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Federal Reserve Bank of Boston
Working Papers
News-driven uncertainty fluctuations
Dongho Song
Jenny Tang
Abstract

We embed a news shock, a noisy indicator of the future state, in a two-state Markov-switching 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.


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Dongho Song & Jenny Tang, News-driven uncertainty fluctuations, Federal Reserve Bank of Boston, Working Papers 18-3, 01 Jan 2018.
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Keywords: Bayesian learning; discrete environment; Minsky moment; news shocks; recursive utility; risk premium; survey forecasts; uncertainty
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