Integrated Micro-Statistical Frameworks for Predictive Validity in Climate and Policy Econometrics

Abstract

This study proposes and applies an integrated micro-statistical framework to evaluate predictive validity in climate and policy econometrics. It has been found that traditional microeconometric models are highly cause interpretive but exhibit low predictivity, and modern statistical methods of learning do not have structural transparency but can make more predictions. By integrating microeconometric estimation, hierarchical multilevel modeling and machine-learned based predictive analytics into a single framework, this article fills this divide. Based on micro-level data on climate exposure, policy support and economic outcomes, the study evaluates the predictive performance based on cross-validated error measures and examines the moderating impact of policy intervention on climate-induced economic vulnerability.The results reveal that the combined model is significantly better in predictive validity with reduced RMSE and MAE and increased predictive R 2 compared to other traditional fixed-effects, multilevel-only, and machine-learn-only models. Findings also indicate that specific policy interventions can go a long way in offsetting the negative impacts of climate surprises on the income stability of households, agricultural output, and energy usage behavior, and with regional and income disparities in their impacts. The stability of results is confirmed by robustness checks based on alternative climate indices, subsample estimations and sensitivity analysis.The article advances predictive analytics and empirical strategies in climate and policy econometrics, methodologically, by bringing together the causal inference and predictive analytics. In substance, it emphasizes the significant role of micro-level heterogeneity as well as the effects of interaction in determining climate resilience. The research paper is concluded with finding that integrated micro-statistical frameworks can be more accurately used in predicting distributional effects and informing adaptive policy formulation in a period of increasing climate uncertainty.