We consider a prototypical representative-agent forward-looking model, and study the low frequency variability of the data when the agent's beliefs about the model are updated through linear learning algorithms. We find that learning in this context can generate strong persistence. The degree of...

This paper studies the properties of multi-step projections, and forecasts that are obtained using either iterated or direct methods. The models considered are local asymptotic: they allow for a near unit root and a local to zero drift. We treat short, intermediate and long term forecasting by...