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“Mean reversion” and “random walk” models of campaign effects

Seeing Nate’s discussion here on random walks, bounces, and trends, I was reminded of a paper that Joe Bafumi, David Park, and I wrote a few years ago.

Basically, general election opinion polls can be modeled well with a “mean reversion” model, in which the outcome is predictable and the polls will eventually converge to this predictable outcome. But journalists and observers tend to implicitly assume a “random walk” model which starts at the current position of the polls and then moves from there. Here’s the paper, here’s the abstract:

Scholars disagree over the extent to which presidential campaigns activate predispositions in voters or create vote preferences that could not be predicted. When campaign related information flows activate predispositions, election results are largely predetermined given balanced resources. They can be accurately forecast well before a campaign has run its course. Alternatively, campaigns may change vote outcomes beyond forcing predispositions to some equilibrium level. We find most evidence for the former: opinion poll data are consistent with Presidential campaigns activating predispositions, with fundamental variables increasing in importance as a presidential election draws near.

And here is a key graph showing votes becoming more predictable during the election campaign:

walk.png

Finally, here’s my article with Gary from 1993, “Why are American Presidential election campaign polls so variable when votes are so predictable?” This article gives lots of evidence supporting the idea that people ultimately decide how to vote based on their enlightened preferences.