This paper proposes a large-scale Bayesian vector autoregression with factor stochastic volatility to investigate the macroeconomic consequences of international uncertainty shocks in G7 countries. The curse of dimensionality is addressed by means of a global-local shrinkage prior that mimics certain features of the wellknown Minnesota prior, yet provides additional flexibility in terms of achieving shrinkage. The factor structure enables us to identify an international uncertainty shock by assuming that it is the joint volatility process that determines the dynamics of the variance-covariance matrix of the common factors. To allow for first and second moment shocks we, moreover, assume that the uncertainty factor enters the VAR equation as an additional regressor. Our findings suggest that the estimated uncertainty measure is strongly connected to global equity price volatility, closely tracking other prominent measures commonly adopted to assess uncertainty. The dynamic responses of a set of macroeconomic and financial variables show that an international uncertainty shock exerts large effects on all economies and variables under consideration.
Factor stochastic volatility vector autoregressive models global propagation of shocks global uncertainty