A Bayesian Approach to Modelling Graphical Vector Autoregressions
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Abstract
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DOI: 10.1111/j.1467-9892.2005.00460.x
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Other versions of this item:
- Corander, Jukka & Villani, Mattias, 2004. "A Bayesian Approach to Modelling Graphical Vector Autoregressions," Working Paper Series 171, Sveriges Riksbank (Central Bank of Sweden).
References listed on IDEAS
- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
Citations
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Cited by:
- P. Giudici & A. Spelta, 2016.
"Graphical Network Models for International Financial Flows,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 128-138, January.
- Paolo Giudici & Alessandro Spelta, 2013. "Graphical network models for international financial flows," DEM Working Papers Series 052, University of Pavia, Department of Economics and Management.
- Yin, Libo & Ma, Xiyuan, 2018. "Causality between oil shocks and exchange rate: A Bayesian, graph-based VAR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 434-453.
- Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
- Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
- 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".
- Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application To Systemic Risk," DEM Working Papers Series 063, University of Pavia, Department of Economics and Management.
- Daniel Felix Ahelegbey & Paolo Giudici, 2014. "Hierarchical Graphical Models, With Application to Systemic Risk," Working Papers 2014:01, Department of Economics, University of Venice "Ca' Foscari".
- 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.
- Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016.
"Bayesian Graphical Models for STructural Vector Autoregressive Processes,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
- 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".
- Ahelegbey, Daniel Felix, 2015. "The Econometrics of Bayesian Graphical Models: A Review With Financial Application," MPRA Paper 92634, University Library of Munich, Germany, revised 25 Apr 2016.
- Daniela Scidá, 2023. "Structural VAR and financial networks: A minimum distance approach to spatial modeling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 49-68, January.
- Daniel Felix Ahelegbey, 2015. "The Econometrics of Networks: A Review," Working Papers 2015:13, Department of Economics, University of Venice "Ca' Foscari".
- Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
- 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.
- Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
- Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
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More about this item
JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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