  Performance of ordinary least squares(OLS) method for the \emph{estimation of
high dimensional stable state transition matrix} $A$(i.e., spectral radius
$\rho(A)<1$) from a single noisy observed trajectory of the linear time
invariant(LTI)\footnote{Linear Gaussian (LG) in Markov chain literature} system
$X_{-}:(x_0,x_1, \ldots,x_{N-1})$ satisfying \begin{equation}
  x_{t+1}=Ax_{t}+w_{t}, \hspace{10pt} \text{ where } w_{t} \thicksim
N(0,I_{n}), \end{equation}
  heavily rely on negative moments of the sample covariance matrix:
$(X_{-}X_{-}^{*})=\sum_{i=0}^{N-1}x_{i}x_{i}^{*}$ and singular values of
$EX_{-}^{*}$, where $E$ is a rectangular Gaussian ensemble $E=[w_0, \ldots,
w_{N-1}]$. Negative moments requires sharp estimates on all the eigenvalues
$\lambda_{1}\big(X_{-}X_{-}^{*}\big) \geq \ldots \geq
\lambda_{n}\big(X_{-}X_{-}^{*}\big) \geq 0$. Leveraging upon recent results on
spectral theorem for non-Hermitian operators in \cite{naeem2023spectral}, along
with concentration of measure phenomenon and perturbation theory(Gershgorins'
and Cauchys' interlacing theorem) we show that only when $A=A^{*}$, typical
order of $\lambda_{j}\big(X_{-}X_{-}^{*}\big) \in \big[N-n\sqrt{N},
N+n\sqrt{N}\big]$ for all $j \in [n]$. However, in \emph{high dimensions} when
$A$ has only one distinct eigenvalue $\lambda$ with geometric multiplicity of
one, then as soon as eigenvalue leaves \emph{complex half unit disc}, largest
eigenvalue suffers from curse of dimensionality:
$\lambda_{1}\big(X_{-}X_{-}^{*}\big)=\Omega\big( \lfloor\frac{N}{n}\rfloor
e^{\alpha_{\lambda}n} \big)$, while smallest eigenvalue
$\lambda_{n}\big(X_{-}X_{-}^{*}\big) \in (0, N+\sqrt{N}]$. Consequently, OLS
estimator incurs a \emph{phase transition} and becomes \emph{transient:
increasing iteration only worsens estimation error}, all of this happening when
the dynamics are generated from stable systems.
