Autocovariance of an AR(1) process observed with white noise (${\gamma }_Y(h)=\frac{{\sigma }_z^2{\varphi }^{|h|}}{1-{\varphi }^2}+{\sigma }_w^2{1}_{\{h=0\}}$)

For a causal AR(1) plus uncorrelated observation white noise, cross covariances vanish by L2 convergence of the autoregressive noise expansion. Its covariance is ${\sigma }_z^2{\varphi }^{|h|}/(1-{\varphi }^2)+{\sigma }_w^2{1}_{\{h=0\}}$. The additional noise changes only lag zero, attenuating the normalized positive-lag correlations.

Filtering the observations by $1-\varphi B$ gives covariance $A={\sigma }_z^2+(1+{\varphi }^2){\sigma }_w^2$ at zero, $C=-\varphi {\sigma }_w^2$ at lag one and zero elsewhere. Set $\nu =(A+\sqrt{A^2-4C^2})/2$ and $\vartheta =C/\nu$. Then $\nu (1+{\vartheta }^2)=A$, $\nu \vartheta =C$ and $|\vartheta |<1$. Applying $(1+\vartheta B{)}^{-1}$ defines actual white-noise innovations with variance $\nu$, proving an at-most-(1,1) ARMA representation without assuming Gaussianity.