On Forecasting Conflict in Sudan: 2009-2012

The paper considers univariate and multivariate models to forecast monthly conflict events in the Sudan over the out-of-sample period 2009 – 2012. The models used to generate these forecasts were based on a specification from a machine learning algorithm fit to 2000 – 2008 monthly data. The idea here is that for policy purposes we need models that can forecast conflict events before they occur. The model that includes previous month’s wheat price performs better than a similar model which does not include past wheat prices (the univariate model). Both models did not perform well in forecasting conflict in a neighborhood of the 2012 “Heglig Crisis”. Such a result is generic, as “outlier or unusual events” are hard for models and policy experts to forecast.