IDENTIFICATION OF AN AUTOREGRESSIVE MODEL FOR MULTIVARIATE ONE-DIMENSIONAL NONSTATIONARY GAUSSIAN RANDOM PROCESSES

Abstract

Identification problems on coefficient matrices of an autoregressive model for multivariate and one-dimensional nonstationary Gaussian random processes are investigated, by appling the Kalman filter incorporated with a weighted global iteration.
The major contributions of the paper are the use of Kalman filter for estimating time varying model parameters and the development of an effective method in terms of computer time.
The results indicate that the coefficients of this recursive equation are identified extremely well at the stage of their stable convergency to optimal ones.