A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

Abstract

In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.

Keywords

• global economic policy uncertainty index
• principal component analysis
• random matrix theory
• complex network
• GARCH-MIDAS model
• future price volitility