IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v45y2018i1p17-25.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1257586
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1257586?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:17-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.