Comparing forecast models of Radical Right voting in four European countries (1973-2008)
AbstractRadical Right Parties (RRPs) have traditionally been seen as 'hard cases' to forecast, with unstable voter bases affected by short-term influences. Building upon our previous work on forecasting the French Front National's vote across time, we construct a comparable model for three other European countries-Austria, Denmark and Norway-with significant RRPs, using economic, cultural and political predictors. We find that the model performs surprisingly well, with the partial exception of Norway, and provides an accurate forecast of RRP electoral performance which improves upon naive endogenous models and, significantly, upon polling estimates. Moreover, the model is firmly rooted in existing explanations of RRP success, allowing a robust explanation not only of variation in these parties' votes, but also of less successful estimates in a small number of country-specific contexts. Overall, we find that standard approaches to electoral forecasting in fact offer a useful tool in the analysis of RRPs.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 26 (2010)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/ijforecast
Electoral forecast Radical Right Evaluating forecasts Regression Time series;
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- Campbell, James E., 2008. "Evaluating U.S. presidential election forecasts and forecasting equations," International Journal of Forecasting, Elsevier, vol. 24(2), pages 259-271.
- Pickup, Mark & Johnston, Richard, 2008. "Campaign trial heats as election forecasts: Measurement error and bias in 2004 presidential campaign polls," International Journal of Forecasting, Elsevier, vol. 24(2), pages 272-284.
- Rallings, Colin & Thrasher, Michael, 1999. "Local votes, national forecasts - using local government by-elections in Britain to estimate party support," International Journal of Forecasting, Elsevier, vol. 15(2), pages 153-162, April.
- Stambough, Stephen J. & Thorson, Gregory R., 1999. "Toward stability in presidential forecasting: the development of a multiple indicator model," International Journal of Forecasting, Elsevier, vol. 15(2), pages 143-152, April.
- Aichholzer, Julian & Willmann, Johanna, 2014. "Forecasting Austrian national elections: The Grand Coalition model," International Journal of Forecasting, Elsevier, vol. 30(1), pages 55-64.
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