Incorporating Model Uncertainty into Policy Analysis Frameworks: A Bayesian Averaging Approach combining computable General Equilibrium (CGE) Model with Metamodelling Techniques

Future sustainable economic development depends heavily on public policy at regional, national, and global levels. Therefore, it is crucial to conduct a thorough policy analysis that ensures consistent and effective policy guidance. However, a major challenge in traditional policy analysis is the uncertainty inherent in the models used. Both policymakers and analysts face fundamental uncertainty regarding which model accurately represents the natural, economic, or social phenomena being analyzed. In this paper, we present a comprehensive framework that explicitly incorporates model uncertainty into the policy decision-making process. Addressing this uncertainty typically requires significant computational resources. To mitigate this, we utilize metamodeling techniques to reduce computational demands. We illustrate the impact of various metamodel types by applying a simplified model to the CAADP policy in Senegal. Our findings highlight that neglecting model uncertainty can lead to inefficient policy decisions and substantial waste of public funds.