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Can Super-Efficiencies Improve Bias Correction? A Bayesian Data Envelopment Analysis Approach

In: Advances in the Theory and Applications of Performance Measurement and Management

Author

Listed:
  • Panagiotis D. Zervopoulos

    (University of Sharjah)

  • Angelos Kanas

    (University of Piraeus
    Hellenic Parliamentary Budget Office)

  • Ali Emrouznejad

    (University of Surrey)

  • Philip Molyneux

    (Abu Dhabi University
    Leeds University Business School, University of Leeds)

Abstract

It has been proven that DEA efficiencies, within the interval (0, 1], are overestimated for finite samples, while asymptotically, this bias reduces to zero. In the extant literature, the statistical inference approaches yielding the best-performing DEA estimates are the smoothed bootstrap and Bayesian DEA methods. All statistical inference techniques apply to DEA models yielding efficiencies between zero and one. This study presents a new Bayesian DEA approach that takes into account efficiencies and super-efficiencies aiming to improve bias correction. We prove that efficiencies and super-efficiencies are interrelated for finite samples. However, bias correction is statistically significant only in the case of efficiencies below one. The new Bayesian super-efficiency DEA approach yields estimates with lower mean absolute error and mean square error than the extant DEA statistical inference techniques referring only to efficiencies with right-censored distributions, where efficiencies are not allowed to exceed unity. Drawing on formal analysis, real-world and simulated data sets, we conclude that the new Bayesian super-efficiency DEA estimates are consistent of DEA parameters.

Suggested Citation

  • Panagiotis D. Zervopoulos & Angelos Kanas & Ali Emrouznejad & Philip Molyneux, 2024. "Can Super-Efficiencies Improve Bias Correction? A Bayesian Data Envelopment Analysis Approach," Lecture Notes in Operations Research, in: Ali Emrouznejad & Emmanuel Thanassoulis & Mehdi Toloo (ed.), Advances in the Theory and Applications of Performance Measurement and Management, pages 21-31, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-61597-9_3
    DOI: 10.1007/978-3-031-61597-9_3
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