论文标题
比较大贝叶斯var的随机波动率规格
Comparing Stochastic Volatility Specifications for Large Bayesian VARs
论文作者
论文摘要
具有各种形式的随机波动率的大型贝叶斯矢量自动加入在经验宏观经济学中变得越来越流行。从业者的主要困难是为其特定应用选择最合适的随机波动率规范。我们开发了贝叶斯模型比较方法 - 基于结合条件蒙特卡洛和自适应重要性采样的边际似然估计器，以在各种随机波动率规格中进行选择。所提出的方法还可以用于在VAR系数上选择适当的收缩先验，这是避免在高维设置中过度拟合的关键组件。使用US的季度数据，我们发现Cholesky随机波动率和因子随机波动率都优于常见的随机波动率规范。但是，它们的出色表现主要归因于可容纳跨变量收缩的更灵活的先验。
Large Bayesian vector autoregressions with various forms of stochastic volatility have become increasingly popular in empirical macroeconomics. One main difficulty for practitioners is to choose the most suitable stochastic volatility specification for their particular application. We develop Bayesian model comparison methods -- based on marginal likelihood estimators that combine conditional Monte Carlo and adaptive importance sampling -- to choose among a variety of stochastic volatility specifications. The proposed methods can also be used to select an appropriate shrinkage prior on the VAR coefficients, which is a critical component for avoiding over-fitting in high-dimensional settings. Using US quarterly data of different dimensions, we find that both the Cholesky stochastic volatility and factor stochastic volatility outperform the common stochastic volatility specification. Their superior performance, however, can mostly be attributed to the more flexible priors that accommodate cross-variable shrinkage.
