The party leadership model : an early forecast of the 2015 British general election

British political parties select their leaders to win elections. The winning margin of the party leader among the selectorate reflects how likely they think she is to win the General Election. The present research compares the winning margins of party leaders in their party leadership elections and uses the results of this comparison to predict that the party leader with the larger winning margin will become the next Prime Minister. I term this process "the Party Leadership Model". The model correctly forecasts 8 out of 10 past elections, while making these forecasts 4 years in advance on average. According to a Bayesian analysis, there is a 95 per cent probability that having the larger winning margin in party leadership elections increases the chances of winning the General Election. Because David Cameron performed better among Conservative MPs in 2005 than Ed Miliband did among Labour MPs in 2010, the model predicts Cameron to become Prime Minister again in 2015. The Bayesian calculation puts his chances of re-election at 75 per cent

Citizens forecast a hung parliament with the Conservatives as the largest party

Can citizens forecast the outcome of the UK election? In this post, Andreas Murr presents the results of his forecasting model, which predicts constituency level results by asking citizens in each constituency which party they think is likely to win in their area. The forecast suggests that there will be a hung parliament, with the Conservatives as the largest party

Truss is good news for Labour’s electoral prospects

The fact that Liz Truss was not the most popular candidate among Conservative MPs bodes ill for the Conservatives. Andreas Murr and Stephen Fisher write that no party has won a general election with a leader that lost the contest for support from their own MPs. They say the Party Leadership Model forecasts that the Conservatives will lose the next general election

Citizen forecasting 2020 : a state-by-state experiment

The leading approaches to scientific election forecasting in the United States consist of structural models, prediction markets and opinion polling. With respect to the last, by far the dominant mode relies on vote intention polling, e.g., “If the election were held tomorrow, who would you vote for?” However, there exists an abiding opinion polling strategy that shows a good deal of promise—citizen forecasting. That is, rather than query on vote intention, query on vote expectation, e.g., “Who do you think will win the upcoming election?” This approach has been pursued most extensively in the United Kingdom (Murr 2016) and the United States (LewisBeck and Tien 1999). Recent performance evaluations have shown that in the United Kingdom vote expectations clearly offer more predictive accuracy than vote intentions (Murr et al. forthcoming) and that in the United States vote expectations appear to be superior to an array of rival forecasting tools (Graefe 2014). However, the timing of the data collection has forced most of the studies using citizen forecasts to forecast elections ex post, i.e., after they occurred. Indeed, to date, there are only two ex ante citizen forecasting papers to have appeared before a national election (Lewis-Beck and Stegmaier 2011; Murr 2016). Both these efforts forecasted British General Elections, with Murr (2016) relatively most accurate among 12 academic forecasts (Fisher and Lewis-Beck 2016). With respect to the United States, the case at hand, none of the work has been ex ante and all studies have focused on the national level, with the exception of a lone study carried out at the state level (Murr, 2015). The latter point seems critical, since the final selection of the president takes place in the Electoral College. The citizen forecasting research here stands unique, being ex ante and focusing on the states. Utilizing survey questions on Amazon.com’s Mechanical Turk (MTurk), administered in July, we render forecasts for the November 2020 presidential contest. This experiment, which has been conducted before-the-fact and looks at the states, provides a strong test of the quality of citizen forecasting in this American election

Asking people in each state who they think will win suggests that the presidential election may be very close.

In new survey research, Andreas E. Murr and Michael S. Lewis-Beck asked people in each of the 50 states and Washington DC who they thought would win their state in the presidential election. Adding up their raw data, their survey suggests that President Trump will win re-election next week with 320 electoral votes to 218 for former Vice President, Democrat Joe Biden

Vote expectations versus vote intentions : rival forecasting strategies

Are ordinary citizens better at predicting election results than conventional voter intention polls? We address this question by comparing eight forecasting models for British general elections: one based on voters’ expectations of who will win and seven based on who voters themselves intend to vote for (including “uniform national swing model” and “cube rule” models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950 – 2017, yielding 449 months with both expectation and intention polls. The large sample size allows us to compare the models’ prediction accuracy not just in the months prior to the election, but over the years leading up to it. In predicting both the winning party and parties’ seat shares, we find that vote expectations outperform vote intent ions models. Vote expectations thus appear an excellent tool for predicting the winning party and its seat share

Using citizen forecasts we predict that with 362 electoral votes, Hillary Clinton will be the next president

Who will be the next US President? Some commentators have argued that voter intention polls are flawed because it is difficult to know who will actually turn out to vote. To get around this problem, Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck use citizen forecasts, a “who do you think will win” survey question, to predict the election result

Citizen forecasting 2019: a big win for the Conservatives

The recent failures of voter intention polls to predict UK election results has led to public scepticism about the usefulness of polls. Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck deploy an alternative approach, which focuses on which party opinion poll respondents expect to win the election (rather than just on their voting intentions). This ‘voter expectations’ model predicts a solid Johnson majority, with the Conservatives gaining 360 seats, and Labour only 190

Citizen forecasts of the 2021 German election

There are various scientific approaches to election forecasting: poll aggregation, structural models, electronic markets, and citizen forecasting. With respect to the German case, the first two approaches—polls and models—perhaps have been the most popular. However, relatively little work has been done deploying citizen forecasting (CF), the approach described in this article. In principle, CF differs considerably from other methods and appears, on its face, quite simple. Before an election, citizens are asked in a national survey who they think will win. As the percentage of expectations for party X increases, the likelihood of an X win is judged to be higher. The method has been applied regularly with success in other established democracies, such as the United Kingdom and the United States

Computing quantities of interest and their uncertainty using Bayesian simulation

When analyzing data, researchers are often less interested in the parameters of statistical models than in functions of these parameters such as predicted values. Here we show that Bayesian simulation with Markov-chain Monte Carlo tools makes it easy to compute these quantities of interest with their uncertainty. We illustrate how to produce customary and relatively new quantities of interest such as variable importance ranking, posterior predictive data, difficult marginal effects, and model comparison statistics to allow researchers to report more informative results