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Asymptotic Theory for Aggregate Efficiency

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Abstract

Applied researchers in the field of efficiency and productivity analysis often need to estimate and inference about aggregate efficiency, such as industry efficiency or aggregate efficiency of a group of distinct firms within an industry (e.g., public vs. private firms, regulated vs. unregulated firms, etc.). While there are approaches to obtain point estimates for such important measures, no asymptotic theory have been derived for it–the gap in the literature that 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 DEA or FDH. As a result, the developed theory opens a path for more accurate and theoretically better grounded statistical inference on aggregate efficiency estimates such as industry efficiency, etc.

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  • Léopold Simar & Valentin Zelenyuk, 2016. "Asymptotic Theory for Aggregate Efficiency," CEPA Working Papers Series WP042016, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:114
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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, December.
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    3. Léopold Simar & Valentin Zelenyuk, 2011. "Stochastic FDH/DEA estimators for frontier analysis," Journal of Productivity Analysis, Springer, vol. 36(1), pages 1-20, August.
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    11. 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.
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    More about this item

    Keywords

    DEA; FDH; Efficiency; Aggregation; Industry Efficiency; Asymptotics; Limiting distribution; Consistency; Convergence; Jackknife; Bias correction;
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