Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure
提出一类大型贝叶斯向量自回归模型，允许非高斯、异方差和序列相关的扰动项，利用克罗内克结构简化计算，在20个宏观变量应用中优于标准模型。
We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic, and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.