There has been recent interest in using ortho-normalized forms of fixed den
ominator model structures for system identification, A key motivating facto
r in the employment of these forms is that of improved numerical properties
. Namely, for white input, perfect conditioning of the least-squares normal
equations is achieved by design. However, for the more usual case of color
ed input spectrum, it is not clear what the numerical conditioning properti
es should be in relation to simpler and perhaps more natural model structur
es. This paper provides theoretical and empirical evidence to argue that in
fact, even though the orthonormal structures are only designed to provide
perfect numerical conditioning for white input, they still provide improved
conditioning for a wide variety of colored inputs.