Saturday, May 16, 2015

First batch of post budget polling

It is the weekend after the Federal Budget and the blizzard of post Budget polling has started. There is a venerable tradition in Australian politics of wall-to-wall polling in the week that follows the Budget.

Mark Graph's first law of polling analysis is that most Budgets are meaningless events in the lives of most punters. Budgets have minimal (if any) impact on week-to-week movements in the national voting intention (particularly at the time of the Budget). Where they do have impact, measures that hurt are more likely to have an impact than measures that help. Furthermore, if a Budget measure does have an impact on voting intention, that impact is typically manifest closer to the time of implementation (when it actually hurts) than at the time of announcement.

Mark's second law: breathless reporting, where every micro-movement in the polls is over analysed, follows the Budget polling. In the melee, noise and signal become confused: one media outlet will proclaim the resurrection of the government and another will declare its Armageddon - all in the same week. The reporting of opinion polls in the week following the Budget is often little more than an exercise in intellectual masturbation for the titillation of those living inside the beltway.

Now that I have got that off my chest, let's look at the polls:
  • ReachTEL has the national two-party preferred voting intention at 47-53 in Labor's favour. This is a movement of one percentage point in the government's favour since 23 April.
  • Galaxy has the national two-party preferred voting intention at 48-52 in Labor's favour. This is a five percentage movement in the government's favour since February 2015.

Before moving to the charts, I need to acknowledge a few things. I have added some early 2014 data that I had missed, including the ACNielsen polling data before it ceased political polling. Because I use a sum-to-zero constraint in my model, these charts are not directly comparable with the earlier charts as a result.

In short, the government's fortunes continue to improve, but at a very slow pace. The government is still some distance from looking competitive were an election called at this time. All-in-all, there is not much to see in the post-Budget opinion polling.


I shifted the graph production process from ggplot in R to matplotlib in python. Of necessity, this saw some analytical tasks move from R to python. In the process, it became clear that the LOWESS (localised regression) package in python statsmodels was not as robust as the conceptually similar LOESS package under R. For comparison, the first two of the remaining charts were produced in R. The subsequent charts were produced in python. I still run the Bayesian model in R/JAGS. However, I now export the model output data to python for chart production.


On the annotated charts, the last result was rounded to zero decimal places. This has been corrected to round to one decimal place.

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