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Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model

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  • Kuosmanen, Timo

Abstract

Electricity distribution network is a prime example of a natural local monopoly. In many countries, electricity distribution is regulated by the government. Many regulators apply frontier estimation techniques such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) as an integral part of their regulatory framework. While more advanced methods that combine nonparametric frontier with stochastic error term are known in the literature, in practice, regulators continue to apply simplistic methods. This paper reports the main results of the project commissioned by the Finnish regulator for further development of the cost frontier estimation in their regulatory framework. The key objectives of the project were to integrate a stochastic SFA-style noise term to the nonparametric, axiomatic DEA-style cost frontier, and to take the heterogeneity of firms and their operating environments better into account. To achieve these objectives, a new method called stochastic nonparametric envelopment of data (StoNED) was examined. Based on the insights and experiences gained in the empirical analysis using the real data of the regulated networks, the Finnish regulator adopted the StoNED method in use from 2012 onwards.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Economics.

Volume (Year): 34 (2012)
Issue (Month): 6 ()
Pages: 2189-2199

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Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:2189-2199

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Web page: http://www.elsevier.com/locate/eneco

Related research

Keywords: Energy markets; Heterogeneity; Nonparametric production analysis; Productive efficiency;

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References

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Citations

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Cited by:
  1. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
  2. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
  3. Agrell, Per J. & Niknazar, Pooria, 2014. "Structural and behavioral robustness in applied best-practice regulation," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 89-103.
  4. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
  5. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
  6. Thomas Triebs & David S. Saal & Pablo Arocena & Subal C. Kumbhakar, 2012. "Estimating Economies of Scale and Scope with Flexible Technology," Ifo Working Paper Series Ifo Working Paper No. 142, Ifo Institute for Economic Research at the University of Munich.
  7. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.

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