Bayesian Graphical Models for Structural Vector Autoregressive Processes
Daniel Felix Ahelegbey (),
Monica Billio () and
Roberto Casarin ()
No 2012:36, Working Papers from Department of Economics, University of Venice "Ca' Foscari"
Vector autoregressive models have widely been applied in macroeconomics and macroeconometrics to estimate economic relationships and to empirically assess theoretical hypothesis. To achieve the latter, we propose a Bayesian inference approach to analyze the dynamic interactions among macroeconomics variables in a graphical vector autoregressive model. The method decomposes the structural model into multivariate autoregressive and contemporaneous networks that can be represented in the form of a directed acyclic graph. We then simulated the networks with an independent sampling scheme based on a single-move Markov Chain Monte Carlo (MCMC) approach. We evaluated the efficiency of our inference procedure with a synthetic data and an empirical assessment of the business cycles hypothesis.
Keywords: Bayesian Graphical models; Markov Chain Monte Carlo; Structural Vector Autoregression; Directed Acyclic Graph; Bayesian Inference; Dynamic Bayesian Network. (search for similar items in EconPapers)
JEL-codes: C11 C15 C53 E17 (search for similar items in EconPapers)
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Journal Article: Bayesian Graphical Models for STructural Vector Autoregressive Processes (2016)
Working Paper: HIERARCHICAL GRAPHICAL MODELS, WITH APPLICATION TO SYSTEMIC RISK (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2012:36
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