Election forecasting in Lithuania: The case of municipal elections

Jastramskis, Mažvydas

doi:10.1016/j.ijforecast.2012.05.003

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Election forecasting in recently democratized countries is difficult -given the scarcity of elections and more unstable political environments-but not impossible (Bunker and Bauchowitz 2016;Cantu et al 2016;Jastramskis 2012;Turgeon and Rennó, 2012;Toros 2012). To circumvent the low-N problem, Turgeon and Rennó (2012) moved to a lower level of analysis.…”

Section: Introductionmentioning

confidence: 99%

Against all Odds: Forecasting Brazilian Presidential Elections in times of political disruption

2022

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When the number of observed elections is low, subnational data can be used to perform electoral forecasts. Turgeon and Rennó (2012) applied this solution and proposed three forecasting models to analyze Brazilian presidential elections (1994-2006). The models, adapted from forecasting models of American and French presidential elections, considers economic and political factors. We extend their analysis to the recent presidential elections in Brazil (2010, 2014 and 2018) and find that the addition of the three recent elections does not improve the accuracy of our forecast models although it strengthens the relationship between the explanatory variables and vote for the incumbent. We also find that models based on the popularity of the incumbent outperform those based on trial-heat polls and that electoral forecast models can survive earthquake elections like the 2018 election that led to the unexpected rise of “outsider” and extremist candidate Jair Bolsonaro.

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