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“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”

Author

Listed:
  • Oscar Claveria

    () (Department of Econometrics. University of Barcelona)

  • Enric Monte

    () (Department of Signal Theory and Communications. Polytechnic University of Catalunya.)

  • Salvador Torra

    () (Department of Econometrics & Riskcenter-IREA. Universitat de Barcelona)

Abstract

By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents’ expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. By mapping the trajectory of economic experts’ expectations prior to the recession we find that when there are brisk changes in expectations before impending shocks, Artificial Neural Networks are more suitable than time series models for modelling expectations. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models show the best forecasting performance. This result demonstrates the usefulness of clustering techniques for selecting the most appropriate method to model and forecast expectations according to their behaviour.

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
  • Handle: RePEc:aqr:wpaper:201508
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    File URL: http://www.ub.edu/irea/working_papers/2015/201511.pdf
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    References listed on IDEAS

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    Keywords

    Business surveys; Self-Organizing Maps; Clustering; Forecasting; Neural networks; Time series models; Nonlinear models JEL classification: C02; C22; C45; C63; E27;

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