政府武器装备在预测州内冲突月死亡人数中的作用：一个半参数层次障碍模型
The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model
摘要：
准确和可解释的预测模型预测国家内部冲突伤亡人数在空间和时间上的细粒度变化对政策制定者和国际非政府组织至关重要。使用计数数据的方法，我们提出了一个分层的障碍回归模型，以解决相应的预测挑战，在每月PRIO网格水平。更准确地说，我们将特定时间点的地方武装冲突强度建模为三阶段过程。我们的方法的第一阶段和第二阶段分别估计我们是否会在国家和网格单元级别观察到任何伤亡，而第三阶段应用截断数据的回归模型来预测前两个阶段的死亡人数。在这一模型框架内，我们侧重于政府武器进口的作用，作为一个过程性因素，使政府加强或阻止战斗。我们进一步认为，一个网格单元的地理偏远一定会缓和这些军事集结的影响。样本外预测证实了我们简约和理论驱动的模型的有效性，该模型能够在预测过程中实现完全透明和准确性。
英文摘要：
Accurate and interpretable forecasting models predicting spatially and temporally fine-grained changes in the numbers of intrastate conflict casualties are of crucial importance for policymakers and international non-governmental organisations (NGOs). Using a count data approach, we propose a hierarchical hurdle regression model to address the corresponding prediction challenge at the monthly PRIO-grid level. More precisely, we model the intensity of local armed conflict at a specific point in time as a three-stage process. Stages one and two of our approach estimate whether we will observe any casualties at the country- and grid-cell-level, respectively, while stage three applies a regression model for truncated data to predict the number of such fatalities conditional upon the previous two stages. Within this modelling framework, we focus on the role of governmental arms imports as a processual factor allowing governments to intensify or deter from fighting. We further argue that a grid cell's geographic remoteness is bound to moderate the effects of these military buildups. Out-of-sample predictions corroborate the effectiveness of our parsimonious and theory-driven model, which enables full transparency combined with accuracy in the forecasting process.
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