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An Application of Clustering Analysis to International Private Indebtedness

Author

Listed:
  • Monteiro Andre

    (Gavea Investimentos)

  • Carneiro Dionisio

    (PUC- RIO)

  • Pedreira Carlos

    (PUC-RIO)

Abstract
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.

Suggested Citation

  • Monteiro Andre & Carneiro Dionisio & Pedreira Carlos, 2005. "An Application of Clustering Analysis to International Private Indebtedness," Computational Economics 0505001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpco:0505001
    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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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/comp/papers/0505/0505001.pdf
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    More about this item

    Keywords

    Vector quantization; Clustering; Self-Organizing Feature Map; Macroeconomic Performance; Private Indebtedness.;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    Statistics

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