By Helmut Lütkepohl
When I labored on my advent to a number of Time sequence research (Lutk ¨ ¨- pohl (1991)), an appropriate textbook for this ?eld was once now not to be had. Given the good significance those equipment have received in utilized econometric paintings, it truly is probably no longer fabulous on reflection that the booklet was once really profitable. Now, nearly one and a part a long time later the ?eld has passed through monstrous improvement and, for that reason, the booklet doesn't hide all subject matters of my very own classes at the topic anymore. for this reason, i began to consider a major revision of the e-book whilst I moved to the eu collage Institute in Florence in 2002. right here within the stunning hills of ToscanyIhadthetimetothink approximately larger tasks back and made up our minds to arrange a considerable revision of my prior ebook. as the label moment variation was once already used for a prior reprint of the e-book, i made a decision to change the identify and thereby wish to sign to capability readers that signi?cant alterations were made relative to my earlier a number of time sequence book.
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Example text
IK 0 (Kp×Kp) ⎡ ⎢ ⎢ Ut := ⎢ ⎣ ut 0 .. ⎤ ⎥ ⎥ ⎥. ⎦ 0 (Kp×1) 16 2 Stable Vector Autoregressive Processes Following the foregoing discussion, Yt is stable if det(IIKp − Az) = 0 for |z| ≤ 1. 10) i=0 Ut Ut ). 11) the process yt is obtained as yt = JY Yt . Because Yt is a well-defined stochastic process, the same is true for yt . Its mean is E(yt ) = Jμ which is constant for ΓY (h)J are also time invariant. 1). Given the definition of the characteristic polynomial of a matrix, we call this polynomial the reverse characteristic polynomial of the VAR(p) process.
Ap−1 Ap IK 0 . . 0 0 ⎥ ⎥ 0 IK 0 0 ⎥ ⎥, .. .. ⎥ .. . . ⎦ 0 0 . . IK 0 (Kp×Kp) ⎡ ⎢ ⎢ Ut := ⎢ ⎣ ut 0 .. ⎤ ⎥ ⎥ ⎥. ⎦ 0 (Kp×1) 16 2 Stable Vector Autoregressive Processes Following the foregoing discussion, Yt is stable if det(IIKp − Az) = 0 for |z| ≤ 1. 10) i=0 Ut Ut ). 11) the process yt is obtained as yt = JY Yt . Because Yt is a well-defined stochastic process, the same is true for yt . Its mean is E(yt ) = Jμ which is constant for ΓY (h)J are also time invariant. 1). Given the definition of the characteristic polynomial of a matrix, we call this polynomial the reverse characteristic polynomial of the VAR(p) process.
P − 1, are obtained from vec ΓY (0) = (I(Kp)2 − A ⊗ A)−1 vec ΣU . 04 0 0 ⎥ ⎥. 42) and so on. A method for computing Γy (0) without explicitly inverting (I − A ⊗ A) is given by Barone (1987). The autocovariance function of a stationary VAR(p) process is positive semidefinite, that is, n n aj Γy (i − j)ai j=0 i=0 ⎡ ⎢ ⎢ = (a0 , . . , an ) ⎢ ⎣ Γy (0) Γy (−1) .. Γy (1) Γy (0) .. ... Γy (n) . . Γy (n − 1) .. . Γy (−n) Γy (−n + 1) . . Γy (0) ⎤⎡ ⎥⎢ ⎥⎢ ⎥⎢ ⎦⎣ a0 a1 .. 43) for any n ≥ 0. Here the ai are arbitrary (K × 1) vectors.