Primary model

The Primary Model, developed by Helmut Norpoth, is an extension of his electoral-cycle model. The model provides a long-term forecast of the election outcome based on electoral histories, plus the candidates’ performance in early primaries. In particular, it uses the vote of the two most recent elections as well as the candidates support in primaries as predictor variables in a linear multiple regression model, which has been estimated based on data from all elections since 1912. The model’s vote equation reads as:

V = A + b1 Vt-1 + b2 Vt-2 + b3 DPRIM + b4 RPRIM

Table 1: Overview of variables used in the Primary model
Variable Description
Vt-1 Incumbent party’s popular two-party vote in the last election
Vt-2 Incumbent party’s popular two-party vote in the second to last election
DPRIM Incumbent-party (Democratic) candidate primary support in New Hampshire and South Carolina
RPRIM Opposition-party (Republican) candidate primary support in New Hampshire and South Carolina
V Incumbent share of the two-party vote
A Constant

2016 forecast

Assuming a hypothetical Clinton-Trump race, the model predicts Trump to gain 52.5% of the two-party vote, compared to 47.5% for Clinton.

Past performance

The Primary Model has, with some modifications, been used to forecast each of the five elections since 1996. The following chart shows the model’s forecasts and the actual results in each election. On average, the model missed the final election outcome by 3.1 percentage points.


Norpoth, H. (2016). Primary model predicts Trump victoryPS: Political Science & Politics, 49 (4), 655–658.