Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm
AbstractIn 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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Bibliographic InfoPaper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0101.
Length: 50 pages
Date of creation: 19 Jan 2001
Date of revision:
Note: ISSN 1485-6441
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Postal: PO Box 1700, STN CSC, Victoria, BC, Canada, V8W 2Y2
Web page: http://web.uvic.ca/econ
More information through EDIRC
Fuzzy logic; fuzzy sets; fuzzy c-means algorithm; pattern recognition; semi-parametric modelling; curse of dimensionality.;
Find related papers by 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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