Analysis of multivariate arma processes with non-stationary innovations

Analysis of multivariate arma processes with non-stationary innovations

The paper considers vector ARMA processes with nonstationary innovations. It is suggested that this class of models provide a very efficient framework for nonstationary problems. A generalization of the Yule-Walker equations relating the underlying process is obtained. Identification procedures are...

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Veröffentlicht in:Communications in statistics. Theory and methods 1990-01, Vol.19 (8), p.2847-2852
1. Verfasser: Shelton Peiris, M.
Format: Artikel
Sprache:eng
Schlagworte:
covariance matrix function
Exact sciences and technology
Hilbert space
identification
Inference from stochastic processes
time series analysis
innovation
Mathematics
nonstationary
prediction
Probability and statistics
Sciences and techniques of general use
Statistics
time series
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Zusammenfassung:The paper considers vector ARMA processes with nonstationary innovations. It is suggested that this class of models provide a very efficient framework for nonstationary problems. A generalization of the Yule-Walker equations relating the underlying process is obtained. Identification procedures are discussed. The associated prediction problem is solved using the Hilbert space approach.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610929008830352