Measuring Violence Risk in Space and Time using Kernel Density Estimation

Augustin Tapsoba

October 3, 2019, 11:00–12:30

Room MF 323

DLPP - Behavior, Institutions, and Development seminar


Being able to assess conflict risk at local level is crucial for preventing political violence or mitigating its consequences. This paper develops a new methodological approach for measuring violence risk across space and time that improves the prediction of future conflict events. Violence is modeled as a stochastic process with an unknown underlying distribution. Each conflict event observed on the ground is interpreted as a random realization of this process and its underlying distribution is estimated using kernel density estimation methods in a three-dimensional space. The optimal smoothing parameters are estimated to maximize the likelihood of future conflict events. An illustration of the practical gains (in terms of out-ofsample forecasting performance) of this new methodology compared to standard space-time autoregressive models is shown using data from Ivory Coast.