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A measure of the degree of linear association between a pair of random variables.

2 votes
1 answer
109 views

Help understanding the step $\sum_{j=0}^n\sum_{k=0}^ng_jg_k\text{Cov}(\epsilon_{n-1},\epsilo...

I understand that the sum over $k$ gets removed and that we want to avoid zero covariance, but how does $+h\sigma^2$ pop up? … Doing the substitution $k=h+j$ then the covariance is just the variance which is onl $\sigma^2$ (no $h$), and then it's multiplied to the sum and not added. …
Parseval's user avatar
  • 221
0 votes
1 answer
82 views

Show that $\text{Cov}[X_r,X_s]=\text{Cov}[X_{r+h},X_{s+h}]$ for $X_t=a+bZ_t+cZ_{t-2}.$

Problem: Let $\{Zt\}$ be a sequence of independent normal random variables, each with mean $0$ and variance $\sigma^2$, and let $a$, $b$, and $c$ be constants. Is $X_t=a+bZ_t+cZ_{t-2}$ a (weakly) sta …
Parseval's user avatar
  • 221
1 vote
1 answer
119 views

Show that $\text{Cov}[Z_t,Z_{t+h}]=\text{Cov}[Z_s,Z_{s+h}].$

Problem: If $X\sim\text{WN}(\mu,\sigma^2).$ Let then $Z$ be the process defined by \begin{equation} Z_t=\sum_{i=0}^na_iX_{t-i} \end{equation} for some coefficients $a_1,...,a_n\in\mathbb{R}$ wit …
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  • 221