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 Nonlinear Models

As previously discussed in Chapter 7 of UTM, model specification involves finding a functional representation that reduces a variable to white noise. In the multivariate context, this can simply be written as

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where X(t) is an m-dimensional vector of variables and V(t) is an m-dimension vector of white noise residuals. Up to now our attention has focused on the linear ...

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