News-driven uncertainty fluctuations - Dongho Song; Jenny Tang (Federal Reserve Bank of Boston, 2018)
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.
Keywords: C11; G12; E32; E37; 330; Bayesian learning; discrete environment; Minsky moment; news shocks; recursive utility; risk premium; survey forecasts; uncertainty