Transcription of Vector Autoregressive Models for Multivariate Time Series
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Vector Autoregressive Models for Multivariate Time Series
This is page 383 Printer: Opaque this11 Vector Autoregressive Models forMultivariate Time IntroductionThevector autoregression(VAR)modelis one of the most successful,flexi-ble, and easy to use Models for the analysis of Multivariate time Series . It isa natural extension of the univariate Autoregressive model to dynamic mul-tivariate time Series . The VAR model has proven to be especially useful fordescribing the dynamic behavior of economic andfinancial time Series andfor forecasting. It often provides superior forecasts to those from univari-ate time Series Models and elaborate theory-based simultaneous equationsmodels. Forecasts from VAR Models are quiteflexible because they can bemade conditional on the potential future paths of specified variables in addition to data description and forecasting, the VAR model is alsoused for structural inference and policy analysis.
such as a linear time trend or seasonal dummy variables may be required to represent the data properly. Additionally, stochastic exogenous variables may be required as well. The general form of the VAR(p)modelwithde-terministic terms and exogenous variables is given by Yt= Π 1Yt−1+Π 2Yt−2+···+ΠpYt−p+ΦDt+GXt+εt (11.4)
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