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Cost Efficiency Analysis of Electricity Distribution

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  • Kamil Makiela and Jacek Osiewalski

Abstract

This paper discusses a Bayesian approach to analyzing cost efficiency of Distribution System Operators when model specification and variable selection are difficult to determine. Bayesian model selection and inference pooling techniques are adopted in a stochastic frontier analysis to mitigate the problem of model uncertainty. Adequacy of a given specification is judged by its posterior probability, which makes the benchmarking process not only more transparent but also much more objective. The proposed methodology is applied to one of Polish Distribution System Operators. We find that variable selection plays an important role and models, which are the best at describing the data, are rather parsimonious. They rely on just a few variables determining the observed cost. However, these models also show relatively high average efficiency scores among analyzed objects.

Suggested Citation

  • Kamil Makiela and Jacek Osiewalski, 2018. "Cost Efficiency Analysis of Electricity Distribution," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    • Handle: RePEc:aen:journl:ej39-4-makiela
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    Citations

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    Cited by:

    1. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    2. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    3. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    4. Krzysztof Wach & Agnieszka Głodowska & Marek Maciejewski & Marek Sieja, 2021. "Europeanization Processes of the EU Energy Policy in Visegrad Countries in the Years 2005–2018," Energies, MDPI, vol. 14(7), pages 1-23, March.
    5. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    6. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

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