Clustering a Sample of Major and Emerging Economies in Function of their Economic Policy Uncertainty: K-means, Agglomerative Hierarchical Clustering and Density-Based Spatial Clustering with Noise
DOI:
https://doi.org/10.18381/eq.v22i1.7355Keywords:
Incertidumbre de la política económica, análisis de agrupamiento, K-means, DBSCAN, agrupación jerárquica aglomerativaAbstract
Objective: This study carries out pattern identification in a sample of 16 major and emerging economies in function of their economic policy uncertainty. Methodology: This paper applies for the groping procedure K-Means, Agglomerative Hierarchical Clustering (AHC), and Clustering and Density-Based Spatial Clustering with Noise (DBSCAN). Data: This research uses the Economic Policy Uncertainty (EPU) Index calculated monthly by the EPU Agency for several countries. In particular, it examines EPU indexes for a sample 16 countries in five crisis periods between 2008 and 2024; the sample was chosen based on data availability. Results: Global crises have created distinct country clusters transcending traditional economic groupings based on development status or geographical location. Notably, in the COVID-19 pandemic it was generated an unprecedented global EPU homogeneity among countries. High-uncertainty clusters consistently emerge, often comprising large economies directly affected by crises. Limitations: There are possible biases in news-based component of EPU indices. Originality: To the best of the authors’ knowledge, multiple clustering techniques for various crisis periods have not been implemented before. Conclusion: Global crises can equalize policy uncertainty, challenging conventional notions of economic resilience. The empirical findings emphasize the importance of considering EPU in a global context for those responsible for improving the design of economic policy.Downloads
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