IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v27y2006i1p141-156.html
   My bibliography  Save this article

A Bayesian Approach to Modelling Graphical Vector Autoregressions

Author

Listed:
  • Jukka Corander
  • Mattias Villani

Abstract

. We introduce a Bayesian approach to model assessment in the class of graphical vector autoregressive processes. As a result of the very large number of model structures that may be considered, simulation‐based inference, such as Markov chain Monte Carlo, is not feasible. Therefore, we derive an approximate joint posterior distribution of the number of lags in the autoregression and the causality structure represented by graphs using a fractional Bayes approach. Some properties of the approximation are derived and our approach is illustrated on a four‐dimensional macroeconomic system and five‐dimensional air pollution data.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jtsera:v:27:y:2006:i:1:p:141-156
    DOI: 10.1111/j.1467-9892.2005.00460.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.2005.00460.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.2005.00460.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Ahelegbey, Daniel Felix & Billio, Monica & Casarin, Roberto, 2024. "Modeling Turning Points in the Global Equity Market," Econometrics and Statistics, Elsevier, vol. 30(C), pages 60-75.
    4. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    5. 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".
    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 & 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.
    8. 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.
    9. 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.
    10. Daniel Felix Ahelegbey, 2015. "The Econometrics of Networks: A Review," Working Papers 2015:13, Department of Economics, University of Venice "Ca' Foscari".
    11. 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.
    12. 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.
    13. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Justin Doran & Bernard Fingleton, 2014. "Economic shocks and growth: Spatio-temporal perspectives on Europe's economies in a time of crisis," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 137-165, November.
    2. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    3. Lisbeth Funding la Cour, 1995. "A Component® based Analysis of the danish Long-run Money Demand Relation," Discussion Papers 95-18, University of Copenhagen. Department of Economics.
    4. Levent, Korap, 2007. "Modeling purchasing power parity using co-integration: evidence from Turkey," MPRA Paper 19584, University Library of Munich, Germany.
    5. Alessia Naccarato & Andrea Pierini & Giovanna Ferraro, 2021. "Markowitz portfolio optimization through pairs trading cointegrated strategy in long-term investment," Annals of Operations Research, Springer, vol. 299(1), pages 81-99, April.
    6. Darrian Collins & Clem Tisdell, 2004. "Outbound Business Travel Depends on Business Returns: Australian Evidence," Australian Economic Papers, Wiley Blackwell, vol. 43(2), pages 192-207, June.
    7. Christian Schoder, 2012. "Effective demand, exogenous normal utilization and endogenous capacity in the long run. Evidence from a CVAR analysis for the US," IMK Working Paper 103-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    8. Muhammad Shahbaz & Vassilios G. Papavassiliou & Amine Lahiani & David Roubaud, 2023. "Are we moving towards decarbonisation of the global economy? Lessons from the distant past to the present," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2620-2634, July.
    9. Karaman Örsal, Deniz Dilan & Droge, Bernd, 2014. "Panel cointegration testing in the presence of a time trend," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 377-390.
    10. Jakšić Saša, 2022. "Modelling Determinants of Inflation in CESEE Countries: Global Vector Autoregressive Approach," Review of Economic Perspectives, Sciendo, vol. 22(2), pages 137-169, June.
    11. António Duarte, 2009. "The Portuguese Disinflation Process: Analysis of Some Costs and Benefits," Transition Studies Review, Springer;Central Eastern European University Network (CEEUN), vol. 16(1), pages 157-173, May.
    12. Njangang Henri & Nembot Ndeffo Luc & Nawo Larissa, 2019. "The Long‐run and Short‐run Effects of Foreign Direct Investment on Financial Development in African Countries," African Development Review, African Development Bank, vol. 31(2), pages 216-229, June.
    13. Lego, Brian & Gebremedhin, Tesfa & Cushing, Brian, 2000. "A Multi-Sector Export Base Model of Long-Run Regional Employment Growth," Agricultural and Resource Economics Review, Cambridge University Press, vol. 29(2), pages 192-197, October.
    14. Ali MNA & Moheddine YOUNSI, 2018. "A monetary conditions index and its application on Tunisian economic forecasting," Journal of Economics and Political Economy, KSP Journals, vol. 5(1), pages 38-56, March.
    15. Franses, Ph.H.B.F. & Paap, R., 1999. "Forecasting with periodic autoregressive time series models," Econometric Institute Research Papers EI 9927-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    16. Hauser, Shmuel & Kedar-Levy, Haim & Milo, Orit, 2022. "Price discovery during parallel stocks and options preopening: Information distortion and hints of manipulation," Journal of Financial Markets, Elsevier, vol. 59(PA).
    17. Jaromir Benes & David Vavra, 2004. "Eigenvalue Decomposition of Time Series with Application to the Czech Business Cycle," Working Papers 2004/08, Czech National Bank.
    18. Çakır, Mustafa Yavuz & Kabundi, Alain, 2013. "Trade shocks from BRIC to South Africa: A global VAR analysis," Economic Modelling, Elsevier, vol. 32(C), pages 190-202.
    19. Stanislav Yugay & Linde Götz & Miranda Svanidze, 2024. "Impact of the Ruble exchange rate regime and Russia's war in Ukraine on wheat prices in Russia," Agricultural Economics, International Association of Agricultural Economists, vol. 55(2), pages 384-411, March.
    20. Apergis, Nicholas & Payne, James E., 2010. "Coal consumption and economic growth: Evidence from a panel of OECD countries," Energy Policy, Elsevier, vol. 38(3), pages 1353-1359, March.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jtsera:v:27:y:2006:i:1:p:141-156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.