Tag: model update

Model Update 10/15

I am writing this week’s update a day early.

So I’ve developed a functional second model that allows for correlation between states. I am working on testing it. Next week I might base the post on the second model if I show that the second model is more accurate than the current model.

This week’s map:

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 0.75-0.85 Lean Blue (lightest blue) 0.85-0.95 Likely Blue(second darkest) >.95 Safe Blue (darkest)

Biden/Trump is likely to lose about 1 in 4 of there lean states and 1 in 10 likely states. The expected number of electors is Biden 335, Trump 203. This adjusts for the uncertainty in winning a lean or likely state. Except for Texas, the likely and lean red states are labeled because of insufficient polling data. CA, WA, OR are likely blue because insufficient polling data.

Analysis:

Not much change in the model. The model is very stable. The number of polls are increasing which is nice. I think Biden remains the likely winner.

My 2020 Model

First up I want to be super clear this is NOT A FORECAST or a prediction of what happens on election day. This is a polling aggregation model. Think of it as a fancy Real Clear Politics average except that this model comes up with good estimates of uncertainty and is a little better at predicting the final outcome. This model only predicts well the election at the very end of the cycle. At about six weeks before the electio

This model may not be final. I am going to test a few new features on historical polling data. If they work they will be added in to the 2020 model.

I want to explain why I do what I do.

For starters, I was a little bit surprised when the Economist came out with their model. It did some things I wanted to do. I agree with most of how it is structured. But I didn’t want to basically copy them. I wanted something novel.

One interesting thing I have discovered in my research is that FiveThirtyEight’s model is not that much better at predicting election outcomes (51% for Trump, 49% for Clinton) than basic polling averages where you average the last few polls. My models from my undergraduate research were better than a polling average but not as good as FiveThirtyEight. I wanted to how accurate could a Bayesian election model be that could be run on a standard laptop in a couple of minutes.

Data Inclusion Criteria

I am using the Economist’s I assume that the results in one poll don’t affect the results in another poll. One type of election polls are tracking polls where they interview the same people multiple times. Tracking polls depend on the previous poll results so I exclude them.