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In many forecasting settings, there is a specific interest in predicting thesign of an outcome variable correctly in addition to its magnitude. Forinstance, when forecasting armed conflicts, positive and negative log-changesin monthly fatalities represent escalation and de-escalation, respectively, andhave very different implications. In the ViEWS forecasting challenge, aprediction competition on state-based violence, a novel evaluation score calledtargeted absolute deviation with direction augmentation (TADDA) has thereforebeen suggested, which accounts for both for the sign and magnitude oflog-changes. While it has a straightforward intuitive motivation, the empiricalresults of the challenge show that a no-change model always predicting alog-change of zero outperforms all submitted forecasting models under the TADDAscore. We provide a statistical explanation for this phenomenon. Analyzing theproperties of TADDA, we find that in order to achieve good scores, forecastersoften have an incentive to predict no or only modest log-changes. Inparticular, there is often an incentive to report conservative pointpredictions considerably closer to zero than the forecaster's actual predictivemedian or mean. In an empirical application, we demonstrate that a no-changemodel can be improved upon by tailoring predictions to the particularities ofthe TADDA score. We conclude by outlining some alternative scoring concepts.

Direction Augmentation in the Evaluation of Armed Conflict Predictions