Robustly Optimal Monetary Policy with Near Rational Expectations

The paper considers optimal monetary stabilization policy in a forward-looking model, when the central bank recognizes that private-sector expectations need not be precisely model-consistent, and wishes to choose a policy that will be as good as possible in the case of any beliefs that are close enough to model-consistency. The proposed method offers a way of avoiding the assumption that the central bank can count on private-sector expectations coinciding precisely with whatever it plans to do, while at the same time also avoiding the equally unpalatable assumption that the central bank can precisely model private-sector learning and optimize in reliance upon a precise law of motion for expectations.
The main qualitative conclusions of the rational-expectations analysis of optimal policy carry over to the weaker assumption of near-rational expectations. It is found that commitment continues to be important for optimal policy, that the optimal long-run inflation target is unaffected by the degree of potential distortion of beliefs, and that optimal policy is even more history-dependent than if rational expectations are assumed.