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

  • David E. A. Giles


  • Robert Draeseke

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.

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Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0101.

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Length: 50 pages
Date of creation: 19 Jan 2001
Date of revision:
Handle: RePEc:vic:vicewp:0101
Note: ISSN 1485-6441
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  1. Gregory Richardson, 1998. "The structure of fuzzy preferences: Social choice implications," Social Choice and Welfare, Springer, vol. 15(3), pages 359-369.
  2. 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.
  3. 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.
  4. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521586115, October.
  5. Finn E. Kydland & Edward C. Prescott, 1990. "Business cycles: real facts and a monetary myth," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr, pages 3-18.
  6. 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.
  7. Murphy, Kevin M & Welch, Finis, 1990. "Empirical Age-Earnings Profiles," Journal of Labor Economics, University of Chicago Press, vol. 8(2), pages 202-29, April.
  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. Mark Coppejans, 2000. "Breaking the Curse of Dimensionality," Econometric Society World Congress 2000 Contributed Papers 0830, Econometric Society.
  11. Kunal Sengupta, 1999. "Choice rules with fuzzy preferences: Some characterizations," Social Choice and Welfare, Springer, vol. 16(2), pages 259-272.
  12. Yu Hsing & David Smyth, 1994. "Kuznets's inverted-U hypothesis revisited," Applied Economics Letters, Taylor & Francis Journals, vol. 1(7), pages 111-113.
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