nice work-can you do an analysis of Howard’s/ALP’s numbers in Bennelong since 1996, predict his demise and make my bloody day!
Terry

I could only use an equivalent model for 2004 because the number of observations are too small to go back any further than that. Of course, using 2004, the dummy variables slightly change, but the election day correction component is exactly the same.

-Will Dwinnell
http://matlabdatamining.blogspot.com/

Yep, Workchoices added about 1.5 points in this model, but it could be more as some of the Rudd variable could actually be Workchoices – but for the model it doesn’t really matter either way. If you take out ACN, the Workchices coefficient lifts to a little over 1.8

I’ve played around a lot with other policy variables doing other things for the site, and none apart from Workchoices really amount to a hill of beans over the last couple of years.

The true Latham effect was little negative on election (those campaign period error correction variables did that) , but before the campaign was positive because of the general slump the ALP found itself in before his leadership.

Rudd added about 2 points to the leadership in this model. Using other models to analyse the Rudd effect, it’s sometimes a bit higher, sometimes a bit lower in TPP terms. The big effect of Rudd was to lift the ALP primary vote – and he did that by around 5 points depending on how you model the primary vote.

Thanks for the links to help explain some of the tools of your modelling. I am becoming quite brain dead, and any additional information to stimulate brain cells is joyfully devoured.

Great work on your behalf but do feel gut instinct says it will be a win but not by your 55.15%

So if I go back and build the equivalent model for the 2004 election and only use the period up to September 2004 as the sample, I’d institute a dummy variable starting at the “troops home by Christmas” remark and going through to the election (the equivalent of which in the 2007 model is the “Workchoices” DV). Once I do that, and then forecast ahead the last month into the election itself, I end up with an ALP TPP of 47.6, which is close to the 47.2 which was the result.

But now for 2007, I dont need that event DV of “troops home by christmas”, I can just let the other variables take the weight of that over the end of the 2004 period and redo the model for the 2007 election.

The way it works is that it accounts for the leadership and honeymoon effects, then adjusts for the big issue going into the election. Yet, by doing it that way, it creates an overweight in the other explanatory variables which is then adjusted in the forecast with the last two variables in the model. Those last two variables look a bit counter intuitive to start with, but it actually accounts for the “running home to momma” effect of late deciders if people start moving away from the government in large amounts because of their dissatisfaction with it, or to the ALP because of their satisfaction with it.So the error correction type function it fullfills is based on both types of voters that move – those that do it because of their earlier dislike of one side, and their earlier attraction to one side.

I’ll happily grant you that it’s a great model for the data it’s trained on , hopefully it’s a great model for the election since I like the conclusions :p However I would like to see how well your methodology predicts unknown data , we can do this by rewinding a bit using known data as a test set and retraining without it , then seeing how well it predicts it.