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

Joshua Chan and Xuewen Yu ()

Journal of Economic Dynamics and Control, 2022, vol. 143, issue C

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.

Keywords: Large vector autoregression; Stochastic volatility; Variational Bayes; Volatility network; Connectedness (search for similar items in EconPapers)
JEL-codes: C11 C32 C55 G21 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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Related works:
Working Paper: 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:eee:dyncon:v:143:y:2022:i:c:s0165188922002093

DOI: 10.1016/j.jedc.2022.104505

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