At its most basic level, this forecast of the 2012 presidential election performed successfully. The model forecasted the reelection of President Obama. With 53.8% of the two-party vote; he ended up with 51.8% (as of November 28, 2012), yielding an error of 2.0 percentage points. Prior to 2012 the average out-of-sample forecasting error was 3.34 points. Here, the model does better than the average of the previous years by almost a point. Running the model with the added data point does not change matters appreciably. The economic item increases slightly and the logged time in the White House item weakens slightly. The overall fit of the model is just a bit lower.

The results of the House of Representatives model were not quite as accurate. The model forecast that the Republicans would pick up 10 seats. Instead, they lost nine seats (as of November 13, 2012). The model was off by 19 seats and the average out-of-sample error prior to 2012 was 15 seats. One possibility for this result is that the larger than average error might be the result of the first election after redistricting. An examination of the out-of-sample forecasts does not support that conjecture.

Looking forward to 2016, the incumbent party will be at a disadvantage. Given that the Democrats will be seeking a third term, the nation will have to be less pessimistic than is typical for the incumbent party to emerge victorious. If the electorate is as pessimistic in 2016 as they were this election season, the Democrats will lose handily.