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Measuring the Connectedness of the Global Economy

Author

Listed:
  • Matthew Greenwood-Nimmo

    (Department of Economics, The University of Melbourne)

  • Viet Hoang Nguyen

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne vietn@unimelb.edu.au https://www.melbourneinstitute.com/staff/vhnguyen/default.html Yongcheol Shin Department of Economics and Related Studies, University of York)

Abstract

We develop a technique to evaluate macroeconomic connectedness in any multi-country macroeconomic model with an approximate VAR representation. We apply our technique to a large Global VAR covering 25 countries and derive vivid representations of the connectedness of the system. We show that the US, the Eurozone and the crude oil market exert a dominant influence in the global economy and that the Chinese and Brazilian economies are also globally significant. Recursive analysis over the period of the global financial crisis shows that shocks to global equity markets are rapidly and forcefully transmitted to real trade flows and real GDP.

Suggested Citation

  • Matthew Greenwood-Nimmo & Viet Hoang Nguyen, 2015. "Measuring the Connectedness of the Global Economy," Melbourne Institute Working Paper Series wp2015n07, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2015n07
    as

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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2015n07.pdf
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    References listed on IDEAS

    as
    1. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    2. Pesaran, Mohammad Hashem & Holly, Sean & Dees, Stephane & Smith, L. Vanessa, 2007. "Long Run Macroeconomic Relations in the Global Economy," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 1, pages 1-20.
    3. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    4. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Yongcheol Shin, 2012. "Probabilistic forecasting of output growth, inflation and the balance of trade in a GVAR framework," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 554-573, June.
    5. Alessandro Rebucci & Ambrogio Cesa-Bianchi & M. Hashem Pesaran & TengTeng Xu, 2012. "China's Emergence in the World Economy and Business Cycles in Latin America," ECONOMIA JOURNAL OF THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION, ECONOMIA JOURNAL OF THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION, vol. 0(Spring 20), pages 1-75, January.
    6. Mr. Papa M N'Diaye & Mr. Dale F Gray & Ms. Natalia T. Tamirisa & Ms. Hiroko Oura & Qianying Chen, 2010. "International Transmission of Bank and Corporate Distress," IMF Working Papers 2010/124, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Generalised Connectedness Measures (GCMs); international linkages; network analysis; macroeconomic connectedness;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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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