Artificial neural networks versus multivariate statistics: An application from economics

John Cooper

Journal of Applied Statistics, 1999, vol. 26, issue 8, 909-921

Abstract: An artificial neural network is a computer model that mimics the brain's ability to classify patterns or to make forecasts based on past experience. This paper explains the underlying theory of the widely used back-propagation algorithm and applies this procedure to a problem from the field of international economics, namely the identification of countries that are likely to seek a rescheduling of their international debt-service obligations. A comparison of the results with those obtained from three multivariate statistical procedures applied to the same data set suggests that neural networks are worthy of consideration by the applied economist.

Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664769921927 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:26:y:1999:i:8:p:909-921

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664769921927

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().