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Sparse Graphical Vector Autoregression: A Bayesian Approach

Listed author(s):
  • Daniel Felix Ahelegbey
  • Monica Billio
  • Roberto Casarin

This paper considers a sparsity approach for inference in large vector autoregressive (VAR) models. The approach is based on a Bayesian procedure and a graphical representation of VAR models. We discuss a Markov chain Monte Carlo algorithm for sparse graph selection, parameter estimation, and equation-specific lag selection. We show the efficiency of our algorithm on simulated data and illustrate the effectiveness of our approach in forecasting macroeconomic time series and in measuring contagion risk among financial institutions.

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File URL: http://www.jstor.org/stable/10.15609/annaeconstat2009.123-124.0333
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Article provided by GENES in its journal Annals Of Economics and Statistics.

Volume (Year): (2016)
Issue (Month): 123-124 ()
Pages: 333-361

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Handle: RePEc:adr:anecst:y:2016:i:123-124:p:333-361
DOI: 10.15609/annaeconstat2009.123-124.0333
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