TY - JOUR
T1 - Belief Distortions and Macroeconomic Fluctuations
AU - Bianchi, Francesco
AU - Ludvigson, Sydney C.
AU - Ma, Sai
N1 - Publisher Copyright:
© 2022 American Economic Association. All rights reserved.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
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U2 - 10.1257/aer.20201713
DO - 10.1257/aer.20201713
M3 - Article
AN - SCOPUS:85134375559
SN - 0002-8282
VL - 112
SP - 2269
EP - 2315
JO - American Economic Review
JF - American Economic Review
IS - 7
ER -