Belief Distortions and Macroeconomic Fluctuations
CEPR Discussion Paper No. DP15003
93 Pages Posted: 28 Jul 2020 Last revised: 4 Feb 2022
Date Written: July 2020
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.
Keywords: beliefs, Biases, Expectations, Machine Learning
JEL Classification: E17, E27, E32, E7, G4
Suggested Citation: Suggested Citation