# A.5. Proof of the Wold Decomposition

Let Xt be a zero-mean covariance stationary process and E[X2] = a2. Then the Xt’s are members of the Hilbert space U0 defined in Section 7.A.2. Let S—TO be the subspace spanned by Xt_j, j > 1, and let Xt be the projection of Xt on S——1,. Then Ut = Xt — Xt is orthogonal to all Xt _ j, j > 1, that is, E[UtXt —j] = 0 for j > 1. Because Ut —j є S—TO for j > 1, the Ut’s are also orthogonal to each other: E[UtUt—j] = 0 for j > 1.

Note that, in general, Xt takes the form Xt = fit, jXt—j, where the

coefficients et, j are such that WYt ||2 = E [Yt2] < to. However, because Xt is covariance stationary the coefficients fit, j do not depend on the time index t, for they are the solutions of the normal equations

TO

Y(m) = E[XtXt—m] = J2 вjE[Xt—jX— m]

j=1

TO

= Y! вj Y (j—m), m = 1, 2, 3,….

j=1

Thus, the projections Xt = J2TO=1 ejXt—j are covariance stationary and so are the Ut’s because

a2 = ||Xt ||2 = || Ut + Xt ||2 = || Ut ||2+||Xt ||2 + 2{Ut, Xt)

= II Ut ||2 +||Xt ||2 = E [Uj] + E[X?];

thus, E [U2] = a2 < a2.

Next, let Zt, m = Y."!= 1 ajUt-j, where aj = {Xt, Ut – j > = E[XU-j]. Then   2

= E [X?] – ajE[XtUt-j]

j=1

m m [ ] m + E E ai ajE [U Uj ] = E [X2] – E a?2 > 0

i=1 j=1 j=1

for all m > 1; hence, 2^= a1? < to. The latter implies that J]°=m a1? — 0 for m —— to, and thus for fixed t, Zt, m is a Cauchy sequence in S–,, and Xt – Ztm is a Cauchy sequence in S-to. Consequently, Zt =Yj=1 ajUt-j є S–, and Wt = Xt – Ej=i aj Ut-j є S-to exist.

As to the latter, it follows easily from (7.8) that Wt є S-7 for every m; hence,

Wt є П S-to. (7.67)

-TO<t<TO

Consequently, E[Ut +m Wt] = 0 for all integers t and m. Moreover, it follows from (7.67) that the projection of Wt on any S1- is Wt itself; hence, Wt is perfectly predictable from any set {Xt – j, j > 1} of past values of Xt as well as from any set {Wt-j, j > 1} of past values of Wt. 