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The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)

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Abstract

Based on the seminal paper of Farrell (J R Stat Soc Ser A (General) 120(3):253–290, 1957), researchers have developed several methods for measuring efficiency. Nowadays, the most prominent representatives are nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA), both introduced in the late 1970s. Researchers have been attempting to develop a method which combines the virtues—both nonparametric and stochastic—of these “oldies”. The recently introduced Stochastic non-smooth envelopment of data (StoNED) by Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) is such a promising method. This paper compares the StoNED method with the two “oldies” DEA and SFA and extends the initial Monte Carlo simulation of Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) in several directions. We show, among others, that, in scenarios without noise, the rivalry is still between the “oldies”, while in noisy scenarios, the nonparametric StoNED PL now constitutes a promising alternative to the SFA ML.

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Notes

  1. Thanks to an editor’s suggestion, we want to point out that a functional form misspecification has a different and in general greater effect when a deterministic, parametric method instead of a stochastic, parametric method is applied. While a misspecification error directly influences the estimated inefficiency term of a deterministic, parametric method, a misspecification error of a stochastic, parametric method is (at least partly) covered by the random noise term, which shows (at least partly) the approximation error.

  2. Axioms: Convexity, Inefficiency (“Free Disposability”), Ray Unboundedness (“Returns to Scale”) and Minimum Extrapolation, see Banker et al. (1984).

  3. This envelopment formulation is usually the preferred form, because it has fewer constraints than the multiplier form (see Coelli et al. (2005)).

  4. The normal-half normal model is the most common model. There are other models which mainly differ in the assumption with respect to the inefficiency distribution, e.g. the normal-exponential model. For a comprehensive treatment of the different models, see Kumbhakar and Lovell (2003).

  5. StataSE 11.2 is used for the implementation of the DGP and the estimation of DEA while General Algebraic Modeling System (GAMS) Version 23.3.2 is used to estimate the other four methods. The codes are available upon request from the authors.

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Acknowledgments

We are deeply indebted to the participants of the 8th Asia-Pacific Productivity Conference (APPC) in Bangkok, Thailand, the 4th Workshop on Efficiency and Productivity Analysis (HAWEPA) in Halle, Germany, the 12th European Workshop on Efficiency and Productivity Analysis (EWEPA) in Verona, Italy, and the 11th IAEE European Conference in Vilnius, Lithuania, for providing valuable comments that have led to a considerable improvement of earlier versions of this paper. Furthermore, we would like to thank Brian Bloch, Finn Førsund, William Greene, Arne und Geraldine Henningsen, Uwe Jensen, Choonjoo Lee, Colin Vance, the editors and two anonymous referees for their helpful comments and suggestions. The authors are responsible for all errors and omissions.

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Authors and Affiliations

  1. Division of Environment and Resources, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Hohenzollernstr. 1-3, 45128, Essen, Germany

    Mark Andor

  2. Finance Center Münster, Westfälische Wilhelms-Universität Münster, Universitätstrasse 14-16, 48143, Munster, Germany

    Frederik Hesse

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  1. Mark Andor
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Correspondence to Mark Andor.

Appendix

Appendix

The appendix can be found online at: http://www.rwi-essen.de/media/content/pages/publikationen/ruhr-economic-papers/REP394appendix.pdf.

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Andor, M., Hesse, F. The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA). J Prod Anal 41, 85–109 (2014). https://doi.org/10.1007/s11123-013-0354-y

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