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Neural network pruning applied to real exchange rate analysis
Author(s) -
Kaashoek Johan F.,
van Dijk Herman K.
Publication year - 2002
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.835
Subject(s) - principal component analysis , artificial neural network , liberian dollar , exchange rate , us dollar , computer science , pruning , econometrics , term (time) , component (thermodynamics) , value (mathematics) , economics , artificial intelligence , monetary economics , machine learning , finance , agronomy , physics , quantum mechanics , biology , thermodynamics
Neural networks are fitted to real exchange rates of several industrialized countries. The size and topology of the networks is found through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell. These pruned neural networks are good approximations to varying non‐linear trends in real exchange rates. Non‐linear dynamic analysis shows that the long‐term equilibrium values of several European currencies correspond to the actual values within the European Monetary System. Based on its long‐term equilibrium value, the Euro appears to be undervalued vis‐à‐vis the US dollar at the introduction of the Euro on 1 January 1999. Copyright © 2002 John Wiley & Sons, Ltd.