Hi Alex
Thank you for taking the time on the reply! I actually watched part of your talk and I have it bookmarked for later. I also looked at the dynamic Bayesian model from Linzer and I started to work on that but I decided it was probably better to get a simple model done first. The only part of that model that seems mysterious to me is the reverse GRW. I like the idea of using a GRW to act almost like a Khalman filter, like Jackman 2005, but I’m not sure I understand why Linzers would go in reverse. I guess to ensure that the national component decays to 0?
Also, and maybe this is a misunderstanding on my part, but figure 3 in the Linzer paper is concerning to me. I understand that the solid horizontal line is the actual outcome and the model is forecasting something close to that. But to me nothing in the data up to 2 months before election day seems to suggest anything that would trend that way. If I were developing this model and I saw that forecast with 2 months to go I would think that something is wrong.
My only guess as to whats happening here given my (poor) understanding of the model is that its regression towards the national polling average (which tends to be closer to 50%). However this would be a bad prediction for all the “safe” states in the US elections. It looks good here for the two swing states obviously.
Anyway, thanks again for your response. It seems like I’m on the right track with a baby model on a baby election. I’ll watch your talk and see if I can pick up any tricks!