DocumentCode :
700758
Title :
Multivariate nonlinear cointegration analysis using artificial neural networks
Author :
Weeren, A.J.T.M. ; Dumortier, F. ; Plasmane, J.E.J.
Author_Institution :
UFSIA (Univ. of Antwerp), Antwerp, Belgium
fYear :
1997
fDate :
1-7 July 1997
Firstpage :
1939
Lastpage :
1944
Abstract :
In system identification we are often confronted with non-stationary timeseries. In the area of econometrics the method of cointegration analysis has become the most popular way to deal with models involving nonstationary data. The main disadvantage of the methodology based on cointegration is that it is basically a linear method and hence it is only capable to find linear relationships. Therefore. in this paper we propose a new method for nonlinear multivariate cointegration analysis.
Keywords :
econometrics; exchange rates; neural nets; time series; artificial neural networks; econometrics; linear method; linear relationships; multivariate nonlinear cointegration analysis; nonstationary time-series; system identification; Analytical models; Artificial neural networks; Error correction; Exchange rates; Feedforward neural networks; Yttrium; Modelling; Neural nets; Nonlinear identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
Type :
conf
Filename :
7082388
Link To Document :
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