Drawing upon recent contributions in the statistical literature, the authors present new results on the convergence of recursive, stochastic algorithms which can be applied to economic models with learning and which generalize previous results. The formal results provide probability bounds for convergence which can be used to describe the local stability under learning of rational expectations equilibria in stochastic models. Economic examples include local stability in a multivariate linear model with multiple equilibria and global convergence in a model with a unique equilibrium. Copyright 1998 by The Review of Economic Studies Limited.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.