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Election forecasts: Cracking the Danish case

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  • Nadeau, Richard
  • Lewis-Beck, Michael S.

Abstract

Election forecasting models based on voting theories and estimated via regression analysis are routinely available for virtually all advanced industrial democracies. Denmark, however, offers an exception, for no such prediction equations have been published on the Danish case. This absence has sometimes been attributed to the puzzling nature of economic voting there, along with the complexity of its multi-party system, which renders formulation of the dependent variable problematic. We attempt to overcome these obstacles, offering a “synthetic” forecasting model for Danish national election outcomes, 1964–2015. The regression model, based on the variables of economic growth and vote intention, performs well, by various tests. Finally, we apply it, ex ante fashion, to the 2019 contest, where the prediction favored the Social Democratic led coalition, an outcome that came to pass.

Suggested Citation

  • Nadeau, Richard & Lewis-Beck, Michael S., 2020. "Election forecasts: Cracking the Danish case," International Journal of Forecasting, Elsevier, vol. 36(3), pages 892-898.
    • Handle: RePEc:eee:intfor:v:36:y:2020:i:3:p:892-898
    DOI: 10.1016/j.ijforecast.2019.09.007
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    References listed on IDEAS

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    1. Drew A. Linzer, 2013. "Dynamic Bayesian Forecasting of Presidential Elections in the States," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 124-134, March.
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