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Measuring the Connectedness of the Global Economy

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  • Greenwood-Nimmo, Matthew
  • Nguyen, Viet Hoang
  • Shin, Yongcheol

Abstract

We develop a technique to exploit forecast error variance decompositions to evaluate the macroeconomic connectedness embedded in any multi-country macroeconomic model with an approximate vector autoregressive (VAR) representation. We apply our technique to a large global VAR model covering 25 countries and derive vivid representations of macroeconomic connectedness. We find that the US exerts a dominant influence in the global economy and that Brazil, China, and the Eurozone are also globally significant. Recursive analysis over the period of the global financial crisis shows that shocks to global equity markets are transmitted rapidly and forcefully to real trade flows and real GDP.

Suggested Citation

  • Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Shin, Yongcheol, 2021. "Measuring the Connectedness of the Global Economy," International Journal of Forecasting, Elsevier, vol. 37(2), pages 899-919.
  • Handle: RePEc:eee:intfor:v:37:y:2021:i:2:p:899-919
    DOI: 10.1016/j.ijforecast.2020.10.003
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    More about this item

    Keywords

    Generalised Connectedness Measures (GCMs); International linkages; Network analysis; Macroeconomic connectedness; Forecast error variance decomposition;
    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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