Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/280675 
Year of Publication: 
2023
Series/Report no.: 
AEI Economics Working Paper No. 2023-14
Publisher: 
American Enterprise Institute (AEI), Washington, DC
Abstract: 
Following Manzan (2021), this paper examines how professional forecasters revise their uncertainty (variance) forecasts. We show that popular first moment "efficiency" tests are not applicable to study variance forecasts and instead employ monotonicity tests developed by Patton and Timmermann (2012). We find strong support for the Bayesian learning prediction of decreasing patterns in the variance of fixed-event density forecasts and their revisions as the forecast horizon declines. We explore the role of financial conditions indices in variance forecasts and document their predictive content for the revision process of US professional forecasters, although the evidence is weaker for euro area forecasters.
Subjects: 
Variance forecasts
survey expectations
Bayesian learning
Document Type: 
Working Paper

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