Sparse Graphical Vector Autoregression: A Bayesian Approach
Daniel Felix Ahelegbey (),
Monica Billio () and
Roberto Casarin ()
Annals of Economics and Statistics, 2016, issue 123-124, 333-361
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.
Keywords: Large VAR; Model Selection; Prior Distribution; Sparse Graphical Models (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 C55 E17 G01 G17 (search for similar items in EconPapers)
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Working Paper: Sparse Graphical Vector Autoregression: A Bayesian Approach (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2016:i:123-124:p:333-361
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