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Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers

Author

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  • Mehmet Ali Cengiz
  • Emre Dünder
  • Talat Şenel

Abstract

More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.

Suggested Citation

  • Mehmet Ali Cengiz & Emre Dünder & Talat Şenel, 2018. "Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 17-25, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:17-25
    DOI: 10.1080/02664763.2016.1257586
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    Cited by:

    1. Tsionas, Mike G. & Philippas, Dionisis & Philippas, Nikolaos, 2022. "Multivariate stochastic volatility for herding detection: Evidence from the energy sector," Energy Economics, Elsevier, vol. 109(C).
    2. Bishan Wu, 2024. "Low-carbon development mechanism of energy industry from the perspective of carbon neutralization," Energy & Environment, , vol. 35(2), pages 628-643, March.
    3. Yu, Yinyun & Li, Congdong & Fu, Yelin & Yang, Weiming, 2023. "A group decision-making method to measure national energy architecture performance: A case study of the International energy Agency," Applied Energy, Elsevier, vol. 330(PA).

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