Alpha-Stable Consistent Model Specification Tests for Heavy-Tailed Neural Networks Environments
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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" 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.
|Date of creation:||01 Mar 1999|
|Contact details of provider:|| Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA|
Web page: http://fmwww.bc.edu/CEF99/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecf9:1041. 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: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.