Diverse Trajectories of Globalization and Forecasting of Economic Phenomena in Subpopulations: A Model-Based and Simulation Approach

The article presents a model-based and simulation-oriented approach to analyzing economic phenomena within subpopulations operating in different globalization contexts. Globalization is a heterogeneous phenomenon with varying trajectories across regions, sectors, and degrees of economic integration. This study addresses a critical gap in the literature by proposing a novel, model-based approach to analyze globalization patterns in European subpopulations, particularly under conditions of limited data availability. Using the Dynamic Time Warping (DTW) algorithm and k-means clustering, we identify and group countries with similar globalization trajectories based on the KOF Globalization Index. Our methodology captures nonlinear similarities and local anomalies, offering a more nuanced understanding of globalization dynamics than traditional linear models. The results reveal four distinct clusters of European countries, each characterized by unique globalization pathways—ranging from rapid growth to stagnation or decline. These findings contribute to the broader debate on globalization’s uneven impacts and provide policymakers with actionable insights for fostering economic integration.