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Inference in Dynamic, Nonparametric Models of Production for General Technologies

In: Advances in the Theory and Applications of Performance Measurement and Management

Author

Listed:
  • Léopold Simar

    (Biostatistique et Sciences Actuarielles (ISBA), LIDAM, UCLouvain)

  • Paul W. Wilson

    (Clemson University)

Abstract

Nonparametric envelopment estimators are often used to estimate the attainable set and its efficient boundary, and to assess efficiency and changes in productivity. Kneip et al. [11] provide asymptotic results that can be used to make inference about expected changes in productivity measured by Malmquist indices and about the sources of productivity changes. These results require convexity of the attainable set, but in a number of situations this assumption is questionable. Recently, Kneip et al. [12] extend these results to allow for possibly non-convex technologies where the DEA estimators are known to be inconsistent. This paper summarizes these results, and explains how researchers should choose the appropriate method in a particular application.

Suggested Citation

  • Léopold Simar & Paul W. Wilson, 2024. "Inference in Dynamic, Nonparametric Models of Production for General Technologies," Lecture Notes in Operations Research, in: Ali Emrouznejad & Emmanuel Thanassoulis & Mehdi Toloo (ed.), Advances in the Theory and Applications of Performance Measurement and Management, pages 9-20, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-61597-9_2
    DOI: 10.1007/978-3-031-61597-9_2
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