Improving our election poll aggregation model

Luke Mansillo saw our election poll aggregation model and writes:

I had a look at the Stan code and I wondered if the model that you, Merlin Heidemanns, and Elliott Morris were implementing was not really Drew Linzer’s model but really Simon Jackman’s model. I realise that Linzer published Dynamic Bayesian Forecasting of Presidential Elections in the States in 2013 but the model looks a lot like Jackman’s 2006 Australian Journal of Political Science article (that was in chapter 9 of his 2009 textbook) mixed with a hierarchical structure in chapter 8 of his textbook. Nevertheless, I was wondering if there was any component to the model that you would have liked to include but couldn’t? I have commented on GitHub that I imagine MRP would be a fine inclusion, or using a non-linear component to the hierarchical structure such as a spline or a Gaussian process would have been cool, or even the use of a neat prior specification horseshoe prior on shrinking in the hierarchical structure or on the bias of a pollster (etc), or perhaps the use of ensemble of model specifications.

Whenever I’ve thought about how I’d do a forecast beyond a national state space model I find myself quickly turning into a kid in a candy store, imagining all sorts of permutations that would be cool but have the cost of time thanks to exploring high dementia all space. Incorporating national and state polling into a dynamic mrp model has been an idea I’ve played around a bit but found that I’ve not had enough survey data in Australia to do it confidently.

A longer version of Mansillo’s comment is here on the Github page, and here’s an analysis that he did with Jackman of an Australian election campaign.

My quick reply is that, no, we didn’t take this from Jackman’s article, but it’s a pretty straightforward model where we just kept adding in features to explain different aspects of the time evolution of public opinion and poll data. As I wrote in my earlier post, it’s vaguely based on my 2010 paper with Kari Lock on Bayesian combination of state polls and election forecasts, but we pretty much started from scratch. I suppose you can get to our model from various paths. I agree with Mansillo that more can be done. Our model is all open-source, so anyone is free to alter it and do better.