  Nyman and Ormerod (2017) show that the machine learning technique of random
forests has the potential to give early warning of recessions. Applying the
approach to a small set of financial variables and replicating as far as
possible a genuine ex ante forecasting situation, over the period since 1990
the accuracy of the four-step ahead predictions is distinctly superior to those
actually made by the professional forecasters. Here we extend the analysis by
examining the contributions made to the Great Recession of the late 2000s by
each of the explanatory variables. We disaggregate private sector debt into its
household and non-financial corporate components. We find that both household
and non-financial corporate debt were key determinants of the Great Recession.
We find a considerable degree of non-linearity in the explanatory models. In
contrast, the public sector debt to GDP ratio appears to have made very little
contribution. It did rise sharply during the Great Recession, but this was as a
consequence of the sharp fall in economic activity rather than it being a
cause. We obtain similar results for both the United States and the United
Kingdom.
