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

MCML Authors

Link to Profile Göran Kauermann PI Matchmaking

Göran Kauermann

Prof. Dr.

Principal Investigator

Applied Statistics in Social Sciences, Economics and Business

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 organizations (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 modeling 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.

article

International Interactions

48.4. Nov. 2021.

Authors

C. Fritz • M. Mehrl • P. W. Thurner • G. Kauermann

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: FMT+21

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