Advanced Search
MyIDEAS: Login

An Artifical Neural Network System of Leading Indicators

Contents:

Author Info

  • Andrew P Blake

Abstract

We construct an artificial neural network to act as a system of leading indicators. We focus on radial basis functions as the architecture and forward selection as the method for determining the number of basis functions in the network. A brief review is given of the advantages of this as a strategy. Using common heuristics to determine scaling, radii and centre population, we find that the results for output growth prediction for six European countries are promising.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://niesr.ac.uk/pubs/dps/dp144.pdf
Our checks indicate that this address may not be valid because: 404 Not Found. If this is indeed the case, please notify (Communications Manager)
Download Restriction: no

Bibliographic Info

Paper provided by National Institute of Economic and Social Research in its series NIESR Discussion Papers with number 144.

as in new window
Length:
Date of creation: Jan 1999
Date of revision:
Handle: RePEc:nsr:niesrd:144

Contact details of provider:
Postal: 2 Dean Trench Street Smith Square London SW1P 3HE
Web page: http://niesr.ac.uk

Related research

Keywords:

References

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Documents de travail du Centre d'Economie de la Sorbonne 10065, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  2. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00505165, HAL.

Lists

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

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:nsr:niesrd:144. 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: (Communications Manager).

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.