Suppose you have a process $x_{t} = \mu +w_{t} -0.8*w_{t-1}$ where $w_{t}$ ~ $wn(0,\sigma_{w}^2)$. How do I calculate the standard error of $var(\bar{x})$ for estimating the mean. I know: $var(\bar{x}) = \frac{1}{n}\sum_{j=-n}^n (1-\frac{|h|}{n})\gamma_{x}(h)$. I am stuck on what to do next. I know that $var(x_{t}) = \sigma_{w}^2 + 0.64*\sigma_{w}^2$. I am not sure how to use $|h|$ in the above expression. I know that $\gamma(0) = 1.64\sigma_{w}^2 , \gamma(1) = -0.8\sigma_{w}^2$ and $\gamma(h>=2) = 0$. So when I evaluate this sum I get $\frac{1}{n}\sum_{j=-1}^1(1-\frac{|h|}{n})\gamma_{x}(h) = \frac{1}{n}[(1-\frac{1}{n})(-0.8\sigma_{w}^2) + 1.64\sigma_{w}^2 + (1-\frac{1}{n})(-0.8\sigma_{w}^2) $ . Is this correct?
EDIT: The solutions manual states that the answer is $var(\bar{x}) = \frac{\sigma_{w}^2}{n}(1-2\frac{n-1}{n}0.8)$ but I am not sure how this was arrived at. I get $\frac{\sigma_{w}^2}{n}(1.68-2\frac{n-1}{n}0.8)$ using the approach above.