We derive the optimal monetary policy in a sticky price model when private agents follow adaptive learning. We show that this slight departure from rationality has important implications for policy design. The central bank faces a new intertemporal trade-off, not present under rational...

A finite number of sellers (n) compete in schedules to supply an elastic demand. The costs of the sellers have uncertain common and private value components and there is no exogenous noise in the system. A Bayesian supply function equilibrium is characterized; the equilibrium is privately...

A Bayesian supply function equilibrium is characterized in a market where firms have private information about their uncertain costs. It is found that with supply function competition, and in contrast to Bayesian Cournot competition, competitiveness is affected by the parameters of the...

This paper shows that price rigidity evolves in an economy populated by imperfectly rational agents who experiment with alternative rules of thumb. In the model, firms must set their prices in face of aggregate demand shocks. Their payoff depends on the level of aggregate demand, as well as on...

We introduce the E-correspondence principle for stochastic dynamic expectations models as a tool for comparative dynamics analysis. The principle is applicable to equilibria that are stable under least squares and closely related learning rules. With this technique it is possible to study,...

This paper performs a welfare analysis of economies with private information when public information is endogenously generated and agents can condition on noisy public statistics in the rational expectations tradition. Equilibrium is not (restricted) efficient even when feasible allocations...

This paper provides novel evidence on exchange rate expectations of both chartists and fundamentalists separately. These groups indeed form expectations differently. Chartists change their expectations more often; however, all professionals’ expectations vary considerably as they generally...