Monetary policy rules and economic stability when agents must learn

In most economic models used for theoretical exploration or policy analysis, there is a crucial role for agents' expectations about future outcomes. Generally, it is assumed that economic agents take their decisions according to rationality principles and that they have a fairly accurate knowledge about the economic environment. In other words, they are assumed to know the model of the economy (Rational Expectations Hypothesis). The latter assumption is somewhat extreme, given the evident lack of agreement, even among professionals, about the correct model of the economy. In this thesis I maintain the hypothesis that agents take their decisions rationally, i. e. in order to maximize their utilities given their budget constraints, but I assume that each agent has to learn about the economic environment. More specifically, I consider economic models for monetary policy analysis. The goal is to study how the introduction of learning in these models can affect the design of monetary policy. Policy recommendations that might be sound under Rational Expectations, might lead to disastrous results under learning. I also use learning as a selection device. Some economic models fail to predict a unique Rational Expectations Equilibrium. Nevertheless, a REE is a sensible prediction of the model only if it can be shown that it is the result of some learning process of the economic agents. REE that are unstable under learning are not plausible equilibria.