The determination of reliable early-warning indicators of economic crises is a hot topic in economic sciences. Pinning down recurring patterns or combinations of macroeconomic indicators is indispensable for adequate policy adjustments to prevent a looming crisis. We investigate the ability of several macroeconomic variables telling crisis countries apart from non-crisis economies. We introduce a self-calibrated clustering-algorithm, which accounts for both similarity and dissimilarity in macroeconomic fundamentals across countries. Furthermore, imposing a desired community structure, we allow the data to decide by itself, which combination of indicators would have most accurately foreseen the exogeneously defined network topology. We quantitatively evaluate the degree of matching between the data-generated clustering and the desired community-structure.

中文翻译：

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经济危机指标：数据驱动的聚类方法

确定可靠的经济危机预警指标是经济科学中的热门话题。固定重复的模式或宏观经济指标的组合对于适当的政策调整以防止迫在眉睫的危机是必不可少的。除了非危机经济体之外，我们还研究了几个宏观经济变量告诉危机国家的能力。我们引入了一种自校准的聚类算法，该算法说明了各国宏观经济基本面的相似性和不相似性。此外，通过施加所需的社区结构，我们可以让数据自行决定，哪种指标组合可以最准确地预测外生定义的网络拓扑。