Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/202919 
Year of Publication: 
2018
Series/Report no.: 
Working Papers No. 18-3
Publisher: 
Federal Reserve Bank of Boston, Boston, MA
Abstract: 
We embed a news shock, a noisy indicator of the future state, in a two-state Markovswitching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a "Minsky moment" - a sudden collapse in asset values. The effect is greatly amplified when agents have a preference for early resolution of uncertainty. We leverage survey recession probability forecasts to solve a sequential learning problem and estimate the full posterior distribution of model primitives. We identify historical periods in which uncertainty and risk premia were elevated because of news shocks.
Subjects: 
Bayesian learning
discrete environment
Minsky moment
news shocks
recursive utility
risk premium
survey forecasts
uncertainty
JEL: 
C11
G12
E32
E37
Document Type: 
Working Paper

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