Multilayered Neural Networks in Approximation of Macroeconomic Dependencies
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Internet version without in-text references, based on: Paweł Rośczak, Multilayered Neural Networks in Approximation of Macroeconomic Dependencies, [In:] Some Aspects of Computer Science, Eds. D. Rutkowska, J. Kacprzyk, A. Cader, K. Przybyszewski, Academic Publishing House EXIT, Warsaw 2011, pp. 378–389, http://www.rosczak.com/index.php/en/macroeconomic/, 2012-01-26.
Abstract
The text undertakes the problem of implementing feedforward, multilayered neural network in macroeconomics. Neural methods are used to approximate macroeconomic dependencies and correlations usually being researched by econometric models. What the paper describes is the key differences between training the neural network and building the econometric model. The text also refers to the results and properties of the neural network system built on the basis of EMIL, i.e. the existing econometric macro model of Sweden economy. It shows that the neural network can be an additional useful tool in researching unclear, nonlinear economic issues.
Model of Sweden Economy 
