Belief Distortions and Macroeconomic Fluctuations

Francesco Bianchi, Sydney Ludvigson and Sai Ma

No 15003, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.

Keywords: beliefs; Biases; Machine learning; Expectations (search for similar items in EconPapers)
JEL-codes: E17 E27 E32 E7 G4 (search for similar items in EconPapers)
Date: 2020-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-mac and nep-upt
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Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Belief Distortions and Macroeconomic Fluctuations (2022)
Working Paper: Belief Distortions and Macroeconomic Fluctuations (2020)
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