✦ LIBER ✦

Subset selection of autoregressive time series models

✍ Scribed by Cathy W. S. Chen

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 155 KB
Volume: 18
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199912)18:7<505::aid-for728>3.0.co;2-u

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✦ Synopsis

We propose a solution to select promising subsets of autoregressive time series models for further consideration which follows up on the idea of the stochastic search variable selection procedure in . It is based on a Bayesian approach which is unconditional on the initial terms. The autoregression stepup is in the form of a hierarchical normal mixture model, where latent variables are used to identify the subset choice. The framework of our procedure is utilized by the Gibbs sampler, a Markov chain Monte Carlo method. The advantage of the method presented is computational: it is an alternative way to search over a potentially large set of possible subsets. The proposed method is illustrated with a simulated data as well as a real data.

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