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Using Multivariate Time Series Models in Systemic Analysis

  • Klaus Ackermann
  • Uta Streit
  • Hansjörg Ebell
  • Arno Steitz
  • Ilse M. Zalaman
  • Dirk Revenstorf
Part of the Springer Series in Synergetics book series (SSSYN, volume 58)

Abstract

Multivariate autoregressive moving-average modeling of relationships between two or more stochastic time series as well as the underlying principles are discussed (Tiao & Box, 1981; Jenkins & Alavi, 1981). The relevance of this particular type of time series analysis with regard to identifying systemic relationships among observed process variables is demonstrated by using examples from pain and marital counseling research. This kind of analysis is especially suited to describe time series for which the relationships between variables are thought to be stable or stationary throughout the time of observation.

Keywords

Time Series Pain Intensity Marital Relationship Random Shock Contemporaneous Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Klaus Ackermann
  • Uta Streit
  • Hansjörg Ebell
  • Arno Steitz
  • Ilse M. Zalaman
  • Dirk Revenstorf

There are no affiliations available

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