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ORDERS AND INITIAL VALUES OF NON‐STATIONARY MULTIVARIATE ARMA MODELS
Author(s) -
Swift A. L.
Publication year - 1990
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1990.tb00063.x
Subject(s) - akaike information criterion , mathematics , autoregressive model , multivariate statistics , estimator , star model , statistics , autoregressive–moving average model , least squares function approximation , first order , autoregressive integrated moving average , time series
. Akaike's stepwise canonical correlations procedure identifies each autoregressive (AR) order (assuming a lesser moving‐average (MA) order) and gives initial parameter estimates of a stationary multivariate autoregressive moving‐average model. We show that a similar procedure is valid for the non‐stationary model when this can be approximated by a large‐order AR‐only model which has consistent least‐squares estimators. The procedure can also be adapted for all MA orders by fixing the overall maximum of any MA order less its corresponding AR order. We perform the canonical correlations procedure on a succession of values of this maximum and compare the results to obtain one or more prospective model structures.