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Time series are data observed over time (either in continuous time or at discrete time periods).

2 votes
1 answer
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Linear process autocovariance function converges to zero as h goes to infinity

Assume that time series $(X_t)$ is given by: \begin{equation} X_t = \sum_{i=0}^{\infty} c_i \varepsilon_{t - i}, \end{equation} where $(\varepsilon_t)$ is a weak white noise $\text{WN}(0, \sigma^2)$ a …
IWillDominate's user avatar
1 vote
Accepted

Linear process autocovariance function converges to zero as h goes to infinity

I found the solution on my own, so I share it with you. We know that \begin{equation} \gamma_X(h) = \sigma^2\sum_{i = 0}^\infty c_ic_{i+h}. \end{equation} To test the convergence consider \begin{equat …
IWillDominate's user avatar
1 vote
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Forecasting $X_{t+2}$ for causal AR(p)

Consider $X_{t+2}$ term: \begin{equation} \begin{split} X_{t+2} & = \sum_{i=1}^p \psi_iX_{t+2-i} \,+Z_{t+2} = \psi_1X_{t+1} + \sum_{i=2}^p\psi_iX_{t+2-i} \,+Z_{t+2} \\ & = \psi_1\left(\sum_{i=1}^p \ps …
IWillDominate's user avatar
1 vote
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Prediction error for ARMA process

Remark: Mind, that we can solve this only is $(X_t)$ is casual. A sufficient condition for that is $|\varphi|<1$. Using the representation with a lag operator $B$ \begin{equation} (1-\varphi B^{12})X_ …
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