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DATA AUGMENTATION AND DYNAMIC LINEAR MODELS
Author(s) -
FrühwirthSchnatter Sylvia
Publication year - 1994
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1994.tb00184.x
Subject(s) - mathematics , hyperparameter , gibbs sampling , convergence (economics) , sampling (signal processing) , inverse , algorithm , mathematical optimization , statistics , computer science , bayesian probability , filter (signal processing) , geometry , economics , computer vision , economic growth
. We define a subclass of dynamic linear models with unknown hyperpara‐meter called d ‐inverse‐gamma models. We then approximate the marginal probability density functions of the hyperparameter and the state vector by the data augmentation algorithm of Tanner and Wong. We prove that the regularity conditions for convergence hold. For practical implementation a forward‐filtering‐backward‐sampling algorithm is suggested, and the relation to Gibbs sampling is discussed in detail.