Research explaining mean reversion of election forecasts: why each new poll provides very very little information about the election outcome, given what we already know

Brad DeLong writes:

There is a huge amount of mean reversion in Nate Silver’s model right now . . . Considering that Silver’s forecast F is roughly F = λ(polls) + (1-λ)(fundamentals), either λ must be really small or truly extraordinary things have happened to Silver’s fundamentals in the past week.

I don’t know what Nate’s model is, but if he’s doing things right, then indeed the weight attached to the recent week of polls must be really small. Each week’s polls provide very very little information about the election outcome, given what we already know.

For more background, see this article (with Noah Kaplan and David Park) on the random walk and mean reversion models and this article (with Kari Lock) on Bayesian combination of state polls and election forecasts.