  Linear dynamical relations that may exist in continuous-time, or at some
natural sampling rate, are not directly discernable at reduced observational
sampling rates. Indeed, at reduced rates, matricial spectral densities of
vectorial time series have maximal rank and thereby cannot be used to ascertain
potential dynamic relations between their entries. This hitherto undeclared
source of inaccuracies appears to plague off-the-shelf identification
techniques seeking remedy in hypothetical observational noise. In this paper we
explain the exact relation between stochastic models at different sampling
rates and show how to construct stochastic models at the finest time scale that
data allows. We then point out that the correct number of dynamical dependences
can only be ascertained by considering stochastic models at this finest time
scale, which in general is faster than the observational sampling rate.
