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Using complex networks to characterize international business cycles
1Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania. Caraiani@ipe.ro
Plos One
|March 14, 2013
Summary
Complex networks reveal international business cycle dynamics. Granger causality networks, unlike correlation networks, better predict GDP fluctuations and identify the US as a key economic influencer.
Area of Science:
- Economics
- Network Science
- Econometrics
Background:
- Complex networks are increasingly applied in economics, with a focus on stock markets.
- This study extends network analysis to the domain of international business cycles.
Purpose of the Study:
- To apply complex network analysis to understand international business cycle synchronization.
- To compare correlation-based networks with Granger causality networks for economic analysis.
Main Methods:
- Construction of complex networks using GDP data from G7 and OECD economies.
- Application of correlation and Granger causality measures to economic data.
- Utilizing various filtering methods to derive stationary GDP components and analyzing network sensitivity to detrending.
Main Results:
- Correlation networks highlight comovement among national economies.
- Granger causality networks demonstrate superior predictive power for GDP fluctuations.
- Directed networks derived from Granger causality reveal the US as a central player influencing the global economy in both G7 and OECD samples.
Conclusions:
- Complex network analysis provides valuable insights into international business cycle dynamics.
- Granger causality networks offer a more nuanced understanding of economic interdependencies and influence.
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