Simplicity Versus Optimality : The Choice of Monetary Policy Rules When Agents Must Learn

The monetary policy rules that are widely discussed--notably the Taylor rule--are remarkable for their simplicity. One reason for the apparent preference for simple ad hoc rules over optimal rules might be the assumption of full information maintained in the computation of an optimal rule. Arguably this makes optimal control rules less robust to model specification errors. In this paper, we drop the full-information assumption and investigate the choice of policy rules when agents must learn the rule that is in use. To do this, we conduct stochastic simulations on a small, estimated forward-looking model, with agents following a strategy of least-squares learning or discounted least-squares learning. We find that the costs of learning a new rule can, under some circumstances, be substantial. These circumstances vary with the preferences of the monetary authority and with the rule initially in place. Policymakers with strong preferences for inflation control must incur substantial costs when they change the rule; but they are nearly always willing to bear those costs. Policymakers with weak preferences for inflation control, on the other hand, may actually benefit from agents' prior belief that a strong rule is in place