TY - JOUR
T1 - Measuring the Connectedness of the Global Economy
AU - Greenwood-Nimmo, Matthew
AU - Hoang Nguyen, Viet
AU - Shin, Yongcheol
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1016/j.ijforecast.2020.10.003
DO - 10.1016/j.ijforecast.2020.10.003
M3 - Article
VL - 37
SP - 899
EP - 919
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 2
ER -