Measuring international uncertainty using global vector autoregressions with drifting parameters
Michael Pfarrhofer ()
Papers from arXiv.org
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.
Date: 2019-08, Revised 2019-12
New Economics Papers: this item is included in nep-ecm and nep-int
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1908.06325
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