Forecasting the Presidential Election: What Can We Learn from Them Models?
Campbell, James E., Mann, Thomas E., Brookings Review
The search for order in a seemingly chaotic and volatile political world is perfectly understandable. The unceremonious reversals of fortune suffered in recent years by George Bush, Bill Clinton, and Newt Gingrich suggest the tenuousness of political advantage and the risks of simple extrapolations from the present to the future. The pattern of media coverage of presidential elections, which chronicles every unforeseen event and strategic choice by the candidates and their handlers and analyzes every blip of reaction in public opinion, reinforces the impression that each election is in flux and wildly unpredictable. So it is not surprising that election forecasting has garnered increasing attention. Over the past several election cycles, scholars have churned out a dizzying array of models that purport to capture the underlying structure of past presidential elections and, on that basis, to predict the outcome of the election that lies some months in the future. Reactions to the forecasts have ranged from reverence to ridicule. Yale University economist Ray Fair gained prominence based on the accuracy of a model he developed in the late 1970s to forecast presidential elections during the 1980s. The convergence of Fair's model and several newcomers around George Bush as the projected winner at a time in 1988 when Michael Dukakis was enjoying a healthy lead captured the fancy of journalists and lent a new respectability to forecasting. But that elevated standing was cut short by the well-publicized failure of two of the models (including Fair's) to anticipate Bill Clinton's comfortable 1992 victory. Soon skeptics were treating forecasts as curiosities on a par with such reputed election bellwethers as which league won the World Series and whether fashion hemlines were going up or down.
We come neither to praise election forecasting nor to bury it, but to explore whether the enterprise does in fact shed light on presidential campaigns and elections. Certainly forecasters have grounds for humility. Building models based on a small number of elections, involving questionable assumptions and rough measures of only a few of the factors that may affect specific election outcomes, they produce forecasts that often have wide confidence intervals and can be highly sensitive to specification choices. It doesn't inspire confidence to see data mined and equations refitted in the aftermath of inaccurate forecasts. But there are reasonable standards to use in judging the utility of various models, from the accuracy of out-of-sample forecasts to the plausibility of the underlying theory of individual behavior and the stability of the estimated effects over time. By using these standards in reviewing recent experience with these models, we can draw useful lessons about presidential elections and provide some baseline expectations with which to view the final months of this year's campaign.
The Lessons of Presidential Election Forecasting
1 The fundamentals of a presidential election are in place before the traditional beginning of the general election campaign on Labor Day.
This is not to say that the election is over before the campaign begins or that idiosyncratic events throughout the campaign make no difference. But forecasting models can succeed - and several have been quite accurate - because the critical factors affecting a presidential election are in place before the fall campaign begins. Among the fundamentals are the general course of the economy, the advantages of presidential incumbency, and, most important, the predisposition of the electorate toward the candidates. Before the fall campaign gets under way, a critical mass of voters has either already decided on a candidate or is strongly predisposed toward one.
Election forecasting offers a useful lesson about when polling becomes meaningful in a presidential election. In keeping with conventional wisdom, early polls should be taken with a grain of salt. …