Cambridge Core
I’m not a huge fan of predictive Social Science. People are not the weather; they are bound to react to our predictions, which may become self-defeating or self-fulfilling in the process. Either scenario is unpleasant for obvious reasons. Predictive models are often subject to herd behaviour. They rarely rely on first principles, which makes them rather less interesting in terms of understanding the underlying dynamics, and may therefore fail rather spectacularly if the underlying, often implicit assumptions fail. This, in turn, tends to leave us with egg on our collective face.
Having said that, and looking at the rather spectacular result of the US presidential election, it’s difficult not to be impressed by Helmut Norpoth’s “Primary Model”, which predicted a solid Trump victory back in March. The Primary Model relies on very little data, has a relatively long lead (time from prediction to event), and a good track record: It has correctly identified the winner ever since it was introduced in 1996. Whether that makes HN a happy man today is a different matter.
The Primary Model’s rather quaint website is here; the link above points to a more accessible contribution by Norpoth to the PS symposium on forecasting the 2016 election. Which brings us back to the collective egg/face problem.
Update
I wrote the original post in the early hours of November 9, when it was clear that Trump had a majority in the Electoral College. Since then, it has become clear that Clinton has won the popular vote, probably by a considerable margin. Because (as a couple of people have noted on Twitter) the Primary Model aims at predicting the popular vote, even Political Science’s consolation prize is gone.
DN
asecondmouse
andrewgelman.com
