Suppose we have $x_1,\cdots,x_n$, such that $x_i$ are i.i.d according to $x_i\sim N(0,1)$ for $i=1,\cdots,n$. Then we know that $Y=\sum_{i=1}^nx_i^2$
\begin{equation} Y\sim \chi^2(n) \end{equation}
Now suppose $\tilde{x}_1,\cdots,\tilde{x}_n$ are not longer independent and follow an AR(1) process - i.e.,
\begin{equation} \tilde{x}_t=\rho \tilde{x}_{t-1}+u_t,\quad u_t \sim N(0,\sigma^2_u)\quad\text{and}\quad \lvert \rho\rvert<1 \end{equation} thus, implying $\tilde{x}_t\sim N(0, \sigma^2_u/1-\rho^2)$. Can anything be said about the distribution of $\tilde{Y}_t=\sum_{i=1}^n\tilde{x}_i^2$? For instance
\begin{equation} \frac{\sqrt{1-\rho^2}}{\sigma_u} \tilde{Y_t}\overset{?}{\sim} \chi^2(?) \end{equation} with perhaps adjusted degrees of freedom?