The role of statistical analysis and econometric modeling in predicting corruption and ensuring economic security

Abstract

The article examines the importance of statistical analysis and econometric modeling in forecasting corruption levels and ensuring economic security. Corruption poses a significant threat to the economic stability and development of states, making effective forecasting of its level crucial for formulating strategies to enhance economic security. The paper explores the use of regression analysis to identify the relationships between corruption levels and various economic, political, and social factors, such as GDP levels, political stability, and governance quality. Panel data, which includes both time series and cross-country variations, allows for an understanding of both the dynamics and spatial aspects of corruption. The article also discusses econometric modeling methods, including multiple regression models and autoregressive models (ARIMA). Multiple regression models assess the simultaneous impact of several factors, which aids in better understanding the causes and consequences of corruption. ARIMA models are used for time series analysis and forecasting future values of corruption levels based on historical data. The advantages of these methods include precise identification of key risk factors and forecasting changes in corruption levels, which is essential for developing effective anti-corruption measures. The article also addresses limitations related to data quality and potential model shortcomings. The conclusion emphasizes the need for further research, including the use of big data and machine learning to improve forecasting accuracy and enhance the understanding of corruption risks in the context of economic security.