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 a Markov chain Monte-Carlo algorithm that takes into account the hierarchical structure of the factors. We organize a panel of 447 series into blocks according to the timing of data releases and use a four-level model to study the dynamics of real activity at both the block and aggregate levels. While the effect of the economic downturn of 2007-09 is pervasive, growth cycles are synchronized only loosely across blocks. The state of the leading and the lagging sectors, as well as that of the overall economy, is monitored in a coherent framework.
|Date of creation:||2009|
|Date of revision:|
|Contact details of provider:|| Postal: 33 Liberty Street, New York, NY 10045-0001|
Web page: http://www.newyorkfed.org/
More information through EDIRC
|Order Information:|| Web: http://www.ny.frb.org/rmaghome/staff_rp/ Email: |
When requesting a correction, please mention this item's handle: RePEc:fip:fednsr:412. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Amy Farber)
If references are entirely missing, you can add them using this form.