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Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria

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  • Peter Sarlin

    () (Department of Information Technologies, Åbo Akademi University)

Abstract

Optimum currency area (OCA) theory attempts to define the geographical region in which it would maximize economic efficiency to have a single currency. In this paper, the focus is on prospective and current members of the Economic and Monetary Union. For this task, a self-organizing neural network, the Self-organizing map (SOM), is combined with hierarchical clustering for a two-level approach to clustering and visualizing OCA criteria. The output of the SOM is a topologically preserved two-dimensional grid. The final models are evaluated based on both clustering tendencies and accuracy measures. Thereafter, the two-dimensional grid of the chosen model is used for visual assessment of the OCA criteria, while its clustering results are projected onto a geographic map.

Suggested Citation

  • Peter Sarlin, 2011. "Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria," Economics Bulletin, AccessEcon, vol. 31(2), pages 1483-1495.
  • Handle: RePEc:ebl:ecbull:eb-10-00756
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    File URL: http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I2-P139.pdf
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    References listed on IDEAS

    as
    1. DomeNico Raguseo & Jan Sebo, 2008. "Optimum Currency Areas theory and the Slovak suitability for the euro adoption," Economics Bulletin, AccessEcon, vol. 6(40), pages 1-14.
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    More about this item

    Keywords

    Self-organizing maps; Optimum Currency Area; projection; clustering; geospatial visualization;

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • F0 - International Economics - - General

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