Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting
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DOI: 10.1016/j.ijforecast.2015.03.002
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- Easaw, Joshy & Fang, Yongmei & Heravi, Saeed, 2021. "Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model," Cardiff Economics Working Papers E2021/34, Cardiff University, Cardiff Business School, Economics Section.
- Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
- Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai, 2021. "Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective," International Journal of Forecasting, Elsevier, vol. 37(2), pages 461-483.
- Nollenberger, Clemens & Unger, Gina-Maria, 2020. "Fundamentals-Based State-Level Forecasts of the 2020 US Presidential Election," SocArXiv cm58f, Center for Open Science.
- Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
- Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
- Isakov, Michael & Kuriwaki, Shiro, 2020. "Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016," OSF Preprints 29pvm, Center for Open Science.
- John Sides & Michael Tesler & Lynn Vavreck, 2016. "The Electoral Landscape of 2016," The ANNALS of the American Academy of Political and Social Science, , vol. 667(1), pages 50-71, September.
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Keywords
Electoral forecasting; U.S. presidential elections; Bayesian statistics;All these keywords.
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