Social networks and citizen election forecasting: The more friends the better

Most citizens correctly forecast which party will win a given election, and such forecasts usually have a higher level of accuracy than voter intention polls. How do citizens do it? We argue that social networks are a big part of the answer: much of what we know as citizens comes from our interactions with others. Previous research has considered only indirect characteristics of social networks when analyzing why citizens are good forecasters. We use a unique German survey and consider direct measures of social networks in order to explore their role in election forecasting. We find that three network characteristics –  size, political composition, and frequency of political discussion – are among the most important variables when predicting the accuracy of citizens’ election forecasts.     

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.     

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.     

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.     

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.     

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.     

Modeling voter choice to predict the final outcome of two-stage elections

Most election forecasting research to date has been conducted in the context of single-round elections. However, more than 40 countries in the world employ a two-stage process, where actual voting data are available between the first and the second rounds to help politicians understand their position in relation to each other and to voter preferences and to help them predict the final outcome of the election. In this study we take advantage of the theoretical foundation on voter behavior from the political science literature and the recent methodological advances in choice modeling to develop a Nested Logit Factor Model of voter choice which we use to predict the final outcome of two stage elections and gain insights about the underlying political landscape. We apply the proposed model to data from the first stage and predict the final outcome of two stage elections based on the inferences made from the first stage results. We demonstrate how our proposed model can help politicians understand their competitive position immediately after the first round of actual voting and test its predictive accuracy in the run-off election across 11 different state governorship elections.     

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.     

Polls apart! Political,research and ethical lessons from UK pressure groups' use of opinion polls

Pressure groups use opinion polls to help further political agendas, as in the case of hunting with dogs. The authors evaluate the two types of poll that have featured in the campaigning: ‘scientific’ (representative) polls and ‘straw’ polls. The shortcomings of straw polls are well known and the new problem of ‘piling in’ where pressure groups direct their supporters to such polls is described, raising a number of potential ethical issues. The apparent discrepancy in ‘scientific’ opinion polls commissioned by the two sides of the debate is examined and an attempt to reconcile the differences is made. The authors' observations raise questions about the value and limitations of polling as well as technical and ethical issues the polling industry and professional bodies need to address. Copyright © 2003 Henry Stewart Publications     

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 multiparty by-elections using Dirichlet regression

By-elections, or special elections, play an important role in many democracies – but whilst there are multiple forecasting models for national elections, there are no such models for multiparty by-elections. Multiparty by-elections present particular analytic problems related to the compositional character of the data and structural zeros where parties fail to stand. I model party vote shares using Dirichlet regression, a technique suited for compositional data analysis. After identifying predictor variables from a broader set of candidate variables, I estimate a Dirichlet regression model using data from all post-war by-elections in the UK (n=468). The cross-validated error of the model is comparable to the error of costly and infrequent by-election polls (MAE: 4.0 compared to 3.6 for polls). The steps taken in the analysis are in principle applicable to any system that uses by-elections to fill legislative vacancies.     

What is the role of perceived leadership capacity in presidential politics?

The relevance of leadership models in presidential leadership, and principally the role of perceived leadership in presidential election years, is an area of study with limited development but increasing importance. This study explores the relationship between young voters' leadership assessment of presidential candidates, Barack Obama and John McCain, and their reports of voting behavior during the 2008 presidential election. Leadership perceptions were collected from 812 respondents prior to the election. Results indicate that candidate leadership assessments have a significant effect on candidate preference after controlling for the impact of party identification and self‐perceived political efficacy. Further, political efficacy significantly impacted respondents' intent to vote in the election after controlling for these same variables. Party affiliation produced significant differences across the political ideology, leadership ratings, political efficacy, and likelihood of voting variables. The study concludes with a discussion of the implications as they pertain to political leadership.     

The 2016 U.S. presidential election and the Stock,FX and VIX markets

The U.S. presidential election is one of the global political events that have the profound effects on the Global Financial Markets (GFMs). The aim of the study is to examine Stock, FX and VIX markets under the U.S. presidential election 2016. The findings strongly suggest that ‘U.S. presidential election effects’ hold in equity and FX markets across the GFMs. The empirical outcome signifies that markets are inefficient in the short-run (election year) and allows the opportunity to make abnormal gains from the market. The ‘Republican president elect’ has shown negative effects on the Nifty50, S&PASX200, and IPC equity markets while FTSE100, DJIA, Top40, EuroStoxx50 and Nikkei225 have reported positive returns. The Trumps’ proposal on international trade has caused major loss in the global currency market against the U.S. dollar. The investors’ sentiment to be measured extremely low on the poll announcement day but VXJ and AXVI based market participants have shown very high degree of concern. The Bearish-run election effects to be observed during the election period while post election period has shown Bull-run effects (Asia-pacific markets).     

Accuracy of German federal election forecasts, 2013 & 2017

The present study reviews the accuracy of four methods (polls, prediction markets, expert judgment, and quantitative models) for forecasting the two German federal elections in 2013 and 2017. On average across both elections, polls and prediction markets were most accurate, while experts and quantitative models were least accurate. However, the accuracy of individual forecasts did not correlate across elections. That is, the methods that were most accurate in 2013 did not perform particularly well in 2017. A combined forecast, calculated by averaging forecasts within and across methods, was more accurate than three of the four component forecasts. The results conform to prior research on US presidential elections in showing that combining is effective in generating accurate forecasts and avoiding large errors.     

Government Popularity and the Economic Cycle

In June 1987 the Conservatives under Mrs. Thatcher were re-elected with a majority of over 100 seats against Labour. They received 42.3 per cent of the total vote, the size of the majority owing much to the significant number of votes received by third parties. But it is also believed that the Conservatives' share of the vote, unchanged from 1983, reflected the performance of the macro economy. In the four years of Mrs. Thatcher's second term, output rose more than 3 per cent a year and by the time of the election inflation was below 1 per cent, interest rates were under 10 per cent, and unemployment had come back below 3 million. If Mrs. Thatcher and her Cabinet colleagues had planned an economic strategy for the next four (or five) years in order to be in a strong position to win a fourth term of office, it might have included the following factors: output growth of 2–3 per cent a year; inflation staying around 1 per cent; interest rates of 10 per cent or thereabouts; unemployment down further from the near-3 million mark of June 1987. On our current forecast, that is, with the exception of output, the economic record of the Conservatives' third (Thatcher/Major) term. Yet the Conservatives have been running neck and neck with Labour in tile opinion polls and, barring unforeseen developments, the coming election will be extremely close, with the possibility still of either a small Conservative majority or a small Labour majority or even a hung Parliament. Why is it that, against the background of a similar economic performance in aggregate, the Conservatives have lost popularity? The arguments are complex and a full explanation would include the introduction of the community charge arid the fall of Mrs. Thatcher herself. But the economy is part of the explanation. The economic literature on Government popularity examines the state of a number of economic variables at the time of the election. At its most extreme, some believe that all a Government has to do to be re-elected is deliver a low mortgage rate in time for the election. Other analysts have explained Government popularity in a simple regression framework, with a lagged dependent variable to capture sluggish adjustment. A weakness of this research is that it implicitly believes all the Government has to do is to ‘get it right on the night’. As long as the economy falls into place by the time of the election, re-election is certain. This implies that the electorate both forgets and forgives, and is indifferent to the course of the economy in the previous three or four years. But it cannot be the case that the electorate evaluates only the average performance of the economy over the lifetime of a Parliament or even the most recent developments. As in any simple utility maximisation problem, it is not just the mean of the distribution that counts but also its variance. In other words the combination of late 1980s' boom and early 1990s' recession counts against the Government in a way that four or five years of steady progress would not have done. If this is correct, it may not be sufficient for the Government to deliver low inflation and interest rates and some recovery in output in time for the election; it may simply be that the electorate remains unforgiving of a five-year track record of boom and bust.     

Comparing the forecasting performances of linear models for electricity prices with high RES penetration

We compare alternative univariate versus multivariate models and frequentist versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts is inspected in four main European markets (Germany, Denmark, Italy, and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian vector autoregressive specifications with exogenous variables dominate other multivariate and univariate specifications in terms of both point forecasting and density forecasting.     

“Instrumentalism” and Friedman’s methodology: a short objection

‘Instrumentalist’ interpretations of economic theories have been held to provide justification for the rightness of being indifferent to the ‘realism’ or otherwise of the assumptions underlying a theory. In this view, what is important for an economist engaged in formulating policy is to have a theory which is capable of generating accurate predictions. This note asserts, with the help of a very simple example, that theories based on unrealistic assumptions are—precisely contrary to its claim—inimical to the interests of an instrumentalist view of theories. Tests of predictive accuracy can be narrow or broad. When narrow, the likelihood is high of there being a large number of theories passing the test—but this could also raise the chances of survival of (unrealistic) theories which may have unintended and undesirable policy outcomes. On the other hand, when the scope of the test of predictive accuracy is widened, the set of contending theories is likely to be small, with the survivors restricted to those based on ‘realistic’ assumptions.     

Financial nowcasts and their usefulness in macroeconomic forecasting

Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.     

Macroeconomic Forecasting using Low‐frequency Filters

We consider univariate low‐frequency filters applicable in real‐time as a macroeconomic forecasting method. This amounts to targeting only low frequency fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains in forecast accuracy can be obtained in comparison with a variety of other methods. There is an inherent arbitrariness in the choice of the cut‐off defining low and high frequencies, which calls for a careful characterization of the implied optimal (for forecasting) degree of smoothing of the key macroeconomic indicators we analyse. We document interesting patterns that emerge: for most variables the optimal choice amounts to disregarding fluctuations well below the standard business cycle cut‐off of 32 quarters while generally increasing with the forecast horizon; for inflation and variables related to housing this cut‐off lies around 32 quarters for all horizons, which is below the optimal level for federal government spending.     

Endogenous party structure

This paper proposes a model of two-party representative democracy on a single-dimensional political space, in which voters choose their parties in order to influence the parties’ choices of representative. After two candidates are selected as the median of each party’s support group, Nature determines the candidates’ relative likability (valence). Based on the candidates’ political positions and relative likability, voters vote for the preferable candidate without being tied to their party’s choice. We show that (1) there exists a nontrivial equilibrium under natural conditions, and (2) the equilibrium party border and the ex ante probabilities of the two-party candidates winning are sensitive to the distribution of voters. In particular, we show that if a party has a more concentrated subgroup, then the party tends to alienate its centrally located voters, and the party’s probability of winning the final election is reduced. Even if voter distribution is symmetric, an extremist party (from either side) can emerge as voters become more politically divided.