How cross-validation can go wrong and what to do about it


Neunhoeffer, Marcel ; Sternberg, Sebastian



DOI: https://doi.org/10.1017/pan.2018.39
URL: https://www.cambridge.org/core/journals/political-...
Additional URL: https://www.marcel-neunhoeffer.com/pdf/papers/pa_c...
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Political Analysis : PA
Volume: 27
Issue number: 1
Page range: 101-106
Place of publication: Cambridge
Publishing house: Cambridge University Press
ISSN: 1047-1987 , 1476-4989
Publication language: English
Institution: School of Social Sciences > Politische Wissenschaft, Quantitative Sozialwissenschaftliche Methoden (Gschwend 2007-)
Außerfakultäre Einrichtungen > Graduate School of Economic and Social Sciences- CDSS (Social Sciences)
Subject: 320 Political science
Abstract: We offer a dynamic Bayesian forecasting model for multi-party elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multi-party nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multi-party setting.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Neunhoeffer, Marcel ; Sternberg, Sebastian (2019) How cross-validation can go wrong and what to do about it. Political Analysis : PA Cambridge 27 1 101-106 [Article]


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