LEARNING ABOUT REGIME CHANGE*

• 成果类型: Article
• 署名作者: Foerster, Andrew; Matthes, Christian
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - San Francisco; Indiana University System; Indiana University Bloomington
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12585
• 发表日期: 2022
• 页码: 1829-1859
• 关键词: expectations; models; debt; time

摘要

Total factor productivity (TFP) and investment specific technology (IST) growth both exhibit regimeswitching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We develop a general perturbation solution algorithm for a wide class of models with unobserved regime-switching. Using our method, we show learning about regime-switching fits the data, affect the responses to regime shifts and intraregime shocks, increase asymmetries in the responses, generate forecast error bias even with rational agents, and raise the welfare cost of fluctuations.