Communications - Scientific Letters of the University of Zilina 2005, 7(2):33-37 | DOI: 10.26552/com.C.2005.2.33-37

Application of Support Vector Machines to the Modelling of Inflation

Dusan Marcek1, Milan Marcek2
1 The Faculty of Management Science and Informatics, University of Zilina, Slovakia and Institute of Computer Science, The Silesian University Opava, Czech Republic
2 KIWA, Ltd., Nitra, Slovakia

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. The theory of classical Phillips curve [8] is used to specify a structural model of inflation. We provide the fit of the models based on econometric approach for the inflation over the period 1993-2003 in the Slovak Republic, and use them as a tool to compare their approximation ability with those obtained using SVM's method. Some methodological contributions are made for SVM implementations to the causal economic modelling.

Keywords: SVM's, time series analysis, quadratic programming, econometric modelling

Published: June 30, 2005  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Marcek, D., & Marcek, M. (2005). Application of Support Vector Machines to the Modelling of Inflation. Communications - Scientific Letters of the University of Zilina, 7(2), 33-37. doi: 10.26552/com.C.2005.2.33-37
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. ADAMČA, J., MARČEK, M., PANČÍKOVÁ, L.: Some results in econometric modelling and forecasting of the inflation in Slovak economics. Journal of Economics, 52, 2004, No. 9, (1080-1093)
    2. CRISTIANINI, N.; SHAWE-TAYLOR, J.: An introduction to support vector machines. Cambridge University Press, 2000
    3. FLETCHER, R.: Practical methods of optimization. John Wiley and Sons, Chichester and NY, 1987
    4. GUNN, S., R.: Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, 1997
    5. JOACHIMS, THORSTEN: Making large-Scale SVM Learning Practical. In Advances in Kernel Methods - Support Vector Learning, chapter 11. MIT, 1999
    6. JOHANSEN, S.: Statistical analysis of cointegrating vectors. Journals of Economic Dynamics and Control, 12, 1988, (231-254) Go to original source...
    7. OSUNA, R., FREUND, GIROSI, F.: An improved training algorithm for support vector machines. In J. Principe, L. Gile, N. Morgan, and E. Wilson, editors, Neural Networks for Sigmal Processing VII - Proceedings of the 1997 IEEE Workshop, New York. IEEE, (276-285)
    8. PHILLIPS, A. W.: The Relation between Unemployment and the Rate of Change of Money Wages in the United Kingdom, 1861-1957, Economica, November 1958 Go to original source...
    9. PINDYCK, R., RUBINFELD, D.: Economic Models and Economic Forecasts. 3d ed., Mc Graw-Hill Publishing Company, New York, 1991
    10. SUYKENS, J., A., K., VAN GESTEL, T., DE BRABANTER, J., DE MOOR, B., VANDEWALLE, J.: Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore, 2002 Go to original source...
    11. VAN GESTEL, T., SUYKENS J., BART DE MOOR, DIRK-EMMA BAESTAENS: Volatility tube support vector machines. Neural Network World 1-2/2000, (287-297)
    12. VAPNIK, V.: Statistical learning theory. John-Wiley, New York, 1990
    13. VAPNIK, V.: The nature of statistical learning theory. Springer Verlag, New York, 1995. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content