Abstract
In this paper, I estimate a simple Bayesian learning model to expectations data from the Survey of Professional Forecasters. I reformulate the model in terms of forecast revisions, which allows one to abstract from differences in priors and to focus the analysis on the relationship between news and revisions. The empirical analysis shows that there is significant heterogeneity in the interpretation of news among forecasters, in particular at longer horizons, while it decreases closer to the forecast target date. The results also indicate a positive relationship between prior sentiment and interpretation of the signal, in the sense that relatively optimistic (pessimistic) forecasters are likely to believe that the signal under (over) estimates the future realization and assign it a low (high) weight in the forecast revision.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
