The long-range presidential election forecast model developed by Jay DeSart is designed to predict the election outcome up to a year in advance. The multiple regression model is based on state electoral histories, national polling data, and two variables that attempt to estimate the context of the election.
The model generates forecasts of state-level outcomes using the following vote equation:
Vi = A + b1
Previous resulti + b2
Prior national polls + b3
Home statei + b4
|State i’s result from the previous election|
|Average of all national head-to-head matchup polls taken in month X prior to the election|
|1 if state i is is the home state of the Democratic candidate, -1 if state is is the home state of the Republican candidate, and 0 otherwise|
|Number of terms the party currently occupying the White House has done so|
|V||Democratic share of the two-party vote in state i|
DeSart, J. A. (2015). State Electoral Histories, Regime Age, and Long-Range Presidential Election Forecasts: Predicting the 2016 Presidential Election. 2015 Iowa Conference on Presidential Politics, Dordt College, Sioux Center, IA, October 29-31.