2. Further research on election forecasting

On January 25, 2016

At 10:42 am

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1. Graefe, A. (2017). Political markets. In K. Arzheimer, J. Evans & M. S. Lewis-Beck (Eds.), The SAGE Handbook of Electoral Behaviour, Volume 2 (pp. 861-882). London: Sage.
2. Graefe, A. (2017). Prediction market performance in the 2016 U.S. presidential election. Foresight – The International Journal of Applied Forecasting, 2017(45), 38-42.
3. Graefe, A. (2016). Issue-handling beats leadership: Issues and Leaders model predicts Clinton will defeat Trump. Research & Politics, DOI: 10.1177/2053168016679364.
4. Graefe, A. (2016). Forecasting proportional representation elections from non-representative expectation surveys. Electoral Studies, 42, 222-228.
5. Graefe, A. (2015). Improving forecasts using equally weighted predictors. Journal of Business Research, 68(8), 1792-1799. [Replication data available here.]
6. Graefe, A. (2014). Accuracy of vote expectation surveys in forecasting elections, Public Opinion Quarterly, 78(S1), 204-232. [Replication data available here.]
7. Graefe, A., & Armstrong, J. S. (2014). Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue. Political Science Research and Methods, 2(1), 141-149.
8. Graefe, A., Küchenhoff, H., Stierle, V. & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems. International Journal of Forecasting, 31(3), 943-951.
9. Graefe, A. (2013). Issue and leader voting in U.S. presidential elections. Electoral Studies, 32(4), 644-657.
10. Jones, Randall J. Jr. & Cuzán, A. G. (2013). Expert judgment in forecasting American presidential elections: A preliminary evaluation, 2013 APSA Annual Meeting Paper.
11. Graefe, A., & Armstrong, J. S. (2013). Forecasting elections from voters’ perceptions of candidates’ ability to handle issues. Journal of Behavioral Decision Making, 26(3), 295-303.
12. Graefe, A., & Armstrong, J. S. (2012). Predicting elections from the most important issue: A test of the take‐the‐best heuristic. Journal of Behavioral Decision Making, 25(1), 41-48.
13. Armstrong, J. S. & Graefe, A. (2011). Predicting elections from biographical information about candidates, Journal of Business Research, 64, 699-706.
14. Cuzán, A. G. (2008). Predicting the results of the 2010 midterm Elections: Judgment, econometrics, and prediction markets, Foresight – The International Journal of Applied Forecasting, Issue 21, 41-44.
15. 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’ biographies, 2011 APSA Annual Meeting Paper.
16. Cuzán, A. G. & Bundrick, C. M. (2009). Predicting presidential elections with equally weighted regressors in Fair’s equation and the fiscal model. Political Analysis 17(3), 333-340.
17. Jones, R. J. Jr. (2008). The state of presidential election forecasting: The 2004 experience. International Journal of Forecasting 24(2), 310-321.
18. Jones, R. J. Jr. & Cuzán, A. G. (2008). Forecasting U.S. Presidential Elections: A Brief Review. Foresight – The International Journal of Applied Forecasting, Issue 10, 29-34.
19. Jones, R. J. Jr., Armstrong, J. S. & Cuzán, A. G. (2007). Forecasting Elections Using Expert Surveys: An Application to U. S. Presidential Elections, 2007 APSA Annual Meeting Paper.
20. Armstrong, J. S. & Cuzán, A. G. (2006). Index methods for forecasting: An application to American presidential elections. Foresight – The International Journal of Applied Forecasting, Issue 3, 10-13.

Andreas has joined the PollyVote in 2007. He is a Professor of Management at Macromedia University in Munich. Andreas has published extensively on election forecasting. For more information see www.andreas-graefe.org.

By Andreas Graefe