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Central Limit Theorems for Aggregate Efficiency

Author

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  • Léopold Simar

    (Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, B1348 Louvain-la-Neuve, Belgium)

  • Valentin Zelenyuk

    (School of Economics and Centre for Efficiency and Productivity Analysis, University of Queensland, Brisbane, Qld 4072, Australia)

Abstract

Applied researchers in the field of efficiency and productivity analysis often need to estimate and make inference about aggregate efficiency, such as industry efficiency or aggregate efficiency of a group of distinct firms within an industry (e.g., public versus private firms, regulated versus unregulated firms, etc.). While there are approaches to obtain point estimates for such important measures, no asymptotic theory has been derived for it. This is the gap in the literature we fill with this paper. Specifically, we develop full asymptotic theory for aggregate efficiency measures when the individual true efficiency scores being aggregated are observed as well as when they are unobserved and estimated via the data envelopment analysis or the free disposal hull. As a result, the developed theory opens a path for more accurate and theoretically better grounded statistical inference (e.g., estimation of confidence intervals and conducting statistical tests) on aggregate efficiency estimates such as industry efficiency, etc.

Suggested Citation

  • Léopold Simar & Valentin Zelenyuk, 2018. "Central Limit Theorems for Aggregate Efficiency," Operations Research, INFORMS, vol. 66(1), pages 137-149, January.
    • Handle: RePEc:inm:oropre:v:66:y:2018:i:1:p:137-149
    DOI: 10.1287/opre.2017.1655
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, September.
    2. Mayer, Andreas & Zelenyuk, Valentin, 2014. "Aggregation of Malmquist productivity indexes allowing for reallocation of resources," European Journal of Operational Research, Elsevier, vol. 238(3), pages 774-785.
    3. Laurens Cherchye & Bram De Rock & Frederic Vermeulen, 2008. "Analyzing Cost-Efficient Production Behavior Under Economies of Scope: A Nonparametric Methodology," Operations Research, INFORMS, vol. 56(1), pages 204-221, February.
    4. Alois Kneip & Léopold Simar & Paul W. Wilson, 2016. "Testing Hypotheses in Nonparametric Models of Production," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 435-456, July.
    5. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
    6. Zelenyuk, Valentin, 2015. "Aggregation of scale efficiency," European Journal of Operational Research, Elsevier, vol. 240(1), pages 269-277.
    7. Dominique Deprins & Léopold Simar & Henry Tulkens, 2006. "Measuring Labor-Efficiency in Post Offices," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 285-309, Springer.
    8. Zelenyuk, Valentin, 2006. "Aggregation of Malmquist productivity indexes," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1076-1086, October.
    9. Wade D. Cook & Joe Zhu, 2008. "CAR-DEA: Context-Dependent Assurance Regions in DEA," Operations Research, INFORMS, vol. 56(1), pages 69-78, February.
    10. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    11. Wade D. Cook & Julie Harrison & Raha Imanirad & Paul Rouse & Joe Zhu, 2013. "Data Envelopment Analysis with Nonhomogeneous DMUs," Operations Research, INFORMS, vol. 61(3), pages 666-676, June.
    12. Wade D. Cook & Joe Zhu, 2006. "Incorporating Multiprocess Performance Standards into the DEA Framework," Operations Research, INFORMS, vol. 54(4), pages 656-665, August.
    13. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2015. "When Bias Kills The Variance: Central Limit Theorems For Dea And Fdh Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 31(2), pages 394-422, April.
    14. Léopold Simar & Valentin Zelenyuk, 2007. "Statistical inference for aggregates of Farrell-type efficiencies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(7), pages 1367-1394.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Fare, Rolf & Zelenyuk, Valentin, 2003. "On aggregate Farrell efficiencies," European Journal of Operational Research, Elsevier, vol. 146(3), pages 615-620, May.
    17. Ruggiero, John, 1996. "On the measurement of technical efficiency in the public sector," European Journal of Operational Research, Elsevier, vol. 90(3), pages 553-565, May.
    18. Joe Zhu, 2004. "Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile Telecommunication Company," Operations Research, INFORMS, vol. 52(2), pages 323-329, April.
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