## Friday, 26 May 2017

### (Too) slowly but surely?

After the tragic events in Manchester and the suspension in the campaigns, things have started again and a couple new polls have been released. Some of the media have also picked up the trend I was observing from my model and so I have re-updated the results.

The increasing trend for Labour does see another little surge, as does the decreasing trend for the Tories. In comparison to my last update, the Lib Dem are slightly picking up again. But all in all, the numbers still tell kind of the same story, I guess.

mean        sd 2.5% median   97.5%
Conservative 369.251 5.1765622  357    370 378.000
Labour       197.886 5.2142298  190    197 211.000
UKIP           0.000 0.0000000    0      0   0.000
Lib Dem       15.085 2.3852598   11     15  19.025
SNP           49.263 2.3965756   44     49  53.000
Green          0.000 0.0000000    0      0   0.000
PCY            0.515 0.8499985    0      0   3.000
Other          0.000 0.0000000    0      0   0.000

These are the summary results as of today (again after discounting past polls). Lib Dem move from a median number of expected seats of 14 to the current estimate of 15; Labour go from 191 to 197 and the Tories go from 376 to 370, still comfortably in the lead.

## Monday, 22 May 2017

### Quick update

This is going to be a very short post. I've been again following the latest polls and have updated my election forecast model $-$ nothing has changed in the general structure, only new data coming as the campaigns evolve.
The dynamic forecast (which considers for each day from 1 to 22 May only the polls available up to that point) show an interesting progression for Labour, who seem to be picking up some more seats. They are still a long way from the Tories, who are slightly declining. Also, the Lib Dems are also going down and the latest results seem to suggest a poor result for Plaid Cymru in Wales too (the model was forecasting up to 4 seats before, where now they are expected to get 0).

The detailed summary as of today is as follows.
mean         sd    2.5% median 97.5%
Conservative 375.109 4.02010949 367.000    376   382
Labour       192.134 3.94862452 186.000    191   200
UKIP           0.000 0.00000000   0.000      0     0
Lib Dem       14.320 2.24781064  10.000     14    18
SNP           50.053 2.12713792  45.975     50    53
Green          0.007 0.08341438   0.000      0     0
PCY            0.377 0.77036645   0.000      0     3
Other          0.000 0.00000000   0.000      0     0

I think the trend seems genuine $-$ Labour go from a median number of predicted seats of 175 at 1st May to the current estimate of 191, the Tories go from 381 to 376 and the Lib Dems from 23 to 14. Probably not enough time to change things substantially (bar some spectacular faux pas from the Tories, I think), though...

I've also played around with the issue of coalitions $-$ there's still some speculation in the media that the "Progressives" (Labour, Lib Dems and Greens) could try and help each other by not fielding a candidate and support one of the other parties in selected constituencies, so as to maximise the chance of ousting the Conservatives. I've simply used the model prediction and (most likely unrealistically!) assumed 100% compliance from the voters, so that the coalition would get the sum of the votes originally predicted for each of the constituent parties. Here's the result.

The Progressive come much closer and the probability of an outright Tory majority is now much smaller, but still...

## Monday, 15 May 2017

### Through time & space

I've continued to fill in the data from the polls and re-run the model for the next UK general election. I think the dynamic element is interesting in principle, mainly because of how the data from the most recent polls could be weighed differently than those further in the past.

Roberto had done an amazing job, building on Linzer's work and using a rather complex model to account for the fact that the polls are temporally correlated and, as you get closer to election day, the historical data are much less informative. This time, I have done something much simpler and somewhat more arbitrary, simply based on discounting the polls depending on how distant they are from "today".

This is the results given by my model in the period from May 1st to May 12th $-$ at every day, I've only included the polls available at that time and discounted using a 10% rate, assuming modern life really runs very fast (which it reasonably does...). Not much is really changing and the predictions in terms of the number of seats won by the parties in England, Wales and Scotland seems fairly stable $-$ Labour is probably gaining a couple of seats, but the story is basically unchanged.

The other interesting thing (which I had done here and here too) is to analyse the predicted geographical distribution of the votes/seats. Now, however, I'm taking full advantage of the probabilistic nature of the model and not only am I plotting on the map the "most likely outcome" (assigning a colour to each constituency, depending on who's predicted to win it). In the graph below, I've also computed the probability that the party most likely to win a given seat actually does so (based on the simulations from the posterior distributions of the vote shares, as explained here) $-$ I've shaded the colours so that lighter constituencies are more uncertain (i.e. the win may be more marginal).

There aren't very many marginal seats (according to the model) and most of the times, the chance of a party winning a constituency exceeds 0.6 (which is fairly high, as it would mean a swing of over 10% from the prediction to overturn this).

This is also the split across different regions $-$ again, not many open battlefields, I think. In London, Hornsey and Wood Green is predicted to go Labour but with a probability of only 54%, while Tooting is predicted to go Tory (with a chance of 58%).

## Friday, 5 May 2017

### Flash forward sampling

Slowly but surely, I've managed to think a bit more about the elections model. Here, I've described how I included some prior information in my model to try and "discount" the evidence provided by the polls, to obtain estimates that may be more reasonable and less affected by the short-term shocks that may (over)influence people's opinions.

However, I wasn't entirely happy with the strategy I had used $-$ the informative priors I had set on the parameters $\alpha_p$ and $\beta_p$ did induce rather precise distributions. In addition, the analysis I have made wasn't making the most of the actual inferential machine I had constructed, because it was estimating the number of seats for the average vote shares profile. But in fact, I can do better than that and actually propagate fully the uncertainty in the vote shares and have an entire posterior distribution of the seats configuration.

So, first off, I think I've refined my priors and I did so by running the model simply through "forward sampling" $-$ in other words, by not including any of the polls in my analysis to better understand what implications were deriving by my choice of priors. By selecting the means and standard deviations for the vectors $\alpha$ and $\beta$, I effectively imply the following prior expectation in terms of the vote share.
The red dots represent the "historical" averages over the past 3 general elections, which I used as a reference point. You could fiddle a bit more with the parameters of the distributions for $\alpha_p$ and $\beta_p$, but I am reasonably happy with the implications of the current choice $-$ I'm expecting the Conservatives to do much better than the historical figure; Labour is expected to be around how they normally do, but there is a chance they'll do worse than "usual" and on average they're also doing worse than in the 2015 election. The Lib Dems are predicted with relatively large uncertainty and still under their historical average $-$ I think this is reasonable and many pundits are also aligned with this. Similarly, the prior effectively gives a very low weight to UKIP $-$ and this is in line with general consensus (I think) as well as the result of last night local elections.

Interestingly,  I can map these results and propagate the uncertainty to estimate the distribution of seats in Parliament (still with no data from the polls included), to produce the following graph.
Again, I think this picture is even more convincing than the analysis of the probabilities and I feel relatively confident with this. (But of course, one could replicate the whole analysis and try different specifications, which I have to some degree).

So it's now time to include the data that are pouring in from the polls. In particular, I now have information collected over the past two weeks or so and I think in a fast-moving election such as this where opinions may be changed by a large number of "facts" and stories, it's useful to "discount" the older data. There are many ways of doing this, more or less formally $-$ I'm using a rather quick and dirty strategy, by applying a simple discount rate defined as a function of time since today.

Each observed poll gets rescaled as $$y^{j*}_{ip}= \frac{y^j_{ip}}{(1+\delta)^t},$$
where $y^{j*}_{ip}$ is the number of voting intentions for party $p$ in poll $i$ under voters of type $j$ (=1 for Leavers and =2 for Remainers); and $\delta$ is an arbitrarily defined discount rate. I've tested a few versions (ranging from 0.03 to 0.1) and the results do not vary dramatically $-$ the larger the discount rate, the more older polls are discounted, which tends to reduce by a minimum of 1 and a maximum of 4 the number of seats associated with the Conservatives. This is because in the very first few polls, the advantage associated with the Tories was bigger than in the most recent).

With a discount rate $\delta=0.1$, the results estimated in terms of seats won are as in the following graph.
So, Conservatives with a median estimated number of seats of 379 (and a 95% interval estimate of 369-391, way above the line of 325 seats that are needed for a majority), Labour with 175 (163-185), Lib Dems with 25 (17-31), SNP with 49 (46-54), Green with 1 and Plaid Cymru with 3 (0-4).

I think this analysis is interesting because it is fairly easy to assess the uncertainty propagated through the model up to the actual quantity of interest (the seats won). Other pundits are being a lot less favourable to the Lib Dems, but I'm kind of happy of how my model works, especially after considering the prior analysis.

Plenty more fun to come $-$ well, depending on your definition of fun...

## Friday, 28 April 2017

### Face value

I found a little more time to think about the election model and fiddle with the set up, as well as use some more recent polls $-$ I have now managed to get 9 polls detailing voting intention for the 7 main parties competing in England, Scotland and Wales.

I think one thing I was not really happy with the basic set up I've used so far is that it kind of takes the polls at "face value", because the information included in the priors is fairly weak. And we've seen in recent times on several occasions that polls are often not what they seem...

So, I've done some more analysis to: 1) test the actual impact of the prior on the basic setting I was using; and 2) think of something that could be more appropriate, by including more substantive knowledge/data in my model.

First off, I was indeed using some information to define the prior distribution for the log "relative risk" of voting for party $p$ in comparison to the Conservatives, among Leavers ($\alpha_p$) and Remainers ($\alpha_p + \beta_p$), but I think that kind of information was really weak. It is helpful to run the model by simply "forward sampling" (i.e. pretending that I had no data) to check what the priors actually imply. As expected, in this case, the prior vote share for each party was close to basically $(1/P)\approx 0.12$. This is consistent with a "vague" structure, but arguably not very realistic $-$ I think nobody is expecting all the main parties to get the same share of the vote before observing any of the polls...

So, I went back to the historical data on the past 3 General Elections (2005, 2010 and 2015) and used these to define some "prior" expectation for the parameters determining the log relative risks (and thus the vote shares).

There are obviously many ways in which one can do this $-$ the way I did it is to first of all weigh the observed vote shares in England, Scotland and Wales to account for the fact that data from 2005 are likely to be less relevant than data from 2015. I have arbitrarily used a ratio of 3:2:1, so that the latest election weighs 3 times as much as the earliest. Of course, if this was "serious" work, I'd want to check sensitivity to this choice (although see below...).

This gives me the following result:

Conservative     0.366639472
Green            0.024220681
Labour           0.300419740
Liberal Democrat 0.156564215
Plaid Cymru      0.006032815
SNP              0.032555551
UKIP             0.078807863
Other            0.034759663

Looking at this, I'm still not entirely satisfied, though, because I think UKIP and possibly the Lib Dem may actually have different dynamics at the next election, than estimated by the historical data. In particular, it seems that UKIP has clear problems in re-inventing themselves, after the Conservatives have by and large so efficiently taken up the role of Brexit paladins. So, I have decided to re-distribute some of the weight for UKIP to the Conservatives and Labour, who were arguably the most affected by the surge in popularity for the Farage army.

In an extra twist, I also moved some of the UKIP historical share to the SNP, to safeguard against the fact that they have a much higher weight when it counts for them (ie Scotland) than the national average suggests. (I could have done this more correctly by modelling the vote in Scotland separately).

These historical shares can be turned into relative risks by simply re-proportioning them by the Conservative share, thus giving me some "average" relative risk for each party (against the reference $=$ Conservatives). I called these values $\mu_p$ and have used them to derive some rather informative priors for my $\alpha_p$ and $\beta_p$ parameters.

In particular, I have imposed that the mixture of relative risks among leavers and remainers would be centered around the historical (revisited) values, which means I'm implying that $$\hat{\phi}_p = 0.52 \phi^L_p + 0.48 \phi^R_p = 0.52 \exp(\alpha_p) + 0.48\exp(\alpha_p)\exp(\beta_p) \sim \mbox{Normal}(\mu_p,\sigma).$$ If I fix the variance around the overall mean $(\sigma^2)$ to some value (I have chosen 0.05, but have done some sensitivity analysis around it), it is possible to do some trial-and-error to figure out what the configuration of $(\alpha_p,\beta_p)$ should be so that on average the prior is centered around the historical estimate.

I can then re-run my model and see what the differences are by assuming the "minimally informative" and the "informative" versions.
Here, the black dots and lines indicate the mean and 95% interval of the minimally informative prior, while the blue dots and lines are the posterior estimated vote shares (ie after including the 9 polls) for that model. The red and magenta dots/lines are the prior and posterior results for the informative model (based on the historical/subjective data).

Interestingly, the 9 polls seem to have quite substantial strength, because they are able to move most of the posteriors (eg the Conservatives, Labour, SNP, Green, Plaid Cymru and Other). The differences between the two versions of the model are not huge, necessarily, but they are important in some cases.

The actual results in terms of seats won are as in the following.

Party        Seats (MIP)  Seat (IP)
Conservative        371        359
Green                 1          1
Labour              167        178
Lib Dem              30         40
Plaid Cymru          10          3
SNP                  53         51

Substantively, the data + model assumptions seem to suggest a clear Conservative victory in both versions. But the model based on informative/substantive prior seems to me a much more reasonable prediction $-$ strikingly, the minimally informative version predicts a ridiculously large number of seats for Plaid Cymru.

The analysis of the swing of votes is shown in the following (for the informative model).
2015/2017         Conservative Green Labour Lib Dem PCY SNP
Conservative               312     0      0      17   0   1
Green                        0     1      0       0   0   0
Labour                      45     0    178       8   0   1
Liberal Democrat             0     0      0       9   0   0
Plaid Cymru                  0     0      0       0   3   0
SNP                          1     0      0       6   0  49
UKIP                         1     0      0       0   0   0

Labour are predicted to now win any new seats and their losses are mostly to the Conservatives and the Lib Dems. This is how the seats are predicted across the three nations.

As soon as I have a moment, I'll share a more intelligible version of my code and will update the results as new polls become available.

## Tuesday, 25 April 2017

### Snap

In the grand tradition of all recent election times, I've decided to have a go and try and build a model that could predict the results of the upcoming snap general election in the UK. I'm sure there will be many more people having a go at this, from various perspectives and using different modelling approaches. Also, I will try very hard to not spend all of my time on this and so I have set out to develop a fairly simple (although, hopefully reasonable) model.

First off: the data. I think that since the announcement of the election, the pollsters have intensified the number of surveys; I have found already 5 national polls (two by Yougov, two by ICM and one by Opinium $-$ there may be more and I'm not claiming a systematic review/meta-analysis of the polls.

Arguably, this election will be mostly about Brexit: there surely will be other factors, but because this comes almost exactly a year after the referendum, it is a fair bet to suggest that how people felt and still feel about its outcome will also massively influence the election. Luckily, all the polls I have found do report data in terms of voting intention, broken up by Remain/Leave. So, I'm considering $P=8$ main political parties: Conservatives, Labour, UKIP, Liberal Democrats, SNP, Green, Plaid Cymru and "Others". Also, for simplicity, I'm considering only England, Scotland and Wales $-$ this shouldn't be a big problem, though, as in Northern Ireland elections are generally a "local affair", with the mainstream parties not playing a significant role.

I also have available data on the results of both the 2015 election (by constituency and again, I'm only considering the $C=632$ constituencies in England, Scotland and Wales $-$ this leaves out the 18 Northern Irish constituencies) and the 2016 EU referendum. I had to do some work to align these two datasets, as the referendum did not consider the usual geographical resolution. I have mapped the voting areas used 2016 to the constituencies and have recorded the proportion of votes won by the $P$ parties in 2015, as well as the proportion of Remain vote in 2016.

For each observed poll $i=1,\ldots,N_{polls}$, I modelled the observed data among "$L$eavers" as $$y^{L}_{i1},\ldots,y^{L}_{iP} \sim \mbox{Multinomial}\left(\left(\pi^{L}_{1},\ldots,\pi^{L}_{P}\right),n^L_i\right).$$ Similarly, the data observed for " $R$emainers" are modelled as $$y^R_{i1},\ldots,y^R_{iP} \sim \mbox{Multinomial}\left(\left(\pi^R_{1},\ldots,\pi^R_P\right),n^R_i\right).$$
In other words, I'm assuming that within the two groups of voters, there is a vector of underlying probabilities associated with each party  ($\pi^L_p$ and $\pi^R_p$) that are pooled across the polls. $n^L_i$ and $n^R_i$ are the sample sizes of each poll for $L$ and $R$.

I used a fairly standard formulation and modelled $$\pi^L_p=\frac{\phi^L_p}{\sum_{p=1}^P \phi^L_p} \qquad \mbox{and} \qquad \pi^R_p=\frac{\phi^R_p}{\sum_{p=1}^P \phi^R_p}$$ and then $$\log \phi^j_p = \alpha_p + \beta_p j$$ with $j=0,1$ to indicate $L$ and $R$, respectively. Again, using fairly standard modelling, I fix $\alpha_1=\beta_1=0$ to ensure identifiability and then model $\alpha_2,\ldots,\alpha_P \sim \mbox{Normal}(0,\sigma_\alpha)$ and $\beta_2,\ldots,\beta_P \sim \mbox{Normal}(0,\sigma_\beta)$.

This essentially fixes the "Tory effect" to 0 (if only I could really do that!...) and then models the effect of the other parties with respect to the baseline. Negative values for $\alpha_p$ indicate that party $p\neq 1$ is less likely to grab votes among leavers than the Tories; similarly positive values for $\beta_p$ mean that party $p \neq 1$ is more popular than the Tories among remainers. In particular, I have used some informative priors by defining the standard deviations $\sigma_\alpha=\sigma_\beta=\log(1.5)$, to mean that it is unlikely to observe massive deviations (remember that $\alpha_p$ and $\beta_p$ are defined on the log scale).

I then use the estimated party- and EU result-specific probabilities to compute a "relative risk" with respect to the observed overall vote at the 2015 election $$\rho^j_p = \frac{\pi^j_p}{\pi^{15}_p},$$ which essentially estimates how much better (or worse) the parties are doing in comparison to the last election, among leavers and remainers. The reason I want these relative risks is because I can then distribute the information from the current polls and the EU referendum to each constituency $c=1,\ldots,C$ by estimating the predicted share of votes at the next election as the mixture $$\pi^{17}_{cp} = (1-\gamma_c)\pi^{15}_p\rho^L_p + \gamma_c \pi^{15}_p\rho^R_p,$$ where $\gamma_c$ is the observed proportion of remain voters in constituency $c$.

Finally, I can simulate the next election by ensuring that in each constituency the $\pi^{17}_{cp}$ sum to 1. I do this by drawing the vote shares as $\hat{\pi}^{17}_{cp} \sim \mbox{Dirichlet}(\pi^{17}_1,\ldots,\pi^{17}_P)$.

In the end, for each constituency I have a distribution of election results, which I can use to determine the average outcome, as well as various measures of uncertainty. So in a nutshell, this model is all about i) re-proportioning the 2015 and 2017 votes based on the polls; and ii) propagating uncertainty in the various inputs.

I'll update this model as more polls become available $-$ one extra issue then will be about discounting older polls (something like what Roberto did here and here, but I think I'll keep things easy for this). For now, I've run my model for the 5 polls I mentioned earlier and this is the (rather depressing) result.
From the current data and the modelling assumption, this looks like the Tories are indeed on course for a landslide victory $-$ my results are also kind of in line with other predictions (eg here). The model here may be flattering to the Lib Dems $-$ the polls seem to indicate almost unanimously that they will be doing very well in areas of a strong Remain persuasion, which means that the model predicts they will gain many seats, particularly where the 2015 election was won with a little margin (and often they leapfrog Labour to the first place).

The following table shows the predicted "swings" $-$ who's stealing votes from whom:

Conservative Green Labour Lib Dem PCY SNP
Conservative                 325     0      0       5   0   0
Green                          0     1      0       0   0   0
Labour                        64     0    160       6   1   1
Liberal Democrat               0     0      0       9   0   0
Plaid Cymru                    0     0      0       0   3   0
Scottish National Party        1     0      0       5   0  50
UKIP                           1     0      0       0   0   0

Again, at the moment, bad day at the office for Labour who fails to win a single new seat, while losing over 60 to the Tories, 6 to the Lib Dems, 1 to Plaid Cymru in Wales and 1 to the SNP (which would mean Labour completely erased from Scotland). UKIP is also predicted to lose their only seat $-$ but again, this seems a likely outcome.

## Thursday, 20 April 2017

### Post-doc

If you fancy becoming like the crazy, purple minion, we have a Research Associated position at the UCL Institute for Global Health (with whom I've been heavily involved in the past year or so, while organising our new MSc Health Economics & Decision Science).

All the relevant details and the link to the application form are available here. The deadline is 13 May.

## Tuesday, 18 April 2017

### Hope & Faith

In a remarkable and unpredictable (may be?) turn of events, the UK Prime Minister has today sort-of-called a general election for this coming June $-$ sort-of, if you don't follow UK politics, because technically a law prevents the PM to call snap elections, unless 66% of Parliament agrees to this and so there will need to be a discussion and then it will be Parliament to actually call the election...

Anyway, current polls give the ruling Conservative party way ahead with 43%. The Labour Party (who are supposed to be the main opposition, but have been in a state of chaos for quite a while now) have 25% $-$ this is compared to the results at the 2015 general election where the Tories got 37% and Labour 30%.

As if the situation weren't bleak enough for people of the left-ish persuasion (with Brexit and all), this doesn't seem to be very good news and many commentators (and perhaps even the PM herself) are predicting a very good result for the Tory, may be even a landslide.

But because of the electoral system, may be the last word has not been said: the fact is that the UK Parliament is elected on a first-pass-the-post basis and so Labour may not actually lose too many seats (as some commentators have suggested).

I went back to the official general election data and looked at the proportion of seats won by the main parties, by the size of the majority $-$ the output is in the graph below.

The story is that while there are some very marginal seats (where Labour won a tiny majority just two years ago), a 5% decrease in the vote may not be as bad as it looks $-$ although the disaffection with Labour is not necessarily uniformly distributed across the country.

More interestingly, I've also linked the data from the 2015 General Election with last year EU referendum $-$ one of the main arguments following the Brexit outcome was that the Remain camp were not able to win in Labour strongholds, particularly in the North-East of England.

The 5 constituencies in which Labour holds a majority of less than 1% are distributed as follows, in terms of the proportion of the Remain vote:

• Brentford and Isleworth: 0.5099906
• City of Chester: 0.4929687
• Ealing Central and Acton: 0.6031031
• Wirral West: 0.5166292
• Ynys Dulas: 0.4902312
(I know I have way too many significant figures here, but I thought it'd be interesting to actually see these values). So, apart from Ealing & Acton (strong Remain), there may be a good chance that the other four constituencies be made by people who are fed up with Labour and could be voting for some other party.

When you actually consider all the constituencies with a Labour majority of less than 10%, then the situation is like in the following graph.

Indeed, many of these are strong Leavers, which may actually be a problem for Labour. A few, on the other hand, may not be affected so much (because the "Remain" effect may counterbalance the apathy for Labour) $-$ although it may well go the other way and parties on a clear Pro-EU platform (eg the Lib Dems) may gain massively.

At the other hand of the spectrum, the corresponding graph for Conservative-hold areas with small majorities is like in the following graph.
For the Tories, the problem may be in Remain areas where they have a small majority $-$ there aren't a massive number of them, but I guess about 40% of these may be fought very hard (because they are relatively close or above 50% in terms of the proportion of Remain)?

Anyway $-$ I'm not sure whether Hope & Faith should be all smiley if you're a Pro-EU migrant. But then again, there is still some hope & faith...