Publikationen

Klicken Sie auf eine der folgenden Kategorien für eine Auflistung relevanter Publikationen der PollyVote-Teammitglieder.

1. Forschung zur PollyVote-Prognose
  1. Graefe, A. (2015). German election forecasting: Comparing and combining methods for 2013. German Politics, 24(2), 195-204. [Replication data available here.]
  2. Graefe, A. (2015). Accuracy gains of adding vote expectation surveys to a combined forecast of US presidential election outcomesResearch & Politics, 2(1), 1-5. [Replication data available here.]
  3. Graefe, A., Armstrong, J. S., Jones, R. J. Jr. & Cuzán, A. G. (2014). Combining forecasts: An application to electionsInternational Journal of Forecasting, 30(1), 43-54. [Replication data available here.]
  4. Graefe, A., Armstrong, J. S., Cuzán, A. G. & Jones, R. J. Jr. (2014). Accuracy of combined forecasts for the 2012 presidential elections: The PollyVotePS: Political Science & Politics, 47(2), 427-431. [Replication data available here.]
  5. Graefe, A., Armstrong, J. S., Jones, R. J. Jr. & Cuzán, A. G. (2013). Combined Forecasts of the 2012 Election: The PollyVoteForesight – The International Journal of Applied Forecasting, Issue 28, 50-51.
  6. Graefe, A., Jones, R. J. Jr., Armstrong, J. S. & Cuzán, A. G. (2012). The PollyVote’s year-ahead forecast of the 2012 U.S. presidential electionForesight – The International Journal of Applied Forecasting, Issue 24, 13-14.
  7. Graefe, A., Armstrong, J. S., Jones, R. J. Jr. & Cuzán, A. G. (2009). Combined Forecasts of the 2008 Election: The PollyVoteForesight – The International Journal of Applied Forecasting, Issue 12, 41-42.
  8. Cuzán, A. G., Armstrong, J. S. & Jones, R. J. Jr. (2005). The Pollyvote: Applying the Combination Principle in Forecasting to the 2004 Presidential Election, Paper presented at the 2005 International Symposium on Forecasting, San Antonio.
  9. Cuzán, A. G., Armstrong, J. S. & Jones, R. J. Jr. (2005). How we computed the PollyVoteForesight: The International Journal of Applied Forecasting, Issue 1, 51-52.
2. Weitere Arbeiten zu Wahlvorhersagen
  1. Graefe, A. (2015). Improving forecasts using equally weighted predictorsJournal of Business Research, 68(8), 1792-1799. [Replication data available here.]
  2. Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting electionsPublic Opinion Quarterly, 78(S1), 204-232. [Replication data available here.]
  3. Graefe, A., & Armstrong, J. S. (2014). Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issuePolitical Science Research and Methods, 2(1), 141-149.
  4. Graefe, A., Küchenhoff, H., Stierle, V. & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problemsInternational Journal of Forecasting, 31(3), 943-951.
  5. Graefe, A. (2013). Issue and leader voting in U.S. presidential electionsElectoral Studies, 32(4), 644-657.
  6. Jones, Randall J. Jr. & Cuzán, A. G. (2013). Expert judgment in forecasting American presidential elections: A preliminary evaluation2013 APSA Annual Meeting Paper.
  7. Graefe, A., & Armstrong, J. S. (2013). Forecasting elections from voters’ perceptions of candidates’ ability to handle issuesJournal of Behavioral Decision Making, 26(3), 295-303.
  8. Graefe, A., & Armstrong, J. S. (2012). Predicting elections from the most important issue: A test of the take‐the‐best heuristicJournal of Behavioral Decision Making, 25(1), 41-48.
  9. Armstrong, J. S. & Graefe, A. (2011). Predicting elections from biographical information about candidatesJournal of Business Research, 64, 699-706.
  10. Cuzán, A. G. (2008). Predicting the results of the 2010 midterm Elections: Judgment, econometrics, and prediction marketsForesight – The International Journal of Applied Forecasting, Issue 21, 41-44.
  11. Graefe, A. & Armstrong, J. S. (2011). Who should be nominated to run in the 2012 U.S. Presidential Election? Long-term forecasts based on candidates’ biographies2011 APSA Annual Meeting Paper.
  12. Cuzán, A. G. & Bundrick, C. M. (2009). Predicting presidential elections with equally weighted regressors in Fair’s equation and the fiscal modelPolitical Analysis 17(3), 333-340.
  13. Jones, R. J. Jr. (2008). The state of presidential election forecasting: The 2004 experienceInternational Journal of Forecasting 24(2), 310-321.
  14. Jones, R. J. Jr. & Cuzán, A. G. (2008). Forecasting U.S. Presidential Elections: A Brief ReviewForesight – The International Journal of Applied Forecasting, Issue 10, 29-34.
  15. Jones, R. J. Jr., Armstrong, J. S. & Cuzán, A. G. (2007). Forecasting Elections Using Expert Surveys: An Application to U. S. Presidential Elections2007 APSA Annual Meeting Paper.
  16. Armstrong, J. S. & Cuzán, A. G. (2006). Index methods for forecasting: An application to American presidential electionsForesight – The International Journal of Applied Forecasting, Issue 3, 10-13.
3. Forschung zu automatisiertem Journalismus
  1. Graefe, Andreas (2016). Guide to Automated Journalism. Tow Center for Digital Journalism, Columbia Journalism School, New York City.
  2. Graefe, A., Haim, M., Haarmann, B. & Brosius, H.-B. (2016). Readers‘ perception of computer-written news: Credibility, expertise, and readabilityJournalism (forthcoming).
  3. Haim, M. & Graefe, A. (2016). Automated news: Better than expected?. ICA 2016 – Annual Conference of the International Communication Association, Fukuoka, Japan, June 9-13, 2016.
  4. Berger, M., Haim, M., Graefe, A., Brosius, H.-B., & Hess, T. (2015). Aktuelles Stichwort: Computational Journalism. Medienwirtschaft, 12(1), 20-23.