Statistical Modeling, Causal Inference, and Social Science: Some thoughts on "the keys to the White House"

Some thoughts on "the keys to the White House"

Vivek Mohta asks (in a comment here) the following:

The conclusion [of some research on election forecasting] seems to be that presidential election results turn primarily on the *performance* of the party controlling the White House. The political views of and campaigning by the challenging candidate (within historical norms) have little to no impact on results.
The most recent paper applying this method is The Keys to the White House: Forecast for 2008. I haven't yet looked at the original paper from 1982 where the method is developed. But there was a reference to his work in the Operations Research Today: "His method is based on a statistical pattern recognition algorithm for predicting earthquakes, implemented by Russian seismologist Volodia Keilis-Borok. In English-language terminology, the technique most closely resembles kernel discriminant function analysis."

My thoughts:

jfalk

Sound advice on overfitting the tossups, but it raises a fundamental conundrum. Plenty of elections weren't close. No one cares if you have a model that gets every runaway election right. So doesn't that leave you in the position of making a model which is only useful in the moderately close elections, whatever that means. To the contrary, I would argue that the close elections are exactly what you ought to be modelling, and that your metric ought not to be a binary classification measure (whose justification I never really understood anyway), but whether or not any of the close elections were predicted by the model to be close elections. Assuming logit or probit models, the close ones ought to have predicted values near zero, and it's not overfitting to try and get the predicted values close to zero. Your vote-shares model obviously steps in this direction...

Vivek Mohta

Thanks for your helpful thoughts and for the Rosenstone reference.

I agree that you lose information by converting a continuous measure to binary. It seems that the Lichtman treatment of the independent variables as binary allows him to use qualitative historical data for about 20 additional elections going back to 1860. However, it requires him to convert richer continuous data to binary post-1948.