An autoregressive (AR) system

where

, and A(n) are general stationary stochastic processes and w(n) is white noise, is well modeled by a similar equation in which the random processes are replaced by their averages, if the resulting constant coefficient model is sufficiently lowpass, low gain (LPLG) [1]. By computer simulation, we investigate the performance of this approximation for various AR systems. It is found that "sufficiently LPLG" implies a very restricted class (very LPLG) for the general case of unknown (large) perturbations on

. Only as the perturbations become small can the very LPLG restriction be relaxed.