Moving-average root reflection
= Moving-average root reflection
Reflecting an inside-unit-circle zero across the unit circle produces an invertible <moving-average model> with the same <spectral density of a stationary process>, after rescaling the driving <white noise> variance. For real $|\theta|>1$, $|1-\theta e^{-i\lambda}|^2=\theta^2|1-\theta^{-1}e^{-i\lambda}|^2$. The transformed driving sequence is a <linear innovation process>; without Gaussianity it need not be <strong white noise>.