EconPapers    
Economics at your fingertips  
 

A radial basis function artificial neural network test for neglected nonlinearity

Andrew Blake and George Kapetanios

Econometrics Journal, 2003, vol. 6, issue 2, 357-373

Abstract: We propose a test for neglected nonlinearity that uses an alternative artificial neural network (ANN) specification to the one commonly used in the literature. We use radial basis functions for the "hidden layer" with basis function centres and radii chosen from the sample data set and selected on the basis of an information criterion. The procedure is straightforward to implement and outperforms, in many cases, the ANN test proposed by Lee et al. (1993) and the analytic variation devised by Terasvirta et al. (1993) Copyright Royal Economic Society, 2003

Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (9)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: A Radial Basis Function Artificial Neural Network Test for Neglected Nonlinearity (1999)
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:ect:emjrnl:v:6:y:2003:i:2:p:357-373

Ordering information: This journal article can be ordered from
http://www.ectj.org

Access Statistics for this article

Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().

 
Page updated 2025-03-19
Handle: RePEc:ect:emjrnl:v:6:y:2003:i:2:p:357-373