Prevendo corridas de dois cavalos em novas democracias: exatidão, precisão e erro
Resumo O objetivo deste artigo é explorar a previsão eleitoral em corridas de dois cavalos em novas democracias. Especificamente, ele aplica um modelo linear dinâmico Bayesiano (nomeado de modelo de dois estágios, TSM) para observar o plebiscito nacional de duas perguntas do Chile em 2020. O objetivo final é testar o TSM em termos de precisão (quão próximo está dos resultados?), exatidão (quão próximo está de outros métodos de previsão?) e erro (quanto se desvia da precisão / exatidão perfeita?). O artigo conclui que, embora o TSM seja um estimador estável, sua exatidão e precisão são afetadas sob certas condições. Usando a diferença nos resultados das duas questões do plebiscito, o artigo discute como mudanças repentinas e inesperadas nas preferências eleitorais podem influenciar as previsões.
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Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211
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Armstrong, J., Green, K., & Graefe, A. (2015). Golden rule of forecasting: Be conservative. Journal of Business Research, 68(8), 1717–1731. https://doi.org/10.1016/j.jbusres.2015.03.031
Bartels, L. M. (1996). Uninformed Votes: Information Effects in Presidential Elections. American Journal of Political Science, 40(1), 194–230. https://doi.org/10.2307/2111700
Berger, J. O., Wolpert, R. L., Bayarri, M. J., DeGroot, M. H., Hill, B. M., Lane, D. A., & LeCam, L. (1988). The Likelihood Principle. Lecture Notes-Monograph Series, 6, iii-v+vii-xii+1-199.
Bernardo, J. M., & Smith, A. F. M. (2009). Bayesian Theory. John Wiley & Sons.
Blais, A., Gidengil, E., Fournier, P., & Nevitte, N. (2009). Information, visibility and elections: Why electoral outcomes differ when voters are better informed. European Journal of Political Research, 48(2), 256–280. https://doi.org/10.1111/j.1475-6765.2008.00835.x
Bodell, M. H. (2016). Bayesian poll of polls for multi-party systems. [Master’s thesis, Linköping University]. https://liu.diva-portal.org/smash/get/diva2:945786/FULLTEXT01.pdf
Bunker, K. (2021). A two-stage model to forecast elections in new democracies. International Journal of Forecasting, 36(4), 1407–1419. https://doi.org/10.1016/j.ijforecast.2020.02.004
Bunker, K., & Bauchowitz, S. (2016). Electoral Forecasting and Public Opinion Tracking in Latin America: An Application to Chile. Política, 54(2), 207–233. http://www.redalyc.org/articulo.oa?id=64551061008
Cantú, F., Hoyo, V., & 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(1), 96–116. https://doi.org/10.1093/ijpor/edv004
Carpini, M. X. D., & Keeter, S. (1997). What Americans Know about Politics and Why It Matters (1st ed.). Yale University Press.
Cassino, D. (2016). Why Pollsters Were Completely and Utterly Wrong. Harvard Business Review, 9. https://hbr.org/2016/11/why-pollsters-were-completely-and-utterly-wrong
Feddersen, T. J., & Pesendorfer, W. (1996). The Swing Voter’s Curse. The American Economic Review, 86(3), 408–424. https://www.kellogg.northwestern.edu/faculty/fedderse/homepage/papers/curse.pdf
Fisher, S. D., & Lewis-Beck, M. S. (2015). Forecasting the 2015 British general election: The 1992 debacle all over again? Electoral Studies, 41. https://doi.org/10.1016/j.electstud.2015.11.016
Fowler, A., & Margolis, M. (2014). The political consequences of uninformed voters. Electoral Studies: An International Journal, 34, 100–110. https://doi.org/10.1016/j.electstud.2013.09.009
Gamerman, D., & Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (2nd ed.). CRC Press.
Ghirardato, P., & Katz, J. N. (2006). Indecision Theory: Weight of Evidence and Voting Behavior. Journal of Public Economic Theory, 8(3), 379–399. https://doi.org/10.1111/j.1467-9779.2006.00269.x
Graefe, A., Armstrong, J. S., Jones, R. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections. International Journal of Forecasting, 30(1), 43–54. https://doi.org/10.1016/j.ijforecast.2013.02.005
Graefe, A., Küchenhoff, H., Stierle, V., & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems. International Journal of Forecasting, 31(3), 943-951. https://www.sciencedirect.com/science/article/pii/S0169207014001769
Haario, H., Saksman, E., & Tamminen, J. (2001). An Adaptive Metropolis Algorithm. Bernoulli, 7(2), 223–242. https://doi.org/10.2307/3318737
Hanretty, C., Lauderdale, B., & Vivyan, N. (2016). Combining national and constituency polling for forecasting. Electoral Studies, 41, 239–243. https://doi.org/10.1016/j.electstud.2015.11.019
Jackman, S. (2005). Pooling the polls over an election campaign. Australian Journal of Political Science, 40(4), 499–517. https://doi.org/10.1080/10361140500302472
Jackson, N. (2018). The Rise of Poll Aggregation and Election Forecasting. In L. R. Atkeson & M. Alvarez (Eds.), The Oxford Handbook of Polling and Survey Methods. https://doi.org/10.1093/oxfordhb/9780190213299.013.28
Jennings, W., Lewis-Beck, M., & Wlezien, C. (2020). Election forecasting: Too far out? International Journal of Forecasting, 36(3), 949–962. https://doi.org/10.1016/j.ijforecast.2019.12.002
Jennings, W., & Wlezien, C. (2016). The Timeline of Elections: A Comparative Perspective. American Journal of Political Science, 60(1), 219–233. https://www.jstor.org/stable/24583060
Larcinese, V. (2007). Does political knowledge increase turnout? Evidence from the 1997 British general election. Public Choice, 131, 387–411. https://doi.org/10.1007/s11127-006-9122-0
Lassen, D. D. (2005). The Effect of Information on Voter Turnout: Evidence from a Natural Experiment. American Journal of Political Science, 49(1), 103–118. https://doi.org/10.2307/3647716
Lewis-Beck, M. S., & Bélanger, É. (2012). Election forecasting in neglected democracies: An introduction. International Journal of Forecasting, 28(4), 767–768. https://doi.org/10.1016/j.ijforecast.2012.04.006
Lewis-Beck, M. S., & Dassonneville, R. (2015). Forecasting elections in Europe: Synthetic models. Research & Politics, 2(1), 1–11. 2053168014565128. https://doi.org/10.1177/2053168014565128
Lewis-Beck, M. S., & Stegmaier, M. (2008). The Economic Vote in Transitional Democracies. Journal of Elections, Public Opinion and Parties, 18(3), 303–323. https://doi.org/10.1080/17457280802227710
Linzer, D. A. (2013). Dynamic Bayesian Forecasting of Presidential Elections in the States. Journal of the American Statistical Association, 108(501), 124–134. https://doi.org/10.1080/01621459.2012.737735
Lock, K., & Gelman, A. (2010). Bayesian Combination of State Polls and Election Forecasts. Political Analysis, 18(3), 337–348. https://doi.org/10.1093/pan/mpq002
Mannes, A. E., Soll, J. B., & Larrick, R. P. (2014). The wisdom of select crowds. Journal of Personality and Social Psychology, 107(2), 276-299. https://doi.org/10.1037/a0036677
Markus, G. B., & Converse, P. E. (1979). A Dynamic Simultaneous Equation Model of Electoral Choice. American Political Science Review, 73(4), 1055–1070. https://doi.org/10.2307/1953989
Matsusaka, J. (1995). Fiscal Effects of the Voter Initiative: Evidence from the Last 30 Years. Journal of Political Economy, 103(3), 587–623. http://www.jstor.org/stable/2138700
McKay, S., & Tenove, C. (2020). Disinformation as a Threat to Deliberative Democracy. Political Research Quarterly, 74(3), 703-717. https://doi.org/10.1177/1065912920938143
Metropolis, N., & Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association, 44(247), 335–341. https://doi.org/10.1080/01621459.1949.10483310
Milner, H. (2002). Civic Literacy: How Informed Citizens Make Democracy Work (1st ed.). Tufts.
Montalvo, J. G., Papaspiliopoulos, O., & Stumpf-Fétizon, T. (2019). Bayesian forecasting of electoral outcomes with new parties’ competition. European Journal of Political Economy, 59, 52–70. https://doi.org/10.1016/j.ejpoleco.2019.01.006
Nadeau, R., Cloutier, E., & Guay, J.-H. (1993). New Evidence about the Existence of a Bandwagon Effect in the Opinion Formation Process. International Political Science Review / Revue Internationale de Science Politique, 14(2), 203–213. https://www.jstor.org/stable/1601152
Palfrey, T. R., & Poole, K. T. (1987). The Relationship between Information, Ideology, and Voting Behavior. American Journal of Political Science, 31(3), 511–530. https://doi.org/10.2307/2111281
Pasek, J. (2015). Predicting Elections: Considering Tools to Pool the Polls. Public Opinion Quarterly, 79(2), 594-619. https://doi.org/10.1093/POQ/NFU060
Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic linear models. In P. Campagnoli, S. Petrone, & G. Petris (Eds.), Dynamic Linear Models with R (pp. 31–84). Springer. https://doi.org/10.1007/b135794_2
Pickup, M., & Johnston, R. (2007). Campaign trial heats as electoral information: Evidence from the 2004 and 2006 Canadian federal elections. Electoral Studies: An International Journal, 26(2), 460–476. https://doi.org/10.1016/j.electstud.2007.03.001
Rigdon, S. E., Jacobson, S. H., Cho, W. K. T., Sewell, E. C., & Rigdon, C. J. (2009). A Bayesian prediction model for the U.S. presidential election. American Politics Research, 37(4), 700–724. https://doi.org/10.1177/1532673X08330670
Roberts, G. O., & Rosenthal, J. S. (2009). Examples of Adaptive MCMC. Journal of Computational and Graphical Statistics, 18(2), 349–367. https://doi.org/10.1198/jcgs.2009.06134
Sehnbruch, K., & Donoso, S. (2020). Social Protests in Chile: Inequalities and other Inconvenient Truths about Latin America’s Poster Child. Global Labour Journal, 11(1). https://doi.org/10.15173/glj.v11i1.4217
Servel. (2020). Gasto y Propaganda Electoral Plebiscito Nacional 2020. Servel. https://www.plebiscitonacional2020.cl/propaganda-electoral-plebiscito-nacional-2020/
Stoetzer, L. F., Neunhoeffer, M., Gschwend, T., Munzert, S., & Sternberg, S. (2019). Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals. Political Analysis, 27(2), 255–262. https://doi.org/10.1017/pan.2018.49
Stoltenberg, E. A. (2013). Bayesian Forecasting of Election Results in Multiparty Systems. [Master’s thesis, University of Oslo]. http://urn.nb.no/URN:NBN:no-37414
Tanner, M. A., & Wong, W. H. (1987). The Calculation of Posterior Distributions by Data Augmentation. Journal of the American Statistical Association, 82(398), 528–540. https://doi.org/10.2307/2289457
Turgeon, M., & Rennó, L. (2012). Forecasting Brazilian presidential elections: Solving the N problem. International Journal of Forecasting, 28(4), 804–812. https://doi.org/10.1016/j.ijforecast.2012.04.003
Walther, D. (2015). Picking the winner(s): Forecasting elections in multiparty systems. Electoral Studies, 40, 1–13. https://doi.org/10.1016/j.electstud.2015.06.003
Wang, S. S.-H. (2015). Origins of Presidential poll aggregation: A perspective from 2004 to 2012. International Journal of Forecasting, 31(3), 898–909. https://doi.org/10.1016/j.ijforecast.2015.01.003
West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer-Verlag. https://doi.org/10.1007/b98971
Whiteley, P., Clarke, H. D., Sanders, D., & Stewart, M. C. (2016). Forecasting the 2015 British general election: The Seats-Votes model. Electoral Studies, 41, 269–273. https://doi.org/10.1016/j.electstud.2015.11.015
Winters, M. S., & Weitz-Shapiro, R. (2013). Lacking Information or Condoning Corruption: When Do Voters Support Corrupt Politicians? Comparative Politics, 45(4), 418–436. https://www.jstor.org/stable/43664074
Altman, D. (2018). Latin America. In M. Qvortrup (Ed.), Referendums Around the World: With a Foreword by Sir David Butler (pp. 185–211). Springer International Publishing. https://doi.org/10.1007/978-3-319-57798-2_6
Armstrong, J., Green, K., & Graefe, A. (2015). Golden rule of forecasting: Be conservative. Journal of Business Research, 68(8), 1717–1731. https://doi.org/10.1016/j.jbusres.2015.03.031
Bartels, L. M. (1996). Uninformed Votes: Information Effects in Presidential Elections. American Journal of Political Science, 40(1), 194–230. https://doi.org/10.2307/2111700
Berger, J. O., Wolpert, R. L., Bayarri, M. J., DeGroot, M. H., Hill, B. M., Lane, D. A., & LeCam, L. (1988). The Likelihood Principle. Lecture Notes-Monograph Series, 6, iii-v+vii-xii+1-199.
Bernardo, J. M., & Smith, A. F. M. (2009). Bayesian Theory. John Wiley & Sons.
Blais, A., Gidengil, E., Fournier, P., & Nevitte, N. (2009). Information, visibility and elections: Why electoral outcomes differ when voters are better informed. European Journal of Political Research, 48(2), 256–280. https://doi.org/10.1111/j.1475-6765.2008.00835.x
Bodell, M. H. (2016). Bayesian poll of polls for multi-party systems. [Master’s thesis, Linköping University]. https://liu.diva-portal.org/smash/get/diva2:945786/FULLTEXT01.pdf
Bunker, K. (2021). A two-stage model to forecast elections in new democracies. International Journal of Forecasting, 36(4), 1407–1419. https://doi.org/10.1016/j.ijforecast.2020.02.004
Bunker, K., & Bauchowitz, S. (2016). Electoral Forecasting and Public Opinion Tracking in Latin America: An Application to Chile. Política, 54(2), 207–233. http://www.redalyc.org/articulo.oa?id=64551061008
Cantú, F., Hoyo, V., & 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(1), 96–116. https://doi.org/10.1093/ijpor/edv004
Carpini, M. X. D., & Keeter, S. (1997). What Americans Know about Politics and Why It Matters (1st ed.). Yale University Press.
Cassino, D. (2016). Why Pollsters Were Completely and Utterly Wrong. Harvard Business Review, 9. https://hbr.org/2016/11/why-pollsters-were-completely-and-utterly-wrong
Feddersen, T. J., & Pesendorfer, W. (1996). The Swing Voter’s Curse. The American Economic Review, 86(3), 408–424. https://www.kellogg.northwestern.edu/faculty/fedderse/homepage/papers/curse.pdf
Fisher, S. D., & Lewis-Beck, M. S. (2015). Forecasting the 2015 British general election: The 1992 debacle all over again? Electoral Studies, 41. https://doi.org/10.1016/j.electstud.2015.11.016
Fowler, A., & Margolis, M. (2014). The political consequences of uninformed voters. Electoral Studies: An International Journal, 34, 100–110. https://doi.org/10.1016/j.electstud.2013.09.009
Gamerman, D., & Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (2nd ed.). CRC Press.
Ghirardato, P., & Katz, J. N. (2006). Indecision Theory: Weight of Evidence and Voting Behavior. Journal of Public Economic Theory, 8(3), 379–399. https://doi.org/10.1111/j.1467-9779.2006.00269.x
Graefe, A., Armstrong, J. S., Jones, R. J., & Cuzán, A. G. (2014). Combining forecasts: An application to elections. International Journal of Forecasting, 30(1), 43–54. https://doi.org/10.1016/j.ijforecast.2013.02.005
Graefe, A., Küchenhoff, H., Stierle, V., & Riedl, B. (2015). Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems. International Journal of Forecasting, 31(3), 943-951. https://www.sciencedirect.com/science/article/pii/S0169207014001769
Haario, H., Saksman, E., & Tamminen, J. (2001). An Adaptive Metropolis Algorithm. Bernoulli, 7(2), 223–242. https://doi.org/10.2307/3318737
Hanretty, C., Lauderdale, B., & Vivyan, N. (2016). Combining national and constituency polling for forecasting. Electoral Studies, 41, 239–243. https://doi.org/10.1016/j.electstud.2015.11.019
Jackman, S. (2005). Pooling the polls over an election campaign. Australian Journal of Political Science, 40(4), 499–517. https://doi.org/10.1080/10361140500302472
Jackson, N. (2018). The Rise of Poll Aggregation and Election Forecasting. In L. R. Atkeson & M. Alvarez (Eds.), The Oxford Handbook of Polling and Survey Methods. https://doi.org/10.1093/oxfordhb/9780190213299.013.28
Jennings, W., Lewis-Beck, M., & Wlezien, C. (2020). Election forecasting: Too far out? International Journal of Forecasting, 36(3), 949–962. https://doi.org/10.1016/j.ijforecast.2019.12.002
Jennings, W., & Wlezien, C. (2016). The Timeline of Elections: A Comparative Perspective. American Journal of Political Science, 60(1), 219–233. https://www.jstor.org/stable/24583060
Larcinese, V. (2007). Does political knowledge increase turnout? Evidence from the 1997 British general election. Public Choice, 131, 387–411. https://doi.org/10.1007/s11127-006-9122-0
Lassen, D. D. (2005). The Effect of Information on Voter Turnout: Evidence from a Natural Experiment. American Journal of Political Science, 49(1), 103–118. https://doi.org/10.2307/3647716
Lewis-Beck, M. S., & Bélanger, É. (2012). Election forecasting in neglected democracies: An introduction. International Journal of Forecasting, 28(4), 767–768. https://doi.org/10.1016/j.ijforecast.2012.04.006
Lewis-Beck, M. S., & Dassonneville, R. (2015). Forecasting elections in Europe: Synthetic models. Research & Politics, 2(1), 1–11. 2053168014565128. https://doi.org/10.1177/2053168014565128
Lewis-Beck, M. S., & Stegmaier, M. (2008). The Economic Vote in Transitional Democracies. Journal of Elections, Public Opinion and Parties, 18(3), 303–323. https://doi.org/10.1080/17457280802227710
Linzer, D. A. (2013). Dynamic Bayesian Forecasting of Presidential Elections in the States. Journal of the American Statistical Association, 108(501), 124–134. https://doi.org/10.1080/01621459.2012.737735
Lock, K., & Gelman, A. (2010). Bayesian Combination of State Polls and Election Forecasts. Political Analysis, 18(3), 337–348. https://doi.org/10.1093/pan/mpq002
Mannes, A. E., Soll, J. B., & Larrick, R. P. (2014). The wisdom of select crowds. Journal of Personality and Social Psychology, 107(2), 276-299. https://doi.org/10.1037/a0036677
Markus, G. B., & Converse, P. E. (1979). A Dynamic Simultaneous Equation Model of Electoral Choice. American Political Science Review, 73(4), 1055–1070. https://doi.org/10.2307/1953989
Matsusaka, J. (1995). Fiscal Effects of the Voter Initiative: Evidence from the Last 30 Years. Journal of Political Economy, 103(3), 587–623. http://www.jstor.org/stable/2138700
McKay, S., & Tenove, C. (2020). Disinformation as a Threat to Deliberative Democracy. Political Research Quarterly, 74(3), 703-717. https://doi.org/10.1177/1065912920938143
Metropolis, N., & Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association, 44(247), 335–341. https://doi.org/10.1080/01621459.1949.10483310
Milner, H. (2002). Civic Literacy: How Informed Citizens Make Democracy Work (1st ed.). Tufts.
Montalvo, J. G., Papaspiliopoulos, O., & Stumpf-Fétizon, T. (2019). Bayesian forecasting of electoral outcomes with new parties’ competition. European Journal of Political Economy, 59, 52–70. https://doi.org/10.1016/j.ejpoleco.2019.01.006
Nadeau, R., Cloutier, E., & Guay, J.-H. (1993). New Evidence about the Existence of a Bandwagon Effect in the Opinion Formation Process. International Political Science Review / Revue Internationale de Science Politique, 14(2), 203–213. https://www.jstor.org/stable/1601152
Palfrey, T. R., & Poole, K. T. (1987). The Relationship between Information, Ideology, and Voting Behavior. American Journal of Political Science, 31(3), 511–530. https://doi.org/10.2307/2111281
Pasek, J. (2015). Predicting Elections: Considering Tools to Pool the Polls. Public Opinion Quarterly, 79(2), 594-619. https://doi.org/10.1093/POQ/NFU060
Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic linear models. In P. Campagnoli, S. Petrone, & G. Petris (Eds.), Dynamic Linear Models with R (pp. 31–84). Springer. https://doi.org/10.1007/b135794_2
Pickup, M., & Johnston, R. (2007). Campaign trial heats as electoral information: Evidence from the 2004 and 2006 Canadian federal elections. Electoral Studies: An International Journal, 26(2), 460–476. https://doi.org/10.1016/j.electstud.2007.03.001
Rigdon, S. E., Jacobson, S. H., Cho, W. K. T., Sewell, E. C., & Rigdon, C. J. (2009). A Bayesian prediction model for the U.S. presidential election. American Politics Research, 37(4), 700–724. https://doi.org/10.1177/1532673X08330670
Roberts, G. O., & Rosenthal, J. S. (2009). Examples of Adaptive MCMC. Journal of Computational and Graphical Statistics, 18(2), 349–367. https://doi.org/10.1198/jcgs.2009.06134
Sehnbruch, K., & Donoso, S. (2020). Social Protests in Chile: Inequalities and other Inconvenient Truths about Latin America’s Poster Child. Global Labour Journal, 11(1). https://doi.org/10.15173/glj.v11i1.4217
Servel. (2020). Gasto y Propaganda Electoral Plebiscito Nacional 2020. Servel. https://www.plebiscitonacional2020.cl/propaganda-electoral-plebiscito-nacional-2020/
Stoetzer, L. F., Neunhoeffer, M., Gschwend, T., Munzert, S., & Sternberg, S. (2019). Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals. Political Analysis, 27(2), 255–262. https://doi.org/10.1017/pan.2018.49
Stoltenberg, E. A. (2013). Bayesian Forecasting of Election Results in Multiparty Systems. [Master’s thesis, University of Oslo]. http://urn.nb.no/URN:NBN:no-37414
Tanner, M. A., & Wong, W. H. (1987). The Calculation of Posterior Distributions by Data Augmentation. Journal of the American Statistical Association, 82(398), 528–540. https://doi.org/10.2307/2289457
Turgeon, M., & Rennó, L. (2012). Forecasting Brazilian presidential elections: Solving the N problem. International Journal of Forecasting, 28(4), 804–812. https://doi.org/10.1016/j.ijforecast.2012.04.003
Walther, D. (2015). Picking the winner(s): Forecasting elections in multiparty systems. Electoral Studies, 40, 1–13. https://doi.org/10.1016/j.electstud.2015.06.003
Wang, S. S.-H. (2015). Origins of Presidential poll aggregation: A perspective from 2004 to 2012. International Journal of Forecasting, 31(3), 898–909. https://doi.org/10.1016/j.ijforecast.2015.01.003
West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer-Verlag. https://doi.org/10.1007/b98971
Whiteley, P., Clarke, H. D., Sanders, D., & Stewart, M. C. (2016). Forecasting the 2015 British general election: The Seats-Votes model. Electoral Studies, 41, 269–273. https://doi.org/10.1016/j.electstud.2015.11.015
Winters, M. S., & Weitz-Shapiro, R. (2013). Lacking Information or Condoning Corruption: When Do Voters Support Corrupt Politicians? Comparative Politics, 45(4), 418–436. https://www.jstor.org/stable/43664074
Bunker, K. (2022). Prevendo corridas de dois cavalos em novas democracias: exatidão, precisão e erro. Revista Latinoamericana De Opinión Pública, 11(1), 81–108. https://doi.org/10.14201/rlop.25374
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