Gokhan Ciflikli and Nils Metternich. Taking time seriously when evaluating predictions in Binary-Time-Series-Cross-Section-Data. [Pre-Print]
We demonstrate that standard classification metrics for binary outcome data are prone to underestimate model performance in a binary-time-series-cross-section context. We argue for temporal residual based metrics to evaluate cross-validation efforts in binary-time-series-cross-section and test these in Monte Carlo experiments and existing empirical studies.
Nils Metternich, Gokhan Ciflikli, and Ali Altaf. Predicting the severity of civil wars: An actor-centric approach. [Pre-Print]
This paper introduces RebelTrack and RebelCast, which consist of a set of functions to measure rebel behaviour, characteristics, and configurations. Using mainly data from the UCDP GED dataset in combination with PRIO grid data, RebelTrack establishes an open-development platform to generate theory and data-driven information on rebel organizations. RebelCast is an intuitive random forest fitter for the resulting RebelTrack output using cross-validation techniques suitable for time-series-cross-section data.
Gokhan Ciflikli. 2018. Learning Conflict Duration: Insights from Predictive Modelling. PhD thesis, The London School of Economics and Political Science (LSE). [PDF]