Optimal Constrained Interest-rate Rules

We show that if policy-makers compute the optimal unconstrained interest-rate rule within a Taylor-type class, they may be led to rules that generate indeterminacy and/or instability under learning. This problem is compounded by uncertainty about structural parameters since an optimal rule that is determinate and stable under learning for one calibration may be indeterminate or unstable under learning under a different calibration. We advocate a procedure in which policymakers restrict attention to rules constrained to lie in the determinate learnable region for all plausible calibrations, and that minimize the expected loss, computed using structural parameter priors, subject to this constraint.