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"

Abstract: 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)
Pages: 30
Date: 2012
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Citations: View citations in EconPapers (24)

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Related works:
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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