Bayesian inference in cyclical component dynamic linear models

Published

Journal Article

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. © 1995 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • West, M

Published Date

  • January 1, 1995

Published In

Volume / Issue

  • 90 / 432

Start / End Page

  • 1301 - 1312

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1995.10476634

Citation Source

  • Scopus