private agents' learning process, determines the speed at which the economy converges to the rational expectation equilibrium … convergence. I assess the relevance of the transition period from the learning to the rational expectations equilibrium when …

Most studies of optimal monetary policy under learning rely on optimality conditions derived for the case when agents …, and makes active use of, the learning algorithm agents follow in forming their expectations. In this setup, monetary … policy can influence future expectations through its e ect on learning dynamics, introducing an additional tradeo between …

We derive optimal monetary policy in a sticky price model when private agents follow adaptive learning. We show that … order to facilitate private sector learning and thus ease the future intratemporal inflation-output gap trade-offs. The … policy recommendation is robust: the welfare loss entailed by optimal policy under learning if the private sector actually …

In this study we consider a linear model with forecast feedback in which boundedly rational agents are learning the … parameter values of the rational expectations equilibrium by the OLS learning procedure. We show strong consistency of the OLS … boundedly rational learning approach to models including non-stationary time series, like processes with polynomial trends or …

estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic …

macroeconomic models in which agents are boundedly rational and use an adaptive learning rule to form expectations of the endogenous … estimate it empirically. Two prominent learning algorithms are considered, namely constant gain and decreasing gain learning …. For each of the two learning rules, the analysis proceeds in two stages. First, the paper derives the asymptotic …

Adaptive learning under constant-gain allows persistent deviations of beliefs from equilibrium so as to more … learning estimates for a general class of dynamic macroeconomic models under constant-gain learning. Our approximation provides … renewed convergence conditions that depend on the learning gain and the model's structural parameters. We validate the …