Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility
Econometrics
2022-06-20 v1
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.
Cite
@article{arxiv.2206.08438,
title = {Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility},
author = {Joshua C. C. Chan and Xuewen Yu},
journal= {arXiv preprint arXiv:2206.08438},
year = {2022}
}