The 2016 presidential election is by all accounts one of the most unusual and anxiety-producing in American history. The Donald Trump candidacy is literally unprecedented. A Trump victory may be unimaginable to many but it is not impossible. Can we forecast the outcome of this extraordinary election based on methods and models developed for past elections?
For decades economists and political scientists have constructed and tested forecasting models for presidential elections. Their objective was not to explain why all Americans vote as they do but to predict the outcome of the next election based on identifying the forces responsible for changes in the vote won by the two major parties. They have sought to specify and measure the fundamental drivers of electoral change as a basis for making an explicit public prediction well before the election. They form a sort of baseline from which we can judge whose election it is to win and whose it is to lose.
The Vote As A Verdict On The Overall Economy
Some of the initial forecasts, most prominently those of Yale economist Ray Fair, rely on a large set of diverse economic indicators, some of which have to be estimated before the forecast is released. The idea behind the model is familiar one — the incumbent party will be punished for bad economic times and rewarded for especially good economic times. Fair also uses an indicator of the incumbent party's consecutive time in the White House, based on the historical evidence that incumbent parties face formidable hurdles in extending their consecutive hold on the White House beyond two terms. Those models eschewing any public opinion measures of the current political climate or standing of the candidates have been most susceptible to large errors in predicting both the winner and the incumbent-party share of the two-party national vote for the presidency.
Adding A Snapshot Of Public Opinion
Political scientists have been more parsimonious in their use of economic indicators — typically relying on a single indicator tied to conditions in the election year. That's because voters tend to be myopic when it comes to evaluating economic performance, in effect asking "what have you done for me lately?" Economic growth in the first half or the second quarter of the election year is one such measure, although real disposable income per capita and an index of leading economic indicators have advantages as well. Importantly, most of these forecasting models have added a public opinion measure that captures the political standing of the incumbent party, even if the president is not a candidate in the upcoming election. One good measure has been the presidential approval rating in June or July of election year. Some scholars have instead use trial-heat polling measures of the two major-party candidates taken relatively early in the campaign.
These latter models of fundamentals incorporating some measure of public opinion have on average had better track records in forecasting the winner and the size of the national vote. For example, Alan Abramowitz's Time for Change forecasting model, based on the incumbent president's net approval rating at midyear in the Gallup Poll, the growth rate of real GDP in the second quarter of the election year, and whether the incumbent president's party has held the White House for more than one term, produced the most accurate prediction of the 2012 presidential election among this set of forecasting models.