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

David Giles and Robert Draeseke

No 101, Econometrics Working Papers from Department of Economics, University of Victoria

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.

Keywords: Fuzzy logic; fuzzy sets; fuzzy c-means algorithm; pattern recognition; semi-parametric modelling; curse of dimensionality. (search for similar items in EconPapers)
JEL-codes: C14 C49 C51 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2001-01-19
Note: ISSN 1485-6441
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0101

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