FOMC Forecasts, Constant-Gain Learning, and Optimism/Pessimism

Stephen Cole

No 2026-03, Working Papers and Research from Marquette University, Center for Global and Economic Studies and Department of Economics

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.

Keywords: summary of economic projections; FOMC; constant-gain learning; sentiment shocks; waves of optimism and pessimism; evolving beliefs; monetary policy (search for similar items in EconPapers)
JEL-codes: C52 D84 E50 E52 E58 E60 E70 E71 (search for similar items in EconPapers)
Date: 2026-06
New Economics Papers: this item is included in nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:mrq:wpaper:2026-03

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