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Fractional Bayesian lag length inference in multivariate autoregressive processes
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, The Institute of Technology. Linköping University, Faculty of Arts and Sciences. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2786-2519
2001 (English)In: Journal of Time Series Analysis, ISSN 0143-9782, E-ISSN 1467-9892, Vol. 22, no 1, 67-86 p.Article in journal (Refereed) Published
Abstract [en]

The posterior distribution of the number of lags in a multivariate autoregression is derived under an improper prior for the model parameters. The fractional Bayes approach is used to handle the indeterminacy in the model selection caused by the improper prior. An asymptotic equivalence between the fractional approach and the Schwarz Bayesian Criterion (SBC) is proved. Several priors and three loss functions are entertained in a simulation study which focuses on the choice of lag length. The fractional Bayes approach performs very well compared to the three most widely used information criteria, and it seems to be reasonably robust to changes in the prior distribution for the lag length, especially under the zero-one loss.

Place, publisher, year, edition, pages
Wiley-Blackwell Publishing Inc., 2001. Vol. 22, no 1, 67-86 p.
Keyword [en]
Fractional marginal likelihood, improper prior, lag length selection, vector autoregression
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-137704DOI: 10.1111/1467-9892.00212ISI: 000175858300004OAI: oai:DiVA.org:liu-137704DiVA: diva2:1098728
Available from: 2017-05-25 Created: 2017-05-25 Last updated: 2017-06-02Bibliographically approved

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Villani, Mattias
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