Using Multivariate Time Series Models in Systemic Analysis
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.
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