An Application of Clustering Analysis to International Private Indebtedness
Carneiro Dionisio and
Carlos Pedreira ()
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Monteiro Andre: Gavea Investimentos
Carneiro Dionisio: PUC- RIO
Computational Economics from University Library of Munich, Germany
This paper presents a procedure for clustering analysis that combines Kohone’s Self organizing Feature Map (SOFM) and statistical schemes. The idea is to cluster the data in two stages: run SOFM and then minimize the segmentation dispersion. The advantages of proposed procedure will be illustrated through a synthetic experiment and a real macroeconomic problem. The procedure is then used to explore the relationship between private indebtedness and some macroeconomic variables commonly used to measure macroeconomic performance. The experiences of thirty-nine countries in the early nineties are analyzed. The procedure outperformed others clustering techniques in the job of identifying consistent groups of countries from the economic and statistical viewpoints. It found out similarities in different countries concerning their respective levels of private indebtedness when added to well accepted parameters to measure macroeconomic performance.
Keywords: Vector quantization; Clustering; Self-Organizing Feature Map; Macroeconomic Performance; Private Indebtedness. (search for similar items in EconPapers)
JEL-codes: C8 (search for similar items in EconPapers)
Note: Type of Document - pdf; pages: 13. published at 'LEARNING AND NONLINEAR MODELS' ISSN 1676-2789 Vol. 1, No. 4, pp. 264-277, Dec 2004
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpco:0505001
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