Multivariate Analysis of Stationary Time Series

Part of the Use R! book series (USE R)

This is the second chapter that presents models confined to stationary time series, but now in the context of multivariate analysis. Vector autoregressive models and structural vector autoregressive models are introduced. The analytical tools of impulse response functions, forecast error variance decomposition, and Granger causality, as well as forecasting and diagnostic tests, are outlined. As will be shown later, these concepts can be applied to cointegrated systems, too.


Impulse Response Granger Causality Impulse Response Function Stationary Time Series Forecast Error Variance 
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Copyright information

© Springer Science+Business Media, LLC 2008

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