Optimal monetary policy in a micro-founded model with parameter uncertainty

"In this paper, we structurally model uncertainty with a micro-founded model, and investigate its implications for optimal monetary policy. Uncertainty about deep parameters of the model implies that the central bank simultaneously faces both uncertainty about the structural dynamic equations and about the social loss function. Considering both uncertainties with cross-parameter restrictions based on the micro-foundations of the model, we use Bayesian methods to determine the optimal monetary policy that minimizes the expected loss. Our analysis shows how uncertainty can lead the central bank to pursue a more aggressive monetary policy, overturning Brainard's common wisdom. As the degree of uncertainty about inflation dynamics increases, the central bank should place much more weight on price stability, and should respond to shocks more aggressively. In addition, when the central bank is uncertain about output dynamics, an aggressive policy response can be justified by the positive correlation between policy multiplier and transmission of natural rate of interest shock as well as the effect of loss-function uncertainty. We also show that combining a more aggressive policy response with a highly inertial interest rate policy reduces Bayesian risk"--Federal Reserve Board web site.