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Relationship between Macroeconomic Indicators and Economic Cycles in U.S

We analyze monthly time series of 57 US macroeconomic indicators (18 leading, 30 coincidental, and 9 lagging) and 5 other trade/money indexes. Using novel methods, we confirm statistically significant co-movements among these time series and identify noteworthy economic events. The methods we use ar...

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Autores principales: Iyetomi, Hiroshi, Aoyama, Hideaki, Fujiwara, Yoshi, Souma, Wataru, Vodenska, Irena, Yoshikawa, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242465/
https://www.ncbi.nlm.nih.gov/pubmed/32439848
http://dx.doi.org/10.1038/s41598-020-65002-3
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author Iyetomi, Hiroshi
Aoyama, Hideaki
Fujiwara, Yoshi
Souma, Wataru
Vodenska, Irena
Yoshikawa, Hiroshi
author_facet Iyetomi, Hiroshi
Aoyama, Hideaki
Fujiwara, Yoshi
Souma, Wataru
Vodenska, Irena
Yoshikawa, Hiroshi
author_sort Iyetomi, Hiroshi
collection PubMed
description We analyze monthly time series of 57 US macroeconomic indicators (18 leading, 30 coincidental, and 9 lagging) and 5 other trade/money indexes. Using novel methods, we confirm statistically significant co-movements among these time series and identify noteworthy economic events. The methods we use are Complex Hilbert Principal Component Analysis (CHPCA) and Rotational Random Shuffling (RRS). We obtain significant complex correlations among the US economic indicators with leads/lags. We then use the Hodge decomposition to obtain the hierarchical order of each time series. The Hodge potential allows us to better understand the lead/lag relationships. Using both CHPCA and Hodge decomposition approaches, we obtain a new lead/lag order of the macroeconomic indicators and perform clustering analysis for positively serially correlated positive and negative changes of the analyzed indicators. We identify collective negative co-movements around the Dot.com bubble in 2001 as well as the Global Financial Crisis (GFC) in October 2008. We also identify important events such as the Hurricane Katrina in August 2005 and the Oil Price Crisis in July 2008. Additionally, we demonstrate that some coincidental and lagging indicators actually show leading indicator characteristics. This suggests that there is a room for existing indicators to be improved.
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spelling pubmed-72424652020-05-30 Relationship between Macroeconomic Indicators and Economic Cycles in U.S Iyetomi, Hiroshi Aoyama, Hideaki Fujiwara, Yoshi Souma, Wataru Vodenska, Irena Yoshikawa, Hiroshi Sci Rep Article We analyze monthly time series of 57 US macroeconomic indicators (18 leading, 30 coincidental, and 9 lagging) and 5 other trade/money indexes. Using novel methods, we confirm statistically significant co-movements among these time series and identify noteworthy economic events. The methods we use are Complex Hilbert Principal Component Analysis (CHPCA) and Rotational Random Shuffling (RRS). We obtain significant complex correlations among the US economic indicators with leads/lags. We then use the Hodge decomposition to obtain the hierarchical order of each time series. The Hodge potential allows us to better understand the lead/lag relationships. Using both CHPCA and Hodge decomposition approaches, we obtain a new lead/lag order of the macroeconomic indicators and perform clustering analysis for positively serially correlated positive and negative changes of the analyzed indicators. We identify collective negative co-movements around the Dot.com bubble in 2001 as well as the Global Financial Crisis (GFC) in October 2008. We also identify important events such as the Hurricane Katrina in August 2005 and the Oil Price Crisis in July 2008. Additionally, we demonstrate that some coincidental and lagging indicators actually show leading indicator characteristics. This suggests that there is a room for existing indicators to be improved. Nature Publishing Group UK 2020-05-21 /pmc/articles/PMC7242465/ /pubmed/32439848 http://dx.doi.org/10.1038/s41598-020-65002-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Iyetomi, Hiroshi
Aoyama, Hideaki
Fujiwara, Yoshi
Souma, Wataru
Vodenska, Irena
Yoshikawa, Hiroshi
Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title_full Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title_fullStr Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title_full_unstemmed Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title_short Relationship between Macroeconomic Indicators and Economic Cycles in U.S
title_sort relationship between macroeconomic indicators and economic cycles in u.s
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242465/
https://www.ncbi.nlm.nih.gov/pubmed/32439848
http://dx.doi.org/10.1038/s41598-020-65002-3
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