ABSTRACT
Let X = { X t } $ X=\{X_t\} $ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/inline-math11_1.tif"/> be a stationary process with E ( X t ) = 0 $ \text{ E}(X_t) = 0 $ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/inline-math11_2.tif"/> and E ( X t 2 ) < ∞ $ \text{ E}(X_t^2) < \infty $ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/inline-math11_3.tif"/> . The autocovariance function (ACVF) γ X ( · ) $ \gamma _X(\cdot ) $ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/inline-math11_4.tif"/> and the autocovariance matrix (ACVM) Σ n ( X ) $ \Sigma _n(X) $ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/inline-math11_5.tif"/> of order n are defined as: γ X ( k ) = c o v ( X 0 , X k ) , k = 0 , 1 , … and Σ n ( X ) = ( ( γ X ( i - j ) ) ) 1 ≤ i , j ≤ n . $$ \gamma _X(k) = cov(X_0, X_k), \ k =0, 1, \ldots \ \ \text{ and}\ \ \Sigma _n (X) = ((\gamma _X(i-j)))_{1 \le i,j \le n}. $$ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429488436/725f3a92-cccc-4b3b-961a-84021a8d495e/content/unmath11_1.tif"/>
