Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm
Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm
复制标题
通过模糊 c 均值聚类算法进行基于模式识别的计量经济建模
DOI：
10.1201/9780203911570-14
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Robert Draeseke
中科院分区：
文献类型：
--
作者：
D. 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.
