| Abstract: | 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 Weidlich, W, and Haag, G, 1983. Concepts and Models of a Quantitative Sociology. Berlin: Springer; 1983.[Crossref] , [Google Scholar]; Lux, 1995 Lux, T, 1995. Herd behavior, bubbles and crashes, The Economic Journal 105 (1995), pp. 881–96.[Crossref], [Web of Science ®] , [Google Scholar], 2009a Lux, T, 2009a. Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey, Journal of Economic Behavior and Organization 72 (2009a), pp. 638–55.[Crossref], [Web of Science ®] , [Google Scholar]). 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(p,?q) and Autoregressive Fractionally Integrated Moving Average, ARFIMA(p,?d,?q) varieties. These tests speak for a slight superiority of the canonical opinion dynamics model over the alternatives in the majority of cases. |
