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.