Against all Odds: Forecasting Brazilian Presidential Elections in times of political disruption
Abstract 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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Abramowitz, A. I. (2008). Forecasting the 2008 Presidential Election with the Time-for- Change Model. PS: Political Science and Politics, 41, 691-695.
Auberger, A. (2010). Popularity and vote: forecasting the 2007 French presidential election. Canadian Journal of Political Science/Revue canadienne de science politique, 43(1), 123-136.
Baker, A., Ames, B., & Rennó, L. (2020). Persuasive Peers. Princeton University Press.
Beck, N., (2001). Time-series–cross-section data: What have we learned in the past few years? Annual Review of Political Science, 4(1), 271-293.
Berry, M. J., & Bickers, K. N. (2012). Forecasting the 2012 presidential election with state-level economic indicators. PS: Political Science & Politics, 45(4), 669-674.
Bunker, K., & Bauchowitz, S. (2016). Pronósticos Electorales y Seguimiento de Opinión Publica en Latino América: Una aplicación a Chile. Política. Revista de Ciencia Política, 54(2), 207-233.
Campbell, J. E. (1992). Forecasting the presidential vote in the states. American Journal of Political Science, 1, 386-407.
Campbell, J. E. (2008). The Trial-Heat Forecast of the 2008 Presidential Vote: Performance and Value Considerations in an Open-Seat Election. PS: Political Science and Politics, 41, 697-701.
Campbell, J. E. (2017). A recap of the 2016 election forecasts. PS, Political Science & Politics, 50, 331-338.
Cantu, F.; Hoyo, V. and Morales, M. A. (2016). The Utility of Unpacking Survey Bias in Multiparty Elections: Mexican Polling Firms in the 2006 and 2012 Presidential Elections. International Journal of Public Opinion Research, 28, 96-116.
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Duque, D, and Smith, A. (2019). The Establishment Upside Down: A Year of Change in Brazil. Revista de Ciencia Política, 39(2), 165-189.
Foucault, M., & Nadeau, R. (2012). Forecasting the 2012 French presidential election. PS: Political Science & Politics, 45(2), 218-222.
Jastramskis, M. (2012). Election forecasting in Lithuania: The case of municipal elections. International Journal of Forecasting, 28(4), 822-829.
Jérôme, B., & Jérôme-Speziari, V. (2004). Forecasting the 2002 elections: Lessons from a political economy model. In M. Lewis-Beck (ed.), The French Voter: Before and After the 2002 Elections (pp. 178-204). Palgrave Macmillan.
Jérôme, B., & Jerôme-Speziari, V. (2012). Forecasting the 2012 French presidential election: Lessons from a region-by-region political economy model. French Politics, 10(4), 373-377.
Jerôme, B., & Jerôme-Speziari, V. (2016). State-Level Forecasts for the 2016 US Presidential Elections: Political Economy Model Predicts Hillary Clinton Victory. PS: Political Science & Politics, 49(4), 680-686.
Jérôme, B., Jérôme, V., Mongrain, P., & Nadeau, R. (2021). State-level forecasts for the 2020 us presidential election: Tough victory ahead for biden. PS: Political Science & Politics, 54(1), 77-80.
Lewis-Beck, M. S., Bélanger, É. and Fauvelle-Aymar, C. (2008). Forecasting the 2007 French Presidential Election: Ségolène Royel and the Iowa Model. French Politics, 6, 106-115.
Pereira, C. and Bertholini, F. (2018). Coalition Management in Multiparty Presidential Regimes. In B. Ames (ed.) Routledge Handbook of Brazilian Politics (pp. 313-330). Routledge.
Powell Jr, G. B. (2004). The quality of democracy: The chain of responsiveness. Journal of Democracy, 15(4), 91-105.
Rennó, L. R. (2020). The Bolsonaro voter: Issue positions and vote choice in the 2018 Brazilian presidential elections. Latin American Politics and Society, 62, 1-23.
Samuels, D. and C. Zucco. (2018). Partisans, antipartisans, and nonpartisans: voting behavior in Brazil. Cambridge University Press.
Toros, E. (2012). Forecasting Turkish Local elections. International Journal of Forecasting, 28, 813-821.
Turgeon, M. and Rennó, L. (2012). Forecasting Brazilian presidential elections: Solving the N problem. International Journal of Forecasting, 28(4), 804-812.
Auberger, A. (2010). Popularity and vote: forecasting the 2007 French presidential election. Canadian Journal of Political Science/Revue canadienne de science politique, 43(1), 123-136.
Baker, A., Ames, B., & Rennó, L. (2020). Persuasive Peers. Princeton University Press.
Beck, N., (2001). Time-series–cross-section data: What have we learned in the past few years? Annual Review of Political Science, 4(1), 271-293.
Berry, M. J., & Bickers, K. N. (2012). Forecasting the 2012 presidential election with state-level economic indicators. PS: Political Science & Politics, 45(4), 669-674.
Bunker, K., & Bauchowitz, S. (2016). Pronósticos Electorales y Seguimiento de Opinión Publica en Latino América: Una aplicación a Chile. Política. Revista de Ciencia Política, 54(2), 207-233.
Campbell, J. E. (1992). Forecasting the presidential vote in the states. American Journal of Political Science, 1, 386-407.
Campbell, J. E. (2008). The Trial-Heat Forecast of the 2008 Presidential Vote: Performance and Value Considerations in an Open-Seat Election. PS: Political Science and Politics, 41, 697-701.
Campbell, J. E. (2017). A recap of the 2016 election forecasts. PS, Political Science & Politics, 50, 331-338.
Cantu, F.; Hoyo, V. and Morales, M. A. (2016). The Utility of Unpacking Survey Bias in Multiparty Elections: Mexican Polling Firms in the 2006 and 2012 Presidential Elections. International Journal of Public Opinion Research, 28, 96-116.
Dubois, E. and Fauvelle-Aymar, C. (2004). Vote Functions in France and the 2002 Election Forecast. In M. Lewis-Beck (ed.), The French Voter: Before and After the 2002 Elections (pp. 205-230). Palgrave-MacMillan.
Duque, D, and Smith, A. (2019). The Establishment Upside Down: A Year of Change in Brazil. Revista de Ciencia Política, 39(2), 165-189.
Foucault, M., & Nadeau, R. (2012). Forecasting the 2012 French presidential election. PS: Political Science & Politics, 45(2), 218-222.
Jastramskis, M. (2012). Election forecasting in Lithuania: The case of municipal elections. International Journal of Forecasting, 28(4), 822-829.
Jérôme, B., & Jérôme-Speziari, V. (2004). Forecasting the 2002 elections: Lessons from a political economy model. In M. Lewis-Beck (ed.), The French Voter: Before and After the 2002 Elections (pp. 178-204). Palgrave Macmillan.
Jérôme, B., & Jerôme-Speziari, V. (2012). Forecasting the 2012 French presidential election: Lessons from a region-by-region political economy model. French Politics, 10(4), 373-377.
Jerôme, B., & Jerôme-Speziari, V. (2016). State-Level Forecasts for the 2016 US Presidential Elections: Political Economy Model Predicts Hillary Clinton Victory. PS: Political Science & Politics, 49(4), 680-686.
Jérôme, B., Jérôme, V., Mongrain, P., & Nadeau, R. (2021). State-level forecasts for the 2020 us presidential election: Tough victory ahead for biden. PS: Political Science & Politics, 54(1), 77-80.
Lewis-Beck, M. S., Bélanger, É. and Fauvelle-Aymar, C. (2008). Forecasting the 2007 French Presidential Election: Ségolène Royel and the Iowa Model. French Politics, 6, 106-115.
Pereira, C. and Bertholini, F. (2018). Coalition Management in Multiparty Presidential Regimes. In B. Ames (ed.) Routledge Handbook of Brazilian Politics (pp. 313-330). Routledge.
Powell Jr, G. B. (2004). The quality of democracy: The chain of responsiveness. Journal of Democracy, 15(4), 91-105.
Rennó, L. R. (2020). The Bolsonaro voter: Issue positions and vote choice in the 2018 Brazilian presidential elections. Latin American Politics and Society, 62, 1-23.
Samuels, D. and C. Zucco. (2018). Partisans, antipartisans, and nonpartisans: voting behavior in Brazil. Cambridge University Press.
Toros, E. (2012). Forecasting Turkish Local elections. International Journal of Forecasting, 28, 813-821.
Turgeon, M. and Rennó, L. (2012). Forecasting Brazilian presidential elections: Solving the N problem. International Journal of Forecasting, 28(4), 804-812.
Bertholini, F., Rennó, L., & Turgeon, M. (2022). Against all Odds: Forecasting Brazilian Presidential Elections in times of political disruption. Revista Latinoamericana De Opinión Pública, 11(1), 129–147. https://doi.org/10.14201/rlop.25882
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