Relationship between Macroeconomic Indicators and Economic Cycles in U.S

Area of Science:

• Economics
• Econometrics
• Time Series Analysis

Background:

• Macroeconomic indicators are crucial for understanding economic health.
• Existing lead/lag analyses may not fully capture complex interdependencies.

Purpose of the Study:

• To analyze co-movements among 57 US macroeconomic indicators using novel methods.
• To establish a new hierarchical order of lead/lag relationships for these indicators.
• To identify significant economic events and assess indicator performance.

Main Methods:

• Complex Hilbert Principal Component Analysis (CHPCA) for identifying co-movements and correlations.
• Rotational Random Shuffling (RRS) for statistical significance testing.
• Hodge decomposition to determine the hierarchical order and understand lead/lag dynamics.
• Clustering analysis for positively serially correlated changes.

Main Results:

• Statistically significant complex correlations and lead/lag relationships were confirmed among US economic indicators.
• A new lead/lag order was established using CHPCA and Hodge decomposition.
• Collective negative co-movements were identified around the Dot.com bubble (2001) and the Global Financial Crisis (2008).
• Specific events like Hurricane Katrina (2005) and the Oil Price Crisis (2008) were pinpointed.
• Coincidental and lagging indicators were found to exhibit leading characteristics.

Conclusions:

• The novel methods provide deeper insights into the complex dynamics of macroeconomic indicators.
• The established lead/lag order offers a refined understanding of economic indicator relationships.
• Findings suggest that current macroeconomic indicators can be improved, potentially enhancing economic forecasting accuracy.