IDEAS home Printed from https://ideas.repec.org/p/cor/louvco/1994007.html
   My bibliography  Save this paper

Another Look at the American Electrical Utility Data

Author

Listed:
  • RITTER, Christian

    (CORE and Institut de Statistique, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

  • SIMAR, Léopold

    (CORE and Institut de Statistique, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

Abstract

The American electric utility data, which are frequently analyzed in the context of frontier models, can be explained by a linear model without inefficiencies. the observed maximum likelihood for this linear model is very mildly smaller than the maximum likelihood for more flexible stochastic frontier models and the log-likelihood-ratio statistic for an approxImate [chi. exp2] test of the simple least squares model against normal exponential and normal-gamma stochastic frontier models is far from significant.

Suggested Citation

  • RITTER, Christian & SIMAR, Léopold, 1994. "Another Look at the American Electrical Utility Data," LIDAM Discussion Papers CORE 1994007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1994007
    as

    Download full text from publisher

    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp1994.html
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Bernstein, David H. & Parmeter, Christopher F., 2019. "Returns to scale in electricity generation: Replicated and revisited," Energy Economics, Elsevier, vol. 82(C), pages 4-15.
    2. Christian Ritter & Léopold Simar, 1997. "Pitfalls of Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 8(2), pages 167-182, May.
    3. David H. Bernstein & Christopher F. Parmeter, 2017. "Returns to Scale in Electricity Generation: Revisited and Replicated," Working Papers 2017-08, University of Miami, Department of Economics.
    4. Behr, Andreas & Tente, Sebastian, 2008. "Stochastic frontier analysis by means of maximum likelihood and the method of moments," Discussion Paper Series 2: Banking and Financial Studies 2008,19, Deutsche Bundesbank.
    5. Oikawa, Koki, 2016. "A microfoundation for stochastic frontier analysis," Economics Letters, Elsevier, vol. 139(C), pages 15-17.

    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:cor:louvco:1994007. 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: Alain GILLIS (email available below). General contact details of provider: https://edirc.repec.org/data/coreebe.html .

    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.