Prediction of the U.S. Employment Links: An Application of an Empirical Bayes Procedure
Wenyu Wang
Journal of Business & Economic Statistics, 1996, vol. 14, issue 2, 243-50
Abstract:
An empirical Bayes procedure is used to adaptively predict monthly employment links (a link being the ratio of all employee counts in a particular month to the corresponding figure in the previous month) for the metropolitan statistical areas throughout the United States. By comparing with the true link of a month which is available only nine to thirteen months after the month has passed, the author's prediction is substantially and uniformly superior in MSAs, months, and states to existing estimators in terms of the average of squared deviations or the average of the absolute relative errors.
Date: 1996
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:14:y:1996:i:2:p:243-50
Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano
More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().