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Belief Distortions and Macroeconomic Fluctuations

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  • Francesco Bianchi
  • Sydney C. Ludvigson
  • Sai Ma

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.

Suggested Citation

  • Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2020. "Belief Distortions and Macroeconomic Fluctuations," NBER Working Papers 27406, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27406
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    22. Giulia Piccillo & Poramapa Poonpakdee, 2021. "Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters," CESifo Working Paper Series 9486, CESifo.

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    JEL classification:

    • E03 - Macroeconomics and Monetary Economics - - General - - - Behavioral Macroeconomics
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics

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