Document Type
Working Paper
Publication Date
6-5-2026
Abstract
This paper uses an adaptive learning framework to study FOMC forecasts from the Summary of Economic Projections (SEP) dataset. FOMC expectations are modeled as the sum of two components: (1) an endogenous learning part and (2) a sentiment part capturing waves of optimism and/or pessimism. The results include key policy takeaways. FOMC forecasts are responsive to incoming macroeconomic information, consistent with adaptive learning, while sentiment is persistent, correlated across GDP growth and inflation forecasts, and becomes quantitatively more important during and around recessions. FOMC participants also rely more on their endogenous/learning model to form expectations, but sentiment plays a larger role during and around recessions. Finally, the model-implied sentiment measure is positively and significantly correlated with an external measure of FOMC sentiment and remains robust across alternative forecasting specifications.
Recommended Citation
Cole, Stephen J., "FOMC Forecasts, Constant-Gain Learning, and Optimism/Pessimism" (2026). Economics Working Papers. 108.
https://epublications.marquette.edu/econ_workingpapers/108
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