Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/266109 
Year of Publication: 
2022
Series/Report no.: 
Staff Report No. 1025
Publisher: 
Federal Reserve Bank of New York, New York, NY
Abstract: 
We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We find that in contrast to theory, for horizons close to two years, there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon shortens, the relationship becomes one-to-one, as the theory would predict.
Subjects: 
Bayesian interface
Bayesian nonparametric
Survey of Professional Forecasters
noisy rational expectations
JEL: 
C1
C11
C13
C15
C32
C58
G12
G13
G15
Document Type: 
Working Paper

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