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A Bayesian Approach to Modelling Graphical Vector Autoregressions

Listed author(s):
  • Corander, Jukka

    (Department of Mathematics and statistics)

  • Villani, Mattias


    (Research Department, Central Bank of Sweden)

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.

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Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 171.

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Length: 19 pages
Date of creation: 01 Oct 2004
Publication status: Published in Journal of Time Series Analysis, 2005, pages 141-156.
Handle: RePEc:hhs:rbnkwp:0171
Contact details of provider: Postal:
Sveriges Riksbank, SE-103 37 Stockholm, Sweden

Phone: 08 - 787 00 00
Fax: 08-21 05 31
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