What is it about?
We analyse the effects of aggregating the level of disagreement in survey-based expectations. With this aim, we construct several indicators based on two metrics of disagreement: the standard deviation of the balance and a geometric measure of discrepancy. We use data from business and consumer surveys in eleven European countries and the Euro Area. We evaluate the dynamic response of economic growth to shocks in agents’ uncertainty gauged by the discrepancy measures in a bivariate vector autoregressive framework. We find that while the effect on economic activity to a shock in aggregate discrepancy is always negative for firms’ disagreement, the effect to consumers’ disagreement is positive in all countries except Italy. To shed some light regarding the effect of aggregating disagreement both across variables and economic agents on forecast accuracy, we also examine the predictive performance of the discrepancy indicators, using them to generate out-of-sample forecasts of economic growth. We do not find evidence that the aggregation of disagreement improves forecast accuracy.
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Why is it important?
The analysis of economic uncertainty gained renewed interest since the 2008 financial crisis. The advent of the coronavirus pandemic and the subsequent economic disruption caused by the lockdown has further increased the efforts done in order to anticipate uncertainty. This study contributes to the existing literature by analysing the effects of aggregating the level of disagreement across variables and different types of agents. We provide a comparative view of firms vs. households of the dynamic relationship between innovations in their expectations about future economic uncertainty and the evolution of economic growth. The obtained findings are especially relevant when using cross-sectional dispersion of survey-based expectations of firms and households.
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This page is a summary of: On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables, Journal of Business Cycle Research, November 2020, Springer Science + Business Media, DOI: 10.1007/s41549-020-00050-2.
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