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Time Series Analysis - ARIMA Models - Wold decomposition theorem

[Home] [Up] [Basics] [AR(1) process] [AR(2) process] [AR(p) process] [MA(1) process] [MA(2) process] [MA(q) process] [ARMA(1,1) process] [ARMA(p,q) process] [Non stationarity] [Differencing] [Behavior] [Inverse Autocorr.] [Unit Root Tests] [Wold's decomp.]

Wold's decomposition theorem

The most fundamental justification for time series analysis (as described in this text) is due to Wold's decomposition theorem, where it is explicitly proved that any (stationary) time series can be decomposed into two different parts. The first (deterministic) part can be exactly described by a linear combination of its own past, the second part is a MA component of a finite order.

A slightly adapted version of Wold's decomposition theorem states that any real-valued stationary process Yt can be written as

Time Series Analysis - ARIMA Models - Wold decomposition theorem


where yt and zt are not correlated.


where yt is deterministic




(zt has an uncorrelated error with zero mean).

Because of its importance for time series analysis in general, and in practice, we will discuss the proof of Wold's decomp. theorem shortly.

Since there seems to be much confusion in literature about this theorem, we will only discuss the proof of another version (than that described above) which can be proved quite easily. In order to make a distinction with the previous description, we will explicitly use other symbols.

Denote a stationary time series Wt with zero mean and finite variance.

In order to forecast the time series by means of a linear combination of its own past


a criterion is used to optimize the parameter values. This criterion is


which is called the sum of squared residuals (SSR).

The normal equations (c.q. eq. (V.I.1-176) differentiated w.r.t. the parameters) is easily found to be


In matrix notation eq. (V.I.1-177) becomes




which is symmetric about both diagonals due to the stationary of Wt and with


On adding an error component et,n to eq. (V.I.1-175) it can be shown that


The first part of (V.I.1-181) is almost trivial


The second part of (V.I.1-181) is


with a RHS equal to zero since the parameters satisfy (V.I.1-177) (Q.E.D.).

On repeating the previous procedure we obtain


Remark that the error components (V.I.1-181) and (V.I.1-184) are uncorrelated due to (V.I.1-181), from which we obviously find


On substituting (V.I.1-184) into (V.I.1-175) it is obvious that


where xt,n(1) depends on the past of Wt-1

Also it is obvious from (V.I.1-183) that


and from (II.II.1-27) and the fact that et,n is independent from the other regressors of (V.I.1-186), that


On repeating the step described above it is easy to obtain




On applying (V.I.1-189) and (V.I.1-190) on Wt-i we obtain


where xt-i,n always depends on it's own past only, and where evidently


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AR(1) process
AR(2) process
AR(p) process
MA(1) process
MA(2) process
MA(q) process
ARMA(1,1) process
ARMA(p,q) process
Non stationarity
Inverse Autocorr.
Unit Root Tests
Wold's decomp.
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