Please use this identifier to cite or link to this item:
Title: A self-organizing map analysis of survey-based agents' expectations before impending shocks for model selection: The case of the 2008 financial crisis
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Previsió econòmica
Desenvolupament econòmic
Xarxes neuronals (Informàtica)
Anàlisi funcional no lineal
Economic forecasting
Economic development
Neural networks (Computer science)
Nonlinear functional analysis
Issue Date: 2-Dec-2015
Publisher: Elsevier
Abstract: This paper examines the role of clustering techniques to assist in the selection of the most indicated method to model survey-based expectations. First, relying on a Self-Organizing Map (SOM) analysis and using the financial crisis of 2008 as a benchmark, we distinguish between countries that show a progressive anticipation of the crisis, and countries where sudden changes in expectations occur. We then generate predictions of survey indicators, which are usually used as explanatory variables in econometric models. We compare the forecasting performance of a multi-layer perceptron (MLP) Artificial Neural Network (ANN) model to that of three different time series models. By combining both types of analysis, we find that ANN models outperform time series models in countries in which the evolution of expectations shows brisk changes before impending shocks. Conversely, in countries where expectations follow a smooth transition towards recession, autoregressive integrated moving-average (ARIMA) models outperform neural networks.
Note: Versió postprint del document publicat a:
It is part of: International Economics, 2016, vol. 146, p. 40-58
Related resource:
ISSN: 2110-7017
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

Files in This Item:
File Description SizeFormat 
659294.pdf1.6 MBAdobe PDFView/Open    Request a copy

Embargat   Document embargat fins el 2-12-2018

This item is licensed under a Creative Commons License Creative Commons