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Let $X_1=0$ and $X_{k+1}=\beta X_k+\epsilon_k$ with $\epsilon_k$ iid normally distributed, $\epsilon_k\sim N(\mu,\sigma^2)$. Let $\mu$ and $\sigma^2$ be fixed.

How can you show the mean of $X_k$ for $k=1,...,n$ converges, e.g. $$\frac1n\sum_{k=1}^nX_k\xrightarrow{P}{} \frac{\mu}{1-\beta} \;?$$

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  • $\begingroup$ It is β<1 or not? $\endgroup$ – Jimmy R. Nov 12 '14 at 20:33
  • $\begingroup$ @Stefanos yes, $-1\leq \beta<1$. $\endgroup$ – user104143 Nov 12 '14 at 20:35
  • $\begingroup$ @Stefanos where's you answer gone? I've started to check it -.- $\endgroup$ – user104143 Nov 12 '14 at 20:52
  • $\begingroup$ I had a bad mistake, I am correcting it. Again $|β|<1$ not $|β|\le1$ $\endgroup$ – Jimmy R. Nov 12 '14 at 20:53
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The final version is based on the helpful (and constructive) comments (see below) that pointed out mistakes and flaws of the previous efforts to answer the question.


By substituting iteratively you find that $$\begin{align*}X_{k+1}&=βX_{k}+\epsilon_k=β(βX_{k-1}+\epsilon_{k-1})+\epsilon_k=β^2X_{k-1}+β\epsilon_{k-1}+\epsilon_k=\ldots\\\\&=β^kX_1+\left(\sum_{l=0}^{k-1}β^l\epsilon_{k-l}\right)\\&=\sum_{l=0}^{k-1}β^l\epsilon_{k-l}\end{align*}$$ since $X_1=0$. Thus \begin{array}{rcrcrr}X_2&=&\epsilon_1\\X_3&=&β\epsilon_1&+&\epsilon_2\\X_4&=&β^2\epsilon_1&+&β\epsilon_2&+&\epsilon_3&\\\ldots&&\ldots&&\ldots&&\ldots\\X_n+1&=&β^{n-1}\epsilon_1&+&β^{n-2}\epsilon_2&+&β^{n-3}\epsilon_3&+\ldots+β\epsilon_{n-1}+\epsilon_n\end{array} Summing up both sides, we obtain $$\begin{align*}\sum_{k=1}^{n}X_{k+1}&=\frac{1-β^n}{1-β}\epsilon_1+\frac{1-β^{n-1}}{1-β}\epsilon_2+\ldots+\frac{1-β^2}{1-β}\epsilon_{n-1}+\frac{1-β}{1-β}\epsilon_n\\&=\sum_{k=1}^{n}\frac{1-β^{n+1-k}}{1-β}\epsilon_k=\ldots\\&=\frac{1}{1-β}\left(\sum_{k=1}^{n}\epsilon_k-β^{n+1}\sum_{k=1}^{n}\frac{1}{β^k}\epsilon_k\right)\end{align*}$$ and finally $$\bar{X}_n=\frac{1}{n(1-β)}\left(\sum_{k=1}^{n}\epsilon_k-β^{n+1}\sum_{k=1}^{n}\frac{1}{β^k}\epsilon_k\right)$$ From this form we can calculate $E[\bar{X}_n]$ and (especially) $Var(\bar{X}_n)$ since the $\epsilon_k$ are independent. Indeed $$E[\bar{X}_n]=\frac{1}{n(1-β)}\left(\sum_{k=1}^{n}μ-β^{n+1}\sum_{k=1}^{n}\frac{1}{β^k}μ\right)=\frac{μ}{1-β}\left(1-\frac{β^{n+1}-1}{n(1-β)}\right) \longrightarrow \frac{μ}{1-β}$$ as $n \to \infty$ and similarly $$\begin{align*}Var[\bar{X}_n]&=\frac{1}{n^2(1-β)^2}\left(\sum_{k=1}^{n}σ^2-(β^2)^{n+1}\sum_{k=1}^{n}\frac{1}{(β^2)k}σ^2\right)\\\\&=\frac{σ^2}{(1-β)^2}\left(\frac{1}{n}-\frac{(β^2)^{n+1}-1}{n(1-β^2)}\right) \, \longrightarrow \, 0\end{align*}$$ as $n \to \infty$. Hence $$E[\bar{X}_n^2]=Var(\bar{X}_n)+E[\bar{X}_n]^2 \longrightarrow 0+\frac{μ^2}{(1-β)^2}$$ which is enough to obtain $\mathcal L^2$ convergence $$E\left[\left(X_n-\frac{μ}{1-β}\right)^2\right]=E\left[\bar{X}_n^2-2\bar{X}_n\frac{μ}{1-β}+\frac{μ^2}{(1-β)^2}\right]\to \frac{2μ^2}{(1-β)^2}-\frac{2μ^2}{(1-β)^2}=0$$ from which you can conclude that $$\overline{X}_n=\frac{1}{n}\sum_{k=1}^{n}X_k\overset{p}\rightarrow \frac{μ}{1-β}$$ since convergence in $\mathcal L^r$ for $1\le r$ implies convergence in probability (see here).


Summing up both sides of the given relation $$\sum_{k=1}^{n}X_{k+1}=β\sum_{k=1}^{n}X_k+\sum_{k=1}^{n}\epsilon_k$$ gives you after simple calculations that $$\bar{X}_n=\frac{1}{n(1-β)}\left(\sum_{k=1}^{n}\epsilon_k-X_{n+1}\right)$$ and thus obtaining a closed form for $X_{n+1}$ (as above) is enough to obtain the result with less calculations than above.

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    $\begingroup$ Indeed the random variables are not independent, but... You already proved that $E(\bar{X_k})\to\mu$. A similar computation would show that $E(\bar{X_k}^2)\to\mu^2$. Thus $\bar{X_k}\to\mu$ in $L^2$, which is more than the desired result. $\endgroup$ – Did Nov 12 '14 at 21:12
  • $\begingroup$ ??? Where is the proof of the (asserted) convergence in $L^1$? OP: Surely you can complete the proof since you accepted the answer? $\endgroup$ – Did Nov 12 '14 at 23:30
  • $\begingroup$ This avoids the main part of the exercise (the computation of the variance) but now the principle of the proof is sound. $\endgroup$ – Did Nov 13 '14 at 20:33

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