We formalize the Keynesian insight that aggregate demand driven by sentiments can generate output ‡uctuations under rational expectations. When production decisions must be made under imperfect information about demand, optimal decisions based on sentiments can generate stochastic self-ful…lling rational expectations equilibria in standard economies without persistent informational frictions, externalities, non-convexities or strategic complementarities in production. The models we consider are deliberately simple, but could serve as benchmarks for more complicated equilibrium models with additional features.We construct a class of models to capture the Keynesian insight that employment and production decisions are based on expected consumer demand, and that realized aggregate demand follows …rms' production and employment decisions. Because of imperfect information in forecasting demand, consumer sentiments can matter in determining equilibrium aggregate supply. We cast the Keynesian insight in a simple dynamic stochastic general equilibrium model and characterize the rational expectations equilibria of this model. We …nd that despite the lack of any externalities orWe are indebted to George-2 equilibria of Aumann (1974, 1987) and of Maskin and Tirole (1987). 4 They emerge naturally from the endogenous signals that induce imperfectly correlated employment and output decisions by …rms. 5;6 In equilibrium there exists a distribution of sentiments such that for every realization of the sentiment shocks, the …rms' expected aggregate demand is equal to the realized aggregate demand, the consumer's expected aggregate income is equal to the realized aggregate output, and the expected prices and real wages are equal to the realized prices and real wages.The rest of the paper is organized as follows. Section 2 presents a simple benchmark model, de…nes rational expectations equilibrium, and then characterizes the fundamental equilibrium and the sentiment-driven equilibrium. In section 2.6, we provide a more abstract and streamlined model further illustrate the mechanisms behind our results. In section 2.7 we show that the fundamental equilibrium is not stable under constant gain learning, while sentiment-driven equilibrium is stable if the gain parameter is not too large. In section 3 we provide explicit microfoundations for the signal and information structures that we consider throughout the paper. Section 4 extends our analysis to other settings and Section 5 concludes.