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Improving election prediction internationally

机译:在国际上改善选举预测

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摘要

This study reports the results of a multiyear program to predict direct executive elections in a variety of countries from globally pooled data. We developed prediction models by means of an election data set covering 86 countries and more than 500 elections, and a separate data set with extensive polling data from 146 election rounds. We also participated in two live forecasting experiments. Our models correctly predicted 80 to 90% of elections in out-of-sample tests. The results suggest that global elections can be successfully modeled and that they are likely to become more predictable as more information becomes available in future elections. The results provide strong evidence for the impact of political institutions and incumbent advantage. They also provide evidence to support contentions about the importance of international linkage and aid. Direct evidence for economic indicators as predictors of election outcomes is relatively weak. The results suggest that, with some adjustments, global polling is a robust predictor of election outcomes, even in developing states. Implications of these findings after the latest U.S. presidential election are discussed.
机译:这项研究报告了一项多年计划的结果,该计划可以从全球汇总数据中预测不同国家的直接行政选举。我们通过涵盖86个国家和500多个选举的选举数据集以及包含来自146轮选举的广泛民意测验数据的单独数据集,开发了预测模型。我们还参加了两个实时预测实验。我们的模型在样本外测试中正确预测了80%到90%的选举。结果表明,全球选举可以成功地建模,并且随着将来选举中可获得更多信息,它们可能变得更加可预测。结果为政治机构和现有优势的影响提供了有力的证据。它们还提供了证据来支持有关国际联系和援助重要性的争论。经济指标作为选举结果的预测指标的直接证据相对较弱。结果表明,即使进行一些调整,即使在发展中国家,全球民意测验也是选举结果的有力预测指标。讨论了最近美国总统大选后这些发现的含义。

著录项

  • 来源
    《Science》 |2017年第6324期|515-520|共6页
  • 作者单位

    Univ Houston, Ctr Int & Comparat Studies, Houston, TX 77004 USA;

    Northeastern Univ, Lazer Lab, Boston, MA 02115 USA|Harvard Univ, Inst Quantitat Social Sci, Cambridge, MA 02138 USA;

    Northeastern Univ, Lazer Lab, Boston, MA 02115 USA|Harvard Univ, Inst Quantitat Social Sci, Cambridge, MA 02138 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 02:51:16

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