ECONOMETRIC MODELS FOR DEVELOPING COMPETITIVENESS POTENTIAL IN THE REGION

Abstract

The contemporary economic landscape is defined by a fundamental shift from traditional factor-based growth models to a sophisticated competency-based paradigm centered on digital transformation and human capital optimization. This research provides a comprehensive econometric analysis of regional competitiveness across the Visegrad Four (V4), the Russian Federation, and the Republic of Uzbekistan. Utilizing a multi-layered methodological framework—comprising Dynamic Panel Data (Arellano-Bond), Data Envelopment Analysis (DEA), and Spatial Econometric Modeling (SAR/SEM)—this study identifies the mechanisms through which digital literacy and innovation clusters catalyze regional output. The analysis reveals a significant disparity between capital regions (NUTS2) and peripheral territories, necessitating a transition toward "Smart Specialization" (S3) strategies. A critical finding is the role of human capital as a two-tiered structure, where "Fundamental" components (health and education) support a "Progressive" level (digital and emotional capital). Furthermore, the study explores the "neighbor effect" and the paradox of negative spatial spillovers caused by administrative barriers. By synthesizing a 166-equation block-recursive model for forecasting and k-means cluster analysis for regional typology, the research proposes actionable policy frameworks for fostering resilient ecosystems through public-private partnerships and the integration of digital competencies into the industrial fabric.