Approval Ratings and Predicting United States Presidential Elections

S. Strong, I. S. Kohli
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Abstract

Predicting the outcomes of United States Presidential Elections has become a favourite project of many data scientists and academics. Creating a model which can accurately predict who the next president will be is a sure-fire way to solidify one’s name as a reputable source of analysis. The objective of this research paper was to look at various data, and see if there are any important variables which modellers have ignored. A dataset was organized, which included many variables, including economic indicators like GDP growth, average presidential approval ratings, and changes in party composition in the House of Representatives. This included information for every presidential election from 1948 to present. The dataset was then bootstrapped and analyzed through a model which used gradient boosted trees. The findings of the model found a surprisingly high significance for the variable of average approval rating. In 5546 out of 10,000 bootstrap runs (55.46%), this variable was the most significant in predicting the outcome of the election. Given the model’s accuracy rating of 79.52% on test sets, there is validity to the claim that average approval rating is an important predictor of election outcome (though not by itself, as interaction with the other variables was necessary for predictions). The model was then used to predict the outcome of the 2020 presidential election, based on current data. After running the model 10,000 times with the 2020 election as the test set, it was found that Donald Trump was re-elected in 366 trials, indicating a 3.66% chance of re-election given current data (which, especially regarding the economic indicators, is subject to change). A sensitivity analysis of the average approval rating found that for his odds of re-election to exceed 50%, his average approvals must climb to at least 60%. Based on the findings of this report, it can be concluded that approval ratings, while generally ignored or minimized in other models, are actually a very significant indicator of election outcome. If this is correct, president Trump is facing serious challenges in 2020, though with one year left, and economic data left to be reported, his odds could change.
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支持率和预测美国总统选举
预测美国总统大选的结果已经成为许多数据科学家和学者最喜欢的项目。建立一个能准确预测下一任总统是谁的模型,是巩固自己作为一个有信誉的分析来源的可靠方法。本研究论文的目的是查看各种数据,看看是否有建模者忽略的重要变量。我们编制了一个数据集,其中包含许多变量,包括GDP增长等经济指标、总统的平均支持率、众议院政党组成的变化。这包括从1948年至今的每一次总统选举的信息。然后通过使用梯度增强树的模型对数据集进行引导和分析。该模型的研究结果发现,平均支持率这一变量具有惊人的高显著性。在1万次bootstrap中,有5546次(55.46%)对选举结果的预测最为重要。考虑到该模型在测试集上的准确率为79.52%,平均支持率是选举结果的重要预测指标(尽管不是它本身,因为与其他变量的相互作用是预测所必需的)的说法是有效的。然后,该模型被用于根据当前数据预测2020年总统大选的结果。在以2020年大选为测试集运行该模型1万次后,发现唐纳德·特朗普在366次试验中再次当选,根据当前数据(特别是经济指标,可能会发生变化),这表明连任的可能性为3.66%。一项对平均支持率的敏感性分析发现,要想让他连任的几率超过50%,他的平均支持率必须攀升至至少60%。根据本报告的研究结果,可以得出结论,在其他模型中通常被忽略或最小化的支持率实际上是选举结果的一个非常重要的指标。如果这是正确的,那么特朗普总统在2020年将面临严峻的挑战,尽管还有一年的时间,经济数据还没有公布,他获胜的几率可能会改变。
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