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Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs

Martin Feldkircher, Florian Huber, Gary Koop, Michael Pfarrhofer

arXiv 8 Mar 2021 · Econometrics

arXiv:2103.04944 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that domestic information is often more important than international information and group the coefficients accordingly. Fast approximations are used to estimate the latter while the former are estimated with precision using Markov chain Monte Carlo techniques. We illustrate, using a huge model of the world economy, that it produces competitive forecasts quickly.

Citation extraction

43
references
69
in-text mentions
44
distinct cited
1
self-citations
17,433
main-text words

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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
1Van den Boom et al (2021) Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation1.00073100%
2Makalic and Schmidt (2016) A simple sampler for the horseshoe estimator0.84333100%
3Canova and Ciccarelli (2009) Estimating multicountry VAR models0.73732100%
4Feldkircher and Huber (2016) The international transmission of US shocks – Evidence from Bayesian global vector autoregressions0.64422100%
5Bhattacharya et al (2015) Dirichlet–Laplace priors for optimal shrinkage0.64422100%
6Canova and Ciccarelli (2016) Panel Vector Autoregressive models: A survey0.64422100%
7Diebold and Yilmaz (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets0.64422100%
8Carvalho et al (2010) The horseshoe estimator for sparse signals0.64422100%
9Korobilis (2021) High-dimensional macroeconomic forecasting using message passing algorithms0.64422100%
10Park and Casella (2008) The Bayesian Lasso0.64422100%

Showing the top 10 of 44 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
1Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model0.40511
2Bayesian Shrinkage in High-Dimensional VAR Models: A Comparative Study0.40511