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This paper addresses whether non-uniqueness of equilibrium is a substantive problem for the analysis of fiscal policy in New-Keynesian (NK) models at the zero lower bound (ZLB). There would be a substantive problem if there were no compelling way to select among different equilibria that give different answers to critical policy questions. We argue that learnability provides such a criterion. We study a fully non-linear NK model with Calvo pricing frictions. Our main finding is that the model we analyze has a unique E-stable rational expectations equilibrium at the ZLB. That equilibrium is also stable-under-learning and inherits all of the key properties of linearized NK models for fiscal policy.

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This paper investigates what features of an economy determine whether convergence under learning is fast or slow. In all of the models that we consider, people's beliefs about model outcomes are central determinants of those outcomes. We argue that under certain circumstances, convergence of a learning equilibrium to the rational expectations equilibrium can be so slow that policy analysis based on rational expectations is very misleading. We also develop new analytic results regarding rates of convergence in learning models.