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Business and consumer expectations and macroeconomic forecasts
Institution:1. Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands;2. Erasmus School of Economics, Tinbergen Institute, Rotterdam, The Netherlands;1. AQR-IREA (Institute of Applied Economics Research), Department of Econometrics, University of Barcelona, Diagonal, 690, 08034 Barcelona, Spain;2. Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC), Jordi Girona, 1-3, 08034 Barcelona, Spain;3. Riskcenter-IREA, Department of Econometrics and Statistics, University of Barcelona, Diagonal, 690, 08034 Barcelona, Spain;1. Narodowy Bank Polski, ul. ?wietokrzyska 11/21, 00-919 Warsaw, Poland;2. Bank of Finland, PO Box 160, 00101 Helsinki, Finland
Abstract:Business and consumer surveys have become an essential tool for gathering information about different economic variables. While the fast availability of the results and the wide range of variables covered have made them very useful for monitoring the current state of the economy, there is no consensus on their usefulness for forecasting macroeconomic developments.The objective of this paper is to analyse the possibility of improving forecasts for selected macroeconomic variables for the euro area using the information provided by these surveys. After analyzing the potential presence of seasonality and the issue of quantification, we tested whether these indicators provide useful information for improving forecasts of the macroeconomic variables. With this aim, different sets of models have been considered (AR, ARIMA, SETAR, Markov switching regime models and VAR) to obtain forecasts for the selected macroeconomic variables. Then, information from surveys has been considered for forecasting these variables in the context of the following models: autoregressive, VAR, Markov switching regime and leading indicator models. In all cases, the root mean square error (RMSE) has been computed for different forecast horizons.The comparison of the forecasting performance of the two sets of models permits us to conclude that, in most cases, models that include information from the surveys have lower RMSEs than the best model without survey information. However, this reduction is only significant in a limited number of cases. In this sense, the results obtained extend the results of previous research that has included information from business and consumer surveys to explain the behaviour of macroeconomic variables, but are not conclusive about its role.
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