Election forecasts: Cracking the Danish case

Election forecasting models based on voting theories and estimated via regression analysis are routinely available for virtually all advanced industrial democracies. Denmark, however, offers an exception, for no such prediction equations have been published on the Danish case. This absence has sometimes been attributed to the puzzling nature of economic voting there, along with the complexity of its multi-party system, which renders formulation of the dependent variable problematic. We attempt to overcome these obstacles, offering a “synthetic” forecasting model for Danish national election outcomes, 1964–2015. The regression model, based on the variables of economic growth and vote intention, performs well, by various tests. Finally, we apply it, ex ante fashion, to the 2019 contest, where the prediction favored the Social Democratic led coalition, an outcome that came to pass.     

Forecasting government support in Irish general elections: Opinion polls and structural models

Election forecasting is a cottage industry among pollsters, the media, political scientists, and political anoraks. Here, we plow a fresh field in providing a systematic exploration of election forecasting in Ireland. We develop a structural forecast model for predicting incumbent government support in Irish general elections between 1977 and 2020 (the Iowa model). We contrast this structural model with forecasts from opinion polls, the dominant means of predicting Ireland’s elections to date. Our results show that with appropriate lead-in time, structural models perform similarly to opinion polls in predicting government support when the dependent variable is vote share. Most importantly, however, the Iowa model is superior to opinion polls in predicting government seat share, the ultimate decider of government fate in parliamentary systems, and especially significant in single transferable vote (STV) systems where vote and seat shares are not always in sync. Our results provide cumulative evidence of the potency of structural electoral forecast models globally, with the takeaway that the Iowa model estimating seat share outpaces other prediction approaches in anticipating government performance in Irish general elections.     

Polls to probabilities: Comparing prediction markets and opinion polls

The forecasting of election outcomes is a hugely popular activity, and not without reason: the outcomes can have significant economic impacts, for example on stock prices. As such, it is economically important, as well as of academic interest, to determine the forecasting methods that have historically performed best. However, the forecasts are often incompatible, as some are in terms of vote shares while others are probabilistic outcome forecasts. This paper sets out an empirical method for transforming opinion poll vote shares into probabilistic forecasts, and then evaluates the performances of prediction markets and opinion polls. We make comparisons along two dimensions, bias and precision, and find that converted opinion polls perform well in terms of bias, while prediction markets are good for precision.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Modeling and forecasting U.S. public construction

This paper extends Lin's flexible accelerator model of dynamic investment behavior of U.S. public construction by relaxing two of the underlying basic assumptions: (1) the coefficient of adjustment is allowed to vary with the level of government expenditure and (2) the regression coefficients are treated as randomly changing over time rather than being viewed as fixed. The new models afford a better explanation of the behavior of U.S. public construction. Importantly, the forecasting ability of the variable-coefficient-of-adjustment model is tested for the three-year period beyond the sample period and compared to both the Lin's original and ARIMA models. It is found that this new model gives better forecasts of public construction for ten quarters ahead. On the basis of the chi-square test of model stability and the mean squared error, it is concluded that the model with variable adjustment coefficients is a better abstraction of economic reality and improves forecasting accuracy.     

Measuring Electoral Control

If voters do not pay attention to whatrepresentatives do, representatives are not stimulatedto be responsive. Therefore, electoral control, theextent to which voters base their vote on thebehaviour of representatives is, at least potentially,an important variable in the explanation of thebehaviour of representatives. Moreover, electoralcontrol seems to be an important variable from anormative point of view. In this article four ways tomeasure the minimal level of local electoral controlusing the outcomes of local elections (electoralstatistics) are presented. The general idea behind themeasures is fairly simple. If local elections arecompletely determined by nonlocal factors, then thelosses and gains of local divisions of nationalparties from one local election to another are`identical' across municipalities. A deviation fromthis pattern can be interpreted as an indication ofthe minimal level of local electoral control in aspecific municipality. The measures are externallyvalidated using data from a survey among council members.     

Choosing union representation: the role of attitudes and emotions

In the United States, most unions are recognised by a majority vote of employees through union representation elections administered by the government. Most empirical studies of individual voting behaviour during union representation elections use a rational choice model. Recently, however, some have posited that voting is often influenced by emotions. We evaluate competing hypotheses about the determinants of union voting behaviour by using data collected from a 2010 representation election at Delta Air Lines, a US‐based company. In addition to the older rational choice framework, multiple regression results provide support for an emotional choice model. Positive feelings toward the employer are statistically significantly related to voting ‘no’ in a representation election, while positive feelings toward the union are related to a ‘yes’ vote. Effect sizes for the emotion variables were generally larger than those for the rational choice variables, suggesting that emotions may play a key role in representation election outcomes.     

Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective

Forecasting election results has been a highly attractive activity among political and social scientists. Different forecasting methods have been proposed, but those based on public opinion polls are the most common. However, there are challenges to using opinion polls, especially because they neglect undecided voters. Due to the significant number of undecided participants and their impact on voting outcomes, we analyze the potential behavior of undecided voters by considering opinion polls and sentiment based on voter expectation from the perspective of the bandwagon effect and the spiral of silence. We establish a hierarchical Bayesian forecasting model to predict voting results, and apply it to the 2016 United States presidential election and the 2016 Brexit referendum. The results of our model suggest that voting outcomes are more predictable when fully utilizing the impact of undecided voters. The results indicate that integrating aggregated polls into the hierarchical Bayesian framework is a strong predictor for forecasting outcomes, and they provide evidence for the influence of sentiment based on voter expectation in forecasting election results.     

Forecasting election results by studying brand importance in online news

This study uses the semantic brand score, a novel measure of brand importance in big textual data, to forecast elections based on online news. About 35,000 online news articles were transformed into networks of co-occurring words and analyzed by combining methods and tools from social network analysis and text mining. Forecasts made for four voting events in Italy provided consistent results across different voting systems: a general election, a referendum, and a municipal election in two rounds. This work contributes to the research on electoral forecasting by focusing on predictions based on online big data; it offers new perspectives regarding the textual analysis of online news through a methodology which is relatively fast and easy to apply. This study also suggests the existence of a link between the brand importance of political candidates and parties and electoral results.     

Aggregating qualitative district-level campaign assessments to forecast election results: Evidence from Japan

The poll aggregation is conducted in the USA and European democracies for electoral forecasting. However, this has not been the case in Japan because the news media report on electoral campaigns with qualitative assessments rather than poll numbers, although these assessments are based on extensive polling. Our study developed an approach to aggregate qualitative district-level election campaign coverage in Japan, applied the method to forecast the outcomes of the 2017 general election for Japan’s Lower House of the National Diet, and assessed the accuracy of the forecast against the actual results. We integrated the qualitative assessments by using the item response theory, which effectively predicted the electoral results. The method proposed in this paper can be applied to aggregate qualitative assessments by experts in other countries, such as the Cook Political Report in the USA. This would improve the accuracy of election forecasting when combined with existing approaches.     

Voting in the U.S. House on Abortion Funding Issues

A bstract . The Supreme Court's ruling in Webster v. Reproductive Health Services increased the political saliency of the abortion issue. Since pro-choice and pro-life groups within the constituencies of U.S. legislators paid closer attention to abortion-related roll call votes after Webster, the legislators' voting behavior on such issues might have changed as a result of the decision. Accordingly, voting model estimates for abortion funding issues before and after Webster are used to examine changes in the role of legislators' personal policy preferences and the role of policy preferences among their constituency on voting on this issue. The results show that legislators, to some extent, vote according to their personal preferences on abortion funding issues. Moreover, the influence of personal preferences on voting behavior did not change substantially after Webster, despite the change in the outcome of the vote.     

The measurement and transmission of macroeconomic uncertainty: Evidence from the U.S. and BRIC countries

We propose a new measure of macroeconomic uncertainty that incorporates a rich information set from U.S. SPF density forecasts. Our measure has two key advantages over traditional measures: (i) it reflects the subjective perceptions of market participants; and (ii) it is an ex ante measure that does not require a knowledge of realized outcomes. We study the features of this measure of macroeconomic uncertainty and explore its impact on real economic activities within the U.S., as well as its spillover effects for BRIC countries.     

Forecasting using supervised factor models

This paper examines the theoretical and empirical properties of a supervised factor model based on combining forecasts using principal components (CFPC), in comparison with two other supervised factor models (partial least squares regression, PLS, and principal covariate regression, PCovR) and with the unsupervised principal component regression, PCR. The supervision refers to training the predictors for a variable to forecast. We compare the performance of the three supervised factor models and the unsupervised factor model in forecasting of U.S. CPI inflation. The main finding is that the predictive ability of the supervised factor models is much better than the unsupervised factor model. The computation of the factors can be doubly supervised together with variable selection, which can further improve the forecasting performance of the supervised factor models. Among the three supervised factor models, the CFPC best performs and is also most stable. While PCovR also performs well and is stable, the performance of PLS is less stable over different out-of-sample forecasting periods. The effect of supervision gets even larger as forecast horizon increases. Supervision helps to reduce the number of factors and lags needed in modelling economic structure, achieving more parsimony.     

Correcting for survey effects in pre‐election polls

Pre‐election polls can suffer from survey effects, causing biases in forecasted election outcomes. We advocate a simple methodology to estimate the magnitude of survey effects, by collecting data both before and after the election. This method is illustrated by means of a field study with data concerning the 2009 European Parliament elections in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, especially for individuals with low intention to vote. For our data, the overall survey effect on party shares is small. This effect can be more substantial, for example, if political orientation and voting intention are correlated in the sample.     

Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)

While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here, I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error (MAE) of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.     

Forecast combination for U.S. recessions with real-time data

This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index, as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.     

Untangling Political Ideology and Party Identification in the United States

By analyzing an election night survey of voters in the 1992 U.S. Presidential election, this article explicates the meaning, relationships, and effects on vote of ideological self-designation (liberal, centrist, conservative) and party identification (Democrat, Independent, Republican). In addition to concern about a candidate's character, different interests about governmental interventions designed to augment economic equity, social equality, and the public's health interpret the meaning of these categories. Using seven social attributes as instruments, a two-stage least-squares analysis and a sensitivity analysis suggest that ideology has a stable net direct effect on party identification. The effect of party identification on ideology is negligible. Concern about a candidate's character and public health interests strongly interpret the effect of ideology on party identification; the effects of interests concerning equity and rights are not as strong. Because the social attributes explain very little variance in vote, whereas more malleable variables – ideology and party identification – have very strong effects, electoral choices now tend to be more changeable than in the past.     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Alternate manpower forecasts for the coming decade: Second guessing the U.S. department of labor

This paper presents alternate detailed forecasts of U.S. occupational requirements in the coming decade obtained from simulations conducted with the Center for Advanced Computation manpower forecasting model. These alternate manpower forecasts, developed on the basis of different mixes of federal social programs and budget priorities, are compared with the long-range employment projections developed by the U.S. Department of Labor, and the differences in methodology are analyzed. The sources of potential error in the Labor Department forecasts are identified, and a potentially more accurate picture of the shape of U.S. manpower requirements in the coming decade is presented.     

Election forecasting: Too far out?

We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much “lead” still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the “optimal” lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US, and France.