Explainable Machine Learning for Macroeconomic and Financial Nowcasting: A Decision-Grade Framework for Business and Policy

Explainability must match accuracy in high-stakes economic predictions: The paper emphasizes that when machine learning is used to forecast GDP growth and inform policy decisions, predictive accuracy alone is insufficient—models must also provide interpretable explanations of why they make specific predictions, a requirement often overlooked in traditional ML applications.

Economic interpretability requires stability and coherence over time: The authors stress that explainability tools used in nowcasting must demonstrate "temporal stability" and "sign coherence," meaning the explanations for why a model predicts economic changes should remain logically consistent and economically sensible across different time periods, not just produce accurate numbers.

Uncertainty quantification is essential for responsible decision-making: The paper advocates for rigorous uncertainty measurement around both predictions and feature importance rankings, using methods like block bootstrapping, so that policymakers and business leaders understand the confidence level of nowcasts rather than treating point predictions as certain facts.

Different ML methods handle economic shocks differently: The research compares various approaches (neural networks, tree ensembles, penalized regressions) and finds they respond differently to regime shifts and economic crises, suggesting that a single "best" model doesn't exist—responsible practice requires understanding each method's strengths and limitations in volatile environments.

Decision-grade nowcasting requires a structured workflow: Rather than optimizing for prediction error alone, the authors propose a reference framework that integrates accuracy, explainability, uncertainty quantification, and economic credibility to create systems suitable for actual business and policy decisions.