This study explores the dynamic relationship between corruption and economic growth through an approach based on a system of stochastic equations. In the context of globalization and economic interdependencies, corruption not only affects investment and distorts markets, but it can also, under certain conditions, temporarily boost economic activity. Using data from the Gross Domestic Product (GDP) and the Corruption Perception Index (CPI), we implement a time-series-based model to capture the interactions between these two variables. Through a coupled vector autoregressive equations system, our model identifies patterns of interdependence between economic fluctuations and perceptions of corruption at a global level. Employing graph theory and Granger causality, we build a network of interconnections that illustrates how corruption dynamics in one country can influence economic growth and corruption perception in others. The results provide a robust tool for analyzing international political-economic relationships and can serve as a basis for designing policies that promote transparency and sustainable development.

翻译：本研究通过基于随机方程组的方法，探讨了腐败与经济增长之间的动态关系。在全球化和经济相互依存的背景下，腐败不仅影响投资、扭曲市场，在某些条件下还可能暂时促进经济活动。利用国内生产总值（GDP）和腐败感知指数（CPI）的数据，我们构建了一个基于时间序列的模型，以捕捉这两个变量之间的相互作用。通过耦合的向量自回归方程组，我们的模型识别了全球经济波动与腐败感知之间的相互依赖模式。运用图论和格兰杰因果关系，我们构建了一个关联网络，该网络展示了一国的腐败动态如何影响其他国家的经济增长和腐败感知。研究结果为分析国际政治经济关系提供了有力工具，并可为设计促进透明度和可持续发展的政策奠定基础。