This paper studies the asset pricing implications of Bayesian learning about the parameters, states, and models determining aggregate consumption dynamics. Our approach is empirical and focuses on the quantitative implications of learning in real-time using post World War II consumption data. We characterize this learning process and provide empirical evidence that revisions in beliefs stemming from parameter and model uncertainty are significantly related to aggregate equity returns. Further, we show that our agent's beliefs regarding the conditional moments of consumption growth are strongly time-varying and exhibit business cycle and/or long-run fluctuations. Much of the long-run behavior is unanticipated ex ante, which implies that about half of the post World-War II observed equity market risk premium and much of the observed return predictability are due to unexpected revisions in beliefs about parameters and models governing consumption dynamics.