Reduced rank models for multiple time series

Raja P. Velu, Gregory C. Reinsel, Dean W. Wichern

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)105-118
Number of pages14
JournalBiometrika
Volume73
Issue number1
DOIs
StatePublished - Apr 1 1986
Externally publishedYes

Keywords

  • Canonical analysis
  • Canonical correlation
  • Multiple time series
  • Reduced rank regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Reduced rank models for multiple time series'. Together they form a unique fingerprint.

Cite this