Refactored Bayesian model

Today, I updated the technicals page for the 2016 election. This is the page where I explain the Bayesian model I use. The page includes additional commentary on the JAGS model, which explains how the various elements of the model interact.

In the process, I re-examined the model I had been using. I became concerned that the prior I had been using for the house effect was not sufficiently uninformative.  I had been using a normal prior, centred on zero, with a standard deviation of five percentage points. I have changed this to a uniform prior between -15 and +15 percentage points. I would expect house effects to be in the range of -2 to +2 percentage points, so the 30 point uniform range should be uninformative.

The change has had little impact on the analysis, so my fears were probably unfounded. Nonetheless, I have retained the uniform prior, as it is clearly less informative than the normal prior.

The other change I have made to the charts is that I now include an extra band in the Bayesian output to indicate the middle 99 per cent of samples. Previously, I had only indicated the median sample with a line, and the ranges for the middle 95, 80 and 50 percent of the samples with increasingly darker shading.

Let's look at the outcome. For the three month analysis, the median estimate of voting intention is unchanged. The first chart is the revised chart, the second chart is the earlier analysis (from here).

Turning now to house effects. The first chart is the updated analysis. The second chart is the earlier analysis. The only significant difference is the extra band in the first chart, that indicates the range for the middle 99 per cent of samples.

These changes in the second decimal place for the medians are very small, and should be ignored.