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Presidential Election Forecasting

 

DeSart Long-Range Forecast Model

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



January 17, 2016 - Rubio Emerges as Republican Most Likely To Win in November

The January update of my long-range forecast shows that Senator Marco Rubio is the Republican who would be most likely to win the general election later this year. Even though Hillary Clinton led all Republican contenders in the polls in December, her lead over Rubio was the smallest by less than 1%. Given the influence of the Regime Age variable and Rubio's home state advantage in the all-important battleground state of Florida, the model projects that should Senator Rubio be the Republican nominee he would have an 84.7% chance of defeating Hillary Clinton and a 96.8% chance of besting Bernie Sanders.

We also see an apparent reversal in Chris Christie's fortunes. Last month, Governor Christie held the lead spot among Republicans, but in the December polling data he's fallen well back again. There is, however, a caveat to reading too much into this. Not many pollsters include a Clinton v. Christie matchup in their surveys, most likely because they assume Christie has little chance of actually winning the GOP nomination. Therefore, it's not clear whether this movement from last month's projection was due to actual movement in the polls or simply the vaguaries of sampling error from one poll to the next.

Below is the complete matchup matrix generated by the model:



December 8, 2015 - Carson Fades. Sanders Surges, Christie on Top but Democrats' Overall Chances Improve

With 11 months to go before the election, my long-range forecast model shows a shift in the landscape. Last month, Ben Carson was the clear frontrunner according to the model. He was projected to have over a 90% likelihood of defeating both Clinton and Sanders. November polls suggest that Carson's standing has faded. The latest projection now shows that while Carson still leads both of the Democrats in head-to-head matchups of the popular vote, his win probability is substantially lower, resulting in a virtual coin-toss with Clinton and just over a 60% probability of defeating Sanders.

The other noticeable development is Sanders' rise in the polls. Last month, the model only projected that Sanders would be able to defeat Ted Cruz. With this update, the model projects that Sanders would also be able to defeat Jeb Bush and Carly Fiorina as well. In addition, the model also presents scenarios that Sanders could benefit from an Electoral College misfire against Carson, Marco Rubio, and Donald Trump. Last month's projection only suggested that Hillary Clinton could possibly be the beneficiary of the technicality of the Electoral College where a candidate can lose the popular vote but win an Electoral College majority.

The final new development in the model is the inclusion of New Jersey Governor Chris Christie in the matchups. There was not sufficient data for Christie to include him in last month's projection. This month, the data show that he is the only Republican candidate with a clear chance of defeating either Clinton or Sanders. However, the overall picture that this update shows is that the probability of a Republican victory is now substantially lower than it was a month ago.

Below is the full matchup matrix:


As before, there are the same caveats to trying to read too much into this data. The bottom line in all of this is that the race remains very close. Regardless of who the final matchup ends up being, the model suggests that it will be a fairly contentious election.




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:


Caveats
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.

References
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.