IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-905320240000046011.html
   My bibliography  Save this book chapter

Improving Predictions of Technical Inefficiency

In: Essays in Honor of Subal Kumbhakar

Author

Listed:
  • Christine Amsler
  • Robert James
  • Artem Prokhorov
  • Peter Schmidt

Abstract

The traditional predictor of technical inefficiency proposed byJondrow, Lovell, Materov, and Schmidt (1982)is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Suggested Citation

  • Christine Amsler & Robert James & Artem Prokhorov & Peter Schmidt, 2024. "Improving Predictions of Technical Inefficiency," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 309-328, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320240000046011
    DOI: 10.1108/S0731-905320240000046011
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320240000046011/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320240000046011/full/epub?utm_source=repec&utm_medium=feed&utm_campaign=repec&title=10.1108/S0731-905320240000046011
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320240000046011/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/S0731-905320240000046011?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.

    More about this item

    Keywords

    Stochastic frontier analysis; inefficiency scores; copulas; local random forest; nonparametrics; machine learning; synthetic data; C14; C23; C53;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:eme:aecozz:s0731-905320240000046011. 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: Emerald Support (email available below). General contact details of provider: .

    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.