Is there a point to election models?

November 16, 2011 § Leave a comment

(kind of in response to Sean Trende’s post)

Useful critiques of predictive election models:

  • It requires a leap of faith to believe that linear regression is anywhere close to right.
  • There are few data points. You can use what you know about Gubernatorial elections to inform Presidential election predictions, but the two levels don’t operate identically.
  • We rely on data that span decades, but politics has changed a lot in that span. Ask the ghost of George Wallace.
  • The model selection usually isn’t transparent. This applies especially when you get isolated variables: 2nd quarter growth but not 1st or 3rd.
  • Effect sizes are often hard to swallow. You really want to knock off 4.4 points if a party is seeking a third term?
  • Outliers can give wacky results. What if there’s 8 percent annualised growth in the relevant quarter? This ties into the linearity point.

Less useful critiques of predictive election models:

  • Lots of predictors are correlated. If you’re doing causal inference, this is a huge problem, but if you’re predicting it’s nearly immaterial.
  • There are lots of different models. It’s hard to choose which one is best, at least based on performance, but we don’t have to believe one because we don’t have to believe any of them.
  • Timing of variable measurements. I know I spent the last post complaining about this, but it shouldn’t be a dealbreaker, as long as all measurements are available before the election. If you use old measurements, you’re only handicapping yourself.
  • “But you left out…” Other variables may matter a lot, but the model is under no obligation to condition on everything.
Basically if you don’t want to interpret it causally it’s a prediction, and if you’re going to predict you should use polling. If you want to interpret it causally, you’re doing causal inference from a regression, with all the usual problems that implies.
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