We study how the use of judgement or “add-factors” in macroeconomic forecasting may disturb the set of equilibrium outcomes when agents learn using recursive methods. We isolate conditions under which new phenomena, which we call exuberance equilibria, can exist in standard macroeconomic...

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both di1er from and are related to E-stability, which governs stability under least squares learning. SG...

A fundamentals based monetary policy rule, which would be optimal when private agents have perfectly rational expectations, is unstable if in fact these agents follow standard adaptive learning rules. This problem can be overcome if private expectations are observed and suitably incorporated...

We consider the effects of replacing least squares learning by stochastic gradient learning in the multivariate "Cobweb" model. Are the stability conditions altered? For this model, we show global convergence of stochastic gradient learning to the unique rational expectations equilibrium...

We consider the impact of anticipated policy changes when agents form expectations using adaptive learning rather than rational expectations. To model this we assume that agents combine limited structural knowledge with a standard adaptive learning rule. We analyze these issues using two...

We examine global economic dynamics under learning in a New Keynesian model in which the interest-rate rule is subject to the zero lower bound. Under normal monetary and fiscal policy, the intended steady state is locally but not globally stable. Large pessimistic shocks to expectations can lead...

We consider an endogenous growth model which includes international trade in capital goods. The model yields several distinct balanced growth solutions which can be classified using stability under adaptive learning. Some of the equilibria can involve growth rates much higher (or lower) than others.