In much of the literature I’m studying it’s one of those terms that occurs frequently yet without a rigorous definition to be found. Specifically, I am told:

For time-indexed random variables (RVs) {Xt}, the

additive decomposition modelis given asXt=ll(Xt−1,Xt−2,…)+fc(Xt−1,Xt−2,…,εt,εt−1,…)

where

- ll is the
long-term level, which is a stochastic process and can be visualised as a smoothed version of {Xt}, not to be confused withtrendswhich are deterministic patterns- fc is the
fluctuation componentwhich represents changes inlocal level, assumedstationaryand withzero mean level- {εt} are
innovations, and are IID mean-zero RVsBut what is the difference in meaning between

trendvs.long-term levelvs.local levelvs.mean level?Additionally, aren’t the

fluctutation componentandinnovationsmodelling the same thing, which is the noise associated with each observation? So why complicate things by including both?

**Answer**

This has to do with the **order of integration**. A stochastic process Xt is said to be integrated of order 0, equivalently Xt~I(0) if it is stationary. If Xt~I(d) with d>0,d∈N, the process is said to be integrated of order d and is then nonstationary. The above decomposition attempts to filter out the stationary components (as fluctuation component and innovations) and the nonstationary stochastic trend component. A **stochastic trend** is different from a **deterministic trend**, and the usage of the word trend in the passage is sloppy.

Now, this makes it all sound more complicated than it is. Lets consider an example. Take εt~(0,σ2) as a white noise process and let εt be iid. Define the following lag polynomial

C1(L)=0.5L+0.25L2−0.75L3−0.05L4

The lag operator L works on time-indexed random variables as Lkε:=εt−k. Suppose now further that Xt is generated as

Xt=Xt−1+C1(L)εt+εt

Then, using the terminology from your excerpt, the long term level would be defined by Xt−1, the seasonal/fluctuation component by C1(L)εt and the innovations by εt. As described in the excerpt, the fluctuation component and the innovations are stationary.

The reason why it is called that way is somewhat hard to see without making further remarks and relates back to the aforementioned order of integration. Usually, we don’t encounter processes that are integrated of orders higher than 1 or 2, so lets consider the above example of integration order 1.

First of, define ut:=C1(L)εt+εt. ut is stationary, so ut~I(0).

Now we can write

Xt=Xt−1+ut⟺Xt−Xt−1=(1−L)Xt=ΔXt=ut

this tells us that Xt~I(1), because its first difference is integrated of order 0. The meaning of this might be hard to grasp, until one realizes what ΔXt=ut actually means. It means that one can rewrite

Xt=∞∑i=1ΔXt=∞∑i=1ut

This might not look dramatic: E(Xt)=0, after all! However, the variance of this process is **not** finite and explodes to ∞. This is why we say the term defines a stochastic trend: while it is not deterministic (like for instance a linear trend), Xt will only be stationary once we have filtered out the nonstationary component and substracted it from Xt. (In this case, as observed previously, ΔXt=Xt−Xt−1=C1(L)εt+εt would have filtered out the nonstationary component and would be stationary.)

If you do not do this, your usual statistical inference procedures do not work anymore, since Xt will converge to a brownian motion by the invariance principle/Functional Central Limit Theorem. These results replace standard CLR results for Autoregressions, Cointegration problems, and so forth.

**Attribution***Source : Link , Question Author : mchen , Answer Author : Jeremias K*