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Cost Efficiency Analysis of Electricity Distribution Sector under Model Uncertainty

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  • Kamil Makieia
  • 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 Makieia & Jacek Osiewalski, 2018. "Cost Efficiency Analysis of Electricity Distribution Sector under Model Uncertainty," The Energy Journal, , vol. 39(4), pages 31-56, July.
    • Handle: RePEc:sae:enejou:v:39:y:2018:i:4:p:31-56
    DOI: 10.5547/01956574.39.4.kmak
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    References listed on IDEAS

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    1. Christian von Hirschhausen & Astrid Cullmann & Andreas Kappeler, 2006. "Efficiency analysis of German electricity distribution utilities - non-parametric and parametric tests," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2553-2566.
    2. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    3. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    4. Tim Coelli & Antonio Estache & Sergio Perelman & Lourdes Trujillo, 2003. "A Primer on Efficiency Measurement for Utilities and Transport Regulators," World Bank Publications - Books, The World Bank Group, number 15149, April.
    5. Tooraj Jamasb and Michael Pollitt, 2005. "Electricity Market Reform in the European Union: Review of Progress toward Liberalization & Integration," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 11-42.
    6. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    7. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    8. Magnus, Jan R, 1979. "Substitution between Energy and Non-Energy Inputs in the Netherlands, 1950-1976," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(2), pages 465-484, June.
    9. Krzysztof Osiewalski & Jacek Osiewalski, 2013. "A Long-Run Relationship between Daily Prices on Two Markets: The Bayesian VAR(2)–MSF-SBEKK Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(1), pages 65-83, March.
    10. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    11. Mehdi Farsi & Massimo Filippini, 2004. "Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 25(1), pages 1-19, August.
    12. Dimitri Dimitropoulos and Adonis Yatchew, 2017. "Is Productivity Growth in Electricity Distribution Negative? An Empirical Analysis Using Ontario Data," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    13. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    14. Diewert, W E, 1971. "An Application of the Shephard Duality Theorem: A Generalized Leontief Production Function," Journal of Political Economy, University of Chicago Press, vol. 79(3), pages 481-507, May-June.
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