Escape Dynamics in Learning Models

• 成果类型: Article
• 署名作者: Williams, Noah
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdy033
• 发表日期: 2019
• 页码: 882-912
• 关键词: large deviations; CONVERGENCE; volatility

摘要

This article illustrates and characterizes how adaptive learning can lead to recurrent large fluctuations. Learning models have typically focused on the convergence of beliefs towards an equilibrium. However in stochastic environments, there may be rare but recurrent episodes where shocks cause beliefs to escape from the equilibrium, generating large movements in observed outcomes. I characterize the escape dynamics by drawing on the theory of large deviations, developing new results which make this theory directly applicable in a class of learning models. The likelihood, frequency, and most likely direction of escapes are all characterized by a deterministic control problem. I illustrate my results with two simple examples.