Abstract
The vector autoregression (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate autoregressive model to dynamic multivariate time series. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from univariate time series models and elaborate theory-based simultaneous equations models. Forecasts from VAR models are quite flexible because they can be made conditional on the potential future paths of specified variables in the model.
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Zivot, E., Wang, J. (2003). Vector Autoregressive Models for Multivariate Time Series. In: Modeling Financial Time Series with S-Plus®. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21763-5_11
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DOI: https://doi.org/10.1007/978-0-387-21763-5_11
Publisher Name: Springer, New York, NY
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