ON THE LOCAL STABILITY OF SUNSPOT EQUILIBRIA UNDER ADAPTIVE LEARNING RULES

We examine local stability under learning of stationary Markov sunspot equilibria (SSEs) in a simple dynamic nonlinear model. Necessary and sufficient conditions for local convergence of a recursive learning algorithm to SSEs are shown to be given (generically) by expectational stability (E-stability) conditions. We distinguish between weak and strong E-stability, where the latter requires stability also with respect to overparametrizations of the sunspot solution. Economic applications are given based on the overlapping generations model.