Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests. In addition, it covers such topics as: joint stationarity; testing for cointegration; testing for causality; and model order and forecast accuracy. Related models explained include transfer function, vector autoregression and error correction models.

Multivariate Linear Model Specification

Univariate time series models have the advantage of being able to explain or to predict a variable only on the basis of current, past, and future information. There is no doubt, however, that in the context of the social sciences, the explanatory power of univariate models can be improved by incorporating the ...

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