Statistical learning for climate-GDP panels: Data cleaning, flexible trend controls, and predictive validation

We assess the panel-regression approach to climate econometrics—the dominant framework for estimating the effect of climate on GDP in global country–year data—using modern statistical learning techniques. Common implementations are sensitive to outliers, do not fully account for the dependence structure across countries and years, and rarely combine formal model selection with genuine out-of-sample evaluation. To address these issues, we implement knowledge-based data cleaning, nonparametric time-trend controls, and out-of-sample validation across 700 + climate variables. Our analysis reveals that widely used models and predictors—such as mean temperature—have little out-of-sample predictive power. A previously overlooked humidity-related variable emerges as the most consistent predictor, though even its performance remains limited. These findings question the robustness of common empirical practices in this literature and point toward a more data-driven approach built on data cleaning, flexible trend controls, and predictive validation.