Using complex networks to characterize international business cycles.

<h4>Background</h4>There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.<h4>Methodology/principal finding...

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Autor principal:	Petre Caraiani
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0794eb322c1a401b943ac6ad07826852
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spelling	oai:doaj.org-article:0794eb322c1a401b943ac6ad078268522021-11-18T07:55:01ZUsing complex networks to characterize international business cycles.1932-620310.1371/journal.pone.0058109https://doaj.org/article/0794eb322c1a401b943ac6ad078268522013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23483979/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.<h4>Methodology/principal findings</h4>We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries' GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.<h4>Conclusion</h4>The use of complex networks is valuable for understanding the business cycle comovements at an international level.Petre CaraianiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58109 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Petre Caraiani Using complex networks to characterize international business cycles.
description	<h4>Background</h4>There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.<h4>Methodology/principal findings</h4>We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries' GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.<h4>Conclusion</h4>The use of complex networks is valuable for understanding the business cycle comovements at an international level.
format	article
author	Petre Caraiani
author_facet	Petre Caraiani
author_sort	Petre Caraiani
title	Using complex networks to characterize international business cycles.
title_short	Using complex networks to characterize international business cycles.
title_full	Using complex networks to characterize international business cycles.
title_fullStr	Using complex networks to characterize international business cycles.
title_full_unstemmed	Using complex networks to characterize international business cycles.
title_sort	using complex networks to characterize international business cycles.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0794eb322c1a401b943ac6ad07826852
work_keys_str_mv	AT petrecaraiani usingcomplexnetworkstocharacterizeinternationalbusinesscycles
_version_	1718422708851572736

Using complex networks to characterize international business cycles.

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