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Did the Time-for-Change Model Predict Donald Trump's Win?

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Partially. It predicted him as a winner of the popular vote (which he did not win), but asked people to account for incompetence.

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Origin and Prevalence

Time for change is a mathematical model that has correctly predicted the winner of the popular vote in every election from 1988-2012. In 2016 they predicted Trump would win.

The prediction:

Based on a net approval rating for Barack Obama of +6 in the Gallup weekly tracking poll for the week of June 27–July 4, an estimated second quarter change in real GDP of 1.2% according to the Bureau of Economic Analysis and the fact that Hillary Clinton is seeking a third consecutive Democratic term in the White House, the Time for Change Model predicts a narrow victory for Donald Trump—51.4% of the major party vote to 48.6%.

Issues and Analysis

In reality Clinton won the popular vote by 2.1%.

The author of the model, in this case hedged -- saying two important assumptions of the model were that each party ran a mainstream candidate and a marginally competent campaign. Based on the lack of these things in Trump's effort, the author of the study believed his model overestimated the popular vote of Trump, and felt a five point popular vote win for Hillary was likely more accurate.

The model remains interesting because while it overestimated popular support, it points to key factors outside of policy and messaging that explain the election. It is based on only a handful of factors:

Variable

Description

2016 value

NETAPP

Incumbent president's net approval rating (approval-dis-approval) in the final Gallup Poll in June

6

G2GDP

Annualized growth rate of real GDP in the second quarter of the election year

1.2

TERM1INC

Presence (1) or absence (0) of a first-term incumbent in the race

0

A

Constant

47.26

V

Incumbent share of the two-party presidential vote

48.6

Looking at these factors may


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