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.     

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.     

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.     

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.     

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.     

GDP forecasts: Informational asymmetry of the SPF and FOMC minutes

This paper emphasizes asymmetric information about the U.S. economy between the FOMC and SPF. Following Stekler and Symington (2016), it extends their text-based FOMC minutes index (FMI) of economic outlook to 1986-2016. Following Ericsson (2016), it employs truncation adjustment indicators and reinterprets the FMI calibrations as the policy-makers forecasts of the GDP growth, which carry information about the staff Greenbook forecasts prepared prior to the bi-quarterly FOMC meetings. Tests confirm unbiasedness and rationality of these forecasts. The encompassing tests indicate that both the FMI and SPF forecasts contain unique information beyond their alternative’s information set and can be weighted equally. The orthogonality tests suggest that the SPF efficiently use all their information set but could gain if the FOMC minutes were published without a lag, while the policy-makers rely mostly on their projections made earlier in the meetings, and could benefit from incorporating the already published SPF forecasts.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

From 2000 Bush–Gore to 2006 Italian elections: voting at fifty-fifty and the contrarian effect

A sociophysical model for opinion dynamics is shown to embody a series of recent western hung national votes all set at the unexpected and very improbable edge of a fifty-fifty score. It started with the Bush–Gore 2000 American presidential election, followed by the 2002 Stoiber–Schröder, then the 2005 Schröder–Merkel German elections, and finally the 2006 Prodi-Berlusconi Italian elections. In each case, the country was facing drastic choices, the running competing parties were advocating very different programs and millions of voters were involved. Moreover, polls were given a substantial margin for the predicted winner. While all these events were perceived as accidental and isolated, our model suggests that indeed they are deterministic and obey to one single universal phenomena associated to the effect of contrarian behavior on the dynamics of opinion forming. The not hung Bush–Kerry 2004 presidential election is shown to belong to the same universal frame. To conclude, the existence of contrarians hints at the repetition of hung elections in the near future.     

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.     

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 accuracy of long-term earnings forecasts in the electric utility industry

This paper examines the accuracy of various methods of forecasting long-term earnings growth for firms in the electric utility industry. In addition to a number of extrapolative techniques, Value Line analyst forecasts are also evaluated. Value Line analyst forecasts for a five-year time horizon are found to be superior to many of the extrapolative models. Among the extrapolative models examined, implied growth and historical book value per share growth rate models performed best. These results provide strong support for using Value Line growth forecasts in cost of capital estimates for electric utilities in the context of utility rate cases. Value Line forecast errors could be explained by changes in dividend payout ratios, the firm's regulatory environment and bond rating changes.     

One model and various experts: Evaluating Dutch macroeconomic forecasts

The Netherlands Bureau for Economic Policy Analysis (CPB) uses a large macroeconomic model to create forecasts of various important macroeconomic variables. The outcomes of this model are usually filtered by experts, and it is the expert forecasts that are made available to the general public. In this paper we re-create the model forecasts for the period 1997-2008 and compare the expert forecasts with the pure model forecasts. Our key findings from the first time that this unique database has been analyzed are that (i) experts adjust upwards more often; (ii) expert adjustments are not autocorrelated, but their sizes do depend on the value of the model forecast; (iii) the CPB model forecasts are biased for a range of variables, but (iv) at the same time, the associated expert forecasts are more often unbiased; and that (v) expert forecasts are far more accurate than the model forecasts, particularly when the forecast horizon is short. In summary, the final CPB forecasts de-bias the model forecasts and lead to higher accuracies than the initial model forecasts.     

Comparing probabilistic forecasts of the daily minimum and maximum temperature

Understanding changes in the frequency, severity, and seasonality of daily temperature extremes is important for public policy decisions regarding heat waves and cold snaps. A heat wave is sometimes defined in terms of both the daily minimum and maximum temperature, which necessitates the generation of forecasts of their joint distribution. In this paper, we develop time series models with the aim of providing insight and producing forecasts of the joint distribution that can challenge the accuracy of forecasts based on ensemble predictions from a numerical weather prediction model. We use ensemble model output statistics to recalibrate the raw ensemble predictions for the marginal distributions, with ensemble copula coupling used to capture the dependency between the marginal distributions. In terms of time series modelling, we consider a bivariate VARMA-MGARCH model. We use daily Spanish data recorded over a 65-year period, and find that, for the 5-year out-of-sample period, the recalibrated ensemble predictions outperform the time series models in terms of forecast accuracy.     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.     

Bayesian VAR forecasts,survey information,and structural change in the euro area

We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into the predictions of a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon but their possible paths over several horizons jointly since the survey information comes in the form of one- and two-year-ahead expectations. As well as improving the accuracy of the variable that we target, the spillover effects on “other-than-targeted” variables are relevant in size and are statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis, and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.     

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.     

Evaluating quantile forecasts in the M5 uncertainty competition

Probabilistic forecasts are necessary for robust decisions in the face of uncertainty. The M5 Uncertainty competition required participating teams to forecast nine quantiles for unit sales of various products at various aggregation levels and for different time horizons. This paper evaluates the forecasting performance of the quantile forecasts at different aggregation levels and at different quantile levels. We contrast this with some theoretical predictions, and discuss potential implications and promising future research directions for the practice of probabilistic forecasting.     

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.     

Demographically based global income forecasts up to the year 2050

Demographic projections of age structure provide the best information available on long-term future human resources and demand. Fairly robust correlations between age structure, GDP and GDP growth have been discovered in current data. In this paper we use these two facts to study the forecasting properties of demographically based models. Extending the forecasts to 2050 suggests that, due to projected fertility decreases, the poor countries of today will start to catch up with developed economies, in which the growth process will stagnate due to the growth of the elderly population. This remains the case whether or not indications of positive longevity effects are taken into account.