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Anděl, Jiří

Statistical analysis of periodic autoregression. (English) Zbl 0537.62073

Apl. Mat. 28, 364-385 (1983).
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

Cited in 1 Review
Cited in 6 Documents

MSC:

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

Keywords:

periodic autoregression; vague prior density; innovation process; changing variances; simulated series; real data
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\textit{J. Anděl}, Apl. Mat. 28, 364--385 (1983; Zbl 0537.62073)
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References:

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