Publications
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Journal articles
Citizen Forecasting in a Mixed Electoral System: The 2021 German Federal Election as a Test Case
International Journal of Forecasting, 2026
Existing studies show that aggregating citizens’ expectations about who will win can predict election outcomes in a majoritarian system. But can so-called citizen forecasting also successfully predict outcomes in mixed-member systems, where constituency results are less important? The existing evidence is mixed and limited in scope. We conducted, therefore, a citizen forecast of the 2021 German federal election by administering an original survey asking citizens who they thought would win in their constituency, what share of the vote each candidate would win in their constituency, and what share of the vote each party would win nationally. Citizens predicted constituency winners and vote shares more accurately than several benchmarks. However, our citizen forecast was based on a non-representative sample from an online-access panel. We conclude that citizen forecasting provides a simple and inexpensive way to predict the various relevant outcomes in mixed-member elections.
@article{leininger2026citizen,
title = {Citizen forecasting in a mixed electoral system: The 2021 German federal election as a test case},
journal = {International Journal of Forecasting},
volume = {42},
number = {1},
pages = {203-215},
year = {2026},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2025.03.007},
url = {https://www.sciencedirect.com/science/article/pii/S0169207025000354},
author = {Arndt Leininger and Andreas E. Murr and Lukas Stötzer and Mark A. Kayser},
keywords = {Forecasting, Elections, Voter expectations, Survey research, Germany},
abstract = {Existing studies show that aggregating citizens’ expectations about who will win can predict election outcomes in a majoritarian system. But can so-called citizen forecasting also successfully predict outcomes in mixed-member systems, where constituency results are less important? The existing evidence is mixed and limited in scope. We conducted, therefore, a citizen forecast of the 2021 German federal election by administering an original survey asking citizens who they thought would win in their constituency, what share of the vote each candidate would win in their constituency, and what share of the vote each party would win nationally. Citizens predicted constituency winners and vote shares more accurately than several benchmarks. However, our citizen forecast was based on a non-representative sample from an online-access panel. We conclude that citizen forecasting provides a simple and inexpensive way to predict the various relevant outcomes in mixed-member elections.}
}Election Forecasting: Political Economy Models
International Journal of Forecasting, 2025
We draw globally on a major election forecasting tool, political economy models. Vote intention polls in pre-election public surveys are a widely known approach; however, the lesser-known political economy models take a different scientific tack, relying on regression analysis and voting theory, particularly the force of “fundamentals.” We begin our discussion with two advanced industrial democracies, the US and UK. We then examine two less frequently forecasted cases, Mexico and Ghana, to highlight the potential for political-economic forecasting and the challenges faced. In evaluating the performance of political economy models, we argue for their accuracy but do not neglect lead time, parsimony, and transparency. Furthermore, we suggest how the political economic approach can be adapted to the changing landscape that democratic electorates face.
@article{lewisbeck2025election,
title = {Election forecasting: Political economy models},
journal = {International Journal of Forecasting},
volume = {41},
number = {4},
pages = {1655-1665},
year = {2025},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2025.02.006},
url = {https://www.sciencedirect.com/science/article/pii/S0169207025000135},
author = {Michael S. Lewis-Beck and John Kenny and Debra Leiter and Andreas Erwin Murr and Onyinye B. Ogili and Mary Stegmaier and Charles Tien},
keywords = {Election forecasting, Political economy models, Presidential elections, Parliamentary elections, Advanced democracies, Developing democracies},
abstract = {We draw globally on a major election forecasting tool, political economy models. Vote intention polls in pre-election public surveys are a widely known approach; however, the lesser-known political economy models take a different scientific tack, relying on regression analysis and voting theory, particularly the force of “fundamentals.” We begin our discussion with two advanced industrial democracies, the US and UK. We then examine two less frequently forecasted cases, Mexico and Ghana, to highlight the potential for political-economic forecasting and the challenges faced. In evaluating the performance of political economy models, we argue for their accuracy but do not neglect lead time, parsimony, and transparency. Furthermore, we suggest how the political economic approach can be adapted to the changing landscape that democratic electorates face.}
}Política y Gobierno, 2025
La predicción científica de elecciones tiene una tradición larga y creciente en democracias bien establecidas; sin embargo, no ha progresado lo suficiente en América Latina. Para abonar a esto, analizamos las predicciones ciudadanas de las elecciones presidenciales mexicanas de 2000 a 2024. Desarrollamos nuevas herramientas estadísticas para estudiar si las predicciones ciudadanas revelan mejor los resultados electorales que el azar y que las encuestas de intención de voto. A partir de 55 encuestas, encontramos que los ciudadanos se vuelven mejores con el tiempo en predecir resultados electorales. Mientras que los ciudadanos, al inicio de la democracia en México, en el año 2000, predecían peor que el azar, en elecciones presidenciales recientes las han predicho de manera correcta. En la actualidad, dichas predicciones están a la par de las encuestas de intención de voto. A pesar de que los ciudadanos son ahora mejores que el azar al predecir el porcentaje de votos, siguen teniendo peores resultados que las encuestas de intención de voto. Para entender por qué, analizamos el sesgo y la varianza de las predicciones ciudadanas por primera vez en México. Encontramos que las principales razones por las que sus predicciones electorales han empeorado son la socialización bajo un régimen de partido único, la falta de experiencia con elecciones libres y justas, y las preocupaciones infundadas sobre si las elecciones son limpias o no.
@article{murr2025predicciones,
author = {Murr, Andreas E.},
title = {Predicciones ciudadanas de las elecciones presidenciales mexicanas, 2000--2024},
journal = {Política y gobierno},
volume = {32},
number = {1},
pages = {1--39},
year = {2025},
month = {2},
issn = {1665-2037},
language = {spanish},
url = {http://politicaygobierno.cide.edu/index.php/pyg/article/view/1751},
note = {English version available at \url{https://www.researchgate.net/publication/393468465_Citizen_forecasts_of_Mexican_presidential_elections_2000-2024}},
}Voters' Expectations in Constituency Elections Without Local Polls
Public Opinion Quarterly, 2024
How do voters form accurate expectations about the strength of political candidates in constituency elections if there are no reliable constituency polls available? We argue that voters can use national election polls and past election results to increase the accuracy of their expectations. A survey experiment during the German federal election of 2021 confirms that the provision of national election polls and past results increases the accuracy of voters’ expectations. The analysis further shows that voters leverage the information to update their beliefs. The results have relevant implications for debates about belief formation in low-information environments.
@article{stoetzer2024voters,
author = {Stoetzer, Lukas F and Kayser, Mark A and Leininger, Arndt and Murr, Andreas E},
title = {Voters’ Expectations in Constituency Elections without Local Polls},
journal = {Public Opinion Quarterly},
volume = {88},
number = {2},
pages = {408-418},
year = {2024},
month = {05},
abstract = {How do voters form accurate expectations about the strength of political candidates in constituency elections if there are no reliable constituency polls available? We argue that voters can use national election polls and past election results to increase the accuracy of their expectations. A survey experiment during the German federal election of 2021 confirms that the provision of national election polls and past results increases the accuracy of voters’ expectations. The analysis further shows that voters leverage the information to update their beliefs. The results have relevant implications for debates about belief formation in low-information environments.},
issn = {1537-5331},
doi = {10.1093/poq/nfae015},
url = {https://doi.org/10.1093/poq/nfae015},
eprint = {https://academic.oup.com/poq/article-pdf/88/2/408/57728018/nfae015.pdf},
}Computing Quantities of Interest and Their Uncertainty Using Bayesian Simulation
Political Science Research and Methods, 2023
When analyzing data, researchers are often less interested in the parameters of statistical models than in functions of these parameters such as predicted values. Here we show that Bayesian simulation with Markov-Chain Monte Carlo tools makes it easy to compute these quantities of interest with their uncertainty. We illustrate how to produce customary and relatively new quantities of interest such as variable importance ranking, posterior predictive data, difficult marginal effects, and model comparison statistics to allow researchers to report more informative results.
@article{murr2023computing,
title={Computing quantities of interest and their uncertainty using Bayesian simulation},
volume={11},
DOI={10.1017/psrm.2022.18},
number={3},
journal={Political Science Research and Methods},
author={Murr, Andreas and Traunmüller, Richard and Gill, Jeff},
year={2023},
pages={623–632}
}Citizen Forecasting: The 2022 French Presidential Elections
PS: Political Science & Politics, 2022
@article{dufrense2022citizen,
title={Citizen Forecasting: The 2022 French Presidential Election},
volume={55},
DOI={10.1017/S1049096522000567},
number={4},
journal={PS: Political Science & Politics},
author={Dufresne, Yannick and Jérôme, Bruno and Lewis-Beck, Michael S. and Murr, Andreas E. and Savoie, Justin},
year={2022},
pages={730–734}
}Citizen Forecasts of the 2021 German Election
PS: Political Science & Politics, 2022
@article{murr2022citizen,
title={Citizen Forecasts of the 2021 German Election},
volume={55},
DOI={10.1017/S1049096521000925},
number={1},
journal={PS: Political Science & Politics},
author={Murr, Andreas E. and Lewis-Beck, Michael S.},
year={2022},
pages={97–101}
}Do Party Leadership Contests Predict British General Elections?
Electoral Studies, 2021
When assessing election forecasts, two important criteria emerge: their accuracy (precision) and lead time (distance to event). Curiously, in both 2010 and 2015 the most accurate forecasts came from models having the longest lead time—albeit at most 12 months. Can we increase the lead time further, supposing we tolerate a small decrease in accuracy? Here, we develop a model with a lead time of more than 3 years. Our Party Leadership Model relies on the votes of MPs when selecting their party leader. We assess the forecasting quality of our model with both leave-one-out cross-validation and a before-the-fact forecast of the 2019 general election. Compared to both simple forecasting methods and other scientific forecasts, our model emerges as a leading contender. This result suggests that election forecasting may benefit from developing models with longer lead times, and that party leaders may influence election outcomes more than is usually thought.
@article{murr2021party,
title = {Do party leadership contests forecast British general elections?},
journal = {Electoral Studies},
volume = {72},
pages = {102342},
year = {2021},
issn = {0261-3794},
doi = {https://doi.org/10.1016/j.electstud.2021.102342},
url = {https://www.sciencedirect.com/science/article/pii/S0261379421000627},
author = {Andreas Erwin Murr},
keywords = {Accuracy, British general elections, Election forecasting, Party leadership contests, Lead time, Leader effects},
abstract = {When assessing election forecasts, two important criteria emerge: their accuracy (precision) and lead time (distance to event). Curiously, in both 2010 and 2015 the most accurate forecasts came from models having the longest lead time—albeit at most 12 months. Can we increase the lead time further, supposing we tolerate a small decrease in accuracy? Here, we develop a model with a lead time of more than 3 years. Our Party Leadership Model relies on the votes of MPs when selecting their party leader. We assess the forecasting quality of our model with both leave-one-out cross-validation and a before-the-fact forecast of the 2019 general election. Compared to both simple forecasting methods and other scientific forecasts, our model emerges as a leading contender. This result suggests that election forecasting may benefit from developing models with longer lead times, and that party leaders may influence election outcomes more than is usually thought.}
}Vote Expectations Versus Vote Intentions: Rival Forecasting Strategies
British Journal of Political Science, 2021
Are ordinary citizens better at predicting election results than conventional voter intention polls? The authors address this question by comparing eight forecasting models for British general elections: one based on voters' expectations of who will win and seven based on who voters themselves intend to vote for (including ‘uniform national swing model’ and ‘cube rule’ models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950–2017, yielding 449 months with both expectation and intention polls. The large sample size permits comparisons of the models' prediction accuracy not just in the months prior to the election, but in the years leading up to it. Vote expectation models outperform vote intention models in predicting both the winning party and parties' seat shares.
@article{murr2021vote,
title={Vote Expectations Versus Vote Intentions: Rival Forecasting Strategies},
volume={51},
DOI={10.1017/S0007123419000061},
number={1},
journal={British Journal of Political Science},
author={Murr, Andreas E. and Stegmaier, Mary and Lewis-Beck, Michael S.},
year={2021},
pages={60–67}
}Citizen Forecasting 2020: A State-by-State Experiment
PS: Political Science & Politics, 2021
@article{murr2021citizen,
title={Citizen Forecasting 2020: A State-by-State Experiment},
volume={54},
DOI={10.1017/S1049096520001456},
number={1},
journal={PS: Political Science & Politics},
author={Murr, Andreas E. and Lewis-Beck, Michael S.},
year={2021},
pages={91–95}
}Social Networks and Citizen Election Forecasting: The More Friends the Better
International Journal of Forecasting, 2018
Most citizens correctly forecast which party will win a given election, and such forecasts usually have a higher level of accuracy than voter intention polls. How do citizens do it? We argue that social networks are a big part of the answer: much of what we know as citizens comes from our interactions with others. Previous research has considered only indirect characteristics of social networks when analyzing why citizens are good forecasters. We use a unique German survey and consider direct measures of social networks in order to explore their role in election forecasting. We find that three network characteristics – size, political composition, and frequency of political discussion – are among the most important variables when predicting the accuracy of citizens’ election forecasts.
@article{leiter2018social,
title = {Social networks and citizen election forecasting: The more friends the better},
journal = {International Journal of Forecasting},
volume = {34},
number = {2},
pages = {235-248},
year = {2018},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2017.11.006},
url = {https://www.sciencedirect.com/science/article/pii/S0169207017301371},
author = {Murr, Andreas and Leiter, Debra and Rascón Ramírez, Ericka and Stegmaier, Mary},
keywords = {Social networks, Election forecasting, Citizen forecasting, Public opinion, Political interest, Expectations, Germany},
abstract = {Most citizens correctly forecast which party will win a given election, and such forecasts usually have a higher level of accuracy than voter intention polls. How do citizens do it? We argue that social networks are a big part of the answer: much of what we know as citizens comes from our interactions with others. Previous research has considered only indirect characteristics of social networks when analyzing why citizens are good forecasters. We use a unique German survey and consider direct measures of social networks in order to explore their role in election forecasting. We find that three network characteristics - size, political composition, and frequency of political discussion – are among the most important variables when predicting the accuracy of citizens’ election forecasts.}
}The Wisdom of Crowds: What do Citizens Forecast for the 2015 British General Election?
Electoral Studies, 2016
Who do you think will win in your constituency? Most citizens correctly answer this question, and groups are even better at answering it. Combining individual forecasts results in the ‘wisdom of crowds’ explained by Condorcet's jury theorem. This paper demonstrates the accuracy of citizen forecasts in seven British General Elections between 1964 and 2010, and reports what citizens interviewed in February and March forecasted for the election in May 2015. ‘Citizen forecasting’ predicts vote shares and winners in constituency elections, and seat numbers and governments in national elections. The paper also introduces a new method for predicting vote shares from citizen forecasts. Citizen forecasts are direct, accurate, and comprehensible. Pollsters should collect them and communicate their results more often.
@article{murr2016wisdom,
title = {The wisdom of crowds: What do citizens forecast for the 2015 British General Election?},
journal = {Electoral Studies},
volume = {41},
pages = {283-288},
year = {2016},
issn = {0261-3794},
doi = {https://doi.org/10.1016/j.electstud.2015.11.018},
url = {https://www.sciencedirect.com/science/article/pii/S0261379415002255},
author = {Andreas E. Murr},
keywords = {Citizen forecasting, Combining forecasts, Condorcet's jury theorem, Election forecasting, Election surveys},
abstract = {Who do you think will win in your constituency? Most citizens correctly answer this question, and groups are even better at answering it. Combining individual forecasts results in the ‘wisdom of crowds’ explained by Condorcet's jury theorem. This paper demonstrates the accuracy of citizen forecasts in seven British General Elections between 1964 and 2010, and reports what citizens interviewed in February and March forecasted for the election in May 2015. ‘Citizen forecasting’ predicts vote shares and winners in constituency elections, and seat numbers and governments in national elections. The paper also introduces a new method for predicting vote shares from citizen forecasts. Citizen forecasts are direct, accurate, and comprehensible. Pollsters should collect them and communicate their results more often.}
}The Wisdom of Crowds: Applying Condorcet's Jury Theorem to Forecasting U.S. Presidential Elections
International Journal of Forecasting, 2015
Increasingly, professional forecasters rely on citizen forecasts when predicting election results. Following this approach, forecasters predict the winning party to be the one which most citizens have said will win. This approach predicts winners and vote shares well, but related research has shown that some citizens forecast better than others. Extensions of Condorcet’s jury theorem suggest that naïve citizen forecasting can be improved by delegating the forecasting to the most competent citizens and by weighting their forecasts by their level of competence. Indeed, doing so increases both the accuracy of vote share predictions and the number of states forecast correctly. Allocating the state’s electoral votes to the candidate who the most weighted delegates say will win yields a simple but successful forecasting model of the US Presidency. The ‘wisdom of crowds’ model predicts eight presidential elections out of nine correctly. The results suggest that delegating and weighting provide easy ways to improve citizen forecasting.
@article{murr2015wisdom,
title = {The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections},
journal = {International Journal of Forecasting},
volume = {31},
number = {3},
pages = {916-929},
year = {2015},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2014.12.002},
url = {https://www.sciencedirect.com/science/article/pii/S0169207014001770},
author = {Andreas E. Murr},
keywords = {Citizen forecasting, Combining forecasts, Condorcet’s jury theorem, Election forecasting, Election surveys, Weighting},
abstract = {Increasingly, professional forecasters rely on citizen forecasts when predicting election results. Following this approach, forecasters predict the winning party to be the one which most citizens have said will win. This approach predicts winners and vote shares well, but related research has shown that some citizens forecast better than others. Extensions of Condorcet’s jury theorem suggest that naïve citizen forecasting can be improved by delegating the forecasting to the most competent citizens and by weighting their forecasts by their level of competence. Indeed, doing so increases both the accuracy of vote share predictions and the number of states forecast correctly. Allocating the state’s electoral votes to the candidate who the most weighted delegates say will win yields a simple but successful forecasting model of the US Presidency. The ‘wisdom of crowds’ model predicts eight presidential elections out of nine correctly. The results suggest that delegating and weighting provide easy ways to improve citizen forecasting.}
}The Party Leadership Model: An Early Forecast of the 2015 British General Election
Research & Politics, 2015
British political parties select their leaders to win elections. The winning margin of the party leader among the selectorate reflects how likely they think she is to win the General Election. The present research compares the winning margins of party leaders in their party leadership elections and uses the results of this comparison to predict that the party leader with the larger winning margin will become the next Prime Minister. I term this process “the Party Leadership Model”. The model correctly forecasts 8 out of 10 past elections, while making these forecasts 4 years in advance on average. According to a Bayesian analysis, there is a 95 per cent probability that having the larger winning margin in party leadership elections increases the chances of winning the General Election. Because David Cameron performed better among Conservative MPs in 2005 than Ed Miliband did among Labour MPs in 2010, the model predicts Cameron to become Prime Minister again in 2015. The Bayesian calculation puts his chances of re-election at 75 per cent.
@article{murr2015party,
title={The party leadership model: An early forecast of the 2015 British general election},
author={Murr, Andreas Erwin},
journal={Research \& Politics},
volume={2},
number={2},
pages={1--9},
year={2015},
publisher={SAGE Publications Sage UK: London, England}
}Modeling Latent Information in Voting Data with Dirichlet Process Priors
Political Analysis, 2015
We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved heterogeneity among immigrant voters. Our approach is based on generalized linear mixed Dirichlet models (GLMDMs) where random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2) smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant literature suggests. When looking at vote choice, we also found that an immigrant's degree of structural integration does not affect the vote in favor of the CDU/CSU, a party that is traditionally associated with restrictive immigration policy.
@article{traunmueller2015,
title={Modeling Latent Information in Voting Data with Dirichlet Process Priors},
volume={23},
DOI={10.1093/pan/mpu018},
number={1},
journal={Political Analysis},
author={Traunmüller, Richard and Murr, Andreas and Gill, Jeff},
year={2015},
pages={1–20}
}Electoral Studies, 2011
Many studies report the “wonders of aggregation” and that groups (often) yield better decisions than individuals. Can this “wisdom of crowds”-effect be used to forecast elections? Forecasting models in first-past-the-post systems need to translate vote shares into seat shares by some formula; however, the seat–vote ratio alters from election to election. To circumvent this problem, this paper proposes citizen forecasting, which aggregates citizens’ local expectations to directly forecast constituencies. Using data from the 2010 British Election Study, this paper finds (1) that groups are better forecasters than individuals, (2) that citizen forecasting correctly predicts a hung parliament, and (3) that marginality and group size are important predictors for “getting it right”.
@article{murr2011wisdom,
title = {“Wisdom of crowds”? A decentralised election forecasting model that uses citizens’ local expectations},
journal = {Electoral Studies},
volume = {30},
number = {4},
pages = {771-783},
year = {2011},
issn = {0261-3794},
doi = {https://doi.org/10.1016/j.electstud.2011.07.005},
url = {https://www.sciencedirect.com/science/article/pii/S0261379411000977},
author = {Andreas Erwin Murr},
keywords = {British election, Citizen forecasting, Expectations model, Election forecasting, Wisdom of crowds},
abstract = {Many studies report the “wonders of aggregation” and that groups (often) yield better decisions than individuals. Can this “wisdom of crowds”-effect be used to forecast elections? Forecasting models in first-past-the-post systems need to translate vote shares into seat shares by some formula; however, the seat–vote ratio alters from election to election. To circumvent this problem, this paper proposes citizen forecasting, which aggregates citizens’ local expectations to directly forecast constituencies. Using data from the 2010 British Election Study, this paper finds (1) that groups are better forecasters than individuals, (2) that citizen forecasting correctly predicts a hung parliament, and (3) that marginality and group size are important predictors for “getting it right”.}
}Book chapters
Bürger:innenprognosen in einem Mischwahlsystem: Die deutsche Bundestagswahl 2021 als Testfall
Wahlen und Wähler (Harald Schoen & Bernhard Weßels, eds.), 2024
Wie viele Wahlkreise gewinnt welche Partei bei der Bundestagswahl? Diese Frage war im Vorfeld der Bundestagswahl 2021 trotz des deutschen Mischwahlsystems unter Fachleuten wie auch einer breiteren Öffentlichkeit von besonderem Interesse. Diesem Bedarf an Vorhersagen bedient in jüngerer Zeit eine zunehmende Zahl von Prognosemodellen, die sich jedoch fast ausschliesslich auf die Zweitstimme abzielen. Für Wahlkreise gibt es nicht nur in Deutschland, sondern auch in reinen Mehrheitswahlsystemen, kaum relevante Umfragen. Wir führten daher eine Wahlerwartungsumfrage durch, um den Wahlausgang in jedem einzelnen Bundestagswahlkreis zu prognostizieren. Wir nennen unseren Ansatz Bürger:innenprognose, weil er auf den Erwartungen der Bürger:innen über das Wahlverhalten ihrer Mitbürger:innen beruht und nicht auf deren selbstberichteten Wahlabsichten. In diesem Beitrag stellen wir unsere Bürger:innenprognose vor, evaluieren ihre Genauigkeit und vergleichen sie mit anderen Ansätzen zur Wahlprognose.
@Inbook{leininger2024buerger,
author="Leininger, Arndt
and Murr, Andreas E.
and Stoetzer, Lukas F.
and Kayser, Mark A.",
editor="Schoen, Harald
and We{\ss}els, Bernhard",
title="B{\"u}rger:innenprognosen in einem Mischwahlsystem: Die deutsche Bundestagswahl 2021 als Testfall",
bookTitle="Wahlen und W{\"a}hler: Analysen zur Bundestagswahl 2021",
year="2024",
publisher="Springer Fachmedien Wiesbaden",
address="Wiesbaden",
pages="383--411",
abstract="Wie viele Wahlkreise gewinnt welche Partei bei der Bundestagswahl? Diese Frage war im Vorfeld der Bundestagswahl 2021 trotz des deutschen Mischwahlsystems unter Fachleuten wie auch einer breiteren {\"O}ffentlichkeit von besonderem Interesse. Diesem Bedarf an Vorhersagen bedient in j{\"u}ngerer Zeit eine zunehmende Zahl von Prognosemodellen, die sich jedoch fast ausschlie{\ss}lich auf die Zweitstimme abzielen. F{\"u}r Wahlkreise gibt es nicht nur in Deutschland, sondern auch in reinen Mehrheitswahlsystemen, kaum relevante Umfragen. Wir f{\"u}hrten daher eine Wahlerwartungsumfrage durch, um den Wahlausgang in jedem einzelnen Bundestagswahlkreis zu prognostizieren. Wir nennen unseren Ansatz B{\"u}rger:innenprognose, weil er auf den Erwartungen der B{\"u}rger:innen {\"u}ber das Wahlverhalten ihrer Mitb{\"u}rger:innen beruht und nicht auf deren selbstberichteten Wahlabsichten. In diesem Beitrag stellen wir unsere B{\"u}rger:innenprognose vor, evaluieren ihre Genauigkeit und vergleichen sie mit anderen Ans{\"a}tzen zur Wahlprognose.",
isbn="978-3-658-42694-1",
doi="10.1007/978-3-658-42694-1_15",
url="https://doi.org/10.1007/978-3-658-42694-1_15"
}The Sage Handbook of Electoral Behaviour (Kai Arzheimer, Jocelyn Evans & Michael Lewis-Beck, eds.), 2017
@incollection{murr2017wisdom,
author = {Murr, Andreas E.},
title = {Wisdom of Crowds},
booktitle = {The {SAGE} Handbook of Electoral Behaviour},
editor = {Arzheimer, Kai and Evans, Jocelyn and Lewis-Beck, Michael S.},
publisher = {SAGE},
address = {London},
year = {2017},
pages = {835--860},
url = {https://www.researchgate.net/publication/308985928_Wisdom_of_Crowds},
}