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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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    4. Born, Benjamin & Enders, Zeno & Müller, Gernot J., 2023. "On FIRE, news, and expectations," Working Papers 42, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
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    12. Karnaukh, Nina & Vokata, Petra, 2022. "Growth forecasts and news about monetary policy," Journal of Financial Economics, Elsevier, vol. 146(1), pages 55-70.
    13. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    14. Francesco Bianchi & Martin Lettau & Sydney C. Ludvigson, 2022. "Monetary Policy and Asset Valuation," Journal of Finance, American Finance Association, vol. 77(2), pages 967-1017, April.
    15. Dmitri V. Vinogradov & Michael J. Lamla & Yousef Makhlouf, 2024. "Survey-based expectations and uncertainty attitudes," Working Papers 2024_02, Business School - Economics, University of Glasgow.
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    20. Robert J. Tetlow, 2022. "How Large is the Output Cost of Disinflation?," Finance and Economics Discussion Series 2022-079, Board of Governors of the Federal Reserve System (U.S.).
    21. Abdalla, Ahmed M. & Carabias, Jose M. & Patatoukas, Panos N., 2021. "The real-time macro content of corporate financial reports: A dynamic factor model approach," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 260-280.
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    23. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
    24. 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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