## Tuesday, June 18, 2019

### Further polling reflections

I have been pondering on whether the polls have been out of whack for some time, or whether it was a recent failure (over the previous 3, 6 or (say) 12 months). In previous posts, I looked at YouGov in 2017, and at monthly polling averages prior to the 2019 election.

Today I want to look at the initial polls following the 2016 election. First, however, let's recap the model I used for the 2019 election. In this model, I excluded YouGov and Roy Morgan from the sum-to-zero constraint on house effects. I have added a starting point reference to these charts (and increased the rounding on the labels from one decimal place to two. However, I would caution on reading these models to two decimal places, the models are not that precise).

What is worth noting is that this series opens on 6 July 2016 some 1.7 percentage points down from the election result of 50.36 per cent of the two-party preferred (TPP) vote for the Coalition on 2 July 2016. The series closes some 3.1 percentage points down from the 18 May 2019 election result. It appears that the core-set of Australian pollsters started some 1.7 percentage points off the mark, and collectively gained a further 1.4 percentage points of error over the period from July 2016 to May 2019.

These initial polls are all from Essential, and they are under-dispersed. (We discussed the under-dispersion problem here, here, here, and here. I will come back to this problem in a future post). The first two Newspolls were closer to the election result, but they then aligned with Essential from then on. The Newspolls from this period are also under-dispersed.

We can see how closely Newspoll and Essential tracked each other on average from the following chart of average house effects. I have Newspoll twice in this chart, based on the original method for allocating of preferences, and (Newspoll2) for the revised allocation of One Nation preferences from late in 2017.

If I had aggregated the polls prior to the 2019 election by anchoring the line to the previous election, I would have achieved a better estimate of the Coalition's performance than I did. Effectively I would have predicted a tie or a very narrow Coalition victory if I had aggregated the polls for this election with an anchor to the previous election.

A good question to ask at this point is why did I not anchor the model to the previous election? The short answer is that I have watched a number of aggregators in past election cycles use an anchored model and end up with worse predictions than those who assumed the house effects across the pollsters cancel each other out on average. I have also assumed that pollsters use elections to recalibrate their polling methodologies, and this recalibration represents a series break. A left-hand side anchored series assumes there have been no series breaks.

In summary, at least 1.7 percentage points of polling error were baked in from the very first polls following the 2016 election. Over the period since July 2016, this error has increased to 3.1 percentage points.

Wonky note: For the anchored model, I changed the priors on house effects from weakly informative normals centred on zero, to uniform priors in the range -6% to +6%. I did this because the weakly informative priors were dragging the aggregation towards the centre of the data points.

The anchored STAN model code follows.
// STAN: Two-Party Preferred (TPP) Vote Intention Model
//     - Updated to for fixed starting point

data {
// data size
int n_polls;
int n_days;
int n_houses;

// assumed standard deviation for all polls
real pseudoSampleSigma;

// poll data
vector[n_polls] y; // TPP vote share
int house[n_polls];
int day[n_polls];

// period of discontinuity event
int discontinuity;
int stability;

// previous election outcome anchor point
real election_outcome;
}

transformed data {
// fixed day-to-day standard deviation
real sigma = 0.0015;
real sigma_volatile = 0.0045;

// house effect range
real lowerHE = -0.06;
real upperHE = 0.06;
}

parameters {
vector[n_days] hidden_vote_share;
vector[n_houses] pHouseEffects;
real disruption;
}

model {
// -- temporal model [this is the hidden state-space model]
disruption ~ normal(0.0, 0.15); // PRIOR
hidden_vote_share[1] ~ normal(election_outcome, 0.00001);

hidden_vote_share[2:(discontinuity-1)] ~
normal(hidden_vote_share[1:(discontinuity-2)], sigma);

hidden_vote_share[discontinuity] ~
normal(hidden_vote_share[discontinuity-1]+disruption, sigma);

hidden_vote_share[(discontinuity+1):stability] ~
normal(hidden_vote_share[discontinuity:(stability-1)], sigma_volatile);

hidden_vote_share[(stability+1):n_days] ~
normal(hidden_vote_share[stability:(n_days-1)], sigma);

// -- house effects model
pHouseEffects ~ uniform(lowerHE, upperHE); // PRIOR

// -- observed data / measurement model
y ~ normal(pHouseEffects[house] + hidden_vote_share[day],
pseudoSampleSigma);
}


#### 1 comment:

1. excellent post Mark. more education for us.thanks