DeSart model

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 Regime age

VariableDescription
Previous resultState i’s result from the previous election
Prior national pollsAverage of all national head-to-head matchup polls taken in month X prior to the election
Home state1 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
Regime ageNumber of terms the party currently occupying the White House has done so
VDemocratic share of the two-party vote in state i
AConstant
Table 1: Overview of variables used in the DeSart model
The forecast of the national popular vote is then calculated by weighting each state by its proportion of the total number of votes cast in the previous election. On his website, Jay DeSart provides more details about the approach as well as conditional model forecasts depending on hypothetical match-ups of candidates.

Reference

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.