Belief Distortions and Macroeconomic Fluctuations

This paper combines a data-rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (“belief distortions”) embedded in survey responses. We find sizable distortions even for professional forecasters, with all respondent-types overwei...

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Bibliographic Details
Published in:The American economic review 2022-07, Vol.112 (7), p.2269-2315
Main Authors: Bianchi, Francesco, Ludvigson, Sydney C., Ma, Sai
Format: Article
Language:English
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Summary:This paper combines a data-rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (“belief distortions”) embedded in survey responses. We find sizable distortions even for professional forecasters, with all respondent-types overweighting the implicit judgmental component of their forecasts relative to what can be learned from publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with belief distortions evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgment and improve predictive accuracy. (JEL C45, D83, E23, E27, E31, E32, E37)
ISSN:0002-8282
DOI:10.1257/aer.20201713