Learning about the Long Run

• 成果类型: Article
• 署名作者: Farmer, Leland E.; Nakamura, Emi; Steinsson, Jon
• 署名单位: University of Virginia; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF POLITICAL ECONOMY
• ISSN/ISSBN: 0022-3808
• DOI: 10.1086/730207
• 发表日期: 2024
• 页码: 3334-3377
• 关键词: term structure; expectations hypothesis; interest-rates; INFORMATION; RISK; inflation; BEHAVIOR; MODEL; predictability; cointegration

摘要

Forecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia.