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Understanding the Great Recession Using Machine Learning Algorithms

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Document pages: 15 pages

Abstract: Nyman and Ormerod (2017) show that the machine learning technique of randomforests has the potential to give early warning of recessions. Applying theapproach to a small set of financial variables and replicating as far aspossible a genuine ex ante forecasting situation, over the period since 1990the accuracy of the four-step ahead predictions is distinctly superior to thoseactually made by the professional forecasters. Here we extend the analysis byexamining the contributions made to the Great Recession of the late 2000s byeach of the explanatory variables. We disaggregate private sector debt into itshousehold and non-financial corporate components. We find that both householdand non-financial corporate debt were key determinants of the Great Recession.We find a considerable degree of non-linearity in the explanatory models. Incontrast, the public sector debt to GDP ratio appears to have made very littlecontribution. It did rise sharply during the Great Recession, but this was as aconsequence of the sharp fall in economic activity rather than it being acause. We obtain similar results for both the United States and the UnitedKingdom.

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