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Presidential Election Forecasting


DeSart Long-Range Forecast Model

Dr. Jay DeSart, Associate Professor of Political Science - Utah Valley University

November 6, 2015 - My newly developed long-range presidential election forecast model is designed to predict the outcome of presidential elections up to a year in advance of the election.

The model, which is based on state electoral histories, national polling data as well as a variable which attempts to estimate next year's election context, predicts that it is more likely than not that a Republican candidate will win next year's presidential election. Given that we do not know yet who will be the nominees, the model generates an expected outcome matrix for different possible matchups amongst the leading candidates for both parties.

I recently presented this model and forecast at the Iowa Conference on Presidential Politics. The model is fairly straightforward. It generates forecasts of state-level outcomes based on 4 variables:
  • The state's result from the previous election
  • The average of all national head-to-head matchup polls taken in October of the year prior to the election
  • A home state dummy variable
  • A regime age variable measuring how many terms the party currently occupying the White House has done so
Based on the state-level predictions, I can then aggregate up to the national-level to create predictions of the national popular vote and Electoral College vote totals. The results of those aggregations are presented in the outcome matrix below:

It is important to note that this is a long-range forecast. A lot can happen between now and Election Day. It is more indicative of what the election context will be like next year, rather than how a specific election matchup will end up. A major factor in these results is the regime age variable. The basic premise behind this variable is the well-documented "time for change" effect (Abramowitz, 1988; Norpoth, 2013). The basic premise behind this variable is that the longer a party holds on to the White House the more difficult it becomes for them to hold on to it. Looking at history, you can see that this is very clearly the case. Since 1952, a party has only been able to win a third consecutive term once when George H.W. Bush won in 1988, and then was unable to win a second term in 1992. Given that it seems more likely than not that a Republican will be successful 2016, regardless of who that candidate will be. It is interesting to note that without the regime age variable in the model, it projects that Hillary Clinton easily wins against every candidate. Ultimately, one key factor in determining the outcome of the election next year will be whether or not the Democrats will be able to successfully make the case that they deserve a third term in the White House

Another factor that this model does not take into account is the presumed effect that a Hillary Clinton candidacy might have as the first female candidate for President. As the clear front runner for the Democratic nomination at this point, it is worth discussing the possible impact that her gender might have. If the historic nature of her candidacy can generate the same level of enthusiasm that surrounded Barack Obama's candidacy as the first African-American candidate, that could possibly counteract the effect of the regime age variable.

Note, however, that despite the fact that the model projects that most of the GOP candidates will win a plurality of the popular vote, and have a better than 50-50 chance of winning the Electoral College vote, it also projects that the most likely outcome in most matchups is a split outcome where the Republican candidate wins the popular vote, but Hillary Clinton wins the Electoral College by a very small margin.  The most certain thing we can conclude from this is that no matter who wins it will likely be a very close election.
You can read more about the model and prediction here: State Electoral Histories, Regime Age, and Long-Range Presidential Election Forecasts: Predicting the 2016 Presidential Election (Note: The figures presented above differ slightly from those presented in the paper due to the fact that additional poll data were released following the preparation of this manuscript.

Abramowitz, Alan I. 1988. "An Improved Model for Predicting Presidential Election Outcomes." PS: Political Science and Politics. 21: 843-847.
Norpoth, Helmut. 2013. "Time For Change: A Forecast of the 2016 Election." Presented at the 2013 Annual Meeting of the American Political Science Association. Chicago, IL. August 29 September 1, 2013.