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Replication Data for: Which Historical Forecast Model Performs Best? An Analysis of 1965-2017 French Presidential Elections

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Which Historical Forecast Model Performs Best? An Analysis of 1965-2017 French Presidential Elections
 
Identifier https://doi.org/10.7910/DVN/ITAY0K
 
Creator Bélanger, Éric
Feitosa, Fernando
Turgeon, Mathieu
 
Publisher Harvard Dataverse
 
Description Historical (or vote function) models predict election outcomes based on past patterns of voter behavior and rely on the use of aggregate-level historical data about political and economic factors. This article explores the relative efficiency of two of these models (the Iowa and the proxy models) in predicting the vote for all left-wing candidates in the 1965-2017 French presidential elections. The results suggest that the proxy model may be a better forecasting tool than the Iowa model given that the difference between the predicted and the actual vote is lower for the proxy model than the Iowa model in six out of ten presidential elections. Leveraging the proximity of the 2022 French presidential election, we then use the proxy model to make a forecast for the upcoming 2022 election. The forecast produced suggests that the odds appear good, although not overly so, for outgoing president Emmanuel Macron to be re-elected.
 
Subject Social Sciences
 
Contributor Feitosa, Fernando