Argentinian Elections

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.

Blame it on turnout? Citizens’ participation and polls’ accuracy

When pre-election polls fail, citizens make choices in an environment where the information is inaccurate. This is bad for democracy. Understanding the conditions under which polls succeed or fail is thus very important for the quality of democracy. Polling firms have often blamed voter turnout when they failed to provide accurate information. There is, however, no systematic test of the impact of voter turnout on polling errors. Using data from 2104 pre-election polls in 206 elections among 33 unique countries from 1942 to 2017, I test whether polling firms have legitimate reason to blame their errors on turnout. Results systematically fail to provide evidence that the quality of pre-election forecasting is a function of voter turnout. This research entails important implications for our understanding of polls’ capacity to predict electoral outcomes and polling firms’ public reactions across time and space.

A Machine Learning Approach for Forecasting US Presidential Election

In this paper, we develop and compare two models for forecasting the 2020 U.S. presidential election using multiple linear regressions (MLR) and the Machine Learning method of Extreme Gradient Boosting (xgboost). We predict each state’s Republican vote share using seven continuous predictors from 1976-2016, as well as dummy columns for each state. After computing 95% confidence intervals for each prediction, we determine the candidates’ electoral college probabilities. The xgboost appears to be a very strong predictor, accounting for 98.6% of the variance with a 3.34% root mean square error (RMSE), whereas the MLR only accounts for 71.8% of the variance and leaves an RMSE of 6.35%. We observe that 1) both models predict a Democratic electoral college landslide in the 2020 elections, 2) Georgia, Iowa, Florida, North Carolina, and Ohio are crucial for the Republicans to win, and 3) Extreme Gradient Boosting is an attractive alternative to MLR in election forecasting.

Pollster problems in the 2016 US presidential election: vote intention, vote prediction

In recent US presidential elections, there has been considerable focus on how well public opinion can forecast the outcome, and 2016 proved no exception. Pollsters and poll aggregators regularly offered numbers on the horse-race, usually pointing to a Clinton victory, which failed to occur. We argue that these polling assessments of support were misleading for at least two reasons. First, Trump voters were sorely underestimated, especially at the state level of polling. Second, and more broadly, we suggest that excessive reliance on non-probability sampling was at work. Here we present evidence to support our contention, ending with a plea for consideration of other methods of election forecasting that are not based on vote intention polls.

Exploring the Power of Social Media in Election Predictions

The forecasting of election’s outcome remained prevailed in prominence from pre-historic times and is still a delightful topic of the current era. The predictions of election results have been started from traditional methods to economic indicators and now is being swung by social media especially sentimental analysis. The present studies discuss the election forecasting methods carried out in diverse nations by the number of researchers till now. Furthermore, different number of approaches for electoral prediction using social media and economic dimensions has been investigated based on previous literature work. The main focus of this work is to study and examine various techniques, methods and parameters used for election predictions in distinct areas. Finally, we suggest some intelligent techniques which will be based upon some parameters such as the development agenda, party type and religionism etc for further modification in election prediction system, so as to enhance the accuracy of political forecasting globally

From SA to the USA: Election forecasting

Elections draw enormous interest worldwide, especially if these involve major countries, and there is much speculation in the media as to possible outcomes from these elections. In many of these recent elections, such as the UK and USA, however, forecasts from market surveys, electoral polls, scientific forecasting models and even exit polls, obtained from voters as they leave the voting stations, failed to predict the correct outcome. Election night forecasts, which endeavour to forecast the ultimate result before the final outcome is known using early results, were also carried out, with some more accurate than others.After successfully predicting most of the metropolitan region results correctly in the South African local 2016 municipal elections, using an election night forecasting model developed for South Africa (SA), the question of adapting the model to work outside of SA on a different electoral system was raised. The focus of this paper is to describe the results obtained for the 2016 USA presidential election, on election night, using an adapted version of the SA model. This paper also addresses the applicability of the model assumptions as well as the data issues involved in forecasting outside of South Africa. It is shown that even with many hurdles experienced in the process the model performed relatively well.