Many thanks for your cogent responses. It is extremely sad for me to see McCain willing to permanently degrade his reputation in what appears now to be the winter of both his discontent and career.

Regarding the power of models based on economic variables, they seems to work in small economies like Australia. Prior to their Federal election a year ago, a model based on leading economic indicators which could accurately “predict” election results going back 40 years was published on:

http://blogs.crikey.com.au/pollbludger/ and http://blogs.crikey.com.au/pollytics/nsw-polling/ (both run by statisticians).

This economics based model did generate an accurate predicton for the 2007 Aussie election (won by the Opposition who then withdrew Australia’s combat troops from the Iraq War).

I agree that the economic crisis was a major force that potentially moved opinion at the same time. National polls provide better time resolution and could resolve this question better. However, it’s still correlative. Detailed surveys of voters’ reasons and so on are even better.

I just gave a talk yesterday to some political scientists, one of whom reminded me of the power of models based on economic variables to predict poll movements during a campaign. It was fascinating and I am going to read about that.

In regard to negative campaigning, the fact is that it works. However, I think the particular approach has been costly to McCain’s reputation.

My question regarding your Median EV Estimator graph is how can one validly designate a single factor to have precipitated a movement in the median EV?

Most problematic would seem to be the latest significant movement: McCain’s floundering during the Congressional funding negotiations drama and the (pseudo?) suspension of his campaign in order to claim pseudo-credit for Congressional action might have been important negative factors along with the Presidential Debate which you designated.

If you are correct that Obama’s first major downturn was primarily related to the damage from “celebrity” attack advertisements, then the current purported decision of McCain’s campaign to go 100% negative in ads and go the mongrel in debates would appear to be a sound, if less than admirable, strategy.

An appropriate disclaimer could warn of the possible flaws with the adjusted information (i.e. as you point out, that trends may not be distributed uniformly across the nation).

On here, I too would like the ability to apply a %variance on a state by state basis, on the electroal map.

Cheers.

We are thinking of what functions to add to the interactive map. Your comment makes me think that adding N% to one state only might be of interest.

I totally understand and largely agree with Sam’s counter point that modeling future polling shifts, even on the basis of very educated assumptions, is a shaky exercise of questionable value.

That said, I think it’s still somewhat interesting and certainly entertaining to see what happens if we apply some assumptions. Ideally I’d like to be able to fiddle with those assumptions and see how much difference they make.

What I find most useful about 538 is the trend and demographic adjustment applied to states which have few recent polls out. For example, there are currently few if any very recent polls for Wisconsin, New Hampshire, Nevada, Montana, and Missouri. On 538, those states have more favorably odds for Obama based on his most recent polling gains nationally and in demographically similar or neighboring states.

On this site, I can fiddle with the meta-margin to account for nation-wide polling shifts, but I can’t very easily account for discrepancies in the “freshness” of the polling data from state to state.

Thanks for the link it was very helpful.

The way I read it you are basically saying that while yes covariation in the polls vrs election day performance is not being modeled here, that the difference between the two is so small as to be in consequential. For instance, while your model currently shows McCain with no chance of winning if the election were held today, if covariation where taken into account the tails would shift to where there might be some non-zero chance of winning. However, since that chance is less than, let’s say, a 2.5% threshold (corresponding to your 95% CI) we are safe to assume that the two models are equivalent.

Basically, what the distribution tells you is what would happen if the population sampled by pollsters were to vote. In that context, the likelihood of a McCain win right now is nearly zero.

The exception is the possibility of systematic error, such as a bias in polls. To affect the above conclusion the bias would have to be at least 2% to have any noticeable effect. (Note that the Meta-Margin is what it would take to bring things to 269 EV-269 EV). There isn’t a known hiding place for a large bias to help McCain. The only candidate I know of is the Bradley effect, which seems to have disappeared starting in the mid-1990s.

gprimos1 – Your point of view is reasonable, but I want to correct one thing. I don’t use estimated sampling error, except to place a lower bound on the SD of median. This only comes up if the polls happen to cluster tightly. More usually, the algorithm uses the poll sample itself to get an estimated SD. This implicitly takes care of variability of accuracy among pollsters.

The extreme similarity between my estimate and Silver’s is partly a coincidence, but is helped by the fact that the election is not far off. His random drift model for future changes is presumably smaller now. All it does is add uncertainty, which I have more or less addressed here. Think of it as a hurricane that is nearing landfall: the accuracy of a prediction goes up when the thing is near enough to see.

He has a 15% win probability for McCain, about 1/6 of the area under the histogram. This suggests that his additional assumptions are equivalent to an assumption that 1/6 of the time, voter sentiment may swing toward McCain by 4% (the Meta-Margin) or more. I’m pretty sure that’s an overestimate.

Whatever a correct model would be, I agree that a comparison with the Meta-Analysis shows you the cumulative effect of all of FiveThirtyEight’s accumulated assumptions. And yes, the best way to see it is to compare the width and spikiness of the distributions.