A Unified Model of Learning to Forecast

• 成果类型: Article
• 署名作者: Evans, George W.; Gibbs, Christopher G.; Mcgough, Bruce
• 署名单位: University of Oregon; University of St Andrews; University of Sydney
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MACROECONOMICS
• ISSN/ISSBN: 1945-7707
• DOI: 10.1257/mac.20220205
• 发表日期: 2025
• 页码: 101-133
• 关键词: rational-expectations; guessing games; interest-rates; rationalizability; environment; equilibria; STABILITY; DYNAMICS; feedback; BEHAVIOR

摘要

We propose a model of boundedly rational and heterogeneous expectations that unifies adaptive learning, k-level reasoning, and replicator dynamics. Level-0 forecasts evolve over time via adaptive learning. Agents revise over time their depth of reasoning in response to forecast errors, observed and counterfactual. The unified model makes sharp predictions for when and how quickly markets converge in Learning-to-Forecast Experiments, including novel predictions for individual and market behavior in response to announced events. We present experimental results that support these predictions. We apply our unified approach in the New Keynesian model to study forward guidance policy. (JEL D83, D84, E12, E31, E32, E71)