A Universal Model for General Gross Domestic Product Across Global Economies

Billy Gao
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Abstract

In addressing the need for a robust economic measure, this paper presents a mathematical model to forecast Gross Domestic Product (GDP) across diverse global economies. Our model, constructed from a dataset spanning 39 years from 16 varied economies, deciphers GDP by dissecting its fundamental components of population and productivity. Through meticulous literature review and data analysis, the research develops four predictive models, using linear and exponential trends, to represent the immediate and projected rates of change in both population and productivity. The research reveals a nuanced dynamic between these elements, identifying productivity, especially in infrastructure, healthcare, telecommunications, and innovation, as a pivotal force in driving economic growth. The study not only underlines the significant influence of these sectors but also the critical role of developed economies in aiding less developed ones to counteract the widening poverty gap. A comprehensive sensitivity analysis within the paper evaluates the impact of these factors on GDP, equipping policymakers with essential insights into enhancing economic progress. By combining immediate and long-term growth metrics derived from twenty-four influential variables into a cohesive predictive model, this research illuminates the complex interplay of forces shaping GDP trajectories. It suggests that while boosting population can yield short-term economic gains, enduring prosperity hinges on amplifying productivity. Moreover, the study points to the potential socio-economic divides that necessitate proactive measures for equitable development. Although challenges such as data dependency and growth discrepancies are acknowledged, the model proposes more frequent data analyses for capturing economic fluctuations accurately. Conclusively, the paper bridges a critical gap in economic modeling literature and provides a pragmatic framework for crafting inclusive economic policies and development strategies, thus making a significant contribution to both theoretical and applied economic fields.
全球经济体一般国内生产总值的通用模型
为了满足对稳健的经济衡量标准的需求,本文提出了一个数学模型,用于预测全球不同经济体的国内生产总值(GDP)。我们的模型由 16 个不同经济体的 39 年数据集构建而成,通过剖析人口和生产率这两个基本组成部分来解读国内生产总值。通过细致的文献回顾和数据分析,研究利用线性和指数趋势建立了四个预测模型,以表示人口和生产率的即时和预测变化率。研究揭示了这些要素之间微妙的动态关系,指出生产力,尤其是基础设施、医疗保健、电信和创新领域的生产力,是推动经济增长的关键力量。这项研究不仅强调了这些领域的重要影响,还强调了发达经济体在帮助欠发达国家消除不断扩大的贫困差距方面所发挥的关键作用。论文中的综合敏感性分析评估了这些因素对国内生产总值的影响,为政策制定者提供了促进经济进步的重要见解。通过将从二十四个有影响力的变量中得出的近期和长期增长指标结合到一个有凝聚力的预测模型中,这项研究揭示了影响国内生产总值轨迹的各种力量之间复杂的相互作用。研究表明,虽然提高人口数量可以带来短期经济收益,但持久的繁荣取决于生产力的提高。此外,研究还指出了潜在的社会经济鸿沟,有必要采取积极措施促进公平发展。虽然承认存在数据依赖和增长差异等挑战,但该模型建议进行更频繁的数据分析,以准确捕捉经济波动。总之,本文弥补了经济建模文献中的一个重要空白,为制定包容性经济政策和发展战略提供了一个务实的框架,从而为理论和应用经济领域做出了重要贡献。
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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