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Measuring international uncertainty using global vector autoregressions with drifting parameters

Michael Pfarrhofer

arXiv 17 Aug 2019 · Econometrics · publishedMacroeconomic Dynamics (2022) · 4 citations (OpenAlex)

arXiv:1908.06325 · PDF · DOI · OpenAlex · Extracted main text

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.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Crespo Cuaresma J, Huber F, and Onorante L (2017) Fragility and the spillovers of international uncertainty shocks1.00053100%
2Mumtaz H, and Theodoridis K (2018) The Changing Transmission of Uncertainty Shocks in the U.S0.87452100%
3Mumtaz H, and Musso A (2019) The Evolving Impact of Global, Region-Specific, and Country-Specific Uncertainty0.87452100%
4Alessandri P, and Mumtaz H (2019) Financial regimes and uncertainty shocks0.81142100%
5Frühwirth-Schnatter S, and Wagner H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models0.81142100%
6Bloom N (2009) The Impact of Uncertainty Shocks0.81142100%
7Diebold FX, and Li C (2006) Forecasting the term structure of government bond yields0.73732100%
8Pesaran MH, Schuermann T, and Weiner SM (2004) Modeling regional interdependencies using a global error-correcting macroeconometric model0.73732100%
9Bitto A, and Frühwirth-Schnatter S (2019) Achieving shrinkage in a time-varying parameter model framework0.73732100%
10Carriero A, Clark TE, and Marcellino M (2018) b), Measuring Uncertainty and Its Impact on the Economy0.73732100%

Showing the top 10 of 55 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Integration or fragmentation? A closer look at euro area financial markets0.00011