There are a number of steps involved in forecasting the candidates to be elected to the Senate. This blog will briefly outline what I have done. Note that "Major parties" as used below is for Labor, the Coalition and the Greens.
Be aware that any estimation of party votes is dependent on polls at any time. These flip around. So the assumed primaries for my estimates will change frequently and the expected outcomes will too.
Everything in this blog is accurate at time of publishing.
Step 1: Total major party percentage
With myriad candidates in the Senate, it is easily shown that the percentage of parties who vote for major parties in the senate is lower than for the House of Representatives. Arguably, this is partially because some voters cannot find or cannot be bothered looking for their preferred party and just vote for any three word slogan close enough to their political viewpoint. For 25-30+ tickets, it is reasonable to assume 85% of all votes are for the major parties. As the number of tickets gets smaller, perhaps up to 90% or more of all votes will be for major parties.NSW Example
It is appropriate to use 85% major party votes for NSW.
Step 2: Individual major party vote
It is important at this step not to be biased but to base data on actual vote at previous elections and adjust using an appropriate poll tracker. My preferred poll tracker is BludgerTrack. Adjustments should made in two different manners. Firstly, the forecast national swing for each party should be applied to the primary votes from the previous election. This provides the best estimate of the green vote. Secondly, for the Coalition and Labor, as these swings define the national 2PP swing an adjustment needs to be made for the difference between the 2PP national and state swing. Thirdly, the major party votes are summed and then proportionally adjusted to get back to the target from step 1.
In 2010, the Greens polled 10.69%. BludgerTrack forecasts a -2% swing to the Greens. The best estimate of Green NSW Senate vote is 8.69%. (unadjusted for step 1)
In 2010, the Coalition polled 38.95% but is facing a national swing of +2.4% (primary). However, the NSW swing for the Coalition is 0.1% weaker than the national swing, so the 2013 NSW Senate vote needs to be adjusted by -0.1%. Estimate of Coalition vote = 38.95 + 2.4 - 0.1 = 41.25%. (unadjusted for step 1)
In 2010, Labor polled 36.54% but is facing a national swing of -1.2% (primary). However, the NSW swing the Labor vote is 0.1% stronger than the national swing, so the 2013 NSW Senate vote needs to be adjusted by +0.1%. Estimate of Labor vote = 36.54 - 1.2 + 0.1 = 35.44%. (unadjusted for step 1)
Applying the step 1 adjustment we get the following forecasts:
Step 3: Individual minor party vote
Ok, here's where the educated guesses starts. We need to assign the groups into categories. It is damn hard to ensure that it all adds up - the tendency is to think "this party will poll x and this party will poll y..." and you'll then find yourself with about 10% too much vote!
I suggest listing the parties in order and then starting with a low number like 2%, and assigning all parties at least 0.05% for ungrouped independents and no-name parties.
The key source of data for estimates should be the previous election, usually adjusted downwards to allow for the higher number of candidates.
2%: Liberal Democratic Party (inflated by their "Group A" position)
1.5%: CDP, KAP, SXP
1%: Shooters and Fishers, One Nation
0.5%: DLP, Palmer, Stop the Greens, Democrats, Family First, Fishing and Lifestyle, No Carbon Tax, Pirate Party
0.2%: HEMP, Wikileaks, Smokers, Coal Seam Gas, Australian Independents, Drug Law Reform, Republicans
0.1%: Socialist Alliance, Socialist Equality Party
0.05%: 18 other parties
Step 4: Party vote variations
In order to conduct Monte Carlo analysis, you need to specify the degree of variability of each party's vote. For simplicity, I assume a linear variation around the mean as assumed above. The higher the party's vote, the greater the certainty about it.
Monte Carlo analysis is necessary as the order of candidate elimination is fragile - very little change in a vote may change the order of elimination or election, so variability is required to allow a more likely scenario to be identified. Monte Carlo analysis is standard practice in many financial modelling applications.
I assume the following variations as a proportion of each party's vote
If a party has up to 1%, the variation is +/-50% of its vote
If a party has up to 5%, the variation is +/-30% of its vote
If a party has up to 10%, the variation is +/- 25% of its vote
If a party has up to 20%, the variation is +/- 20% of its vote
If a party exceeds 20%, the variation is +/-15% of its vote.
Forecast LNP vote is 41%, forecast LNP variation is +/-6.15%
Forecast Socialist Alliance vote is 0.1%, variation is +/-0.05%
Step 5: Normalise to 100%
After applying random variation to all tickets, the sum will no longer add to 100%. It is necessary to proportionally reset the total to 100% by scaling all forecast votes up or down. In most scenarios, this step is minimal and does not result in tickets moving outside the range of variation specified above.
Step 6: Press the button
Wait one minute for 1000 Monte Carlo simulations to be run in Microsoft Excel, based on the preferences in officially registered group ticket votes.
Step 7: Publish the results
With the revised NSW numbers, here are the likelihoods of each partyin a rough left to right basis
Left: expected value 2.96 seats
Labor: 2 seats: 100% likely, 3 seats 15% likely
Greens: 78% likelihood of election
Right: expected value 3.04 seats
Coalition: 2 seats guaranteed, 3 seats 78% likely
Liberal Democrats: 16% likelihood of election
Shooters and fishers: 4.3%
One Nation: 0.4%
Building Australia: 0.2%
Stable Population: 0.2%
1. Can you suggest any improvement to my forecast votes?
2. Can you suggest any improvement to my forecast party variation?
3. Any general comments?
If you wish to suggest any variations to forecast votes or forecast variation, I will be happy to remodel these scenarios as suggested, and publish the results below.