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Nonparametric Frontier Analysis Using Stata

Author

Listed:
  • Oleg Badunenko

    (Universität zu Köln = University of Cologne)

  • Pavlo Mozharovskyi

    (IP Paris - Institut Polytechnique de Paris, IDS - Département Images, Données, Signal - Télécom ParisTech, S2A - Signal, Statistique et Apprentissage - LTCI - Laboratoire Traitement et Communication de l'Information - IMT - Institut Mines-Télécom [Paris] - Télécom Paris - IMT - Institut Mines-Télécom [Paris] - IP Paris - Institut Polytechnique de Paris)

Abstract

In this article, we describe five new Stata commands that fit and provide statistical inference in nonparametric frontier models. The tenonradial and teradial commands fit data envelopment models where nonradial and radial technical efficiency measures are computed (Färe, 1998, Fundamentals of Production Theory; Färe and Lovell, 1978, Journal of Economic Theory 19: 150–162; Färe, Grosskopf, and Lovell, 1994a, Production Frontiers). Technical efficiency measures are obtained by solving linear programming problems. The teradialbc, nptestind, and nptestrts commands provide tools for making statistical inference regarding radial technical efficiency measures (Simar and Wilson, 1998, Management Science 44: 49–61; 2000, Journal of Applied Statistics 27: 779–802; 2002, European Journal of Operational Research 139: 115–132). We provide a brief overview of the nonparametric efficiency measurement, and we describe the syntax and options of the new commands. Additionally, we provide an example showing the capabilities of the new commands. Finally, we perform a small empirical study of productivity growth. Copyright 2016 by StataCorp LP.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Oleg Badunenko & Pavlo Mozharovskyi, 2016. "Nonparametric Frontier Analysis Using Stata," Post-Print hal-03189227, HAL.
    • Handle: RePEc:hal:journl:hal-03189227
    DOI: 10.1177/1536867X1601600302
    as

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