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.

Model Order and Forecast Accuracy

Tests involving forecast error and prediction error play a major role in the identification of time series models. We have seen how the concept of variance decomposition depends on the use of one-step-ahead forecasts. In selecting the lag order of a model, tests of final prediction error are employed; and in the ...

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