% Citizen Forecasting in a Mixed Electoral System: The 2021 German Federal Election as a Test Case
@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
@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.}
}


% Predicciones ciudadanas de las elecciones presidenciales mexicanas, 2000--2024 [Citizen Forecasts of Mexican Presidential Elections, 2020–2024]
@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
@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
@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
@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
@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?
@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
@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
@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
@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?
@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
@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
@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
@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}
}


% "Wisdom of crowds"? A decentralised election forecasting model that uses citizens' local expectations
@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”.}
}


% Bürger:innenprognosen in einem Mischwahlsystem: Die deutsche Bundestagswahl 2021 als Testfall
@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"
}


% "Wisdom of Crowds"
@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},
}