论文标题
具有随机波动率的大贝叶斯VAR的快速准确推断
Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility
论文作者
论文摘要
我们提出了在大贝叶斯var的背景下对数挥发性的联合后验分布的新变异近似。与基于本地近似值的现有方法相反，新提案提供了一个全球近似值，考虑了联合分布的全部支持。在一项蒙特卡洛（Monte Carlo）研究中，我们表明，与现有替代方案相比，新的全球近似值超过了一个数量级。我们用96个变量的VAR具有随机波动率来衡量全球银行网络连接性，以说明了提出的方法。
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.
