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Belief Distortions and Macroeconomic Fluctuations

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  • Bianchi, Francesco
  • Ludvigson, Sydney
  • Ma, Sai

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.

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  • Bianchi, Francesco & Ludvigson, Sydney & Ma, Sai, 2020. "Belief Distortions and Macroeconomic Fluctuations," CEPR Discussion Papers 15003, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:15003
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    More about this item

    Keywords

    beliefs; Biases; Machine learning; Expectations;
    All these keywords.

    JEL classification:

    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G4 - Financial Economics - - Behavioral Finance

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