Learning with bounded memory in stochastic models

Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to a rational expectations equilibrium (REE). The properties of dynamics arising from such rules are studied for,standard models with steady states. If the REE in linear-models is in a certain sense expectationally stable (E-stable), then the dynamics are asymptotically stationary and forecasts are unbiased, but the economy has excess volatility. We also provide similar local results for a class of nonlinear models with small noise. (C) 2002 Elsevier Science B.V. All rights reserved.