Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-20T02:43:47.943Z Has data issue: false hasContentIssue false
  • English
  • Français

The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election

Published online by Cambridge University Press:  09 September 2021

Thomas Gschwend Open the ORCID record for Thomas Gschwend [Opens in a new window] ,
Klara Müller ,
Simon Munzert Open the ORCID record for Simon Munzert [Opens in a new window] ,
Marcel Neunhoeffer Open the ORCID record for Marcel Neunhoeffer [Opens in a new window]  and
Lukas F. Stoetzer Open the ORCID record for Lukas F. Stoetzer [Opens in a new window]
Thomas Gschwend
Affiliation:
University of Mannheim, Germany
Klara Müller
Affiliation:
University of Mannheim, Germany
Simon Munzert
Affiliation:
The Hertie School, Germany
Marcel Neunhoeffer
Affiliation:
Ludwig-Maximilians-University Munich, Germany
Lukas F. Stoetzer
Affiliation:
Humboldt University of Berlin, Germany
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Forecasting the 2021 German Elections
Information
PS: Political Science & Politics , Volume 55 , Issue 1 , January 2022 , pp. 85 - 90
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Erikson, Robert S., and Wlezien, Christopher. 2013. “Forecasting with Leading Economic Indicators and the Polls 2012.” PS: Political Science & Politics 46 (1): 38–39.Google Scholar
Graefe, Andreas. 2017. “The PollyVote’s Long-Term Forecast for the 2017 German Federal Election.” PS: Political Science & Politics 50 (3): 693–95.Google Scholar
Heidemanns, Merlin, Gelman, Andrew, and Morris, G. Elliott. 2020. “An Updated Dynamic Bayesian Forecasting Model for the US Presidential Election.” Harvard Data Science Review 2 (4). https://doi.org/10.1162/99608f92.fc62f1e1.CrossRefGoogle Scholar
Jérôme, Bruno, Jérôme-Speziari, Véronique, and Lewis-Beck, Michael S.. 2013. “A Political-Economy Forecast for the 2013 German Elections: Who to Rule with Angela Merkel?” PS: Political Science & Politics 46 (3): 479–80.Google Scholar
Kayser, Mark A., and Leininger, Arndt. 2017. “A Länder-Based Forecast of the 2017 German Bundestag Election.” PS: Political Science & Politics 50 (3): 689–92.Google Scholar
Lewis-Beck, Michael S., and Dassonneville, Ruth. 2015. “Forecasting Elections in Europe: Synthetic Models.” Research & Politics 2 (1): 1–11.CrossRefGoogle Scholar
Linzer, Drew A. 2013. “Dynamic Bayesian Forecasting of Presidential Elections in the States.” Journal of the American Statistical Association 108 (501): 124–34.CrossRefGoogle Scholar
Munzert, Simon, Stoetzer, Lukas F., Gschwend, Thomas, Neunhoeffer, Marcel, and Sternberg, Sebastian. 2017. “Zweitstimme.org. Ein Strukturell-Dynamisches Vorhersagemodell Für Bundestagswahlen.” Politische Vierteljahresschrift 58 (3): 418–41.CrossRefGoogle Scholar
Neunhoeffer, Marcel, Gschwend, Thomas, Müller, Klara, Munzert, Simon, and Stoetzer, Lukas F.. 2021. “Replication Data for: The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election.” Harvard Dataverse. https://doi.org/10.7910/DVN/EDTKNW.CrossRefGoogle Scholar
Neunhoeffer, Marcel, Gschwend, Thomas, Munzert, Simon, and Stoetzer, Lukas F.. 2020. “Ein Ansatz Zur Vorhersage der Erststimmenanteile bei Bundestagswahlen.” Politische Vierteljahresschrift 61 (1): 111–30.CrossRefGoogle Scholar
Neunhoeffer, Marcel, Stoetzer, Lukas F., Gschwend, Thomas, Munzert, Simon, and Sternberg, Sebastian. 2018. “Replication Data for: Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals.” Harvard Dataverse. https://doi.org/10.7910/DVN/MLYNX0.CrossRefGoogle Scholar
Norpoth, Helmut, and Gschwend, Thomas. 2010. “The Chancellor Model: Forecasting German Elections.” International Journal of Forecasting 26 (1): 42–53.CrossRefGoogle Scholar
Norpoth, Helmut, and Gschwend, Thomas. 2017. “Chancellor Model Predicts a Change of the Guards.” PS: Political Science & Politics 50 (3): 686–88.Google Scholar
Silver, Nate. 2020. “FiveThirtyEight 2020 US Presidential Election Forecast.” https://projects.fivethirtyeight.com/2020-election-forecast.Google Scholar
Stan Development Team. 2021. “Stan Modeling Language User’s Guide and Reference Manual, 2.26.” https://mc-stan.org.Google Scholar
Stoetzer, Lukas F., Neunhoeffer, Marcel, Gschwend, Thomas, Munzert, Simon, and Sternberg, Sebastian. 2019. “Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals.” Political Analysis 27 (2): 255–62.CrossRefGoogle Scholar
Supplementary material: Link

Gschwend et al. Dataset

Dataset

https://doi.org/10.7910/DVN/EDTKNW
Link
Supplementary material: PDF

Gschwend et al. supplementary material

Gschwend et al. supplementary material

Download Gschwend et al. supplementary material(PDF)
PDF 2.3 MB