The time-for-change model, developed by Alan Abramowitz, predicts the two-party popular vote based on the state of the economy, the incumbent’s popularity, and the time the incumbent president has been in office.
|Table 1: Overview of variables used in the time-for-change model|
||Incumbent president’s net approval rating (approval-dis-approval) in the final Gallup Poll in June||6|
||Annualized growth rate of real GDP in the second quarter of the election year||1.2|
||Presence (1) or absence (0) of a first-term incumbent in the race||0|
|V||Incumbent share of the two-party presidential vote||48.6|
The model’s vote equation reads as:
V = A + 0.108
NETAPP + 0.543
Q2GDP + 4.313
The 2016 time-for-change model predicts a narrow victory for Donald Trump with 51.4% of the major party vote (compared to 48.6% for Hillary Clinton).
The time-for-change model has been used to forecast U.S. presidential elections since 1992. From its first application in 1992 to 2008, the model underwent only minor changes. For forecasting the 2012 election, however, Abramowitz added
POLARIZATION as a fourth variable. The following chart shows the model’s forecasts and the actual election results for each election since 1992. On average across the six elections, the time-for-change model missed the final results by only 1.7 percentage points.
Abramowitz, A. (2016). Will time for change mean time for Trump? PS: Political Science & Politics, 49(4), 659-660.