Optimal Constrained Interest‐Rate Rules
指出，在泰勒规则类中计算最优无约束利率规则可能导致不确定性和学习不稳定性，且参数不确定性会加剧此问题。作者提出一种约束程序，确保规则在所有合理校准下确定且可学习，并最小化预期损失。
We show that if policymakers 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.