TY - JOUR
T1 - Belief Distortions and Macroeconomic Fluctuations
AU - Bianchi, Francesco
AU - Ludvigson, Sydney C.
AU - Ma, Sai
N1 - Funding Information:
* Bianchi: Department of Economics, Johns Hopkins University, and Department of Economics, Duke University, CEPR, and NBER (email: fb36@duke.edu); Ludvigson: Department of Economics, CEPR, and NBER (email: sydney.ludvigson@nyu.edu); Ma: Federal Reserve Board of Governors (email: sai.ma@frb.gov). Emi Nakamura was the coeditor for this article. Ludvigson acknowledges financial support from the C. V. Starr Center for Applied Economics at NYU. We thank Marios Angeletos and Fabrice Collard for providing data on their estimated cyclical shocks, and Michael Boutros, Josue Cox, Justin Shugarman, and Yueteng Zhu for excellent research assistance. We are grateful to Marios Angeletos, Rudi Bachmann, Fabrice Collard, Andrew Foerster, Xavier Gabaix, David Hershleifer, Cosmin Ilut, Anil Kashyp, Yueran Ma, Laura Veldkamp, and to seminar participants at the Bank of Israel, Chicago Booth, Duke, the Federal Reserve Board, King’s Business School, MIT, the Richmond Federal Reserve Bank, UC Berkeley, the 2021 annual meeting of the American Economic Association, the 2022 annual meeting of the American Finance Association, the July 2020 NBER Behavioral Macro workshop, the 2019 New Approaches for Modeling Expectations in Economics Conference (London), the 2019 Conference on Applied Macro-Finance (Melbourne), the 2020 Federal Reserve System Econometrics Meeting, and the 2020 Stanford Institute for Theoretical Economics Workshop on Asset Pricing, Macro Finance, and Computation for many helpful comments. The views expressed are those of the authors and do not necessarily reflect those of the Federal Reserve Board or the Federal Reserve System.
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 -