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Asymptotic Properties of Some Non-Parametric Hyperbolic Efficiency Estimators

In: Exploring Research Frontiers in Contemporary Statistics and Econometrics

Author

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  • Paul W. Wilson

    (Clemson University, Department of Economics)

Abstract

A hyperbolic measure of technical efficiency was proposed by Fare et al. (The Measurement of Efficiency of Production, Kluwer-Nijhoff Publishing, Boston, 1985) wherein efficiency is measured by the simultaneous maximum, feasible reduction in input quantities and increase in output quantities. In cases where returns to scale are not constant, the non-parametric data envelopment analysis (DEA) estimator of hyperbolic efficiency cannot be written as a linear program; consequently, the measure has not been used in empirical studies except where returns to scale are constant, allowing the estimator to be computed by linear programming methods. This paper develops an alternative estimator of the hyperbolic measure proposed by Fare et al. (The Measurement of Efficiency of Production, Kluwer-Nijhoff Publishing, Boston, 1985). Statistical consistency and rates of convergence are established for the new estimator. A numerical procedure allowing computation of the original estimator is provided, and this estimator is also shown to be consistent, with the same rate of convergence as the new estimator. In addition, an unconditional, hyperbolic order-m efficiency estimator is developed by extending the ideas of Cazals et al. (J. Econometric. 106:1–25, 2002). Asymptotic properties of this estimator are also given.

Suggested Citation

  • Paul W. Wilson, 2011. "Asymptotic Properties of Some Non-Parametric Hyperbolic Efficiency Estimators," Springer Books, in: Ingrid Van Keilegom & Paul W. Wilson (ed.), Exploring Research Frontiers in Contemporary Statistics and Econometrics, chapter 0, pages 115-150, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2349-3_6
    DOI: 10.1007/978-3-7908-2349-3_6
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    Cited by:

    1. Cazals, Catherine & Florens, Jean-Pierre & Simar, Léopold, 2025. "Single Index Models for Nonparametric Conditional Frontiers," LIDAM Discussion Papers ISBA 2025022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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