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

Author

Listed:
  • 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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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Michał Niewiadomski & Agata Niemczyk & Zofia Gródek-Szostak & Marcin Surówka, 2024. "The Elusive Phenomenon: Unveiling Deconsumption in the EU," Sustainability, MDPI, vol. 16(11), pages 1-24, June.
    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. 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.
    6. 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.
    7. 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.
    8. Antoni, Giacomo Degli, 2007. "Do Social Relations Affect Economic Welfare? A Microeconomic Empirical Analysis," Knowledge, Technology, Human Capital Working Papers 9330, Fondazione Eni Enrico Mattei (FEEM).
    9. 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.
    10. 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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    Keywords

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    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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