Bayesian Inference in Cyclical Component Dynamic Linear Models
作者:
Mike West,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 432
页码: 1301-1312
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476634
出版商: Taylor & Francis Group
关键词: Autoregressive component dynamic linear model;Cyclical time series;Dynamic linear model;Markov chain Monte Carlo
数据来源: Taylor
摘要:
Dynamic linear models (DLM's) with time-varying cyclical components are developed for the analysis of time series with persistent though time-varying cyclical behavior. The development covers inference on wavelengths of possibly several persistent cycles in nonstationary time series, permitting explicit time variation in amplitudes and phases of component waveforms, decomposition of stochastic inputs into purely observational noise and innovations that impact on the waveform characteristics, with extensions to incorporate ranges of (time-varying) time series and regression terms wihin the standard DLM context. Bayesian inference via iterative stochastic simulation methods is developed and illustrated. Some indications of model extensions and generalizations are given. In addition to the specific focus on cyclical component models, the development provides the basis for Bayesian inference, via stochastic simulation, for state evolution matrix parameters and variance components in DLM's, building on recent work on Gibbs sampling for state vectors in such models by other authors.
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