Data Augmentation and Dynamic Linear Models

Publication: Working/Discussion PaperWU Working Paper

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Abstract

We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
Publication statusPublished - 1992

Publication series

NameForschungsberichte / Institut für Statistik
No.28

WU Working Paper Series

  • Forschungsberichte / Institut für Statistik

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