Replication data for: Forecasting the onset of genocide and politicide: Annual out-of-sample forecasts on a global dataset, 1988–2003

We present what is, to the best of our knowledge, the first published set of annual out-of-sample forecasts of genocide and politicide based on a global dataset. Our goal is to produce a prototype for a real-time model capable of forecasting one year into the future. Building on the current literature, we take several important steps forward.We implement an unconditional two-stage model encompassing both instability and genocide, allowing our sample to be the available global data, rather than using conditional case selection or a case-control approach. We explore factors exhibiting considerable variance over time to improve yearly forecasting performance. And we produce annual lists of at-risk states in a format that should be of use to policymakers seeking to prevent such mass atrocities. Our out-of-sample forecasts for 1988–2003 predict 90.9% of genocide onsets correctly while also predicting 79.2% of non-onset years correctly, an improvement over a previous study using a case-control in-sample approach. We produce 16 annual forecasts based only on previous years’ data, which identify six of 11 cases of genocide/politicide onset within the top 5% of at-risk countries per year. We believe this represents substantial progress towards useful real-time forecasting of such rare events. We conclude by suggesting ways to further enhance predictive performance.

据我们所知，本文首次发布了基于全球数据集的种族灭绝（genocide）与政治屠杀（politicide）年度样本外（out-of-sample）预测集。本研究旨在构建可实现未来一年期预测的实时模型原型。立足现有研究文献，本研究取得了多项重要进展：我们构建了涵盖社会动荡与种族灭绝的无条件两阶段模型，可直接将现有全球数据作为研究样本，而非采用条件式案例选择或案例对照法；为提升年度预测性能，本研究考察了随时间波动幅度较大的各类影响因素；此外，本研究以易于供预防此类大规模暴行的政策制定者参考的格式，生成了年度高风险国家名单。针对1988-2003年的样本外预测结果显示，本研究对90.9%的种族灭绝爆发事件实现了正确预测，同时对79.2%的非爆发年份也做出了准确预判，相较于此前采用案例对照样本内（in-sample）法的相关研究实现了性能提升。本研究仅基于过往年份数据生成了16组年度预测结果，在每年排名前5%的高风险国家中，成功识别出11起种族灭绝/政治屠杀爆发事件中的6起。我们认为，这一成果在针对此类罕见事件的实用化实时预测领域取得了显著进展。最后，本文提出了进一步提升预测性能的改进方向。