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

Oscar Claveria (), Enric Monte () and Salvador Torra ()
Additional contact information
Enric Monte: Polytechnic University of Catalunya
Salvador Torra: Faculty of Economics, University of Barcelona

No 201511, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

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.

Keywords: Business surveys; Self-Organizing Maps; Clustering; Forecasting; Neural networks; Time series models; Nonlinear models JEL classification:C02; C22; C45; C63; E27 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-for and nep-ore
Date: 2015-03, Revised 2015-03
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