National Elections are more about a national message than high variability in any given state. I simulate a national outcome first, then simulate individual state outcomes based on both the national outcome and how each individual state tends to perform relative to the national outcome (for example, Vermont currently looks 17 points better for Obama than the national outcome, Alabama looks 21 points worse). Simulating in this manner ensures that states tends to move together with the national outcome, but are allowed to vary individually.
Each individual election is simulated by drawing a random sample from a normal distribution, then the results are aggregated to give summary statistics (for example, Obama won the Electoral Vote 84.1% of the time the last time I ran 25,000 simulations). The mean and variance of the distributions are calculated as follows:
The mean is based on polling average. For national outcomes the mean is simply the current polling average. For state outcomes it is the national poll average + the state adjustment (+17 for Vermont, -21 for Alabama).
The detailed outcomes from my 9/13 simulation are below.
May I ask why you are using the logistic function instead of just the raw poll results (i.e. if Obama's poll result in a state is 53%, why not just say his probability of winning in that state is 53%)?ReplyDelete
Historical data simply doesn't support bear out an approach like that, and neither does common sense.Delete
As a demonstration let's look at Kansas. The model currently gives Romney 100% chance to win in Kansas, an 18 point lead there. If we assumed there were no undecided voters in Kansas that comes out to 59% - 41%; if the model assigned President Obama a 41% chance of winning Kansas it would be very very wrong to do so.