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A multi-parametric method for bias correction of DEA efficiency estimators

Author

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  • Panagiotis D. Zervopoulos
  • Sokratis Sklavos
  • Angelos Kanas
  • Gang Cheng

Abstract

This paper emphasises the sensitivity of the data envelopment analysis (DEA) efficiency estimators to sampling variations of the production frontier and dimensionality of the production set. It has been proven that DEA yields asymptotic unbiased estimates. A DEA (smoothed) bootstrap method is widely being applied to address the inaccuracy of DEA estimators. The combination of DEA and a modified bootstrap expression enhances the statistical properties of DEA estimators without overcoming the inherent limitations of each of the two methods. This paper provides a non-resampling multi-parametric method to deal with the sensitivity of DEA estimators. The new method is applied to scaled samples, and the bias-corrected efficiency estimators are compared against their population counterparts. A comparative analysis among the standard bootstrap, the smoothed bootstrap, and the new method shows that the new method’s estimations provide a better fit to the population than the estimations of the standard and smoothed bootstrap.

Suggested Citation

  • Panagiotis D. Zervopoulos & Sokratis Sklavos & Angelos Kanas & Gang Cheng, 2019. "A multi-parametric method for bias correction of DEA efficiency estimators," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(4), pages 655-674, April.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:4:p:655-674
    DOI: 10.1080/01605682.2018.1457478
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    Cited by:

    1. Chiang Kao & Shiang-Tai Liu, 2022. "Stochastic efficiencies of network production systems with correlated stochastic data: the case of Taiwanese commercial banks," Annals of Operations Research, Springer, vol. 315(2), pages 1151-1174, August.
    2. Pankaj Dutta & Aayush Jain & Asish Gupta, 2020. "Performance analysis of non-banking finance companies using two-stage data envelopment analysis," Annals of Operations Research, Springer, vol. 295(1), pages 91-116, December.

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