Relationship between Macroeconomic Indicators and Economic Cycles in U.S. (vol 10, 8420, 2020)

被引:0
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作者
Iyetomi, Hiroshi
Aoyama, Hideaki
Fujiwara, Yoshi
Souma, Wataru
Vodenska, Irena
Yoshikawa, Hiroshi
机构
[1] Niigata University, Department of Mathematics, Niigata
[2] Kyoto University, GSAIS, Kyoto
[3] Research Institute of Economy, Trade and Industry (RIETI), Tokyo
[4] University of Hyogo, Graduate School of Simulation Studies, Kobe
[5] Nihon University, College of Science and Technology, Funabashi
[6] Boston University, Metropolitan College, Boston, 02215, MA
[7] Rissho University, Faculty of Economics, Tokyo
关键词
D O I
10.1038/s41598-020-70100-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. © 2020, The Author(s).
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