Abstract
A modelling strategy based on the application of fuzzy inference system is shown to provide a powerful and efficient method for the identification of non-linear and linear economic relationships. The procedure is particularly suitable for the estimation of ill-defined systems in which there is considerable uncertainty about the nature and range of key input variables. In addition, no prior knowledge is required about the form of the underlying relationships. Trend, cyclical and irregular components of the model can all be processed in a single pass. The potential benefits of the fuzzy logic approach are illustrated using a model to explain regime changes in Brazilian nominal interest rates. The results suggest that the relationships in the model are basically non-linear.
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Luna, I., Maciel, L., da Silveira, R.L.F., Ballini, R. (2010). Estimating the Brazilian Central Bank’s Reaction Function by Fuzzy Inference System. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_33
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DOI: https://doi.org/10.1007/978-3-642-14058-7_33
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