2026-04-13T05:45:02Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/3289212026-01-23T03:23:32Zcom_2072_1033col_2072_452950
RECERCAT author Claveria González, Oscar author Monte Moreno, Enrique author Torra Porras, Salvador 2016-08-03 © <2015>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Peer ReviewedPostprint (published version) http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Attribution-NonCommercial-NoDerivs 3.0 Spain Àrees temàtiques de la UPC::Economia i organització d'empreses::Macroeconomia Àrees temàtiques de la UPC::Matemàtiques i estadística Macroeconomics Cluster analysis Business surveys indicators Expectations Self-organizing maps Artificial neural networks Time series models Forecasting Macroeconomia Anàlisi de conglomerats A self-organizing map analysis of survey-based agents expectations before impending shocks for model selection: the case of the 2008 financial crisis Article