A dynamic index model for large cross sections
This paper shows how standard methods can be used to formulate and estimate a dynamic index model for random fields—stochastic processes indexed by time and cross section where the time-series and cross-section dimensions are comparable in magnitude. We use these to study dynamic comovements of sectoral employment in the U.S. economy. The dynamics of employment in sixty sectors is well explained using only two unobservable factors; those factors are also strongly correlated with GNP growth.
|Date of creation:||1992|
|Date of revision:|
|Contact details of provider:|| Postal: 90 Hennepin Avenue, P.O. Box 291, Minneapolis, MN 55480-0291|
Phone: (612) 204-5000
Web page: http://minneapolisfed.org/
More information through EDIRC
|Order Information:|| Web: http://www.minneapolisfed.org/pubs/ Email: |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Robert B. Litterman, 1985.
"Forecasting with Bayesian vector autoregressions five years of experience,"
274, Federal Reserve Bank of Minneapolis.
- Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
When requesting a correction, please mention this item's handle: RePEc:fip:fedmem:77. 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: (Janelle Ruswick)
If references are entirely missing, you can add them using this form.