Statesman and scholar Alexis de Tocqueville (1876) once noted, “History is a gallery of pictures in which there are few originals and many copies.” In other words, history has a habit of repeating itself, and we can deduce cycles and patterns that likely will recur. Such stability and inertia should bode well for prediction. Nevertheless, when it comes to election forecasting, especially in the United States, most prognostications rely on short-term political fundamentals measuring macroeconomic performance or government or leader popularity. In this contribution, we adopt a structural approach but depart from existing literature by focusing on historical party and governance dynamics in the vein of de Tocqueville to establish whether they offer solid guidance regarding the performance of Democrats in US congressional elections. Our ex-post political history models provide solid predictions of which party will control Congress and the Democrats’ seat tally in each chamber between 1946 and 2022. This creates conditions to assume that political history may help us forecast Campaign 2024. Our study applied this political history model to predict the 2024 congressional elections. It forecast Democrats to lose control of the Senate with a net loss of three seats and estimated an exceptionally close race for House control, with the point estimate for the House suggesting that the Democrats would fall short of winning control.

Our political economy model, as it has come to be called, has offered up forecasts of the American presidential election outcome since the early 1980s. The model, based on referendum theory, as measured by the job performance of the president and the economy (1948 to the present), yields a forecast from data available in the summer of the election year. We consider alternative specifications of this parsimonious model, examining the possible effects of other economic measures, Covid-19, and incumbency advantage on forecasting. The current point estimate of the core political economy model predicts the Democratic candidate will receive 48 percent of the two-party popular vote, which translates into a narrow Electoral College loss for the incumbent party. This point forecast, however, comes with a considerable amount of uncertainty. There is an 11-point spread around our point estimate, which effectively means we have a horserace on our hands, with both horses close to the finish line.

The surprising election of Donald Trump to the presidency calls for a comprehensive assessment of what motivated voters to opt for a controversial political novice rather than a provocative but experienced political veteran. Our study provides a novel exploration of the Trump victory through the prism of the defeated candidate—Hillary Rodham Clinton (HRC). Losing candidates’ perceptions are usually not subject to academic analyses. Nevertheless, these people often hold substantial sway in their parties and thus understanding their views on the loss is essential, especially as a party regroups after defeat. Using HRC’s memoir What Happened, we devise the Hillary Hypotheses, her rationale for her electoral defeat. Using the 2016 American National Election Study (ANES), we provide the first systematic test of a losing candidate’s rationale for their defeat. We show that more often than not, HRC’s assumptions are supported. However, we find little evidence to support HRC’s most crucial assertion, namely that the e-mail scandal and specifically James Comey’s intervention ten days before Election Day cost her the presidency. Our findings have implications for understanding why Donald Trump won, but more broadly the contribution explores an understudied aspect of elections—a defeated candidate’s impression of their loss.

Are ordinary citizens better at predicting election results than conventional voter intention polls? The authors address this question by comparing eight forecasting models for British general elections: one based on voters' expectations of who will win and seven based on who voters themselves intend to vote for (including ‘uniform national swing model’ and ‘cube rule’ models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950–2017, yielding 449 months with both expectation and intention polls. The large sample size permits comparisons of the models' prediction accuracy not just in the months prior to the election, but in the years leading up to it. Vote expectation models outperform vote intention models in predicting both the winning party and parties' seat shares.

Changes in voters' behavior and in the campaign strategies that political parties pursue are likely to have increased the importance of campaigns on voters' electoral choices. As a result, scholars increasingly question the usefulness and predictive power of structural forecasting models, that use information from “fundamental” variables to make an election prediction several months before Election Day. In this paper, we empirically examine the expectation that structural forecasting models are increasingly error-prone. For doing so, we apply a structural forecasting model to predict elections in six established democracies. We then trace the predictive power of this model over time. Surprisingly, our results do not give the slightest indication of a decline in the predictive power of structural forecasting models. By showing that information on long-term factors still allows making accurate predictions of electoral outcomes, we question the assumption that campaigns matter more now than they did in the past.

Considerable research shows the presence of an economic vote, with governments rewarded or punished by voters, depending on the state of the economy. But how stable is this economic vote? A current argument holds its effect has increased over time, because of weakening long-term social and political forces. Under these conditions, short-term forces, foremostly the economic issue, can come to the fore. A counter-argument, however, sees the economic vote effect in decline, due to globalization. Against these rival hypotheses rests the status-quo argument: the economic vote effect remains unchanged. To test these claims, we estimate carefully specified models of the incumbent vote, at both the individual and aggregate levels. Western European elections provide the data, with particular attention to Denmark, Germany, Great Britain, Italy, The Netherlands, Norway, and Sweden. Perhaps surprisingly, we find the economic vote to be stable over time, a ‘standing decision’ rule that voters follow in national elections.

In political science research these days, the R2 is out of fashion. A chorus of our best methodologists sounds notes of caution, at varying degrees of pitch. Berry and Feldman (1985, 15) remark in their popular regression monograph: “A researcher should be careful to recognize the limitations of R2 as a measure of goodness of fit.” In their more general statistics text, Hanushek and Jackson (1977, 59) claim that “one must be extremely cautious in interpreting the R2 value for an estimation and particularly in comparing R2 values for models that have been estimated with different data sets.” Perhaps the most pointed attack comes from Achen (1982, 61), who argues that the R2 “measures nothing of serious importance.” His contention is that it should be abandoned, and the standard error of the regression (SEE) substituted as a goodness-of-fit measure. Developing these lines of inquiry further, King (1986) provides the latest set of criticisms. Accordingly, “In most practical political science situations, it makes little sense to use [the R2]” (King 1986, 669). And, concerning the “proportion of variance explained” definition more particularly, “it is not clear how this interpretation adds meaning to political analyses.” (King 1986, 678).