# Filtering a Stationary Time Series

In the first part of the MAT8181 course, on linear (univariate) time series, I forgot to mention an important theorem. Let $(X_t)_{t\in\mathbb{Z}}$ be a stationary time series, and $(a_t)_{t\in\mathbb{Z}}$ a sequence of real numbers such that

$\sum_{t\in\mathbb{Z}} \vert a_t\vert < \infty$

then the time series $(Y_t)_{t\in\mathbb{Z}}$ defined as

$Y_t=\sum_{k\in\mathbb{Z}} a_k X_{t-k}= \left( \sum_{k\in\mathbb{Z}} a_k L^k\right) X_t$

is a stationary time series. Further, one can get easily that

$\gamma_Y(h)=\text{cov}(Y_t,Y_{t-k})=\sum_{r,s\in\mathbb{Z}} a_r a_s \underbrace{\gamma_X(h+r-s)}_{\text{cov}(X_{t-r},X_{t-s+h})}$

This result can be used, if necessary in the exercises (that might save some time actually). I did not include this property in my notes because it is a bit technical to establish that this sum exists, and that the time series is stationary. It is rather simple with the spectral density (since $f_Y(\omega)=\vert A(e^{i\omega})\vert^2f_X(\omega)$ where $A(\cdot)$ stands for the filter generating function), but I did not mention the spectral density since it requires some knowledge on Fourier analysis…

Cite this blog post
Arthur Charpentier (2014, March 13). Filtering a Stationary Time Series. Freakonometrics. Retrieved May 21, 2024, from https://doi.org/10.58079/ouum

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