# Measuring international uncertainty using global vector autoregressions with drifting parameters

## TL;DR

This paper develops a Bayesian global VAR model with drifting parameters and stochastic volatility to measure and analyze the time-varying effects of international macroeconomic uncertainty shocks across six economies.

## Contribution

It introduces a novel Bayesian hierarchical GVAR framework with drifting coefficients and endogenous uncertainty measures, capturing cross-country heterogeneity and time variation.

## Key findings

- Uncertainty shocks have significant real and financial impacts in all countries.
- Effects of shocks vary across economies and over time.
- The model effectively captures time-varying international macroeconomic uncertainty.

## Abstract

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.