Introduction to the special issue: New approaches to learning in macroeconomic models

The research questions addressed by the literature on learning in macroeconomics can be classified into four categories: First, there are issues related to the convergence and stability under learning in models with unique rational expectations equilibria. Authors here are concerned mainly with the learnability of a rational expectations equilibrium, as a measure of that equilibrium's plausibility as an observed outcome in an actual economy. Second, there are issues related to convergence and stability under learning in models with multiple rational expectations equilibria. In this case, learnability serves as an equilibrium selection device, helping economists decide which equilibria are the more likely to be actually observed among the many that exist under rational expectations. A third set of issues involves the examination of transitional dynamics that accompanies the equilibrium selection process. Following some type of unexpected strcutural change or change in policy regime, for instance, economies necessarily must follow temporary transitional paths to a rational expectations equilibrium associated with the new reality. Learning is sometimes used to help model such transitional dynamics. Finally, there are issues related to the examination of learning dynamics that are intrinsically different, even asymptotically, from the dynamics of the rational expectations versions of the models. In these cases, the learning dynamics do not converge to the rational expectations fixed points, and (unexploitable) expectational errors persist indefinitely. Some authors have tried to make use of this possibility in order to build explanations of otherwise puzzling macroeconomic phenomena based on constantly changing expectations. © 2001, Cambridge University Press. All rights reserved.