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The chancellor model: Forecasting German elections

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  • Norpoth, Helmut
  • Gschwend, Thomas

Abstract

Our forecast model for German Bundestag elections relies on three predictors: (1) the popularity of the incumbent chancellor (hence the christening of it as the "Chancellor Model"); (2) the long-term partisan balance in the German electorate; and (3) the cost of ruling, as captured by the tenure of the government in office. The model forecasts the vote share of the governing parties (typically two, such as Social Democrats and Greens, or Christian Democrats and Free Democrats), except for instances of a grand coalition. The coefficients of the predictors are estimated based on elections since 1949, the beginning of the Federal Republic. The out-of-sample forecasts of the model deviate from the actual results by just over one percentage point, on average. The first real-time test of the model came in 2002. The forecast issued three months before Election Day picked the incumbent vote share to the decimal (47.1% for the SPD-Greens coalition); for the 2005 election, called a year early, our forecast three weeks before Election Day was just three-tenths of a percentage off the mark. For the upcoming election, we offer separate forecasts, conditional at this moment, for each of the two parties in the grand coalition.

Suggested Citation

  • Norpoth, Helmut & Gschwend, Thomas, 2010. "The chancellor model: Forecasting German elections," International Journal of Forecasting, Elsevier, vol. 26(1), pages 42-53, January.
  • Handle: RePEc:eee:intfor:v:26:y::i:1:p:42-53
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    References listed on IDEAS

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    1. Gschwend, Thomas, 2004. "Ticket-Splitting and Strategic Voting," Sonderforschungsbereich 504 Publications 05-06, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    2. Bornier Jean Magnan de & Norpoth H. & Lewis-Beck M.S. & Lafay J.D., 1991. "Economics and Politics The calculus of support," Journal des Economistes et des Etudes Humaines, De Gruyter, vol. 2(4), pages 579-581, December.
    3. Abramowitz, Alan I., 2008. "It's about time: Forecasting the 2008 presidential election with the time-for-change model," International Journal of Forecasting, Elsevier, vol. 24(2), pages 209-217.
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    2. Dassonneville, Ruth & Hooghe, Marc, 2012. "Election forecasting under opaque conditions: A model for Francophone Belgium, 1981–2010," International Journal of Forecasting, Elsevier, vol. 28(4), pages 777-788.
    3. Soeren Enkelmann, 2013. "Government Popularity and the Economy First Evidence from German Micro Data," Working Paper Series in Economics 274, University of Lüneburg, Institute of Economics.
    4. Antoine Auberger, 2021. "Vote, popularity, economic conditions and French legislative elections [Vote, popularité, conditions économiques et élections législatives françaises]," Working Papers hal-03480853, HAL.
    5. Antoine Auberger, 2021. "Vote, popularity, unemployment and French presidential elections [Vote, popularité, chômage et élections présidentielles françaises]," Working Papers hal-03480855, HAL.
    6. Quinlan, Stephen & Lewis-Beck, Michael S., 2021. "Forecasting government support in Irish general elections: Opinion polls and structural models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1654-1665.
    7. Sören Enkelmann, 2014. "Government popularity and the economy: first evidence from German microdata," Empirical Economics, Springer, vol. 46(3), pages 999-1017, May.
    8. Alexander Bauer & Andreas Bender & André Klima & Helmut Küchenhoff, 2020. "KOALA: a new paradigm for election coverage," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 101-115, March.
    9. Renaud Foucart & Marjorie Gassner & Emilie Van Haute, 2012. "Une typologie des résultats électoraux basée sur le comportement des électeurs volatiles en Belgique," CEVIPOL Working Papers 3, ULB -- Universite Libre de Bruxelles.

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