用逐州模型预测2022年美国众议院选举：共和党人没有红地毯待遇

IF 1 4区社会学 Q3 POLITICAL SCIENCE

Polity Pub Date : 2023-05-23 DOI:10.1086/725240

Bruno Jérôme, V. Jerome, Philippe Mongrain, R. Nadeau

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引用次数: 1

摘要

在2022年11月中期选举之前，我们提出了一个模型来预测美国众议院选举的总体结果。该模型建立在两个公认的传统之上，即选票流行函数和“区域化”的汇总横截面时间序列模型。拟议的众议院模型的灵感来自之前应用于总统选举的逐州政治经济（2SPE）模型，该模型基于地方和国家数据。2020年，2SPE模型为乔·拜登提供了51.69%的两党全国普选选票（0.6个百分点的误差），并正确预测了47个州和哥伦比亚特区的获胜者。众议院的模型进行了创新，包括各州长期选举的总统支持率数据，以及追踪

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Forecasting the 2022 U.S. House Elections with a State-by-State Model: No Red-Carpet Treatment for the Republicans

Before the November 2022 midterms, we proposed a model to forecast the aggregate results of elections to the U.S. House of Representatives. This model rests on two well-established traditions, that of vote-popularity functions and that of “regionalized” pooled cross-sectional time-series models. The proposed House model is inspired by the State-by-State Political Economy (2SPE) Model previously applied to presidential elections, which is based on local and national data. In 2020, the 2SPE Model gave Joe Biden 51.69% of the two-party nationwide popular vote (a 0.6-point error) and correctly predicted the winner in forty-seven states plus the District of Columbia. The House model innovates by including presidential popularity data by state formidterm elections as well as variables tracing the trajectory of

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来源期刊

Polity POLITICAL SCIENCE-

CiteScore

1.60

自引率

0.00%

发文量

期刊介绍： Since its inception in 1968, Polity has been committed to the publication of scholarship reflecting the full variety of approaches to the study of politics. As journals have become more specialized and less accessible to many within the discipline of political science, Polity has remained ecumenical. The editor and editorial board welcome articles intended to be of interest to an entire field (e.g., political theory or international politics) within political science, to the discipline as a whole, and to scholars in related disciplines in the social sciences and the humanities. Scholarship of this type promises to be highly "productive" - that is, to stimulate other scholars to ask fresh questions and reconsider conventional assumptions.