Let’s Be Honest about Election Forecasting

2020 ◽  
Vol 54 (1) ◽  
pp. 107-110
Author(s):  
Jennifer Nicoll Victor
Keyword(s):  
2009 ◽  
Vol 19 (3) ◽  
pp. 333-358 ◽  
Author(s):  
Richard Nadeau ◽  
Michael S. Lewis‐Beck ◽  
Éric Bélanger

Author(s):  
Maria Celeste Ratto ◽  
Michael S. Lewis-Beck

Election forecasts, based on public opinion polls or statistical structural models, regularly appear before national elections in established democracies around the world. However, in less established democratic systems, such as those in Latin America, scientific election forecasting by opinion polls is irregular and by statistical models is almost non-existent. Here we attempt to ameliorate this situation by exploring the leading case of Argentina, where democratic elections have prevailed for the last thirty-eight years. We demonstrate the strengths—and the weaknesses—of the two approaches, finally giving the nod to structural models based political and economic fundamentals. Investigating the presidential and legislative elections there, 1983 to 2019, our political economy model performs rather better than the more popular vote intention method from polling.


2017 ◽  
Vol 28 (11) ◽  
pp. 1750132 ◽  
Author(s):  
Trevor Fenner ◽  
Eric Kaufmann ◽  
Mark Levene ◽  
George Loizou

Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Contextual and selection effects tend to produce extreme values in the tails of rank-ordered distributions of both census data and district-level election outcomes. Models that account for this nonlinearity generally outperform linear models. Fitting nonlinear functions based on rank-ordering census and election data therefore improves the fit of aggregate voting models. This may help improve ecological inference, as well as election forecasting in majoritarian systems. We propose a generative multiplicative decrease model that gives rise to a rank-order distribution and facilitates the analysis of the recent UK EU referendum results. We supply empirical evidence that the beta-like survival function, which can be generated directly from our model, is a close fit to the referendum results, and also may have predictive value when covariate data are available.


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