Chapter 4: Vector Autoregressions. - GitHub Pages

Chapter 4: Vector Autoregressions. Jesu s Bueren EUI. VAR Jesu s Bueren 1. Introduction Introduction This Chapter describes the dynamic interactions among a set of variables collected in an (n 1) Vector yt . A p-th order Vector autoregression , VAR(p), is a Vector generalization of an AR(p): yt = c + 1 yt 1 + + p yt p + t (1). The (n 1) Vector t is a Vector generalization of white noise: E ( t ) = 0. (. for t = . E ( t ) =. 0 otherwise VAR Jesu s Bueren 2. Introduction Introduction The first row of the Vector system specifies that: (1) (1). y1t = c1 + 1,1 y1,t 1 + + 1,n yn,t 1. (2) (2). + 1,1 y1,t 2 + + 1,n yn,t 2.)

One of the key questions that can be addressed with vector autoregression is how useful some variables are for forecasting others. In a bivariate VAR describing x and y, y does not Granger-cause x in case if it cannot help forecast x. Granger causality and …

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  Vector, Vector autoregression, Autoregression

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