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Forecasting elections at the constituency level: A correction-combination procedure

机译:在选区一级预测选举:更正合并程序

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Scholarly efforts to forecast parliamentary elections have targeted the national level predominantly, disregarding the outcomes of constituency races. In doing so, they have frequently failed to account for systematic bias in the seats-votes curve, and been unable to provide candidates and campaign strategists with constituency-level information. On the other hand, existing accounts of constituency-level election forecasting suffer from data sparsity, leading to a lack of precision. This paper proposes a correction-combination procedure that allows for the correction of individual constituency-level forecast models for election-invariant bias, then combines these models based on their past performances. I demonstrate the use of this procedure through out-of-sample forecasts of 299 district races at the 2013 German federal election. (C) 2017 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译:学者们在预测议会选举方面的努力主要针对国家一级,而无视选民选举的结果。这样一来,他们往往无法解决席位投票曲线中的系统性偏见,也无法向候选人和竞选战略家提供选民级别的信息。另一方面,现有的选区一级选举预测帐户存在数据稀疏性,导致缺乏准确性。本文提出了一种校正组合程序,该程序可以校正选举不变偏差的各个选区水平的预测模型,然后根据它们的过去表现对其进行组合。我通过在2013年德国联邦选举中对299个地区比赛进行样本外预测来证明该程序的使用。 (C)2017国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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