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.

Testing for Joint Stationarity, Normality, and Independence

The hypothesis testing framework for the time series properties of joint stationarity, normality, and independence is less developed in the multivariate case, compared to the univariate case. This is especially true when testing for joint stationarity. The only joint stationarity test presented here is the ...

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