Belief Distortions and Macroeconomic Fluctuations
- (pp. 2269-2315)
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 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.Citation
Bianchi, Francesco, Sydney C. Ludvigson, and Sai Ma. 2022. "Belief Distortions and Macroeconomic Fluctuations." American Economic Review, 112 (7): 2269-2315. DOI: 10.1257/aer.20201713Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- E23 Macroeconomics: Production
- E27 Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications
- E31 Price Level; Inflation; Deflation
- E32 Business Fluctuations; Cycles
- E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications