Application of Dynamic Models and an Support Vector Machine to Inflation Modelling
AbstractIn Support Vector Machines (SVM's), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. Based on work  we investigate the quantifying of econometric structural model parameters of inflation in Slovak economics. Dynamic and SYM's modelling approaches are used for automated specification of a functional form of the model. Based on dynamic modelling, we provide the fit of inflation models over the period 1993-2003 in the Slovak Republic, and use them as a tool to compare their forecasting abilities with those obtained using SYM's method. Some methodological contributions are made to dynamic and SYM's modelling approaches in economics and to their use in data mining systems. The study discusses, analytically and numerically demonstrates the quality and interpretability of the obtained results. The SYM's methodology is extended to predict the time series models.
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Bibliographic InfoArticle provided by The Czech Econometric Society in its journal Bulletin of the Czech Econometric Society.
Volume (Year): 13 (2006)
Issue (Month): 23 ()
Support vector machines; data mining; learning machines; time senes analysis and forecasting; dynamic modelling;
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