RésuméOn compte actuellement un grand nombre de modèles politico-économiques ayant pour objectif de prédire l'issue des élections au Congrès américain ou le sort des candidats à la présidence des États-Unis. Bien qu'un certain nombre de modèles aient vu le jour pour la France, l'Allemagne et le Royaume-Uni au cours des dernières années, le Canada, à l'instar de la majorité des démocraties, n'a reçu jusqu’à maintenant que bien peu d'attention. Cet article vise par conséquent à développer un modèle ancré dans une théorie du vote capable de prédire suffisamment à l'avance la part des voix récoltées par le parti sortant lors des scrutins fédéraux canadiens. Le Canada étant un système multipartite, différents modèles de régressions apparemment indépendantes sont également proposés afin de déterminer s'il est possible de prédire simultanément les scores de plusieurs formations politiques.

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Changes in voters' behavior and in the campaign strategies that political parties pursue are likely to have increased the importance of campaigns on voters' electoral choices. As a result, scholars increasingly question the usefulness and predictive power of structural forecasting models, that use information from “fundamental” variables to make an election prediction several months before Election Day. In this paper, we empirically examine the expectation that structural forecasting models are increasingly error-prone. For doing so, we apply a structural forecasting model to predict elections in six established democracies. We then trace the predictive power of this model over time. Surprisingly, our results do not give the slightest indication of a decline in the predictive power of structural forecasting models. By showing that information on long-term factors still allows making accurate predictions of electoral outcomes, we question the assumption that campaigns matter more now than they did in the past.

Serious election forecasting has become a routine activity in most Western democracies, with various methodologies employed, for example, polls, models, prediction markets, and citizen forecasting. In the Netherlands, however, election forecasting has limited itself to the use of polls, mainly because other approaches are viewed as too complicated, given the great fragmentation of the Dutch party system. Here we challenge this view, offering the first structural forecasting model of legislative elections there. We find that a straightforward Political Economy equation managed an accurate forecast of the 2017 contest, clearly besting the efforts of the pollsters.