Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.     

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.     

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.     

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.     

The paradox of gender voting: An exploratory analysis

Voting studies have documented that white collar workers tend to vote for conservative parties and blue collar workers for parties that advance working class interests. In the United States during the era (1980–1992) of Republican Presidents Reagan and Bush, women who worked in white collar jobs tended to vote for the Democratic party and men for the more conservative Republican party. This shift created the paradox of gender voting, which this article explains. The effect of gender on vote is small but attains statistical significance due to the suppressor effect of occupation. Two interpretations of the gender effect are tested: thematerialist holds that women's occupation and class identification affect this gap; thepost-materialist suggests that affluence is pivotal. The materialist interpretation fits the data from the survey of the national election of 1984 — the middle-most election of the three won by the Republicans— but the post-materialist interpretation may explain the vote of the very affluent.To Morris Rosenberg and Roberta Simmons, fine sociologists who clarified the suppressor effect and causes of self-esteem.     

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.     

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.     

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.     

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.     

Voter turnout in a multidimensional policy space

Many factors influence the likelihood of citizens turning out to vote. In this paper we focus our attention on issue voting, that is, on the likelihood that different policies offered by politicians affect the probability of voting. If voters consider both the benefits and the costs of voting, rational voters will only vote when politicians offer differentiated policies. In a multidimensional policy space this implies that citizens only vote when they perceive enough difference on the issues they care about the most. We investigate the role of voter abstention due to indifference in a unidimensional and a multidimensional policy setting using data from the US National Election Studies for 1972–2000 and find support for our predictions: voters perceiving a small difference between the platforms of the Democratic and Republican parties are less likely to vote; and voters who perceive the two parties as more different on a larger number of issues are significantly more likely to vote.     

Brexit and world growth – what are the risks?

• The initial global market reaction to the UK Brexit vote was very negative and in our view overdone. Nevertheless, we expect the uncertainty to linger for a while, with the vote having refocused investors on existing vulnerabilities in the world economy. Our new forecasts see the main negative impacts on growth being in the UK, the Eurozone and Japan. Risks to our new forecasts remain skewed to the downside, with a significant danger of world growth dropping below 2% this year.
• Our new forecasts see UK growth dropping to 1.4% a year in 2017–18, down from 2.2–2.3% a year before. In the Eurozone, growth will be around 0.2% a year weaker in 2017–18 and Japan is also a loser as a result of the risk aversion‐driven stronger yen, with growth at just 0.3% in 2017 from 0.5%.
• The size of the initial global market sell‐off makes no sense in the context of the likely impact from a weaker UK. In part, it seems to have reflected the pricing in of very negative scenarios in the Eurozone. But investors may also be worrying about other global problems glossed over in recent months.
• One risk to our forecast is that confidence effects on businesses and consumers are larger than we expect – but such effects are often overstated. Another danger is that more of the recent financial market weakness will ‘stick’ than our new baseline forecasts assume.
• Our world recession indicator is already at elevated levels and suggests a significant danger of world growth slipping below 2% this year; not a recession, but it might feel like one. Global policymakers need to act quickly to head off the risks.

     

Differing behaviours of forecasters of UK GDP growth

The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus.     

Modeling Item Nonresponse in Questionnaires

The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company.     

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.     

The paradox of rising wage growth and falling inflation

• ? Strong labour markets and rising wages in advanced economies stand in sharp contrast to recent declines in economists’ inflation forecasts and market expectations. In our view, though, these developments are not necessarily contradictory. Even if wage growth edges higher, we think demand factors will limit any pick‐up in prices. Instead, we expect firms’ margins will be squeezed.
• ? Although the labour share has risen more sharply than we had expected over the past couple of years, we are sceptical that this will translate into substantially stronger underlying inflation. Not only has the rise been small, it has been employment rather than wages that has surprised to the upside. The strength of employment is probably more about firms’ production preferences than workers’ capitalising on a stronger negotiating position.
• ? True, wages adjusted for productivity now look high by historical standards. But neither theory or empirical evidence suggests that this must inevitably lead to stronger CPI inflation in the short‐term. Our forecast for flat wage growth in 2019 and the absence of strong cost pressures elsewhere are also a comfort.
• ? Inflation tends to be more responsive to demand indicators – and the recent GDP growth soft patch suggests any further pick‐up in underlying inflation pressures will be limited (see Chart below).
• ? More generally, we think that the consensus view on inflation for the key advanced economies is high. Market‐based inflation expectations are typically lower than our own, which may reflect the perception that inflation risks are skewed to the downside. Positive economic surprises could lead downside risks to narrow, but ageing expansions and secular stagnation worries suggest this is unlikely, limiting any future pick‐up in bond yields.

     

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.     

Single-stage election procedures for large electorates

The Condorcet efficiency of single-stage election procedures is considered under the assumption of impartial culture for large electorates. The most efficient ranked voting rule is either Borda rule or a truncated scoring rule. A decision rule is established to determine the number of candidates, k, that individuals should be required to vote for, whether or not ranking should be required, and the scoring rule that should be used if ranking is required. This decision depends upon the number of candidates available and the probabilities that individuals will vote if they must rank k candidates or simply report k candidates.     

A Study of BYOD adoption from the lens of threat and coping appraisal of its security policy

Why would employees adopt bring your own device (BYOD)? Would employees feel risk-taking to perform their work by using their own devices? Would peer pressure and company policy help encourage their employees to BYOD and how? Using the Technology Threat Avoidance Theory (TTAT), we hypothesize the intention of adopting BYOD is due to the accessing of security policy by threat and coping appraisal. Moreover, we predict perceived usefulness, perceived ease of use, social influence, organizational commitment and job security are essential for formulating the adoption intention. In this study, 450 random employees were surveyed on their adoption perception of BYOD in their respective companies. The results support most of our hypotheses. We uncover perceived cost and privacy protection within the TTAT framework reflect no significance while organizational commitment and job security posit the strongest influences on employees’ BYOD adoption intention. This finding suggested that in order to roll out a successful and sustainable adoption intention on BYOD, organizations must consider measurements to build up employees’ job security as well as generate a strong sense of organization commitment. Specifically, our analyses show adoption intention is also affected by gender, age, and education level.     

Nowcasting GDP with a pool of factor models and a fast estimation algorithm

We propose a novel mixed-frequency dynamic factor model with time-varying parameters and stochastic volatility for macroeconomic nowcasting and develop a fast estimation algorithm. This enables us to generate forecast densities based on a large space of factor models. We apply our framework to nowcast US GDP growth in real time. Our results reveal that stochastic volatility seems to improve the accuracy of point forecasts the most, compared to the constant-parameter factor model. These gains are most prominent during unstable periods such as the Covid-19 pandemic. Finally, we highlight indicators driving the US GDP growth forecasts and associated downside risks in real time.