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Keywords:
estimating autoregressive matrices; matrixvariate$t$-distribution; multivariate processes; periodic autoregression; test of periodicity; test of fit; vector autoregression; asymptotic posterior chi-square distribution; confidence regions; Bayes approach
estimating autoregressive matrices; matrixvariate$t$-distribution; multivariate processes; periodic autoregression; test of periodicity; test of fit; vector autoregression; asymptotic posterior chi-square distribution; confidence regions; Bayes approach
Summary:
The model of periodic autoregression is generalized to the multivariate case. The autoregressive matrices are periodic functions of time. The mean value of the process can be a non-vanishing periodic sequence of vectors. Estimators of parameters and tests of statistical hypotheses are based on the Bayes approach. Two main versions of the model are investigated, one with constant variance matrices and the other with periodic variance matrices of the innovation process.
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