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Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm

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  • David E. A. Giles
  • Robert Draeseke

Abstract

In this paper we consider the use of fuzzy modelling in the context of econometric analysis of both time-series and cross-section data. We discuss and demonstrate a semi-parametric methodology for model identification and estimation that is based on the Fuzzy c-Means algorithm that is widely used in the context of pattern recognition, and the Takagi-Sugeno approach to modelling fuzzy systems. This methodology is exceptionally flexible and provides a computationally tractable method of dealing with non-linear models in high dimensions. In this respect it has distinct theoretical advantages over non-parametric kernel regression, and we find that these advantages also hold empirically in terms of goodness-of-fit in a selection of economic applications.

Suggested Citation

  • David E. A. Giles & Robert Draeseke, 2001. "Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm," Econometrics Working Papers 0101, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicewp:0101
    Note: ISSN 1485-6441
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    File URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/ewp0101.pdf
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    References listed on IDEAS

    as
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    7. Jozsef, Sandor & Korosi, Gabor & Matyas, Laszlo, 1992. "A possible new approach of panel modelling," Structural Change and Economic Dynamics, Elsevier, vol. 3(2), pages 357-374, December.
    8. Coppejans, Mark, 2000. "Breaking the Curse of Dimensionality," Working Papers 00-13, Duke University, Department of Economics.
    9. Lindstrom, Tomas, 1998. "A fuzzy design of the willingness to invest in Sweden," Journal of Economic Behavior & Organization, Elsevier, vol. 36(1), pages 1-17, July.
    10. Peter Jacobsen & David Giles, 1998. "Income distribution in the United States: Kuznets' inverted-U hypothesis and data non-stationarity," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 7(4), pages 405-423.
    11. Finn E. Kydland & Edward C. Prescott, 1990. "Business cycles: real facts and a monetary myth," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 14(Spr), pages 3-18.
    12. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    13. Robert Draeseke & David E. A. Giles, 1999. "A Fuzzy Logic Approach to Modelling the Underground Economy," Econometrics Working Papers 9909, Department of Economics, University of Victoria.
    14. Kunal Sengupta, 1999. "Choice rules with fuzzy preferences: Some characterizations," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 16(2), pages 259-272.
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    Citations

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    Cited by:

    1. Chad Stroomer & David E.A. Giles, 2003. "Income Convergence and trade Openness: Fuzzy Clustering and Time Series Evidence," Econometrics Working Papers 0304, Department of Economics, University of Victoria.
    2. David E.A. Giles & Hui Feng, 2003. "Testing For Convergence in Output and in 'Well-Being' in Industrialized Countries," Econometrics Working Papers 0302, Department of Economics, University of Victoria.
    3. David E. A. Giles & Carl Mosk, 2003. "Ruminant Eructation and a Long-Run Environmental Kuznets' Curve for Enteric Methane in New Zealand: Conventional and Fuzzy Regression Analysis," Econometrics Working Papers 0306, Department of Economics, University of Victoria.
    4. David Giles & Chad Stroomer, 2006. "Does Trade Openness Affect the Speed of Output Convergence? Some Empirical Evidence," Empirical Economics, Springer, vol. 31(4), pages 883-903, November.
    5. Giacomo Degli Antoni, 2007. "Do Social Relations Affect Economic Welfare? A Microeconomic Empirical Analysis," Working Papers 2007.32, Fondazione Eni Enrico Mattei.
    6. Hui Feng & David E. Giles, 2007. "Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence," Econometrics Working Papers 0710, Department of Economics, University of Victoria.
    7. Morillas, Antonio & Díaz, Bárbara, 2007. "Qualitative Answering Surveys And Soft Computing," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 3-19, May.
    8. David E. Giles & Chad N. Stroomer, 2004. "Identifying the Cycle of a Macroeconomic Time-Series Using Fuzzy Filtering," Econometrics Working Papers 0406, Department of Economics, University of Victoria.
    9. Amir Safari & Detlef Seese, 2009. "Non-parametric estimation of a multiscale CHARN model using SVR," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 105-121.

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    More about this item

    Keywords

    Fuzzy logic; fuzzy sets; fuzzy c-means algorithm; pattern recognition; semi-parametric modelling; curse of dimensionality.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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