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.
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© 2008 Springer Science+Business Media, LLC
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(2008). Multivariate Analysis of Stationary Time Series. In: Analysis of Integrated and Cointegrated Time Series with R. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75967-8_2
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DOI: https://doi.org/10.1007/978-0-387-75967-8_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75966-1
Online ISBN: 978-0-387-75967-8
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