Reduced rank models for multiple time series

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

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


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
Issue number1
StatePublished - Apr 1 1986
Externally publishedYes


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

ASJC Scopus subject areas

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


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