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

Martin Feldkircher, Florian Huber, Gary Koop and Michael Pfarrhofer

Papers from arXiv.org

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.

Date: 2021-03, Revised 2022-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (7)

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Related works:
Journal Article: APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2103.04944

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