Optimal monetary policy when agents are learning

Earlier research on optimal monetary policy under learning uses optimality conditions derived under rational expectations. In this paper instead, we derive optimal monetary policy when the central bank knows the algorithm followed by agents to form their expectations and makes active use of the learning behavior. There is a well known intratemporal tradeoff between inflation and output gap stabilization. We show there is also an intertemporal tradeoff generated by the central banks possibility to influence future expectations. The optimal interest rate rule reacts more aggressively to out-of-equilibrium inflation expectations than what would be optimal under rational expectations, as the central bank exploits its possibility to ”drive” future expectations closer to equilibrium. Moreover, if beliefs are updated according to recursive least squares, the optimal policy is time-varying.