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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
    1. Alessio Moneta, 2008. "Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis," Empirical Economics, Springer, vol. 35(2), pages 275-300, September.
    2. Jukka Corander & Mattias Villani, 2006. "A Bayesian Approach to Modelling Graphical Vector Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 141-156, January.
    3. Hoover,Kevin D., 2001. "Causality in Macroeconomics," Cambridge Books, Cambridge University Press, number 9780521002882, July - De.
    4. David A. Bessler & Seongpyo Lee, 2002. "Money and prices: U.S. Data 1869-1914 (A study with directed graphs)," Empirical Economics, Springer, vol. 27(3), pages 427-446.
    5. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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    7. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
    8. Corander, Jukka, 2003. "Bayesian graphical model determination using decision theory," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 253-266, May.
    9. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
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    12. Marco Grzegorczyk & Dirk Husmeier & Jörg Rahnenführer, 2011. "Modelling non-stationary dynamic gene regulatory processes with the BGM model," Computational Statistics, Springer, vol. 26(2), pages 199-218, June.
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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.;

    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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