Note: the following page is the approach I took to the 2016 Federal Election. The Ba

yesian Aggregation page is being/has been updated for the 2019 election ...

This page provides some technical background on the Bayesian poll
aggregation models used on this site. The page has been updated for the
2016 Federal election.

###
General overview

The aggregation or

data fusion models I use are known as

hidden Markov models. They are are sometimes referred to as

state space models or latent process models.

I
model the national voting intention (which cannot be observed directly;
it is "hidden") for each and every day of the period under analysis.
The only time the national voting intention is not hidden, is at an
election. In some models (known as anchored models), we use the election
result to anchor the daily model we use.

In the
language of modelling, our estimates of the national voting intention
for each day being modeled are known as states. These "states" link
together to form a

Markov process,
where each state is directly dependent on the previous state and a
probability distribution linking the states. In plain English, the
models assume that the national voting intention today is much like it
was yesterday. The simplest models assume the voting intention today is
normally
distributed around the voting intention yesterday.

The
model is informed by irregular and noisy data from the selected polling
houses. The challenge for the model is to ignore the noise and find the
underlying signal. In effect, the model is solved by finding the the
day-to-day pathway with the

maximum likelihood given the known poll results.

To
improve the robustness of the model, we make provision for the long-run
tendency of each polling house to systematically favour either the
Coalition or Labor. We call this small tendency to favour one side or
the other a "house effect". The model assumes that the results from each
pollster diverge
(on average) from the from real population voting intention by a small,
constant number of
percentage points. We use the calculated house effect to adjust the raw
polling data from each polling house.

In estimating the house effects, we can take one of a number of approaches. We could:

- anchor the model to an election result on a particular day, and use that anchoring to establish the house effects.
- anchor the model to a particular polling house or houses; or
- assume that collectively the polling houses are unbiased, and that collectively their house effects sum to zero.

Typically, I use the first and third approaches in my
models. Some models assume that the house effects sum to zero. Other
models assume that the house effects can be determined absolutely by
anchoring the hidden model for a particular day or week to a known
election outcome.

There are issues with both
approaches. The problem with anchoring the model to an election outcome
(or to a
particular polling house), is that pollsters are constantly reviewing
and, from time to time, changing their polling practice. Over time these
changes affect the reliability of the model. On the other hand, the
sum-to-zero assumption is rarely correct. Of the two approaches,
anchoring tends to perform better.

Another adjustment
we make is to allow discontinuities in the
hidden process when either party changes its leadership. Leadership
changes can see immediate changes in the voting preferences. For the
anchored models, we allow for discontinuities with the Gillard to Rudd
and Abbott to Turnbull leadership changes. The unanchored models have a
single discontinuity with the Abbott to Turnbull leadership change.

Solving a model necessitates integration over a series of complex
multidimensional probability distributions. The definite integral is
typically impossible to solve algebraically. But it can be solved using a
numerical method based on Markov chains and random numbers known as

Markov Chain Monte Carlo (MCMC) integration.
I use a
free software product called

JAGS to solve these models.

###
**The dynamic linear model of TPP voting intention with house effects summed to zero**

This is the simplest model. It has three parts:

- The
*observational* part of the model assumes two factors
explain the difference between published poll results (what we observe)
and the national voting intention on a particular day (which, with the
exception of elections, is hidden):

- The first factor is the margin of error from classical statistics. This is the random error associated with selecting a sample; and
- The second factor is the systemic biases (house effects) that affect each pollster's published estimate of the population voting intention.

- The
*temporal* part of the model assumes that the actual
population voting intention on any day is much the same as it was on the
previous day (with the exception of discontinuities). The model
estimates the (hidden) population voting intention for every day under
analysis.

- The
*house effects* part of the model assumes that house effects are distributed around zero and sum to zero.

This model builds on original work by

Professor Simon Jackman. It is encoded in

JAGS as follows:

model {
## -- draws on models developed by Simon Jackman
## -- observational model
for(poll in 1:n_polls) {
yhat[poll] <- houseEffect[house[poll]] + hidden_voting_intention[day[poll]]
y[poll] ~ dnorm(yhat[poll], samplePrecision[poll]) # distribution
}
## -- temporal model - with one discontinuity
hidden_voting_intention[1] ~ dunif(0.3, 0.7) # contextually uninformative
hidden_voting_intention[discontinuity] ~ dunif(0.3, 0.7) # ditto
for(i in 2:(discontinuity-1)) {
hidden_voting_intention[i] ~ dnorm(hidden_voting_intention[i-1], walkPrecision)
}
for (j in (discontinuity+1):n_span) {
hidden_voting_intention[j] ~ dnorm(hidden_voting_intention[j-1], walkPrecision)
}
sigmaWalk ~ dunif(0, 0.01)
walkPrecision <- pow(sigmaWalk, -2)
## -- house effects model
for(i in 2:n_houses) {
houseEffect[i] ~ dunif(-0.15, 0.15) # contextually uninformative
}
houseEffect[1] <- -sum( houseEffect[2:n_houses] )
}

Professor Jackman's original JAGS code can be found in the file

*kalman.bug*, in the zip file link

on this page, under the heading

*Pooling the Polls Over an Election Campaign*.

###
The anchored **dynamic linear model of TPP voting intention**

This
model is much the same as the previous model. However, it is run with
data from prior to the 2013 election to anchor poll performance. It
includes two discontinuities, with the ascensions of Rudd and Turnbull.
And, because it is anchored, the house effects are not constrained to
sum to zero.

model {
## -- observational model
for(poll in 1:n_polls) {
yhat[poll] <- houseEffect[house[poll]] + hidden_voting_intention[day[poll]]
y[poll] ~ dnorm(yhat[poll], samplePrecision[poll]) # distribution
}
## -- temporal model - with two or more discontinuities
# priors ...
hidden_voting_intention[1] ~ dunif(0.3, 0.7) # fairly uninformative
for(j in 1:n_discontinuities) {
hidden_voting_intention[discontinuities[j]] ~ dunif(0.3, 0.7) # fairly uninformative
}
sigmaWalk ~ dunif(0, 0.01)
walkPrecision <- pow(sigmaWalk, -2)
# Up until the first discontinuity ...
for(k in 2:(discontinuities[1]-1)) {
hidden_voting_intention[k] ~ dnorm(hidden_voting_intention[k-1], walkPrecision)
}
# Between the discontinuities ... assumes 2 or more discontinuities ...
for( disc in 1:(n_discontinuities-1) ) {
for(k in (discontinuities[disc]+1):(discontinuities[(disc+1)]-1)) {
hidden_voting_intention[k] ~ dnorm(hidden_voting_intention[k-1], walkPrecision)
}
}
# after the last discontinuity
for(k in (discontinuities[n_discontinuities]+1):n_span) {
hidden_voting_intention[k] ~ dnorm(hidden_voting_intention[k-1], walkPrecision)
}
## -- house effects model
for(i in 1:n_houses) {
houseEffect[i] ~ dnorm(0, pow(0.05, -2))
}
}

###
The latent Dirichlet process for primary voting intention

This model is more complex. It takes advantage of the Dirichlet (pronounced

*dirik-lay*)
distribution, which always sums to 1, just as the primary votes for all
parties would sum to 100 per cent of voters at an election. A weakness
is a transmission mechanism from day-to-day that uses a "tightness of
fit" parameter, which has been arbitrarily selected.

The
model is set up a little differently to the previous models. Rather
than pass the vote share as a number between 0 and 1; we pass the size
of the sample that indicated a preference for each party. For example,
if the poll is of 1000 voters, with 40 per cent for the Coalition, 40
per cent for Labor, 11 per cent for the Greens and and 9 per cent for
Other parties, the multinomial we would pass across for this poll is
[400, 400, 110, 90].

More broadly, this model is
conceptually very similar to the sum-to-zero TPP model: with an
observational component, a dynamic walk of primary voting proportions
(modeled as a

hierarchical Dirichlet process), a
discontinuity for Turnbull's ascension, and a set of house effects that
sum to zero across polling houses and across the parties.

data {
zero <- 0.0
}
model {
#### -- observational model
for(poll in 1:NUMPOLLS) { # for each poll result - rows
adjusted_poll[poll, 1:PARTIES] <- walk[pollDay[poll], 1:PARTIES] +
houseEffect[house[poll], 1:PARTIES]
primaryVotes[poll, 1:PARTIES] ~ dmulti(adjusted_poll[poll, 1:PARTIES], n[poll])
}
#### -- temporal model with one discontinuity
tightness <- 50000 # kludge - today very much like yesterday
# - before discontinuity
for(day in 2:(discontinuity-1)) {
# Note: use math not a distribution to generate the multinomial ...
multinomial[day, 1:PARTIES] <- walk[day-1, 1:PARTIES] * tightness
walk[day, 1:PARTIES] ~ ddirch(multinomial[day, 1:PARTIES])
}
# - after discontinuity
for(day in discontinuity+1:PERIOD) {
# Note: use math not a distribution to generate the multinomial ...
multinomial[day, 1:PARTIES] <- walk[day-1, 1:PARTIES] * tightness
walk[day, 1:PARTIES] ~ ddirch(multinomial[day, 1:PARTIES])
}
## -- weakly informative priors for first and discontinuity days
for (party in 1:2) { # for each major party
alpha[party] ~ dunif(250, 600) # majors between 25% and 60%
beta[party] ~ dunif(250, 600) # majors between 25% and 60%
}
for (party in 3:PARTIES) { # for each minor party
alpha[party] ~ dunif(10, 250) # minors between 1% and 25%
beta[party] ~ dunif(10, 250) # minors between 1% and 25%
}
walk[1, 1:PARTIES] ~ ddirch(alpha[])
walk[discontinuity, 1:PARTIES] ~ ddirch(beta[])
## -- estimate a Coalition TPP from the primary votes
for(day in 1:PERIOD) {
CoalitionTPP[day] <- sum(walk[day, 1:PARTIES] *
preference_flows[1:PARTIES])
CoalitionTPP2010[day] <- sum(walk[day, 1:PARTIES] *
preference_flows_2010[1:PARTIES])
}
#### -- house effects model with two-way, sum-to-zero constraints
## -- vague priors ...
for (h in 2:HOUSECOUNT) {
for (p in 2:PARTIES) {
houseEffect[h, p] ~ dunif(-0.1, 0.1)
}
}
## -- sum to zero - but only in one direction for houseEffect[1, 1]
for (p in 2:PARTIES) {
houseEffect[1, p] <- 0 - sum( houseEffect[2:HOUSECOUNT, p] )
}
for(h in 1:HOUSECOUNT) {
# includes constraint for houseEffect[1, 1], but only in one direction
houseEffect[h, 1] <- 0 - sum( houseEffect[h, 2:PARTIES] )
}
## -- the other direction constraint on houseEffect[1, 1]
zero ~ dsum( houseEffect[1, 1], sum( houseEffect[2:HOUSECOUNT, 1] ) )
}

###

###
The anchored Dirichlet primary vote model

The anchored Dirichlet
model follows. It draws on elements from the anchored TPP model and
Dirichlet model above. It is the most complex of these models. It also
takes the longest time to produce reliable results. For this model, I
run 460,000 samples, taking every 23rd sample for analysis. On my aging
Apple iMac and JAGS 4.0.1 it takes around 100 minutes to run.

model {
#### -- observational model
for(poll in 1:NUMPOLLS) { # for each poll result - rows
adjusted_poll[poll, 1:PARTIES] <- walk[pollDay[poll], 1:PARTIES] +
houseEffect[house[poll], 1:PARTIES]
primaryVotes[poll, 1:PARTIES] ~ dmulti(adjusted_poll[poll, 1:PARTIES], n[poll])
}
#### -- temporal model with multiple discontinuities
# - tightness of fit parameters
tightness <- 50000 # kludge - today very much like yesterday
# Up until the first discontinuity ...
for(day in 2:(discontinuities[1]-1)) {
multinomial[day, 1:PARTIES] <- walk[day-1, 1:PARTIES] * tightness
walk[day, 1:PARTIES] ~ ddirch(multinomial[day, 1:PARTIES])
}
# Between the discontinuities ... assumes 2 or more discontinuities ...
for( disc in 1:(n_discontinuities-1) ) {
for(day in (discontinuities[disc]+1):(discontinuities[(disc+1)]-1)) {
multinomial[day, 1:PARTIES] <- walk[day-1, 1:PARTIES] * tightness
walk[day, 1:PARTIES] ~ ddirch(multinomial[day, 1:PARTIES])
}
}
# After the last discontinuity
for(day in (discontinuities[n_discontinuities]+1):PERIOD) {
multinomial[day, 1:PARTIES] <- walk[day-1, 1:PARTIES] * tightness
walk[day, 1:PARTIES] ~ ddirch(multinomial[day, 1:PARTIES])
}
# weakly informative priors for first day and discontinutity days ...
for (party in 1:2) { # for each minor party
alpha[party] ~ dunif(250, 600) # minors between 25% and 60%
}
for (party in 3:PARTIES) { # for each minor party
alpha[party] ~ dunif(10, 200) # minors between 1% and 20%
}
walk[1, 1:PARTIES] ~ ddirch(alpha[])
for(j in 1:n_discontinuities) {
for (party in 1:2) { # for each minor party
beta[j, party] ~ dunif(250, 600) # minors between 25% and 60%
}
for (party in 3:PARTIES) { # for each minor party
beta[j, party] ~ dunif(10, 200) # minors between 1% and 20%
}
walk[discontinuities[j], 1:PARTIES] ~ ddirch(beta[j, 1:PARTIES])
}
## -- estimate a Coalition TPP from the primary votes
for(day in 1:PERIOD) {
CoalitionTPP[day] <- sum(walk[day, 1:PARTIES] *
preference_flows[1:PARTIES])
}
#### -- sum-to-zero constraints on house effects
for(h in 1:HOUSECOUNT) {
for (p in 2:PARTIES) {
houseEffect[h, p] ~ dnorm(0, pow(0.05, -2))
}
}
# need to lock in ... but only in one dimension
for(h in 1:HOUSECOUNT) { # for each house ...
houseEffect[h, 1] <- -sum( houseEffect[h, 2:PARTIES] )
}
}

###
Beta model of primary vote share for Palmer United

A
simplification of the Dirichlet distribution is the Beta distribution. I
use the Beta distribution (in a similar model to the Dirichlet model
above), to track the primary vote share of the Palmer United Party. This
model does not provide for a discontinuity in voting associated with
the change of Prime Minister.

model {
#### -- observational model
for(poll in 1:NUMPOLLS) { # for each poll result - rows
adjusted_poll[poll] <- walk[pollDay[poll]] + houseEffect[house[poll]]
palmerVotes[poll] ~ dbin(adjusted_poll[poll], n[poll])
}
#### -- temporal model (a daily walk where today is much like yesterday)
tightness <- 50000 # KLUDGE - tightness of fit parameter selected by hand
for(day in 2:PERIOD) { # rows
binomial[day] <- walk[day-1] * tightness
walk[day] ~ dbeta(binomial[day], tightness - binomial[day])
}
## -- weakly informative priors for first day in the temporal model
alpha ~ dunif(1, 1500)
walk[1] ~ dbeta(alpha, 10000-alpha)
#### -- sum-to-zero constraints on house effects
for(h in 2:HOUSECOUNT) { # for each house ...
houseEffect[h] ~ dnorm(0, pow(0.1, -2))
}
houseEffect[1] <- -sum(houseEffect[2:HOUSECOUNT])
}

###
Code and data

I have made most of my data and code base available on

Google Drive.
Please note, this is my live code base, which I play with quite a bit.
So, there will be times when it is broken or in some stage of being
edited.

What I have not made available is the Excel
spreadsheets into which I initially place my data. These live in the
(hidden) raw-data directory. However, the collated data for the Bayesian
model lives in the intermediate directory, visible from the above link.