Random matrix theory and the evolution of business cycle synchronisation 1886-2006
Paul Ormerod

TL;DR
This paper applies random matrix theory and hierarchical clustering to analyze long-term business cycle synchronization among capitalist economies, challenging previous claims of a steady increase in synchronization over the 20th century.
Contribution
It introduces the use of random matrix theory to assess business cycle synchronization, providing a more robust analysis that questions prior findings of a secular trend.
Findings
No significant synchronization before WWI.
Weak synchronization during 1920-38 and 1948-72.
Meaningful synchronization only in 1973-2006.
Abstract
The major study by Bordo and Helbing (2003) analyses the business cycle in Western economies 1881-2001. They examine four distinct periods in economic history, and conclude that there is a secular trend towards greater synchronisation for much of the 20th century. Their analysis, in common with the standard economic literature on business cycle synchronisation, relies upon the estimation of an empirical correlation matrix of time series data of macroeconomic aggregates. However because of the small number of observations and economies, the empirical correlation matrix may contain considerable noise. Random matrix theory was developed to overcome this problem. I use random matrix theory, and the associated technique of agglomerative hierarchical clustering, to examine the evolution of business cycle synchronisation between the capitalist economies in the long-run. Contrary to the…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Theoretical and Computational Physics · Chaos control and synchronization
