IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this paper

A multiple testing procedure for neural network model selection

Listed author(s):
  • Michele La Rocca


    (Dept. of Economics and Statistics, University of Salerno, Italy)

  • Cira Perna

    (Dept. of Economics and Statistics, University of Salerno, Italy)

One of the most critical issues when using neural networks is how to select appropriate network architectures for the problem at hand. Practitioners usually refer to information criteria which might lead to over-parameterized models with heavy consequence on overfitting and poor ex-post forecast accuracy. Moreover, since model selection criteria depend on sample information, their actual values are subject to statistical variations. So, to compare multiple models in terms of their out of sample predictive ability, a test procedure is needed. But, in such context there is always the possibility that any satisfactory results obtained may simply be due to chance rather than any merit inherent in the model yielding to the result. The problem can be particularly serious when using neural network models which are basically atheoretical. In this paper we propose a strategy for neural network model selection which is based on a sequence of tests and, to avoid the data snooping problem, familywise error rate is controlled by a proper technique. The procedure requires the implementation of resampling techniques in order to obtain valid asymptotic critical values for the test. Some simulations results and applications to real data are discussed.

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
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.

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 497.

in new window

Date of creation: 04 Jul 2006
Handle: RePEc:sce:scecfa:497
Contact details of provider: Web page:

More information through EDIRC

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:sce:scecfa:497. 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 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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.