Relationship between Macroeconomic Indicators and Economic Cycles in U.S.

Hiroshi Iyetomi,Wataru Souma,Irena Vodenska,Hideaki Aoyama,Yoshi Fujiwara,Hiroshi Yoshikawa

doi:10.1038/s41598-020-65002-3

Abstract

We analyze monthly time series of 57 US macroeconomic indicators (18 leading, 30 coincident, 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 coincident and lagging indicators actually show leading indicator characteristics. This suggests that there is a room for existing indicators to be improved.

Highlights

We analyze monthly time series of 57 US macroeconomic indicators (18 leading, 30 coincident, and 9 lagging) and 5 other trade/money indexes
Using the similarity measure we found that each aspect of the eigenvectors of Complex Hilbert Principal Component Analysis (CHPCA) has a corresponding eigenvector of principal component analysis (PCA)
Understanding the challenges ahead of us, we suggest that the roles of macroeconomic indicators might change with time and with fluctuating economic dynamics, and real-time analysis using noise-reducing methodologies might be appropriate to offer improved forecasting of the business cycle

Summary

Methods

In order to overcome the shortcomings illustrated in the introduction, we employ novel analytical tools for identifying statistically significant correlation in the time series data, and isolate co-movements in this paper. Many of these methods are described in the book by some of the present authors[13], to make the present paper self-contained, we give the following concise review. In order to identify which eigenmodes of C∼ are the significant mode (signal) and which are the noise, we employ RRS23,25 In this method, we cut off the intercorrelation between the time series by randomly shuffling each of them independently and carrying out the CHPCA analysis

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Findings

Discussion