Skip Navigation

Biometrika 1986 73(1):105-118; doi:10.1093/biomet/73.1.105
© 1986 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by VELU, R. P.
Right arrow Articles by WICHERN, D. W.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Reduced rank models for multiple time series

RAJA P. VELU, GREGORY C. REINSEL and DEAN W. WICHERN

Department of Marketing, University of Wisconsin Whitewater, Wisconsin 53190, U.S.A.
Department of Statistics, University of Wisconsin Madison, Wisconsin 53706, U.S.A.
Department of Business Analysis, Texas A&M University College Station, Texas 77843, U.S.A.

This paper is concerned with the investigation of reduced rank coefficient models for multiple time series. In particular, autoregressive processes which have a structure to their coefficient matrices similar to that of classical multivariate reduced rank regression are studied in detail. The estimation of parameters and associated asymptotic theory are derived. The exact correspondence between the reduced rank regression procedure for multiple autoregressive processes and the canonical analysis of Box & Tiao (1977) is briefly indicated. To illustrate the methods, U.S. hog data are considered.

Key Words: Canonical analysis • Canonical correlation • Multiple time series • Reduced rank regression


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BiometrikaHome page
D. Pena
Dimension reduction in time series and the dynamic factor model
Biometrika, June 1, 2009; 96(2): 494 - 496.
[Abstract] [PDF]

Home page
 National Institute Economic ReviewHome page
J. Eklund and G. Kapetanios
A Review of Forecasting Techniques for Large Data Sets
National Institute Economic Review, January 1, 2008; 203(1): 109 - 115.
[Abstract] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.