  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.
