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

Author

Listed:
  • Asmild, Mette
  • Hougaard, Jens Leth
  • Olesen, Ole B.

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 provides good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space. Potential applications include tests for whether benchmark units dominate more (or less) observations than expected.

Suggested Citation

  • Asmild, Mette & Hougaard, Jens Leth & Olesen, Ole B., 2013. "Testing over-representation of observations in subsets of a DEA technology," European Journal of Operational Research, Elsevier, vol. 230(1), pages 88-96.
    • Handle: RePEc:eee:ejores:v:230:y:2013:i:1:p:88-96
    DOI: 10.1016/j.ejor.2013.03.038
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    References listed on IDEAS

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    1. 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.
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    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Over-representation; Data density; Binomial test; Benchmarks;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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