Belief Distortions and Macroeconomic Fluctuations
结合大数据与机器学习算法，估计调查数据中随时间变化的系统性预期误差（信念扭曲），发现专业预测者也会高估主观判断成分，通胀和GDP增长预测在乐观与悲观间大幅摆动，AI可纠正人类判断错误。
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.