Statistical analysis of periodic autoregression. (English) Zbl 0537.62073

This article describes methods for estimating parameters and testing hypotheses in a periodic autoregression. The statistical analysis is based on the Bayes approach. The parameters of the model are supposed to be random variables with a vague prior density. The innovation process can have either constant or periodically changing variances. Theoretical results are demonstrated on two simulated series and on two sets of real data.
Reviewer: H.Hietikko


62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62F15 Bayesian inference
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