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Sparse Graphical Vector Autoregression: A Bayesian Approach

Author

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  • Daniel Felix Ahelegbey
  • Monica Billio
  • Roberto Casarin

Abstract

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.

Suggested Citation

  • Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    • Handle: RePEc:adr:anecst:y:2016:i:123-124:p:333-361
    DOI: 10.15609/annaeconstat2009.123-124.0333
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    Cited by:

    1. Fianu, Emmanuel Senyo & Ahelegbey, Daniel Felix & Grossi, Luigi, 2022. "Modeling risk contagion in the Italian zonal electricity market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 656-679.
    2. Billio, Monica & Casarin, Roberto & Costola, Michele & Iacopini, Matteo, 2024. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Econometrics and Statistics, Elsevier, vol. 29(C), pages 113-131.
    3. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    4. Agosto, Arianna & Ahelegbey, Daniel Felix & Giudici, Paolo, 2020. "Tree networks to assess financial contagion," Economic Modelling, Elsevier, vol. 85(C), pages 349-366.
    5. Monica Billio & Roberto Casarin & Michele Costola & Lorenzo Frattarolo, 2019. "Opinion Dynamics and Disagreements on Financial Networks," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(4), pages 24-51, December.
    6. Daniel Felix Ahelegbey & Paolo Giudici, 2020. "Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises," DEM Working Papers Series 188, University of Pavia, Department of Economics and Management.
    7. Daniel Felix Ahelegbey, . "The econometrics of Bayesian graphical models: a review with financial application," Journal of Network Theory in Finance, Journal of Network Theory in Finance.
    8. Casarin, Roberto & Costola, Michele & Yenerdag, Erdem, 2018. "Financial bridges and network communities," SAFE Working Paper Series 208, Leibniz Institute for Financial Research SAFE, revised 2018.
    9. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    10. Mike West, 2020. "Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 1-31, February.
    11. Daniel Felix Ahelegbey, 2015. "The Econometrics of Networks: A Review," Working Papers 2015:13, Department of Economics, University of Venice "Ca' Foscari".
    12. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    13. Ahelegbey, Daniel Felix & Giudici, Paolo & Hashem, Shatha Qamhieh, 2021. "Network VAR models to measure financial contagion," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    14. Teye, Alfred Larm & Ahelegbey, Daniel Felix, 2017. "Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 56-64.

    More about this item

    Keywords

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    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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