Many critics of Nate Silver and other modelers have popped up out of the woodworks lately. I’m sure small improvements can be made to his models and to others. But at the end of the day, I prefer math models to educated guesses. All models are an abstraction of reality. They may still be useful (and I believe that many of the forecasting models are–our world runs on models).
But let’s be clear: all rely on proxies and/or data that is available. Both proxies and data introduce sources of errors into the predictions.
Earlier, I blogged about different pieces of information used by different Presidential election forecasting models [Link]. Here, I compared the 13 Keys to the White House model with Nate Silver’s national model (that he is no longer using this close to the election) and Ezra Klein’s national model. They are used economic data as part of the their forecast
Now that we’re getting closer to the election, many models use state polls for forecasts. Now, the national polls are no longer relevant because the election hinges on a few, key states. This implies that state-level forecasts are key, but that’s not necessarily the case. Fewer and fewer people respond to polls. In 1997, 90% of voters were contacted for the polls and 36% responded. In 2012, 36% were contacted and 9% responded [Link]. That’s right, 9%!! It’s getting hard to make the case that polls matter. Some no longer rely on opinion polls.
Here’s a list of forecasting models with a description of the information they use. This list isn’t comprehensive–I’m doing the best I can with shrouded information surrounding some of these models.
- A variety of information from short-and long-term economic indicators, incumbency, charisma of the incumbant and challenger. Polls are not used. This model, however, can make predictions a year before the election (it predicted that Obama would be reelected).
- state polls, economic indicators, historic information about poll biases, post-convention bounce correction factors, etc.
Election Analytics (at U of Illinois)
- state election polls (read more in my previous post here)
- state level economic indicators…and that’s it
- polls about who voters think will win rather than opinion polls that are traditionally used. This isn’t a forecasting model per se since they focus on forecasting state outcomes, but it’s in the same vein
- state polls (accounts for poll biases in a simple manner).
- state polls (I’m not sure if they account for poll biases), economic indicators (the president’s net approval-disapproval rating in June of the election year; the percent change in GDP from Q1 to Q2 of the election year; and whether the incumbent party has held the presidency for two or more terms) to contribute to a Bayesian “prior” for how the race will unfold. The methodology will be published in the Journal of the American Statistical Association (forthcoming).
Here are some related posts:
- forecasting the election using simulation or dynamic programming
- it’s the economy stupid: the three pieces of information you need to forecast the Presidential election
- how close did Romney get to not being the Republican nominee?
- election poll confidence intervals: they are not really 95% confidence intervals
- here’s my last post: moving from polls to forecasts