Model Update 10/23

This is the fit of the new correlated model this map was made on Thursday. I ran the model again today. You may notice lots of changes from the other model. This is expected because what is happening is that this model better “learns” from similar states and uses the last election’s results as a starting point. There are lots of different forms of this model. I struggled to choose a single model and when this ends up in my dissertation, I’m going to be talking about multiple models.

Edit: Here is the google drive link for the daily model updates. This new model is labeled “correlated_fit” and then the date.

Scale: 0-.05 Safe Red (darkest) 0.05-0.15 Likely Red (second darkest) 0.15-0.25 Lean Red (light red) 0.25-0.75 Tossup (brown) 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Average electoral votes: 359

95% credible interval for electoral college: 290-416

Analysis:

I think there might be a slight underestimation of the uncertainty in the electoral college outcome. I’m reading about a 99% probability Biden wins if the election was held today. I think that’s probably high but Biden should still win provided there isn’t some new crazy event. When applied to 2016 data this model read about a 60% chance for Clinton. I am not putting a lot of faith in the electoral college probability because I can’t reliably vet it using past data. It’s really hard to model the correlation between states.

This model is a polling aggregation model and not a forecast. So this fit is like if the election was held today. Since early voting is common and the election is so close this model is now predictive.

There are some things I’m a little skeptical of. I compared this model to the Economist’s model because they have some similarities. I think the estimate for Iowa is too high for Biden, although I would not rule out a Biden win in Iowa. I am wondering if the model is being overconfident in Michigan, Pennsylvania, and Wisconsin.