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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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  • Andor, Mark
  • Hesse, Frederik

Abstract

Based on the seminal paper of Farrell (1957), researchers have developed several methods for measuring effi ciency. 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 (2010) 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 (2010) 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.

Suggested Citation

  • Andor, Mark & Hesse, Frederik, 2013. "The StoNED Age: The Departure Into a New Era of Efficiency Analysis? – A Monte Carlo Comparison of StoNED and the "Oldies" (SFA and DEA)," Ruhr Economic Papers 394, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  • Handle: RePEc:zbw:rwirep:394
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    Cited by:

    1. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    2. repec:kap:regeco:v:55:y:2019:i:3:d:10.1007_s11149-019-09383-y is not listed on IDEAS
    3. Géraldine Henningsen & Arne Henningsen & Uwe Jensen, 2015. "A Monte Carlo study on multiple output stochastic frontiers: a comparison of two approaches," Journal of Productivity Analysis, Springer, vol. 44(3), pages 309-320, December.
    4. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
    5. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    6. repec:gam:jsusta:v:9:y:2017:i:12:p:2080-:d:120466 is not listed on IDEAS
    7. Maria Nieswand & Stefan Seifert, 2016. "Operational Conditions in Regulatory Benchmarking Models: A Monte Carlo Analysis," Discussion Papers of DIW Berlin 1585, DIW Berlin, German Institute for Economic Research.
    8. Stefan Seifert, 2014. "Effizienzanalysemethoden in der Regulierung deutscher Elektrizitäts- und Gasversorgungsunternehmen," DIW Roundup: Politik im Fokus 40, DIW Berlin, German Institute for Economic Research.
    9. repec:taf:applec:v:49:y:2017:i:55:p:5651-5661 is not listed on IDEAS
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    11. repec:spr:empeco:v:54:y:2018:i:1:d:10.1007_s00181-016-1204-3 is not listed on IDEAS
    12. Mark Andor & Christopher Parmeter, 2017. "Pseudolikelihood estimation of the stochastic frontier model," Applied Economics, Taylor & Francis Journals, vol. 49(55), pages 5651-5661, November.
    13. Keshvari, Abolfazl, 2017. "A penalized method for multivariate concave least squares with application to productivity analysis," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1016-1029.
    14. repec:kap:jproda:v:50:y:2018:i:3:d:10.1007_s11123-018-0539-5 is not listed on IDEAS

    More about this item

    Keywords

    efficiency; stochastic non-smooth envelopment of data (StoNED); data envelopment analysis (DEA); stochastic frontier analysis (SFA); Monte Carlo simulation;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D2 - Microeconomics - - Production and Organizations
    • L5 - Industrial Organization - - Regulation and Industrial Policy
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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