An Artifical Neural Network System of Leading Indicators
AbstractWe construct an artificial neural network to act as a system of leading indicators. We focus on radial basis functions as the architecture and forward selection as the method for determining the number of basis functions in the network. A brief review is given of the advantages of this as a strategy. Using common heuristics to determine scaling, radii and centre population, we find that the results for output growth prediction for six European countries are promising.
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Bibliographic InfoPaper provided by National Institute of Economic and Social Research in its series NIESR Discussion Papers with number 144.
Date of creation: Jan 1999
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- Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Documents de travail du Centre d'Economie de la Sorbonne 10065, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00505165, HAL.
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