We predict conflict better than we thought! Taking time seriously when evaluating predictions in Binary-Time-Series-Cross-Section-Data

Efforts to predict civil war onset, its duration, and subsequent peace have dramatically increased. Nonetheless, by standard classification metrics the discipline seems to make little progress. Some remedy is promised by particular cross-validation strategies and machine learning tools, which increase accuracy rates substantively. However, in this research note we provide convincing evidence that the predictive performance of conflict models has been much better than previously assessed. We demonstrate that standard classification metrics for binary outcome data are prone to underestimate model performance in a binary-time-series-cross-section context. We argue for temporal residual based metrics to evaluate cross-validation efforts in binary-time-series-cross-section and test these in Monte Carlo experiments and existing empirical studies.