Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study

Diego Vallarino
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Abstract

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

各国人均 GDP 集群化进程的时间动态:综合生存研究
本研究采用五种不同的生存机器学习模型,特别是深度学习算法(DeepSurv)和生存随机森林,深入研究了一个国家追求目标人均 GDP 水平的时间动态。这种细致入微的视角超越了静态评估,提供了对塑造长期经济轨迹的发展过程的全面理解。其经济影响强调了计算风险和战略脆弱性缓解之间所需的复杂平衡。这些研究结果可指导决策者制定具有弹性的经济战略,以便在当代经济环境固有的复杂性中实现可持续发展和增长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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