Replication Data for: Forecasting Partisan Collective Accountability During the 2024 U.S. Presidential & Congressional Elections
收藏DataCite Commons2025-02-12 更新2025-04-15 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/9ZWATQ
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资源简介:
This article considers both presidential approval and party brand differentials, as measured by the generic ballot, to forecast the 2024 U.S. presidential and congressional elections. While both variables are leveraged to forecast collective partisan election outcomes, we consider the variables together as distinct determinants of partisan fortunes at both the executive and legislative levels. First, using a novel time-series of mass national opinion since 1937, we show that presidential approval and generic brands are distinct conceptual and empirical measures of mass public assessments of collective institutions. Second, in a series of fully specified models validated with out-of-sample predictions, we show that presidential approval is the main predictor of presidential elections while, perhaps surprisingly, the vast bulk of the incumbent party's performance in congressional elections is explained by partisan brands. Lastly, we forecast the 2024 U.S. national elections and find that Republicans are well positioned to both win back the White House this November. By contrast, our model forecasts control of both chambers of the U.S. Congress to be essentially a tied contest.
提供机构:
Harvard Dataverse
创建时间:
2024-09-16



