House Election Forecasts

Andrew Gelman comments offers some comments on some simulations by Joe Bafumi, Bob Erikson, and Christopher Wlezien, which predicts Democrats gaining 32 seats in the House of Representatives on Tuesday.

Compared to our paper on the topic, the paper by Bafumi et al. goes further by predicting the average district vote from the polls. (We simply determine what is the vote needed by the Democrats to get aspecified numer of seats, without actually forecsasting the vote itself.) In any case, the two papers use similar methodology (although, again, with an additional step in the Bafumi et al. paper). In some aspects, their model is more sophisticated than ours (for example, they fit separate models to open seats and incumbent races).

Slightly over-certain?

The only criticism I’d make of this paper is that they might be understating the uncertainty in the seats-votes curve (that is, the mapping from votes to seats). The key point here is that they get district-by-district predictions (see equations 2 and 3 on page 7 of their paper) and then aggregate these up to estimate the national seat totals for the two parties. This aggregation does include uncertainty, but only of the sort that’s independent across districts. In our validations (see section 3.2 of our paper), we found the out-of-sample predictive error of the seats-votes curve to be quite a bit higher than the internal measure of uncertainty obtained by aggregating district-level errors. We dealt with this by adding an extra variance term to the predictive seats-votes curve.