This chapter provides a survey of the recent work on learning in the context of macroeconomics. Learning has several roles. First, it provides a boundedly rational model of how rational expectations can be achieved. Secondly, learning acts as a selection device in models with multiple REE...

We study the impact of adaptive learning for the design of a robust monetary policy using a small open-economy New Keynesian model. We find that slightly departing from rational expectations substantially changes the way the central bank deals with model misspecification. Learning induces an...

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the...

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...

Under the assumption of bounded rationality, economic agents learn from their past mistaken predictions by combining new and old information to form new beliefs. The purpose of this paper is to examine how the policy-maker, by affecting private agents' learning process, determines the speed at...

A counter example to the Grossman and Stiglitz (1980) finding of the impossibility of informationally efficient markets is produced using discrete choice dynamics to govern the evolution of the trader population that choose between being informed or uninformed based on past performance.

We provide conditions for local stability and instability of an equilibrium point in certain systems of nonautonomous nonstochastic difference equations. In the systems under study the influence of time is present through a positive scalar "gain" parameter which converges in the limit to zero....

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...

In this article, we investigate the possibility of sunspot equilibria to emerge from a process of learning and adaptation on agents’ beliefs. We consider both finite state Markov sunspots and sunspots in autoregressive form, and derive conditions for the existence of an heterogeneous...

In a model of incomplete, heterogeneous information, with externalities and strategic interactions, we analyze the possibility for learning to act as coordination device. We build on the framework proposed by Angeletos and Pavan (2007) and extend it to a dynamic multiperiod setting where agents...