hull;

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  1. Bogetoft, Peter & Hougaard, Jens Leth, 2001. "Rational Inefficiencies," Unit of Economics Working papers 24191, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.
  2. Wheelock, David C & Wilson, Paul W, 1999. "Technical Progress, Inefficiency, and Productivity Change in U.S. Banking, 1984-1993," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(2), pages 212-34, May.
  3. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
  4. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  5. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
  6. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
  7. Ole Olesen & N. Petersen, 2003. "Identification and Use of Efficient Faces and Facets in DEA," Journal of Productivity Analysis, Springer, vol. 20(3), pages 323-360, November.
  8. Nam Anh Tran & Gerald Shively & Paul Preckel, 2010. "A new method for detecting outliers in Data Envelopment Analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 17(4), pages 313-316.
  9. J. Hartigan, 1985. "Statistical theory in clustering," Journal of Classification, Springer, vol. 2(1), pages 63-76, December.
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