New Early-Warning Indicators of Macro-Financial Risk: A Mixture-of-Experts Approach

Predicting macro-financial crises in developing economies is a critical but difficult task, complicated by significant data challenges. Machine learning techniques have shown improved predictive performance relative to traditional parametric models, yet this often comes at the cost of lower model explainability, limiting their use for policy makers. To strike a balance between predictive performance and explainability, this paper proposes a Mixture-of-Experts framework that combines ensemble models and traditional logistic regression to generate country-level crisis risk scores which can be decomposed into sub-risks, such as fiscal, financial, and balance of payments, to aid understanding of the drivers of risk within and across countries. Leveraging a global panel dataset spanning from 1990 to 2025 for 161 countries, the Mixture-of-Experts framework is shown to meaningfully outperform standard crisis prediction models in the literature. This paper therefore offers a practical and flexible framework for technical audiences developing early-warning systems in the presence of data gaps.