  This paper investigates the time-varying impacts of international
macroeconomic uncertainty shocks. We use a global vector autoregressive
specification with drifting coefficients and factor stochastic volatility in
the errors to model six economies jointly. The measure of uncertainty is
constructed endogenously by estimating a scalar driving the innovation
variances of the latent factors, which is also included in the mean of the
process. To achieve regularization, we use Bayesian techniques for estimation,
and introduce a set of hierarchical global-local priors. The adopted priors
center the model on a constant parameter specification with homoscedastic
errors, but allow for time-variation if suggested by likelihood information.
Moreover, we assume coefficients across economies to be similar, but provide
sufficient flexibility via the hierarchical prior for country-specific
idiosyncrasies. The results point towards pronounced real and financial effects
of uncertainty shocks in all countries, with differences across economies and
over time.
