| Abstract: |
We study the design of optimal monetary policy under uncertainty in a dynamic
stochastic general equilibrium model. We use a Markov jump-linear-quadratic (MJLQ)
approach to study policy design, proxying the uncertainty by different discrete modes
in a Markov chain, and by taking mode-dependent linear-quadratic approximations of
the underlying model. This allows us to apply a powerful methodology with convenient
solution algorithms that we have developed. We apply our methods to a benchmark
new-Keynesian model, analyzing how policy is affected by uncertainty, and how
learning and active testing affect policy and losses. |
Banco Central de Chile