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 Cointegration

The concept of cointegration, as developed by Granger and others, examines the presence or absence of an equilibrium relationship between two variables over time (e.g., Engle & Granger, 1987; Granger, 1986). An understanding of cointegration is necessary in order to invoke and to explain the structure of ...

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