An early probabilistic forecast for the 2020 presidential election đ February 23, 2020
An early probabilistic forecast for the 2020 presidential election đ February 23, 2020
My models predict a close race fought across 11 swing states
Welcome! Iâm G. Elliott Morris, a data journalist at The Economist and blogger of polls, elections, and political science. Happy Sunday! This is my weekly email where I write about politics using data and share links to what Iâve been reading and writing.Â
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An early probabilistic forecast for the 2020 presidential election
My models predict a close race fought across 11 swing states
Iâm sure many of you astute news-watchers have noticed Donald Trumpâs improving approval ratings over the last couple weeks. Indeed, FiveThirtyEight gave Trump his highest ratings everâa net approval of -5âlast week. And though I think this increase is exaggerated for a few reasons, Trumpâs footing has probably increased slightly over the past few months. (Itâs worth noting that his net rating has since fallen back down to earth a bit.)
I took to Twitter last week to wonder aloud about what might happen in the election if Trumpâs net approval rating had indeed improved to -5 (45% approval and 50% disapproval. I put together a suite of statistical models to get a rough idea, but I ended up coming up with a relatively fully-fledged early forecast of the 2020 election.
The typical approach to coming up with a pre-election forecast is to train a predictive model on the relationship between the state of the economy, the presidentâs approval rating and his eventual vote share. Using data from Alan Abramowitzâthe political scientist behind the Time for Change modelâI can predict a range of outcomes for Trumpâs vote share under different approval ratings and GDP growth.
But instead of just rehashing the old methods (most political scientists train a simple linear regression model between these variables) I added a little twist. In addition to presidential approval ratings and GDP growth, I used a variable to account for the chance that the influence these variables have on votersâ choices has changed over time. We have reason to believe that the state of the economy, in particular, plays less a role now than in the mid-1900s. Then I fed the data into a cross-validated model that uses elastic net regularization to avoid over-fitting and provide for some basic implementation of model selection.
I shared the results in this tweet:
Yet there are a few weaknesses to this approach. Primarily, the high likelihood of a mismatch the national popular vote and the electoral college outcome poses an obvious barrier to learning much from predictions of the former. Some analysts try to remedy this issue by translating the predicted national popular vote to state-level predictions by adding the implied election-to-election swing in the national vote to the last state-level results. This method has a pretty good track record historically, including performing admirably in 2016.
Here, I added another twist on the historical methods. It seems to me that a state-level âfundamentalsâ forecastâthe name we give to models that predict the election with economic indicators and political measurements with a lot of lead timeâought to take into account election-to-election swing in state-level politics as well as the swing in the national vote share. There could be some underlying demographic factors driving, e.g., Arizona to the left and Florida to the right that weâre missing by just adding a national delta to the previous state-level election results. So I trained another set of models to predict short-term trends in state-level partisan lean. This way, we have a prediction for how much the Democratic share of the two-party vote in each state will vary from the national popular vote that takes trends in state-level politics into account. The predictions for 2020 look like so:
Then, all we have to do to get predictions for November is add together the predictions for the national popular vote and state-level lean in Democratic vote share. The results look like this:
In such a scenario, neither the Democratic candidate nor Donald Trump would have enough electoral votes from states in which theyâre favored to win the election:
The election would be close, in other words. But how close? How likely is a Democratic or Republican electoral college majority?
We can quantify the uncertainty in our forecasts by plugging in the predictions to a computer program that simulates the election a million times, in each âtrialâ varying the Democratsâ predicted state-level vote by between roughly -14 and +14 percentage points to account for the error in the national and state-level models. We get a distribution of possible electoral votes that looks like this, with the more likely outcomes having higher bars.
Tally up the predicted votes and you get a 57% chance of Democratic electoral college victory.
Some caveats
As always, there are caveats in these models. I mentioned already that the national popular vote model is weakened by its inability to predict state-level outcomes. Itâs also weakened by the chance that the election cycle does not unfold as history predicts. The model thinks that the president will both become more popular as the election season wears on and that he will outperform his approval rating in the battle box. While these are probably good binary predictions to make (both are likely to happen) itâs hard to know how much Donald Trump will stray from the projected path.
Thereâs also the caveat that the model is trained on the historical relationship between the presidentâs approval ratings in June, but weâve inputted Trumpâs approval rating in late February. So there is even more error in the model in projecting the presidentâs June approval rating with his rating in February.
And it could be that our prediction for state-level lean is off. We try to take this into account in the simulation, but sometimes, just using last yearâs partisan lean is better than trying to predict it forward.
The good news is that we can explore these scenarios. If we predict that Democrats will win 52.5% of the two-party vote come Novemberâroughly what theyâre polling at todayâthe model spits out these predictions, for example:
Similarly, if we simulate the range of possible elections where Trump is polling at -5 approval todayâa projected national vote share of 50%âthe expected probability of a Democratic electoral college majority falls to roughly 35%.
The point is that our predictions rely heavily upon both the starting data and the assumptions we feed into our models. Those assumptions can change over time, and we should revisit this subject in the summer when we have data that better fit the model.
Notes on interpretation
Iâve titled this newsletter âAn early probabilistic forecast for the 2020 presidential electionâ but Iâd rather not have everyone sharing these forecasts as if they are gospel. Instead, as I noted on Twitter, I believe we think about these predictions as Bayesians, using them as an informative prior on the electoral environment that we update as we (a) get close to the election and (b) get more polling data. In fact, thatâs how our final 2020 forecasting model will behave, combining these prior predictions with data from polls to come up with a posterior predictionâone that combines the information from all these sources.
Posts for subscribers
February 20: If 45% of adults approve of Trump, how many will vote for him?
Polls donât provide a clear answer.
Links and Other Stuff
After Nevada, is it Bernieâs nomination to lose?
I enjoyed Nate Silverâs post-Nevada take. Nate Cohnâs from last week is also worth reading.
What I'm Reading and Working On
Iâll have a piece out on swing voters in the 2020 election this week. Make sure youâre signed up to The Economistâs US politics newsletter to get it.
Thanks for reading!
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