Forecasting with leading economic indicators - a non-linear approach
Leading economic indicators have a long tradition in forecasting future economic activity. Recent developments, however, suggest that there is scope for adding extensions to the methodology of forecasting major economic fluctuations. In this paper, the author tries to develop a new model, which would outperform the forecast accuracy of classical leading indicators model. The use of artificial neural networks is proposed here. For demonstration a case study for Slovene economy is included. The main finding is that, at the twelve months forecasting horizon, a stable and improved forecast accuracy could be achieved for in- and out-of-sample data.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 2003 (2003)
Issue (Month): 1 ()
|Contact details of provider:|| Postal: |
Phone: (02) 24 09 51 11
Fax: (02) 24 22 06 57
Web page: http://www.vse.cz/
More information through EDIRC
|Order Information:|| Postal: Editorial office Prague Economic Papers, University of Economics, nám. W. Churchilla 4, 130 67 Praha 3, Czech Republic|
Web: http://www.vse.cz/pep/ Email:
When requesting a correction, please mention this item's handle: RePEc:prg:jnlpep:v:2003:y:2003:i:1:id:207. 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: (Vaclav Subrta)
If references are entirely missing, you can add them using this form.