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 Causality

In any multivariate setting, researchers are interested in testing for the exogeneity of a variable. Such testing is closely related to the concept of causality due to Granger (1969) and it is often the latter that is used to test for the former. Causality between two or more variables is one of the most ...

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