Expectations, Learning and Macroeconomic Persistence

This paper presents an estimated model with learning and provides evidence that learning can improve the fit of popular monetary DSGE models and endogenously generate realistic levels of persistence. The paper starts with an agnostic view, developing a model that nests learning and some of the structural sources of persistence, such as habit formation in consumption and inflation indexation, that are typically needed in monetary models with rational expectations to match the persistence of macroeconomic variables. I estimate the model by likelihood-based Bayesian methods, which allow the estimation of the learning gain coefficient jointly with the "deep" parameters of the economy. The empirical results show that when learning replaces rational expectations, the estimated degrees of habits and indexation drop near zero. This ?nding suggests that persistence arises in the model economy mainly from expectations and learning. The posterior model probabilities show that the specification with learning fits significantly better than does the specification with rational expectations. Finally, if learning rather than mechanical sources of persistence provides a more appropriate representation of the economy, the implied optimal policy will be different. The policymaker will also incur substantial costs from misspecifying private expectations formation.