IDEAS home Printed from
   My bibliography  Save this paper

A spreading method to improve efficiency prediction




In efficiency analysis by means of a stochastic frontier production function, the composite error variable includes the inefficiency component. For this reason, individual prediction cannot be made directly from an estimation of the error in the model. In order to solve this problem, Jondrow et al (1982), and Battese and Coelli (1988) separately developed two different procedures, based on the expectation operator of the conditional distributions. Although the two predictors are different, each suffers from a shrinkage effect with respect to the distribution of theoretical efficiency. Our study of the behaviour of these two predictors leads us to conclude that the value of the gamma parameter has a great influence on the above-mentioned effect, producing a truncation of the distribution that could be more than 50%, so that the extreme values of the efficiency can never be estimated by the predictors considered. We also propose a method that spreads out the predicted efficiencies in order to minimise the shrinkage effect. The Monte Carlo results demonstrate that the corrected predictions have a better behaviour than the original predictors.

Suggested Citation

  • Rafaela Dios-Palomares & Jose Miguel Martínez Paz, 2004. "A spreading method to improve efficiency prediction," Economic Working Papers at Centro de Estudios Andaluces E2004/31, Centro de Estudios Andaluces.
  • Handle: RePEc:cea:doctra:e2004_31

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    2. Kumbhakar, Subal C. & Löthgren, Mickael, 1998. "A Monte Carlo Analysis of Technical Inefficiency Predictors," SSE/EFI Working Paper Series in Economics and Finance 229, Stockholm School of Economics.
    3. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Efficiency; Frontier models; Monte Carlo methods.;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:cea:doctra:e2004_31. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Susana Mérida). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.