On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables
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On the Aggregation of Survey‑Based Economic Uncertainty Indicators Between Different Agents and Across Variables Oscar Claveria1 Received: 22 April 2020 / Accepted: 9 November 2020 © Springer Nature Switzerland AG 2020
Abstract 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. These findings are especially relevant when using crosssectional dispersion of survey-based expectations of firms and households. Keywords Uncertainty · Economic growth · Disagreement · Expectations · Firms · Households · Business and consumer surveys JEL Classification C32 · E23 · E27 · E71
1 Introduction The analysis of economic uncertainty has gained renewed interest since the Great Recession. Despite the evidence that uncertainty shocks have an effect on real activity (Baker et al. 2016; Bloom 2009; Paloviita and Viren 2014), the elusive nature of uncertainty and the difficulty of measuring it, has meant that until recently its impact * Oscar Claveria [email protected] 1
AQR‑IREA, University of Barcelona, Diagonal, 690, 08034 Barcelona, Spain
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Vol.:(0123456789)
Journal of Business Cycle Research
on the economy were not further explored. Dibiasi and Sarferaz (2020) emphasise the importance of defining what is understood by economic uncertainty. Based on the different ways in which economic agents form their expectations regarding unknown future events, Knight (1921) differentiated risk from uncertainty. While under risk, agents are able to allocate probabilities over future outcomes, uncertainty would be defined as the state in which agents are no longer able to form expectations about future events. As noted by Rossi et al. (2020), disagreement on the probability distribution of future outcomes would be a special case of Knightian uncertainty, since disagreeing on probability distributions automatically implies that the probability distributions are not correctly specified. The unobservable nature of economic uncertainty has given rise to different approaches to proxy it. Some authors have opted to
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