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Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks

Author

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  • Orea, Luis

    () (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Growitsch, Christian

    () (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Jamasb, Tooraj

    () (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

Abstract

Supervised dimension reduction methods have been extensively applied in different scientific fields like biology and medicine in recent years. However, they have hardly ever been used in micro economics, and in particular cost function modeling. Nonetheless, these methods can also be useful in regulation of natural monopolies such as gas, water, and electricity networks, where firms’ cost and performance can be affected by a large number of environmental factors. In order to deal with this ‘dimensionality’ problem we propose using a supervised dimension reduction approach that aims to reduce the dimension of data without loss of information. Economic theory suggests that in the presence of other relevant production (cost) drivers, the traditional all-inclusive assumption is not satisfied and, hence, production or cost predictions (and efficiency estimates) might be biased. This paper shows that purging the data using a partial regression approach allows us to address this issue when analyzing the effect of weather and geography on cost efficiency in the context of the Norwegian electricity distribution networks.

Suggested Citation

  • Orea, Luis & Growitsch, Christian & Jamasb, Tooraj, 2012. "Using Supervised Environmental Composites in Production and Efficiency Analyses: An Application to Norwegian Electricity Networks," EWI Working Papers 2012-18, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
  • Handle: RePEc:ris:ewikln:2012_018
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    References listed on IDEAS

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    1. Bura, E. & Yang, J., 2011. "Dimension estimation in sufficient dimension reduction: A unifying approach," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 130-142, January.
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    3. Giannakis, Dimitrios & Jamasb, Tooraj & Pollitt, Michael, 2005. "Benchmarking and incentive regulation of quality of service: an application to the UK electricity distribution networks," Energy Policy, Elsevier, vol. 33(17), pages 2256-2271, November.
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    5. Zhu, Joe, 1998. "Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities," European Journal of Operational Research, Elsevier, vol. 111(1), pages 50-61, November.
    6. Growitsch, Christian & Jamasb, Tooraj & Wetzel, Heike, 2012. "Efficiency effects of observed and unobserved heterogeneity: Evidence from Norwegian electricity distribution networks," Energy Economics, Elsevier, pages 542-548.
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    8. Efstathia Bura & R. Dennis Cook, 2001. "Estimating the structural dimension of regressions via parametric inverse regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 393-410.
    9. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
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    Cited by:

    1. Massimo Filippini & Luis Orea, 2014. "Applications of the stochastic frontier approach in Energy Economics," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 35-42.
    2. Orea, Luis & Jamasb, Tooraj, 2014. "Identifying efficient regulated firms with unobserved technological heterogeneity: A nested latent class approach to Norwegian electricity distribution networks," Efficiency Series Papers 2014/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).

    More about this item

    Keywords

    supervised composites; environmental conditions; electricity networks;

    JEL classification:

    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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