# Forecast error for AR and MA process

AR(p) process is denoted by: $X_t=\mu+\alpha_1(X_{t-1}-\mu)+\alpha_2(X_{t-2}-\mu)+...\alpha_p(X_{t-p}-\mu)+Z_t$

I don't understand forecast error.
Let $\epsilon_{t+l}$ be the forecast error at $l$ step ahead forecast .
Then it says that it can be shown that
$\epsilon _{t+l}=Z_{t+l}+\alpha_1Z_{t+l-1}+\alpha_1^2Z_{t+l-2}+....+\alpha_1^{l-1}Z_{t+1}$
How is this obtained?

My second question is for a MA(1) process it is obtained that
$V(\hat \epsilon_{t+1})=\hat\sigma^2$ ; if $l=1$
$V(\hat \epsilon_{t+1})=\hat\sigma^2 (1+\beta_1^2)$ ; if $l>=2$

Can someone please tell me the method as to how these errors were obtained.

It could be useful despite I can't understand the different notation $Z_t$ and $X_t$:
We can suppose to simplify: $\mu_t=0$ and $l=0$. You can add and substitute these values by the way. We can obtain similar expression by a recursive form. Imagine the AR(1) $$X_t=\alpha_1 X_{t-1}+\epsilon_t (1)$$ and then, how we obtained the $X_{t-1}$?
$$X_{t-1}=\alpha_1 X_{t-2}+\epsilon_{t-1} (2)$$ So, you can substitute the expression (2) in the expression (1): $$X_{t}=\alpha_1^2X_{t-2}+\alpha_1\epsilon_{t-1}+\epsilon_t$$ If you repeat this process for $X_{t-2}$, $X_{t-3}$,$X_{t-4}$...you'll get something like your expression: $$X_t=\epsilon_t+ \alpha^1\epsilon_{t-1}+\alpha^2\epsilon_{t-2}+...+\alpha^i\epsilon_{t-i}$$ It demonstrates the MA model is a AR model with reiterative replacements. So, AR models is used for long memory structures and MA for short memory ones.
• According to my notation the error term in AR(1) is $Z_t$ (Your $\epsilon _t$).My $\epsilon_{t+l}=X_{t+l}-\hat X_{t+l}$.$X_t=\epsilon_t+ \alpha^1\epsilon_{t-1}+\alpha^2\epsilon_{t-2}+...+\alpha^i\epsilon_{t-i}$ gives a expression for $X_t$ but it doesn't give the forecast error, does it?Forecast error should be the expression for $X_t-\hat X_t$ right? – clarkson Dec 21 '14 at 16:21