A Bayesian Approach to Modelling Graphical Vector Autoregressions

Jukka Corander and Mattias Villani
Additional contact information
Jukka Corander: Department of Mathematics and statistics, Postal: P.O. Box 68, FIN-00014, University of Helsinki, Finland

No 171, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

Abstract: We introduce a Bayesian approach to model assessment in the class of graphical vector autoregressive (VAR) processes. Due to the very large number of model structures that may be considered, simulation based inference, such as Markov chain Monte Carlo, is not feasible. Therefore, we derive an approximate joint posterior distribution of the number of lags in the autoregression and the causality structure represented by graphs using a fractional Bayes approach. Some properties of the approximation are derived and our approach is illustrated on a four-dimensional macroeconomic system and five-dimensional air pollution data.

Keywords: Causality; Fractional Bayes; graphical models; lag length selection; vector autoregression (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2004-10-01
New Economics Papers: this item is included in nep-ets
References: Add references at CitEc
Citations:

Published in Journal of Time Series Analysis, 2005, pages 141-156.

Downloads: (external link)
http://www.riksbank.com/upload/WorkingPapers/WP_171.pdf (application/postscript)

Related works:
Journal Article: A Bayesian Approach to Modelling Graphical Vector Autoregressions (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hhs:rbnkwp:0171

Access Statistics for this paper

More papers in Working Paper Series from Sveriges Riksbank (Central Bank of Sweden) Sveriges Riksbank, SE-103 37 Stockholm, Sweden. Contact information at EDIRC.
Bibliographic data for series maintained by Lena Löfgren ().