Selected Publications

We present results of an ex-ante forecast of party-specific vote shares at the German Federal Election 2017. To that end, we combine data from published trial heat polls with structural information. The model takes care of the multi-party nature of the setting and allows making statements about the probability of certain events, such as the plurality of votes for a party or the majority for coalition options in parliament. The forecasts of our model are continuously being updated on the platform The value of our approach goes beyond the realms of academia: We equip journalists, political pundits, and ordinary citizens with information that can help make sense of the parties’ latent support and ultimately make voting decisions better informed.
In PVS, 2017.

Recent Publications & Work in Progress

A Partisan Treatment in a High Salience Election: Evidence from a Field Experiment in Germany

Details PDF Code Dataset A structural-dynamic forecasting model for German federal elections

Details PDF Code Dataset

Forecasting Elections in Multi-party Systems: A Backwards Random-walk Approach

Details PDF

Is Random Forest Really Better than Logistic Regression for Predicting Civil War Onsets?

Details PDF Code Dataset


I am a teaching instructor for the following courses at University of Mannheim:

  • Fall 2017: Tutorial Multivariate Analyses, Graduate (in English)
  • Spring 2017: Tutorial Advanced Quantitative Methods, Graduate (in English)
  • Fall 2016: Tutorial Multivariate Analyses, Graduate (in English)

Furthermore I have been teaching at University of Applied Sciences Ludwigshafen:

  • Spring 2017: Applied Marketing Research, Graduate (in German)