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Uniqueness of estimated k-step prediction models of ARMA processes

✍ Scribed by Petre Stoica; Torsten Söderström

Publisher
Elsevier Science
Year
1984
Tongue
English
Weight
465 KB
Volume
4
Category
Article
ISSN
0167-6911

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✦ Synopsis

The direcr estimation of the k-step prediction models of ARMA processes is discussed. The emphasis is on the uniqueness properties of the parameter estimates of such models, obtained by using either a prediction error method (PEM) or a pseudo-linear regression (PLR) algorithm.

The main result is that both PEM and PLR, when applied to such models, have a certain uniqueness property.

More specifically, it is shown that all the limit models corresponding to either method behave precisely as the true optimal predictor.

Furthermore. if a minimal parameterization is used, then the true predictor is the unique limit model both for PEM and for PLR.

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