Modelling the dynamics of EU economic sentiment indicators: an interaction-based approach

This article estimates a simple univariate model of expectation or opinion formation in continuous time adapting a ‘canonical’ stochastic model of collective opinion dynamics (Weidlich and Haag, 1983; Lux, 1995, 2009a). This framework is applied to a selected data set on survey-based expectations from the rich EU business and consumer survey database for 12 European countries. The model parameters are estimated through Maximum Likelihood (ML) and numerical solution of the transient probability density functions for the resulting stochastic process. The model's success is assessed with respect to its out-of-sample forecasting performance relative to univariate Time Series (TS) models of the Autoregressive Moving Average model, ARMA(<italic>p</italic>, <italic>q</italic>) and Autoregressive Fractionally Integrated Moving Average, ARFIMA(<italic>p</italic>, <italic>d</italic>, <italic>q</it alic>) varieties. These tests speak for a slight superiority of the canonical opinion dynamics model over the alternatives in the majority of cases.