This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high-dimensional macroeconomic learning problems, which entail parameter, model, and state...

Parameter learning strongly amplifies the impact of macro shocks on marginal utility when the representative agent has a preference for early resolution of uncertainty. This occurs as rational belief updating generates subjective long-run consumption risks. We consider general equilibrium models...

Recent evidence suggests that younger people update beliefs more in response to aggregate shocks than older people. We embed this generational learning bias in an equilibrium model where agents have recursive preferences and are uncertain about exogenous aggregate dynamics. The departure from...

Parameter learning strongly amplifies the impact of macro shocks on marginal utility when the representative agent has a preference for early resolution of uncertainty. This occurs as rational belief updating generates subjective long-run consumption risks. We consider general equilibrium models...

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...