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.
Volume (Year): 2003 (2003)
Issue (Month): 1 ()
|Contact details of provider:|| Postal: nam. W. Churchilla 4, 130 67 Praha 3|
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: (Frantisek Sokolovsky)
If references are entirely missing, you can add them using this form.