Toward robust early-warning models: A horse race, ensembles and model uncertainty

TL;DR

This paper evaluates various statistical and machine learning models for crisis prediction, demonstrating that ensemble methods and advanced algorithms outperform traditional approaches in robustness and accuracy.

Contribution

It introduces a comprehensive comparison of models, ensemble techniques, and uncertainty estimation methods for early-warning systems, with empirical validation on European data.

Findings

Machine learning models outperform traditional statistical methods.

Ensemble approaches provide more robust vulnerability assessments.

Uncertainty estimation enhances model reliability.

Abstract

This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground. Generally, our results show that the conventional statistical approaches are outperformed by more advanced machine learning methods, such as k-nearest neighbors and neural networks, and particularly by model aggregation approaches through ensemble learning.