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Forecasting U.S. Presidential Elections: A Brief Review
Abstract
With the November 2008 U.S. presidential election looming, Randall and Alfred describe the enduring forecasting models that have been created by economists and political scientists for predicting the results of this quadrennial ritual. The most stable models since 1996 have consistently forecast the election winner, with an average error of less than 3%. While not all of the players have issued their forecasts for this year’s final vote, the models suggest that the outlook for the Republican Party is negative. Copyright International Institute of Forecasters, 2008
... Then, the researchers would often use these models to provide a forecast of the election result. As summarized by Jones and Cuzán (2008), the majority of models focus on economic indicators, often accompanied by a measure of public opinion. Of the 14 quantitative models in their review (all of which were regression models), twelve used a measure of the state of economy, seven used a measure of the incumbent's popularity, and five used a combination of these variables. ...
... On the other hand, individual differences among candidates such as their personalities, their experience, or their positions on the issues are often assumed to have little impact on the election outcomes. None of the models in the review by Jones and Cuzán (2008) incorporates variables that would capture such information. As a result, it is often difficult to use the forecasts of such models as an aid to those involved with political campaigns. ...
The state of election forecasting has progressed to the point where it is possible to develop highly accurate forecasts for major elections. However, one area that has received little attention is the use of forecasting as an aid to those involved with political campaigns. In the run-up to the presidential primaries, we use the bio-index model to test the chances of potential nominees to defeat President Obama in the 2012 U.S. presidential election. This model uses the index method to incorporate 58 biographical variables (e.g., age, marital status, height, appearance) for making a conditional forecast of the incumbent’s vote-share, depending on who is the opposing candidate. These variables were selected based on received wisdom and findings from prior research. For example, several studies found candidates’ perceived attractiveness or facial competence to be related to their chances of winning an election. The model is particularly valuable for making long-term forecasts of who will win an election and missed the correct winner only twice for the 29 elections from 1896 to 2008. Thus, the model can help candidates to decide whether they should run for office and can advise political interests in deciding whom to support in the primaries and caucuses. The forecasts from the bio-index model are compared to forecasts from polls and prediction markets.
... However, one interesting question which has emerged from this line of research is to what extent these heuristics continue to effect voting decisions in the modern world (Antonakis & Dalgas, 2009;Jones & Cuzán, 2008;Lawson, Lenz, Baker & Myers;Laustsen & Peterson, 2016). Recently, we have argued that the underlying psychological mechanism responsible for this unique ability can best be conceptualized as a type of internal regulatory variable -the "leader index" -which determines both when such coordination is needed and who best to follow (cf Tooby & Cosmides, 2005;. ...
THE EVOLUTIONARY ORIGINS AND PSYCHOLOGY OF
CHARISMATIC LEADERSHIP
... Some of the major contributions date back to the early 1980s (Lewis-Beck and Rice, 1984;Rosenstone, 1983). Since 1996, the most stable approaches have correctly forecast the election winner with an average error of less than 3% (Jones and Cuzán, 2008). These forecasts have often been quantitative models based on economic measures (Alesina, Londregan and Rosenthal, 1993;Nadeau and Lewis-Beck, 2001;Lewis-Beck and Stegmaier, 2000). ...
The purpose of this article is to explore electoral forecasting and public opinion tracking in Latin America. We review different approaches used to estimate the true value of public opinion, and assess the range of their application. We focus on election night forecasting and campaign variation tracking in Latin America. We propose a two-stage model based on poll aggregation and Bayesian inference. We present data from two presidential elections in Chile. We test the model and show that it provides the most accurate election night forecasting point estimate and the most comprehensive campaign variation tracking method. Finally, we discuss the advantages and limitations of our model, and suggest a route for future research.
... However, one interesting question which has emerged from this line of research is to what extent these heuristics continue to effect voting decisions in the modern world (Antonakis & Dalgas, 2009;Jones & Cuzán, 2008;Laustsen & Peterson, 2017;Lawson et al., 2010). Recently, the underlying evolved psychological mechanism responsible for this unique ability has been conceptualized as a type of internal regulatory variable-the "leader index"-which determines both when such coordination is needed and if so, who is the best to follow (cf. ...
The present research replicates and extends previous literature on the evolutionary contingency hypothesis of leadership emergence. Using artificially masculinized versus feminized versions of the faces of the candidates for the 2016 U.S. presidential elections, we demonstrated that different contextual cues produced systematic variation in both preferences for and personality impressions of leadership. We describe results of an online study (N = 298), demonstrating that followers who perceived a match between the contextual prime (intergroup conflict or cooperation) and a leader candidate’s relevant physical cues (masculinized or feminized versions of their faces) both (a) preferred them as leaders and (b) rated them more positively on personality attributes commonly associated with effective leadership such as trustworthiness, warmth, competence, and charisma.
... The forecast of the big-issue model was derived from . For an overview of the predictor variables used in most of the econometric models, see Jones and Cuzán (2008). ...
When deciding for whom to vote, voters should select the candidate they expect to best handle issues, all other things equal. A simple heuristic predicted that the candidate who is rated more favorably on a larger number of issues would win the popular vote. This was correct for nine out of ten U.S. presidential elections from 1972 to 2008. We then used simple linear regression to relate the incumbent’s relative issue ratings to the actual two-party popular vote shares. The resulting model yielded out-of-sample forecasts that were competitive with those from the Iowa Electronic Markets and other established quantitative models. This model has implications for political decision-makers, as it can help to track campaigns and to decide which issues to focus on.
... The forecast of the big-issue model was derived from . For an overview of the predictor variables used in most of the econometric models, see Jones and Cuzán (2008). ...
The field of forecasting is concerned with making statements about matters that are currently unknown. The terms "forecast," "prediction," "projections," and "prognosis" are interchangeable as commonly used. Forecasting is also concerned with the effective presentation and use of forecasts.
... For forecasting U.S. presidential elections, data for the majority of regression models is limited to about 25 elections. In fact, most models use no more than 15 observations and include from two to sometimes as many as seven explanatory variables (Jones & Cuzán 2008). Given that the number of potential variables is large and the number of observations small, forecasting of U.S. Presidential elections lends itself to the use of index models. ...
Using the index method, we developed the PollyBio model to predict election outcomes. The model, based on 49 cues about candidates' biographies, was used to predict the outcome of the 28 U.S. presidential elections from 1900 to 2008. In using a simple heuristic, it correctly predicted the winner for 25 of the 28 elections and was wrong three times. In predicting the two-party vote shares for the last four elections from 1996 to 2008, the model's out-of-sample forecasts yielded a lower forecasting error than 12 benchmark models. By relying on different information and including more variables than traditional models, PollyBio improves on the accuracy of election forecasting. It is particularly helpful for forecasting open-seat elections. In addition, it can help parties to select the candidates running for office.
... Lewis-Beck and Rice (1992), Campbell and Garand (2000), and Jones (2002). For overviews of the variables used in the most popular models see Jones and Cuzán (2008) and Holbrook (2010 Prior research demonstrated that combining predictions from election forecasting models is beneficial to forecast accuracy. Bartels and Zaller (2001) used various combinations of structural variables that are included in prominent presidential election models to construct 48 different models. ...
We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.
... The track record of quantitative models is mixed. Among the best-known and historically better performing models are those by Abramowitz, Campbell, Erikson and Wlezien, and Fair (Jones and Cuzán 2008). The structure of these models has remained relatively unchanged over time. ...
In the PollyVote, we evaluated the combination principle to forecast the five U.S. presidential elections between 1992 and 2008. We combined forecasts from three or four different component methods: trial heat polls, the Iowa Electronic Markets (IEM), quantitative models and, in the 2004 and 2008 contests, periodic surveys of experts on American politics. The forecasts were combined within as well as across components. On average, combining within components reduced forecast error – and increased predictive accuracy – by 17% to 40%. Combining across components led to additional error reductions ranging from 7% to 68%, depending on the forecast horizon. In addition, across all five elections, the PollyVote predicted the correct election winner on all but 4 out of 957 days. The gains from applying the combination principle to election forecasting were much larger than those obtained in other fields.
... The forecasts for Fair's model were obtained from his website (http://fairmodel.econ.yale.edu). For an overview of the predictor variables used in most of the models, see Jones and Cuzán (2008). ...
We used the take-the-best heuristic to develop a model to forecast the popular two-party vote shares in U.S. presidential elections. The model draws upon information about how voters expect the candidates to deal with the most important issue facing the country. We used cross-validation to calculate a total of 1000 out-of-sample forecasts, one for each of the last 100 days of the ten U.S. presidential elections from 1972 to 2008. Ninety-seven per cent of forecasts correctly predicted the winner of the popular vote. The model forecasts were competitive compared to forecasts from methods that incorporate substantially more information (e.g., econometric models and the Iowa Electronic Markets). The purpose of the model is to provide fast advice on which issues candidates should stress in their campaign. Copyright © 2010 John Wiley & Sons, Ltd.
... Most of the established econometric models use between two to five predictor variables, thereby usually including a measure of the economy's state and some measure of the incumbent's popularity. For an overview of the predictor variables used in most of the econometric models, see Jones and Cuzán (2008). ...
When deciding for whom to vote, voters should select the candidate they expect to best handle issues, all other things equal. A simple heuristic predicted that the candidate who is rated more favorably on a larger number of issues would win the popular vote. This was correct for nine out of ten U.S. presidential elections from 1972 to 2008. We then used simple linear regression to relate the incumbent's relative issue ratings to the actual two-party popular vote shares. The resulting model yielded out-of-sample forecasts that were competitive with those from the Iowa Electronic Markets and established quantitative models. The issue-index model has implications for political decision makers, as it can help to track campaigns and to decide which issues to focus on.
The purpose of this paper is to examine the role of housing prices in the outcome of the 2008 U.S. presidential election. Based on data for the 50 states, this study postulates a multivariate model in which the impact of home values is exposed while controlling for other explanatory factors might have exerted independent effects on the Democratic victory. Results of estimation indicate a significant inverse effect of property price appreciation on Democratic votes.
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