A recent study from the Federal Reserve Bank of San Francisco suggests that consumer sentiment and the tone of news coverage can be as effective in predicting recessions as traditional economic data. The study, released on July 17, was conducted by economists including Nicolas Petrosky-Nadeau, Yeji Sung, and Daniel J. Wilson.
The researchers explored whether "soft" data, such as consumer sentiment and economic-policy uncertainty, could forecast recessions as reliably as "hard" statistics like jobs and prices. Their findings indicate that sentiment models can sometimes outperform hard data models, particularly in identifying recession risks one month in advance. However, these sentiment models also tended to produce more false alarms.
The authors clarified that their study does not suggest that sentiment is superior to hard data. Instead, they emphasize that both types of information work best when used together. The research utilized various sentiment indicators, including consumer surveys and an economic-policy uncertainty index, to assess recession risks from August 1999 to May 2026, a period that encompasses three recessions.
For individuals and businesses trying to gauge economic trends, this study provides a nuanced perspective on the importance of collective sentiment. However, the authors noted that the paper reflects their views and does not represent the Federal Reserve's official stance.
