Belief Distortions and Macroeconomic Fluctuations

Francesco Bianchi, Sydney C. Ludvigson, Sai Ma

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)2269-2315
    Number of pages47
    JournalAmerican Economic Review
    Issue number7
    StatePublished - Jul 2022

    ASJC Scopus subject areas

    • Economics and Econometrics


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