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This paper investigates the time-varying impacts of internationalmacroeconomic uncertainty shocks. We use a global vector autoregressivespecification with drifting coefficients and factor stochastic volatility inthe errors to model six economies jointly. The measure of uncertainty isconstructed endogenously by estimating a scalar driving the innovationvariances of the latent factors, which is also included in the mean of theprocess. To achieve regularization, we use Bayesian techniques for estimation,and introduce a set of hierarchical global-local priors. The adopted priorscenter the model on a constant parameter specification with homoscedasticerrors, but allow for time-variation if suggested by likelihood information.Moreover, we assume coefficients across economies to be similar, but providesufficient flexibility via the hierarchical prior for country-specificidiosyncrasies. The results point towards pronounced real and financial effectsof uncertainty shocks in all countries, with differences across economies andover time.

Measuring international uncertainty using global vector autoregressions with drifting parameters