Subspace shrinkage in conjugate Bayesian vector autoregressions
提出一种共轭贝叶斯VAR模型，其子空间收缩先验将大VAR与因子模型结合，自动估计收缩强度和因子数量，模拟中成功检测因子数，并用美国宏观数据提升预测效果。
Summary Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical properties of our prior, we show that it successfully detects the number of factors in simulations and that it leads to forecast improvements using US macroeconomic data.