Let {at}tez be xt = 1xt-1+ at, te Z where at ~ WN(0, 0²) and 1 € (−1, 1) is a fixed parameter, whence xt is a stationary process. (a) Derive the mean function t = Ext for t = Z. (b) Derive the autocovariance function Yt,t+h = Cov(xt, xt+h) for t, h = Z. (c) For ₁ € (-1, 1), derive the autocorrelation function (ACF) Pt,t+h = Cor(t, xt+h) for t, h = Z. Describe the shape of the ACF.
Let {at}tez be xt = 1xt-1+ at, te Z where at ~ WN(0, 0²) and 1 € (−1, 1) is a fixed parameter, whence xt is a stationary process. (a) Derive the mean function t = Ext for t = Z. (b) Derive the autocovariance function Yt,t+h = Cov(xt, xt+h) for t, h = Z. (c) For ₁ € (-1, 1), derive the autocorrelation function (ACF) Pt,t+h = Cor(t, xt+h) for t, h = Z. Describe the shape of the ACF.
MATLAB: An Introduction with Applications
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
Transcribed Image Text:Let {xt}tez be
xt =
1xt-1+at, te z
WN(0, σ²) and 1 € (−1, 1) is a fixed parameter, whence x is a stationary process.
Ext for te Z.
where at~
(a) Derive the mean function µt
=
(b) Derive the autocovariance function Yt,t+h= Cov(t, xt+h) for t, h € Z.
(c) For $1 € (−1,1), derive the autocorrelation function (ACF) Pt,t+h = Cor(xt, Xt+h) for t, h € Z.
Describe the shape of the ACF.
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