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A Bayesian Approach to Modelling Graphical Vector Autoregressions

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Author Info

  • Corander, Jukka

    (Department of Mathematics and statistics)

  • Villani, Mattias

    ()
    (Research Department, 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.

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Bibliographic Info

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
Date of revision:
Publication status: Published in Journal of Time Series Analysis, 2005, pages 141-156.
Handle: RePEc:hhs:rbnkwp:0171

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Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
Phone: 08 - 787 00 00
Fax: 08-21 05 31
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Web page: http://www.riksbank.com/
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Related research

Keywords: Causality; Fractional Bayes; graphical models; lag length selection; vector autoregression;

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Cited by:
  1. Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application To Systemic Risk," DEM Working Papers Series 063, University of Pavia, Department of Economics and Management.

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