DeSart long-range 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:

V_i = A + b₁ Previous result_i + b₂ Prior national polls + b₃ Home state_i + b₄ Regime age

Variable	Description
`Previous result`	State i’s result from the previous election
`Prior national polls`	Average of all national head-to-head matchup polls taken in month X prior to the election
`Home state`	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
`Regime age`	Number of terms the party currently occupying the White House has done so
V	Democratic share of the two-party vote in state i
A	Constant

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.