Gresham's Law of Model Averaging

• 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 Urbana-Champaign; Hanyang University; Federal Reserve System - USA; Simon Fraser University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20160665
• 发表日期: 2017
• 页码: 3589-3616
• 关键词: large deviations; Nash equilibrium; stock-market; long-run; uncertainty; volatility; forecasts

摘要

A decision maker doubts the stationarity of his environment. In response, he uses two models, one with time-varying parameters, and another with constant parameters. Forecasts are then based on a Bayesian model averaging strategy, which mixes forecasts from the two models. In reality, structural parameters are constant, but the (unknown) true model features expectational feedback, which the reduced-form models neglect. This feedback permits fears of parameter instability to become self-confirming. Within the context of a standard asset-pricing model, we use the tools of large deviations theory to show that even though the constant parameter model would converge to the rational expectations equilibrium if considered in isolation, the mere presence of an unstable alternative drives it out of consideration.