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Election polling is not dead: A Bayesian bootstrap method yields accurate forecasts

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  • Olsson, Henrik

Abstract

We present a new Bayesian bootstrap method for election forecasts that combines traditional polling questions about people’s own intentions with their expectations about how others will vote. It treats each participant’s election winner expectation as an optimal Bayesian forecast given private and public evidence available to that individual. It then infers the independent evidence and aggregates it across participants. The bootstrap forecast outperforms aggregate national polls in the 2020 U.S. election, as well as the forecasts based on traditional polling questions posed on large national probabilistic samples before the 2018 and 2020 U.S. elections. The bootstrap forecast puts most weight on people’s expectations about how their social contacts will vote, which might incorporate information about voters who are difficult to reach or who hide their true intentions. Beyond election polling, the new method is expected to improve the validity of other social science surveys.

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  • Olsson, Henrik, 2021. "Election polling is not dead: A Bayesian bootstrap method yields accurate forecasts," OSF Preprints nqcgs, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:nqcgs
    DOI: 10.31219/osf.io/nqcgs
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    References listed on IDEAS

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    1. M. Galesic & W. Bruine de Bruin & M. Dumas & A. Kapteyn & J. E. Darling & E. Meijer, 2018. "Asking about social circles improves election predictions," Nature Human Behaviour, Nature, vol. 2(3), pages 187-193, March.
    2. Will Jennings & Christopher Wlezien, 2018. "Election polling errors across time and space," Nature Human Behaviour, Nature, vol. 2(4), pages 276-283, April.
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