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Belief Distortions and Macroeconomic Fluctuations

Francesco Bianchi, Sydney C. Ludvigson and Sai Ma

American Economic Review, 2022, vol. 112, issue 7, 2269-2315

Abstract: 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-codes: C45 D83 E23 E27 E31 E32 E37 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (20)

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Working Paper: Belief Distortions and Macroeconomic Fluctuations (2020) Downloads
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DOI: 10.1257/aer.20201713

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