Learning and Model Validation

• 成果类型: Article
• 署名作者: Cho, In-Koo; Kasa, Kenneth
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Hanyang University; Simon Fraser University
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdu026
• 发表日期: 2015
• 页码: 45-82
• 关键词: rational-expectations; large deviations; uncertainty; CONVERGENCE; BEHAVIOR; output

摘要

This paper studies adaptive learning with multiple models. An agent operating in a self-referential environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric specification test. If the current model passes the test, it is used to construct an optimal policy. If it fails the test, a new model is selected. As the rate of coefficient updating decreases, one model becomes dominant, and is used almost always. Dominant models can be characterized using the tools of large deviations theory. The analysis is used to address two questions posed by Sargent's Phillips Curve model.