Dynamic Hierarchical Factor Models

This paper uses multi-level 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. A four-level model is estimated to study block- and aggregate-level dynamics in a panel of 445 series related to different categories of real activity in the United States. The model illustrates the importance of block-level variations in the data