Heterogeneous Beliefs and Learning in Forward Looking Economic Models

We study a class of forward looking economic models with heterogeneous agents in a bounded rationality setting. The agents employ the same recursive learning rule to update beliefs but are characterized by different memory parameters. The peculiarity of the learning mechanism is that the learning rate is not vanishing in the limit. Differently from what is obtained in the case of a vanishing learning step, i.e., the stability conditions in the heterogeneous agents case are those of the representative agent model, we show that heterogeneity matters for the expectational stability of a stationary perfect foresight equilibrium and that the stability parameter restrictions with heterogeneous agents are stronger than in the case of homogeneous agents