论文标题
使用机器学习算法了解大萧条
Understanding the Great Recession Using Machine Learning Algorithms
论文作者
论文摘要
Nyman和Ormerod（2017）表明，随机森林的机器学习技术有可能发出衰退的预警。将方法应用于一系列的财务变量，并尽可能复制真正的事前预测情况，在1990年以来，四步前进的预测的准确性明显优于专业预报员实际上做出的预测。在这里，我们通过检查每个解释变量对2000年代后期的大衰退的贡献来扩展分析。我们将私营部门的债务分解为其家庭和非金融公司组成部分。我们发现，家庭和非财务公司债务都是大萧条的关键决定因素。我们在解释模型中发现了相当多的非线性。相比之下，公共部门的债务与GDP比率似乎几乎没有做出的贡献。在大衰退期间，它确实急剧上升，但这是由于经济活动的急剧下降而不是原因。我们为美国和英国获得了类似的结果。
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.
