Dynamic Hierarchical Factor Models
提出多层级因子模型，区分板块冲击与共同冲击，并用MCMC算法估计，在445个美国实际经济活动序列的四层模型中展示了板块变异的重要性。
Abstract This paper uses multilevel factor models to characterize within- and between-block variations as well as idiosyncratic noise in large dynamic panels. Block-level shocks are distinguished from genuinely common shocks, and the estimated block-level factors are easy to interpret. The framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks. The model is estimated using an MCMC algorithm that takes into account the hierarchical structure of the factors. The importance of block-level variations is illustrated in a four-level model estimated on a panel of 445 series related to different categories of real activity in the United States.