The uncertain of the current economic conditions is the most challenging aspect of the decision-making process, so we must conduct advanced modeling exercises that can cope with the incomplete and unclear data. We, in this paper suggest that a framework combining Grey Systems Theory with econometric models serves the purpose of economic forecasting and policy optimization under uncertain conditions. The Grey is a Theory of Grey Systems that helps to provide solutions for problems with limited or imprecise information combined with econometric methodologies such as regression and time series analysis in order to improve the accuracy of predictions and risk assessments. The hybrid model was validated with macroeconomic indicators such as GDP growth, inflation rates, and trade balances, thus proving that it outperformed conventional econometric approaches through reducing forecasting errors and better-quantified uncertainty. The empirical results pointed out the potentials of our model for financial market analysis, macroeconomic policy formulation, and risk mitigation strategies. By creating a synergy between data-driven econometrics and uncertainty-resilient grey modeling, this research represents a completely new and adjustable approach to economic decision-making in complexity and dynamism of environments.
