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Inference for Aggregate Efficiency: Theory and Guidelines for Practitioners

Author

Listed:
  • Simar, Léopold

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Zelenyuk, Valentin
  • Zhao, Shirong

Abstract

We expand the recently developed framework for the inference for aggregate efficiency, by extending the existing theory and providing guidelines for practitioners. In particular, we develop the central limit theorems (CLTs) for aggregate input-oriented efficiency, analogous to the output-oriented framework established by Simar and Zelenyuk (2018). To further improve the finite sample performance of the developed CLTs, we propose a simple yet easy to implement method through using the bias- corrected individual efficiency estimate to improve the variance estimator. The extensive Monte-Carlo experiments confirmed the developed CLTs for aggregate input- oriented efficiency and also confirmed the better performance of our proposed method in the finite sample sizes. Finally, we use two well-known empirical data sets to illustrate the differences between all the existing methods to facilitate the use by practitioners.

Suggested Citation

  • Simar, Léopold & Zelenyuk, Valentin & Zhao, Shirong, 2023. "Inference for Aggregate Efficiency: Theory and Guidelines for Practitioners," LIDAM Discussion Papers ISBA 2023016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  • Handle: RePEc:aiz:louvad:2023016
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    References listed on IDEAS

    as
    1. Simar, Leopold & Zelenyuk, Valentin, 2018. "Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores," LIDAM Discussion Papers ISBA 2018020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Simar, Léopold & Zelenyuk, Valentin, 2020. "Improving finite sample approximation by central limit theorems for estimates from Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1002-1015.
    3. Oleg Badunenko & Daniel J. Henderson & Valentin Zelenyuk, 2008. "Technological Change and Transition: Relative Contributions to Worldwide Growth During the 1990s," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 461-492, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Efficiency ; Non-parametric Efficiency Estimators ; Data Envelopment Analysis ; Free Disposal Hull ; Aggregate Efficiency;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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