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Application of Dynamic Models and an Support Vector Machine to Inflation Modelling

Author

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  • Dušan Marček
  • Milan Marček

Abstract

In Support Vector Machines (SVM's), a non-linear model is estimated based on solving a Quadratic Programming (QP) problem. Based on work [1] 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.

Suggested Citation

  • Dušan Marček & Milan Marček, 2006. "Application of Dynamic Models and an Support Vector Machine to Inflation Modelling," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 13(23).
  • Handle: RePEc:czx:journl:v:13:y:2006:i:23:id:147
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    File URL: http://ces.utia.cas.cz/bulletin/index.php/bulletin/article/view/147
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    References listed on IDEAS

    as
    1. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    2. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.
    3. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, pages 787-821.
    4. Jiri Witzany, 2011. "A Two Factor Model for PD and LGD Correlation," Bulletin of the Czech Econometric Society, The Czech Econometric Society.
    5. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago.
    6. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, pages 1-34.
    7. Seidler, Jakub & Horvath, Roman & Jakubík, Petr, 2009. "Estimating expected loss given default in an emerging market: the case of Czech Republic," Journal of Financial Transformation, Capco Institute, vol. 27, pages 103-107.
    8. De Graeve, F. & Kick, T. & Koetter, M., 2008. "Monetary policy and financial (in)stability: An integrated micro-macro approach," Journal of Financial Stability, Elsevier, pages 205-231.
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    More about this item

    Keywords

    Support vector machines; data mining; learning machines; time senes analysis and forecasting; dynamic modelling;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

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