A Dynamic Index Model for Large Cross Sections
In: Business Cycles, Indicators and Forecasting
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 study dynamic co-movements of sectoral employment in the US 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.
(This abstract was borrowed from another version of this item.)
|This chapter was published in: ||This item is provided by National Bureau of Economic Research, Inc in its series NBER Chapters with number
7195.||Handle:|| RePEc:nbr:nberch:7195||Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
More information through EDIRC
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:nbr:nberch:7195. 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: ()
If references are entirely missing, you can add them using this form.