The application of clustering analysis to international private indebtedness

The main goal of this paper is to apply a combination of statistical and connectionist schemes to examine, via clustering analysis, private indebtedness in different countries. Thirty-nine such experiences are used. The relationship between private debts and some macroeconomic variables are discussed in some detail. The clustering performance is improved by taking advantage of specific properties and capacities of each method. The procedures are also applied to a controlled numerical example.

An Application of Clustering Analysis to International Private Indebtedness

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.Vector quantization, Clustering, Self-Organizing Feature Map,Macroeconomic Performance, Private Indebtedness.