Linear dynamical systems are widely used in many differentfields from engineering to economics. One simple but important class of suchsystems is called the single-input transfer function model. Suppose that allvariables of the system are sampled for a period using a fixed samplerate. The central issue of this paper is the determination of the smallestsampling rate that will yield a sample that will allow the investigator toidentify the discrete-time representation of the system. A critical samplingrate exists that will identify the model. This rate, called the Nyquistrate, is twice the highest frequency component of the system. Sampling at alower rate will result in an identification problem that is serious. Thestandard assumptions made about the model and the unobserved innovationerrors in the model protect the investigators from the identificationproblem and resulting biases of undersampling. The critical assumption that is needed to identify an undersampled system is that at least one of theexogenous time series be white noise.
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Volume (Year): 3 (1999) Issue (Month): 04 (December) Pages: 602-609 Download reference. The following formats are available: HTML
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