Postelection analysis of presidential election/poll data
Jiming Jiang, Yuanyuan Li, Peter X. K. Song
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 3
Abstract
This paper concerns analyses of the 2016 and 2020 U.S. presidential election data, including the data of preelection polls and the actual elections. Our analyses unveil statistical evidence of discrepancy between the polls and real elections that is consistent across these two elections. Specifically, the polls had consistently overestimated advantages of the Democratic candidates or, equivalently, underestimated the true population support of the Republican candidate, Donald Trump, in both elections. The analyses are stratified by state, reflecting the U.S. electoral college system by the means of small area estimation. We have found recurrent patterns suggesting that the polls have been underestimating the Republican candidate, especially in swing states of critical importance. Our findings also suggest an improvement of the 2020 polling methods to mitigate the size of underestimation. We show that a small-area model built upon the actual election data from one election can provide a better prediction than the poll-based projection to another election involving the same Republican candidate. Ranking of pollsters, based on prediction bias, using mixed model prediction is also considered.
Keywords
empirical BLUP, measure of uncertainty, mixed-effects model, opinion polls, projection, small area estimation
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