Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility

Joshua Chan and Xuewen Yu ()

Papers from arXiv.org

Abstract: We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness.

Date: 2022-06
New Economics Papers: this item is included in nep-dem
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Related works:
Journal Article: Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility (2022)
Working Paper: Fast and accurate variational inference for large Bayesian VARs with stochastic volatility (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2206.08438

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