Forecasting two-horse races in new democracies: Accuracy, precision and error
Abstract The purpose of this article is to explore electoral forecasting in two-horse races in new democracies. Specifically, it applies a Bayesian dynamic linear model (coined the Two-Stage Model, TSM) to look at the 2020 Chilean two-question national plebiscite. The ultimate objective is to test the TSM in terms of accuracy (how close the forecast is to the election results), precision (how close the forecast is to other methods of prediction) and error (how the forecast deviates from perfect accuracy/precision). The article finds that while the TSM does appear to be a stable estimator, its accuracy and precision is affected under certain conditions. Using the difference in the results for each of the two questions, the article discusses how sharp and unexpected shifts in electoral preferences can affect forecasts.
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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
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
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). Forecasting two-horse races in new democracies: Accuracy, precision and error. Revista Latinoamericana De Opinión Pública, 11(1), 81–108. https://doi.org/10.14201/rlop.25374
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