A Customized Machine Learning Algorithm for Discovering the Shapes of Recovery: Was the Global Financial Crisis Different?

In this paper, we modify a conventional machine learning technique to classify recession-and-recovery events emerging in the countries’ business cycles. We do this by analyzing output dynamics in time windows of the same size for a large set of countries. We show with quarterly GDP series that, despite the simplicity of the method, it is possible to describe analytically the shapes of recovery (‘shapelets’) that can be considered representative in a sample of 95 events coming from 47 advanced and emerging economies. The proposed methodology allows to depurate the number of shapelets empirically relevant, and also to produce groupings with economic meaning that are strongly associated with recession features such as depth, duration, cumulative losses, and others. Furthermore, we find that the relative frequency of these clusters can vary with the type of crisis. In particular, in the recent global financial crisis, shapelets describing severe recession events were very likely but mild recessions were also common.