based on vector white noise process other than Et. Let h denote a nonsingular (k x k) matrix, and define ut= hEt Then certainly ut is white noise E(ut) E(utu) HQ2H for t=T 0fort≠ Moreover, from( 9) we could writ H+H-Het+yiH-HEt-1+y2H-HEt-2+y3H-HEt-3+ u+Jout +J1ut-1+J2ut-2+J3ut-3+ where J。=yH1 (11) For example, H could be any matrix that diagonalizes n2 HSH=D with d a diagonal matrix. For such a choice of h, the element of ut are uncor- related with one another: a E(utu=HSH=D Thus, it is always possible to write a stationary V AR(p) process as a infinite moving average of a white noise vector ut whose elements are mutually uncorre- lated 2.1.3 Computation of Autocovariances of an Stationary V AR(p) Pro- cess We now consider to express the second moments for yt following a VAR(P) Recall that as in the univariate AR(p) process, the Yule-Walker equation arebased on vector white noise process other than εt . Let H denote a nonsingular (k × k) matrix, and define ut = Hεt . Then certainly ut is white noise: E(ut) = 0 and E(utu 0 τ ) =  HΩH0 for t = τ 0 for t 6= τ . Moreover, from (9) we could write yt = µ + H−1Hεt + Ψ1H−1Hεt−1 + Ψ2H−1Hεt−2 + Ψ3H−1Hεt−3 + ... = µ + J0ut + J1ut−1 + J2ut−2 + J3ut−3 + ...., where Js = ΨsH−1 . (11) For example, H could be any matrix that diagonalizes Ω, HΩH0 = D, with D a diagonal matrix. For such a choice of H, the element of ut are uncor￾related with one another:a E(utu 0 t ) = HΩH0 = D. Thus, it is always possible to write a stationary V AR(p) process as a infinite moving average of a white noise vector ut whose elements are mutually uncorre￾lated. 2.1.3 Computation of Autocovariances of an Stationary V AR(p) Pro￾cess We now consider to express the second moments for yt following a V AR(p). Recall that as in the univariate AR(p) process, the Yule-Walker equation are 9