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Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting

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  • Lauderdale, Benjamin E.
  • Linzer, Drew

Abstract

U.S. presidential election forecasts are of widespread interest to political commentators, campaign strategists, research scientists, and the public. We argue that most fundamentals-based political science forecasts overstate what historical political and economic factors can tell us about the probable outcome of a forthcoming presidential election. Existing approaches generally overlook the uncertainty in coefficient estimates, decisions about model specifications, and the translation from popular vote shares to Electoral College outcomes. We introduce a Bayesian forecasting model for state-level presidential elections that accounts for each of these sources of error, and allows for the inclusion of structural predictors at both the national and state levels. Applying the model to presidential election data from 1952 to 2012, we demonstrate that, for covariates with typical levels of predictive power, the 95% prediction intervals for presidential vote shares should span approximately ±10% at the state level and ±7% at the national level.

Suggested Citation

  • Lauderdale, Benjamin E. & Linzer, Drew, 2015. "Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting," International Journal of Forecasting, Elsevier, vol. 31(3), pages 965-979.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:965-979
    DOI: 10.1016/j.ijforecast.2015.03.002
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    References listed on IDEAS

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    1. Kosuke Imai & Dustin Tingley, 2012. "A Statistical Method for Empirical Testing of Competing Theories," American Journal of Political Science, John Wiley & Sons, vol. 56(1), pages 218-236, January.
    2. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
    3. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    4. Montgomery, Jacob M. & Nyhan, Brendan, 2010. "Bayesian Model Averaging: Theoretical Developments and Practical Applications," Political Analysis, Cambridge University Press, vol. 18(2), pages 245-270, April.
    5. Abramowitz, Alan I., 2008. "It's about time: Forecasting the 2008 presidential election with the time-for-change model," International Journal of Forecasting, Elsevier, vol. 24(2), pages 209-217.
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    Cited by:

    1. 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.
    2. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    3. 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.
    4. Kang, Seungwoo & Oh, Hee-Seok, 2024. "Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling," International Journal of Forecasting, Elsevier, vol. 40(1), pages 124-141.
    5. Nollenberger, Clemens & Unger, Gina-Maria, 2020. "Fundamentals-Based State-Level Forecasts of the 2020 US Presidential Election," SocArXiv cm58f, Center for Open Science.
    6. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    7. 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.
    8. 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.
    9. 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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