Belief Distortions and Macroeconomic Fluctuations

Abstract

This paper combines a data rich environment with a machine learning algorithm to provide estimates of time-varying systematic expectational errors (belief distortions) about the macroeconomy embedded in survey responses. We find that such distortions are large on average even for professional forecasters, with all respondent-types over-weighting their own forecast relative to other information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by quantitatively large amounts. To investigate the dynamic relation of belief distortions with the macroeconomy, we construct indexes of aggregate (across surveys and respondents) expectational biases in survey forecasts. Over-optimism is associated with an increase in aggregate economic activity. Our estimates provide a benchmark to evaluate theories for which information capacity constraints, extrapolation, sentiments, ambiguity aversion, and other departures from full information rational expectations play a role in business cycles.