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.     

When are prediction market prices most informative?

Prediction markets are a popular platform for the elicitation of incentivised crowd predictions. This paper examines the variation in the information contained in prediction market prices by studying Intrade prices on U.S. elections around the release of opinion polls. We find that poll releases stimulate an immediate uptick in trading activity. However, much of this activity involves relatively inexperienced traders, meaning that the price efficiency declines in the immediate aftermath of a poll release, and does not recover until more experienced traders enter the market in the following hours. More generally, this suggests that information releases do not necessarily improve prediction market forecasts, but instead may attract noise traders who temporarily reduce the price efficiency.     

Information flow between prediction markets,polls and media: Evidence from the 2008 presidential primaries

Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.     

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.     

Forecasting Austrian national elections: The Grand Coalition model

Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft  ), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18$n=18$), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades.     

A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls

Human dynamics and sociophysics build on statistical models that can shed light on and add to our understanding of social phenomena. We propose a generative model based on a stochastic differential equation that enables us to model the opinion polls leading up to the 2017 and 2019 UK general elections and to make predictions relating to the actual results of the elections. After a brief analysis of the time series of the poll results, we provide empirical evidence that the gamma distribution, which is often used in financial modelling, fits the marginal distribution of this time series. We demonstrate that the proposed poll-based forecasting model may improve upon predictions based solely on polls. The method uses the Euler–Maruyama method to simulate the time series, measuring the prediction error with the mean absolute error and the root mean square error, and as such could be used as part of a toolkit for forecasting elections.     

Combining forecasts: An application to elections

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.     

Analysing tax evasion dynamics via the Ising model

We develop a model of tax evasion based on the Ising model. We augment the model using an appropriate enforcement mechanism that may allow policy makers to curb tax evasion. With a certain probability tax evaders are subject to an audit. If they get caught they behave honestly for a certain number of periods. Simulating the model for a range of parameter combinations, we show that tax evasion may be controlled effectively by using punishment as an enforcement mechanism.     

Multi-step forecasting in emerging economies: An investigation of the South African GDP

To forecast at several, say h, periods into the future, a modeller faces a choice between iterating one-step-ahead forecasts (the IMS technique), or directly modeling the relationship between observations separated by an h-period interval and using it for forecasting (DMS forecasting). It is known that structural breaks, unit-root non-stationarity and residual autocorrelation may improve DMS accuracy in finite samples, all of which occur when modelling the South African GDP over the period 1965–2000. This paper analyzes the forecasting properties of 779 multivariate and univariate models that combine different techniques of robust forecasting. We find strong evidence supporting the use of DMS and intercept correction, and attribute their superior forecasting performance to their robustness in the presence of breaks.     

Discussion of the paper “Deciding between alternative approaches in macroeconomics”

The excellent article by David Hendry describes how to nest “theory-driven” and “data-driven” approaches when deciding between alternative models in macroeconomics. The article’s final conclusion is that theory allows the econometrician to select a set of variables, while data allows him/her to select across a wide range of alternatives: lag selection, structural breaks, functional forms, etc. The aim of this discussion is to provide the reader with an illustration of this proposed mixing of theory and data in one of the fields mentioned in the paper, macroeconomic forecasting.     

Poles' evaluation of the benefits of the Polish economic transformation: A rectification of the misleading of Jeffrey Sachs

In his protracted dispute with Jan Adam over the early benefits of the Polish shock therapy approach to economic reform, a key, and still intact, component of Jeffrey Sachs' case has been a relatively favourable 1991 opinion poll about Poles' perceptions of the benefits of economic reform. This reported that a substantial majority of Poles felt their standard of living had improved as a result of the first two years of economic reform. This was an apparently powerful argument in favour of Poland's particular shock therapy approach to economic reform, as residents of other East European countries, some subject to more gradualist reforms, responded much less positively to the identical question put as part of the same research programme. However, the cited result should be seen as posing a mystery that is at odds with other monthly polls in Poland and seems incompatible with the results of the 1991 Polish Parliamentary elections, which showed a return of support to post-communist parties. A deeper analysis of opinion polls which follows solves the mystery, when it is shown how the cited result was in error and was published in a form diametrically opposite to the true result.