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AT: Too Far Off To Predict
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AT: Too Far Off To PredictNot too early to make elections predictionsDavid Rothschild, PhD Economics @ Wharton, 3-2-2012, “Two economist from Yahoo Labs,” IEEE, http://spectrum.ieee.org/podcast/at-work/innovation/obama-wins But the mother lode of predictions this year is the 2012 U.S. elections. Two billion dollars might easily be spent between now and November, so if you think it’s too soon to predict Super Bowl XLVI, you probably also think it’s too early to call the presidential race. You’d be wrong. President Obama will be reelected with 303 votes in the Electoral College, winning 26 states and the District of Columbia, including California and New York by wide margins and squeaking out a win in key battleground states Ohio, by 50.3 percent, and Pennsylvania, by just under 52 percent. So predict two economists at Yahoo Labs, Patrick Hummel and David Rothschild , who is my guest today. He has a Ph.D. in applied economics from the Wharton School of Business at the University of Pennsylvania, where his dissertation involved creating forecasts just like this one. He’s been with Yahoo Labs, in New York City, since May of last year, and he joins us by phone from there. David, welcome to the podcast. David Rothschild: Thank you very much for having me. Steven Cherry: David, maybe the most striking thing about your election model is that it doesn’t really know who the Republican candidate is—and almost doesn’t care. But before we get to that, maybe you could tell us about modeling in general and how yours was built. David Rothschild: Sure. This is a fundamental model, and the basis of fundamental models are dropping out polls and prediction markets and thinking about fundamental data that’s available well before the election. And so this is based off such things as presidential approval ratings in mid-June, economic indicators, incumbency, ideological indicators, biographical details, and of course past election results. Those are the main categories, and what you’ve just read off was making some expectations on what those economic indicators and presidential approval will be later in the summer. But there’s really two main reasons to be making fundamental models like this. The first is that it does allow us to make fairly accurate predictions well ahead of the election, which is fun and interesting but also meaningful to those people involved in elections. And the second thing is that by making these fundamental models that take away polls and prediction markets, it lets us look and see how this fundamental data does correlate with election results. These are things that pundits knock back and forth on a regular basis, and here we can add a little bit of data and clarity to those discussions. Steven Cherry: You mentioned “fairly accurate”—I mean, you have some measure, right? You applied your model to other elections. David Rothschild: That’s correct. So this model in particular was calibrated based off the last 10 cycles. We’re looking at state-by-state elections, so we’re looking at 510 somewhat independent elections. Obviously there are national currents as well as idiosyncrasies between the states in any given election cycle, but especially for things like past election results you get to train it on a fairly large number of different elections, if you look at 10 cycles and the 51 different electoral college elections. And that’s what it’s based off of, but then we work to systematically drop data in order to continuously have an out-of-sample look at the data as well when we calibrate it. Steven Cherry: We should note what the margin of error is. David Rothschild: Sure. So what we’re looking at here—and the easiest way to think about it is, is that there’s a mean absolute error in the expected vote share—so the amount of the two-party vote share that either candidate will receive—it’s about three percentage points. Steve Cherry: And there are a lot of state results—I guess, actually about 15 of them, including some big ones—within that, right? David Rothschild: Sure. And in the way you read it off is fun, exciting, but if you look at the tables we provide, some of these states we’re giving a probability of victory within 40–60 percent, so there are at least three states here, Virginia, Ohio, and New Hampshire, which are within just a few percentage points of flipping over from one candidate to the other. Steven Cherry: And if somebody runs as a third-party candidate, a serious third party candidate—I mean, for example, if Ron Paul ran and might get 10 percent of the vote, that might throw everything out, right? David Rothschild: I wouldn’t say it would throw everything out. It is something that we’ve looked at. It’s something we forget, but there have been three serious third-party challengers in the last 11 cycles, I guess. We had Wallace in ’68 and Anderson in ’80 and Perot in kind of ’92 and ’96, so it is something that we’ve seen before, though it is hard to tell exactly at this stage which candidates it affected the most. But it is something that the data has seen before. Steven Cherry: Now your model does assume that Mitt Romney is Obama’s candidate [but] only for the purpose of picking the Republican candidate’s home state, I guess? David Rothschild: That’s correct. It doesn’t make very much of a difference in this model, and I think that’s one of the main interesting findings of this, is that, quite frankly, you can make a fairly accurate prediction pretty far away from the election and do it without even knowing the candidates. Now, this is not to say that the candidates and campaigns don’t make a difference; as you mentioned, probably well over a billion dollars will probably be spent by each side, so first of all we’re talking about the net effect of the campaign. So if one candidate spent over a billion dollars and the other candidate wasn’t able to equal that, you’re likely to see some major impact. So we’re talking about the net effect, and there’s also some error, and this error, a lot of it is idiosyncratic between the states in a given year. But still, these are things that are affected by the campaigns and candidates, and that’s really where you can think their impact is made. 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