In this paper we perform an in—depth investigation of relative merits of two adaptive learning algorithms with constant gain, Recursive Least Squares (RLS) and Stochastic Gradient (SG), using the Phelps model of monetary policy as a testing ground. The behavior of the two learning...

We provide sufficient conditions and necessary conditions for stability of an economy under structural mixed recursive least squares/stochastic gradient heterogeneous learning of agents with possibly different degrees of inertia. We have found a unifying condition which is sufficient for...

While under recursive least squares learning the dynamics of the economy converges to rational expectations equilibria (REE) which are E–stable, some recent examples propose that E–stability is not a sufficient condition for learnability. In this paper, we provide some further...

We use a continuous-time approximation approach to analyze dynamics of a model where government adaptively learns the Phillips curve while running monetary policy (Phellps problem). This approach is based on approximating the discrete-time dynamics with learning by a limiting continuous-time...

The assumption of perfectly rational representative agents is now commonly questioned. This paper explores the equilibrium properties of boundedly rational heterogeneous agents. We combine an adaptive learning process in a modified cobweb model within a Stackleberg framework. We assume that...

This paper studies a game theoretic model where agents choose between two updating rules to predict a future endogenous variable. Agents rationally choose between these predictors based on relative performance. Conditions for evolutionary stability and stability under learning are found for the...

The monetary policy literature has recently devoted considerable attention to Taylor-type rules, in which the interest rate set by the central bank depends on measures of inflation and aggregate output. We show that if policy-makers attempt to choose the optimal rule within a Taylor-type...

We examine the dynamic properties of equilibrium stock returns in an incomplete information economy in which the agents need to learn the hidden state of the endowment process. We consider both the case of optimal Bayesian learning and suboptimal learning, including near-rational learning, over-...

Most studies of optimal monetary policy under learning rely on optimality conditions derived for the case when agents have rational expectations. In this paper, we derive optimal monetary policy in an economy where the Central Bank knows, and makes active use of, the learning algorithm agents...