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ORCiD profile

Papers, conferences, pre-prints, etc.

* = not peer-reviewed

• * Andrew Halterman, Philip A. Schrodt, Andreas Beger, Benjamin E. Bagozzi, and Grace I. Scarborough. 2023. “Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks”. arXiv.

• * Andrew Halterman, Benjamin E. Bagozzi, Andreas Beger, Philip A. Schrodt, and Grace I. Scarborough. 2023. “PLOVER and POLECAT: A New Political Event Ontology and Dataset”. SocArXiv.

• Daniel M. Benjamin, Fred Morstatter, Ali E. Abbas, Andres Abeliuk, Pavel Atanasov, Stephen Bennett, Andreas Beger, Saurabh Birari, David V. Budescu, Michele Catasta, Emilio Ferrara, Lucas Haravitch, Mark Himmelstein, KSM Tozammel Hossain, Yuzhong Huang, Woojeong Jin, Regina Joseph, Jure Leskovec, Akira Matsui, Mehrnoosh Mirtaheri, Xiang Ren, Gleb Satyukov, Rajiv Sethi, Amandeep Singh, Rok Sosic, Mark Steyvers, Pedro A Szekely, Michael D. Ward, Aram Galstyan. 2023. “Hybrid forecasting of geopolitical events”. AI Magazine 44(1)
  (open access)

• Andreas Beger, Richard K Morgan, and Michael D. Ward. 2021. “Reassessing the Role of Theory and Machine Learning in Forecasting Civil Conflict”. Journal of Conflict Resolution.
  [ GitHub ]

• Emma Baillie, Piers DL Howe, Andrew Perfors, Tim Miller, Yoshihisa Kashima, and Andreas Beger. 2021. “Explainable models for forecasting the emergence of political instability”. PLoS ONE 16(7): e0254350.
  [ OSF Code & Data ]

• Fred Morstatter, Aram Galstyan, Gleb Satyukov, Daniel Benjamin, Andres Abeliuk, Mehrnoosh Mirtaheri, KSM Tozammel Hossain, Pedro Szekely, Emilio Ferrara, Akira Matsui, Mark Steyvers, Stephen Bennet, David Budescu, Mark Himmelstein, Michael Ward, Andreas Beger, Michele Catasta, Rok Sosic, Jure Leskovec, Pavel Atanasov, Regina Joseph, Rajiv Sethi, and Ali Abbas. 2019. “SAGE: A Hybrid Geopolitical Event Forecasting System”. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Demos: 6557-6559.

• Andreas Beger and Daniel Hill, Jr. 2019. “Examining repressive and oppressive state violence using the Ill-Treatment and Torture data”. Conflict Management and Peace Science 36(6): 626–644.
  [ GitHub ]

• Richard Morgan, Andreas Beger, and Adam Glynn. 2019. “Varieties of Forecasts: Predicting Adverse Regime Transitions”. V-Dem Working Paper 2019:89.

• Kentaro Fukumoto, Andreas Beger, and Will H. Moore. 2019. “Bayesian modeling for overdispersed event-count time series”. Behaviormetrika 46: 435–452.

• Shana Scogin, Johannes Karreth, Andreas Beger, and Rob Williams. 2019. “BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation”. Journal of Open Source Software.
  [ R package (CRAN) ]

• * Andreas Beger and Michael D. Ward. 2019. “Assessing Amazon Turker and automated machine forecasts in the Hybrid Forecasting Competition”. 7th Annual Asian Political Methodology Meeting, Kyoto, Japan.
  [ PDF ]

• Andreas Beger, Daniel W. Hill, Jr., Nils. W. Metternich, Shahryar Minhas and Michael D. Ward. 2017. “Splitting It Up: The spduration Split-Population Duration Regression Package for Time-varying Covariates”. The R Journal 9(2): 474-486.
  [ R package (CRAN) ]

• Michael D. Ward and Andreas Beger. 2017. “Lessons from near real-time forecasting of irregular leadership changes”. Journal of Peace Research 54(2).
  [ GitHub | Appendix (PDF) ]

• Andreas Beger, Cassy L. Dorff, and Michael D. Ward. 2016. “Irregular Leadership Changes in 2014: Forecasts using ensemble, split-population duration models”. International Journal of Forecasting 32(1): 98-111.
  [ GitHub | PDF ]

• Andreas Beger, Cassy L. Dorff, and Michael D. Ward. 2014. “Ensemble Forecasting of Irregular Leadership Changes”. Research & Politics 1(3).
  [ GitHub | PDF ]

• * Andreas Beger, Cassy L. Dorff, and Michael D. Ward. 2014. “Irregular Leadership Changes in 2014: Forecasts using ensemble, split-population duration models”. arXiv.
  (This is a technical report written for the Political Instability Task Force.)

Other manuscripts

Unpublished papers that I am not further working on:

2016. “Precision-recall curves”.

• PDF
• http://ssrn.com/abstract=2765419
• For rare outcomes (*cough*, a lot of IR), ROC curves and the area under them are not a great measure of model fit. Look at (the area under) precision-recall curves as well.

2012. “Using front lines to predict deaths in the Bosnian civil war”.

• PDF
• To be useful for forecasting and prediction, a statistical model needs to be feasible given the data it requires. This paper examines the relationship between front lines and other, time-invariant variables, and killings during the Bosnian civil war from 1992 to 1995. It uses a Bayesian spatial count model to estimate and compare model fit to other, more established conflict models. One of the dissertation papers.

2012. “Explaining and predicting interstate war deaths”.

• PDF, http://ssrn.com/abstract=2765421
• This paper is about predicting interstate war battle deaths. Data on 89 interstate wars between 1815 and 1991 is used to estimate a truncated regression model that provides the basis for out-of sample forecasts for two other wars. Also a dissertation paper.

2012. “Predicting the intensity and location of violence in war”.

• PDF
• My three-papers-wrapped-together Ph.D. dissertation.

2008. “Simulating the Effects of Selection Bias in the Minorities at Risk Project”.

• PDF
• How much of a problem is it that the Minority at Risk project collects information only for ethnic groups that are “at risk”, i.e. selection on the dependent variable?