IDEAS home Printed from https://ideas.repec.org/p/sce/scecf9/1041.html
   My bibliography  Save this paper

Alpha-Stable Consistent Model Specification Tests for Heavy-Tailed Neural Networks Environments

Author

Listed:
  • Jonathan Hill

    (University of Colorado)

Abstract

This paper investigates applications of stable-law limiting theory to model specification tests in which non-linearities are sought in data that exhibit bounded maximal moments. Utilizing the stable-laws allows us for the first time to prove that consistent conditional moment tests (CM) of a functional form within neural network environments are not chi-squared in distribution. In addition, we prove that CM tests suffer a dramatic loss in power when moments greater than two are infinite. Furthermore, we offer for the first time a set of computationally cheapest statistics that are stable-functionals of suitable moment conditions. The new statistics are suitable for all iid and serially dependent data processes and are directly applicable to neural network learning in financial time-series models. The stable-law statistics are invariant to moment condition failure, remain maximally powerful under mild conditions, and do not require a restrictive orthogonality condition under the null hypothesis. Simulation experiments indicate that CM tests are far more likely to predict non-linearity erroneously in data than true chi-squared distributions imply. Moreover, in comparison, for certain data environments, the new stable-law statistics demonstrate perfect power for all levels of moment condition failure.

Suggested Citation

  • Jonathan Hill, 1999. "Alpha-Stable Consistent Model Specification Tests for Heavy-Tailed Neural Networks Environments," Computing in Economics and Finance 1999 1041, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:1041
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sce:scecf9:1041. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.