Desirability of Nominal GDP Targeting under Adaptive Learning
在微观基础模型中，用递归学习稳定性评估名义GDP目标制，检验理性预期假设是否成立，对研究货币政策规则的经济学者有参考价值。
Nominal GDP targeting has been advocated by a number of authors since it produces relative stability of inflation and output. However, all of the papers assume rational expectations on the part of private agents. In this paper I provide an analysis of this assumption. I use stability under recursive learning as a criterion for evaluating nominal GDP targeting in the context of amodel with explicit micro-foundations which is currently the workhorse for the analysis of monetary policy.