If the V AR(p) process is stationary, we can take expectation of both side of (1)to calculate the mean u of the process =c+重1+重2+…+重p, 更p) Equation(1)can then be written in terms of deviations from the mean (yt-p)=重1(yt-1-1)+重2y-2-1)+…+更p(y-p-1)+Et.(3) 2.1.1 Conditions for Stationarity As in the case of the univariate AR(p)process, it is helpful to rewrite(3)in terms of a VAR(1) process. Toward this end, define P×1) 重1重2更3 更 I00 O IK 0 F 0 0 000 000 0 00000 0If the V AR(p) process is stationary, we can take expectation of both side of (1) to calculate the mean µ of the process: µ = c + Φ1µ + Φ2µ + ... + Φpµ, or µ = (Ik − Φ1 − Φ2 − ... − Φp) −1 c. Equation (1) can then be written in terms of deviations from the mean as (yt − µ) = Φ1(yt−1 − µ) + Φ2(yt−2 − µ) + ... + Φp(yt−p − µ) + εt . (3) 2.1.1 Conditions for Stationarity As in the case of the univariate AR(p) process, it is helpful to rewrite (3) in terms of a V AR(1) process. Toward this end, define ξt =         yt − µ yt−1 − µ . . . yt−p+1 − µ         (kp×1) (4) F =           Φ1 Φ2 Φ3 . . . Φp−1 Φp IK 0 0 . . . 0 0 0 IK 0 . . . 0 0 0 0 0 . . . 0 0 0 0 0 . . . 0 0 0 0 0 . . . 0 0 0 0 0 . . . 0 0           (kp×kp) (5) and vt =         εt 0 . . . 0         (kp×1) . 6