International business cycle spillovers since the 1870s

Abstract

This article considers the evolution of international business cycle interdependencies among 27 developed and developing countries since the beginning of 1870s, utilizing the generalized vector autoregressive (VAR)-based spillover index of Diebold and Yilmaz (2012), which allows the construction of a time-varying measure of business cycle spillovers. We find that, on average, 65% of the forecast error variance of the 27 countries’ business cycle shocks is due to international spillovers. However, the magnitude of international business cycle spillovers varies considerably over time. There is a clear increasing trend since the end of World War II and until the mid-1980s. After that, international business cycle interdependencies declined during the period that was dubbed the Great Moderation and stabilized around the beginning of the twenty-first century. During the Great Recession of 2008–2009, international business cycle spillovers increased to unprecedented levels. Finally, developed countries are consistently ranked as net transmitters of cyclical shocks to developing counties throughout the sample.

Keywords:

• business cycle
• spillover
• variance decomposition
• vector autoregression
• developing country
• developed country

JEL Classification:

• C32
• E32
• F01
• F62
• N10

Acknowledgements

The authors like to thank the editor (Mark Taylor) and two anonymous reviewers for helpful comments on a previous version of this article. The usual disclaimer applies.

Supplemental data

Supplemental data for this article can be accessed here .

Notes

¹ This classification referring to the most recent period is used throughout the article.

² See http://www.conference-board.org/data/economydatabase/ and http://www.worldeconomics.com/Data/MadisonHistoricalGDP/Madison\%20Historical\%20GDP\%20Data.efp

³ For robustness, we have cross-checked the results using per capita GDP growth instead of the filtered variables. The findings are qualitatively very similar and thus omitted for the sake of brevity; The HP-filtered data can be accessed at http://dx.doi.org/10.1080/00036846.2014.937040.

⁴ However, we explore the robustness of our results by using Cholesky factorization with alternative orderings of the variables, as discussed below, and our results remain very similar.

⁵ The approximate nature of the result is due to the fact that the contributions of the variables do not sum to 1 under the generalized decomposition framework and have to be normalized (see Equation 3).

⁶ We have explored the robustness of our results using alternative forecasting horizons (i.e. 8 and 12 years) and the results remain qualitatively similar.

⁷ Our results reported below remain robust to alternative choices of window length (i.e. 20, 40 and 80 years).