A recent analysis by the Federal Reserve indicates that consumer sentiment and the tone of news can predict recessions similarly to traditional economic data. The study, released on July 17, was conducted by economists from the Federal Reserve Bank of San Francisco, including Nicolas Petrosky-Nadeau, Yeji Sung, and Daniel J. Wilson.
The researchers explored whether soft data, such as consumer sentiment and news tone, could serve as effective indicators of economic downturns. Their findings suggest that sentiment models can outperform traditional hard data in predicting recessions one month ahead. While these models may generate more false alarms, they also flagged a greater number of months leading into past recessions.
The study emphasizes that soft data should complement hard statistics rather than replace them, as both types of information provide valuable insights into recession risks. The analysis spanned from August 1999 to May 2026, covering three recessions and utilizing various sentiment inputs.
For those in Melissa, Texas, trying to gauge economic trends, this research offers reassurance that collective sentiment is a significant factor in understanding potential downturns. However, the authors note that the paper reflects their views and not the official stance of the Federal Reserve.
