  Most of the analytical techniques used in the business cycle synchronisation
literature rely upon the estimation of an empirical correlation matrix of time
series data of macroeconomic aggregates, real GDP usually being the key
variable. But the small number of available observations and small number of
economies mean that the empirical correlation matrix may contain considerable
noise. Random matrix theory was developed in physics to overcome this problem.
The largest eigenvalue of the correlation matrix informs us directly about the
degree to which movements of the economies are genuinely correlated. The
evolution of business cycle synchronisation can be analysed with the temporal
evolution of the largest eigenvalue over a fixed window of data. I analyse
quarterly real GDP data 1981Q1-2008Q1 for the core EU economies - Germany,
France, Italy, Spain, Netherlands, Belgium - along with the UK, which is a
member of the EU but not the Euro, and the US as a comparator. The core EU
economies have shown varying but strong synchronisation over the whole period.
In contrast, the UK and the US are much more synchronised with each other than
they are with the core EU economies.
