Sampling Dynamical Systems
AbstractLinear 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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Bibliographic InfoArticle provided by Cambridge University Press in its journal Macroeconomic Dynamics.
Volume (Year): 3 (1999)
Issue (Month): 04 (December)
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- Jorda, Oscar & Marcellino, Massimiliano, 2003.
"Time-Scale Transformations of Discrete-Time Processes,"
03-2, University of California at Davis, Department of Economics.
- Oscar Jordà & Massimiliano Marcellino, 2004. "Time-scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
- Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 32, University of California, Davis, Department of Economics.
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