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Model-based pre-election polling for national and sub-national outcomes in the US and UK

机译:基于模型的选举前民意调查,用于美国和英国的国家和次国家级选举结果

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摘要

We describe a strategy for applying multilevel regression and post-stratification (MRP) methods to pre-election polling. Using a combination of contemporaneous polling, census data, past election polling, past election results, and other sources of information, we are able to construct probabilistic, internally consistent estimates of national votes and the sub-national electoral districts that determine seats or electoral votes in many electoral systems. We report on the performance of the general framework in three applications that were conducted and released publicly in advance of the 2016 UK Referendum on EU Membership, the 2016 US Presidential Election, and the 2017 UK General Election. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译:我们描述了将多层回归和后分层(MRP)方法应用于选举前民意测验的策略。通过同时使用民意调查,普查数据,过去的选举投票,过去的选举结果和其他信息源,我们能够构建概率一致,内部一致的国家投票估计值,并确定地方或选举投票的地方选举区域在许多选举系统中。我们在2016年英国欧盟成员国公投,2016年美国总统大选和2017年英国大选之前公开发布的三个申请中报告了总体框架的绩效。 (C)2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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