The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model

TL;DR

This paper introduces a hierarchical hurdle regression model to accurately forecast monthly conflict fatalities at a fine spatial scale, emphasizing the impact of government weapons imports and geographic remoteness.

Contribution

It presents a novel three-stage count data model that incorporates governmental arms imports and geographic factors for conflict prediction.

Findings

The model effectively predicts conflict fatalities with high accuracy.

Governmental weapons imports significantly influence conflict intensity.

Geographic remoteness moderates the effect of military buildups.

Abstract

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.