The Identifying Information in Vector Autoregressions with Time-Varying Volatilities: An Application to Endogenous Uncertainty

Abstract

We develop a structural vector autoregression with stochastic volatility in which one of the variables can impact both the mean and the variance of the other variables. We provide conditional posterior distributions for this model, develop an MCMC algorithm for estimation, and show how stochastic volatility can be used to provide useful restrictions for the identiÖcation of structural shocks. We then use the model with US data to show that some variables have a signiÖcant contemporaneous feedback e§ect on macroeconomic uncertainty, and overlooking this channel can lead to distortions in the estimated e§ects of uncertainty on the economy.