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Bayesian Graphical Models for Structural Vector Autoregressive Processes

Author

Listed:
  • Daniel Felix Ahelegbey

    (Department of Economics, University Of Venice C� Foscari)

  • Monica Billio

    (Department of Economics, University Of Venice C� Foscari)

  • Roberto Casarin

    (Department of Economics, University Of Venice C� 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.

Suggested Citation

  • Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2012. "Bayesian Graphical Models for Structural Vector Autoregressive Processes," Working Papers 2012:36, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2012:36
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian Graphical models; Markov Chain Monte Carlo; Structural Vector Autoregression; Directed Acyclic Graph; Bayesian Inference; Dynamic Bayesian Network.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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