A Political-Economy Forecast for the 2013 German Elections: Who to Rule with Angela Merkel?

Jérôme, Bruno; Jérôme-Speziari, Veronique; Lewis‐Beck, Michael S.

doi:10.1017/s1049096513000814

Cited by 20 publications

(14 citation statements)

References 2 publications

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“…2008 (Kayser u. Leininger 2016), als auch durch Mehrfachgleichungsmodelle, die den Stimmanteil einer jeden Partei prognostizieren (Jérôme et al 2013;Selb u. Munzert 2016) . Die Verteilung der Parteibindungen in der Wählerschaft erlaubt es bereits Erwartungen zu bilden, welchen Stimmenanteil eine Partei unter "normalen Umständen" erhalten sollte (Converse 1966;Falter u. Rattinger 1983 (Gschwend u. Norpoth 2000.…”

Section: Literaturunclassified

“…Anmerkungen: Koeffizientenschätzungen des strukturellen Modells, 1953 (Gschwend u. Norpoth 2000Kayser u. Leininger 2016;Küntzler 2017), oder modellierten die jeweiligen Anteile in separaten Regressionsgleichungen, die dann simultan geschätzt wurden (Jérôme et al 2013). Tatsächlich birgt unser Vorgehen sowohl Vor-als auch Nachteile.…”

Section: Abbildung 2: Koeffizientenschätzungen Des Strukturellen Modeunclassified

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Nicht was Du denkst! Was wir wirklich von Wahlvorhersagen lernen können

Strijbis¹

2017

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As this forum demonstrates, the competition between election forecasting models along the US model becomes standard also in Germany. This raises the question about the scientific relevance of this undertaking. It is often argued that election forecasts provide a particularly hard and hence good test for the falsification of theories of electoral behavior. In this contribution, I contest this argument and show that the forecast models presented in this issue hardly contribute to the falsification of theories. Instead I refer to an often overlooked relevance of election forecasts consisting in the models' potential for opera-tionalizing important variables. I illustrate my argument by referring to the literature on prediction markets. The Chancellor Model predicts that the 2017 Bundestag election will most likely confirm Angela Merkel for another term as head of the federal government. As of June this year, she leads her Social Democratic challenger Martin Schulz by a wide enough margin in chancellor popularity to overcome any fatigue with her tenure after three terms. Besides continuing with the Grand Coalition, Merkel also has the option of entering a Jamaicacoalition (with the FDP and Greens), which would command a majority in the new Bundestag. A shift in chancellor popularity, however, would change the forecast. In the event that Schulz ultimately surpasses Merkel in chancellor popularity, as he did in late February, the SPD candidate would be more likely to head the next German government.

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Section: Literaturunclassified

Section: Abbildung 2: Koeffizientenschätzungen Des Strukturellen Modeunclassified

Nicht was Du denkst! Was wir wirklich von Wahlvorhersagen lernen können

Strijbis¹

2017

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“…Five recent articles have applied some kind of structural model to the German or Swedish cases (J erôme et al, 2013;Norpoth and Gschwend, 2010;Graefe, 2015;Kayser and Leininger, 2013;Sundell and Lewis-Beck, 2014). The question then inescapably arises how these have dealt with the theoretical problems outlined above.…”

Section: How Previous Studies Have Dealt With These Issuesmentioning

confidence: 99%

Picking the winner(s): Forecasting elections in multiparty systems

Walther

2015

Electoral Studies

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a b s t r a c tFrom the 1970s onwards, a wide range of forecasting techniques have been developed in the literature on electoral forecasting. However, these models have primarily been applied in two-party, presidential democracies, with the US being by far the most popular country to investigate. The question thus arises whether the same techniques that have proved successful in this context can also be applied to the more complex, multiparty democracies in northern Europe. This paper seeks to answer this question and in the process makes two main contributions. Firstly, the popular dynamic linear model (Jackman, 2005) is tried and tested in Germany and Sweden where it is shown that reasonable forecasts can be made despite the complexity of the systems and the emergence of new parties. A novelty is then introduced when cyclical changes in party support are modelled through a seasonal component. This extension of the dynamic linear model helps to significantly lower the error in early forecasts and is thus something that could be useful in future applications of the model.

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“…Anmerkungen: Koeffizientenschätzungen des strukturellen Modells, 1953 (Gschwend u. Norpoth 2000, 2001Kayser u. Leininger 2016;Küntzler 2017), oder modellierten die jeweiligen Anteile in separaten Regressionsgleichungen, die dann simultan geschätzt wurden (Jérôme et al 2013 (Jennings u. Wlezien 2016). 10 Unser Modell stellt die erste Anwendung dieses rückwärtsgerichteten random walk-Modells in einem Mehrparteienkontext dar.…”

Section: Introductionunclassified

Zweitstimme.org. Ein strukturell-dynamisches Vorhersagemodell für Bundestagswahlen

Munzert¹,

Stötzer²,

Gschwend³

et al. 2017

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Abstract:We present results of an ex-ante forecast of party-specific vote shares at the German Federal Election 2017. To that end, we combine data from published trial heat polls with structural information. The model takes care of the multi-party nature of the setting and allows making statements about the probability of certain events, such as the plurality of votes for a party or the majority for coalition options in parliament. The forecasts of our model are continuously being updated on the platform zweitstimme.org. The value of our approach goes beyond the realms of academia: We equip journalists, political pundits, and ordinary citizens with information that can help make sense of the parties' latent support and ultimately make voting decisions better informed.

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A Political-Economy Forecast for the 2013 German Elections: Who to Rule with Angela Merkel?

Cited by 20 publications

References 2 publications

Nicht was Du denkst! Was wir wirklich von Wahlvorhersagen lernen können

Nicht was Du denkst! Was wir wirklich von Wahlvorhersagen lernen können

Picking the winner(s): Forecasting elections in multiparty systems

Zweitstimme.org. Ein strukturell-dynamisches Vorhersagemodell für Bundestagswahlen

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