The Time for Change forecasting model has correctly predicted the winner of the national popular vote in every presidential election since 1988. This model is based on three predictors — the incumbent president’s approval rating at midyear (late June or early July) in the Gallup Poll, the growth rate of real GDP in the second quarter of the election year, and whether the incumbent president’s party has held the White House for one term or more than one term. Using these three predictors, it is possible to forecast the incumbent party’s share of the major party vote with a high degree of accuracy around three months before Election Day.
What does the Time for Change model predict for 2016? Estimated weights of the three predictors for the 2016 presidential election are displayed in Table 1. These estimates are based on the results of an OLS regression analysis using data on the 17 presidential elections between 1948 and 2012. The estimated coefficients for all three predictors are highly statistically significant and the model has an impressive adjusted R-squared of .90.
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