Belief Distortions and Macroeconomic Fluctuations

This paper combines a data-rich environment with 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, all respondent-types overweighting the implicit judgmental component their forecasts relative what can be learned from publicly available information. Forecasts inflation and GDP growth oscillate between optimism pessimism by large margins, belief evolving dynamically response cyclical shocks. The results suggest that artificial intelligence algorithms productively deployed correct human judgment improve predictive accuracy. (JEL C45, D83, E23, E27, E31, E32, E37)