The Economist model never showed Trump as a clear favorite. In 2020, it said he has had less than 10% chance of winning the electoral college. In October 2020 it said that his chances were particularly bad; 4% chance of winning the electoral college, and <1% chance of winning the popular vote.
I am interested in the fundamentals of this model and why it was sure at that point of what qas happening. Is it the early voting, polls, a combination, or something else?
In comparison to the 538 model, the economist model uses less "fat tails".
538 uses a t-distribution to account for "black swan" events: things that, although they are unlikely, would have a big impact on the polls. This means that 538 assigns a small probability to some very unlikely outcomes (Trump wins California, or Biden wins Utah) The Economist model doesn't. It uses a normal distribution which has thinner tails, and so treats these kind of results as essentially impossible. Moreover the 538 model uses data back to 1936 to try to estimate how much polls can change between now and the Election. The Economist uses only more recent data.
The effect of these modelling assumptions is to make extreme events more likely. For Trump to win now would require either a very significant failure in polling or a large swing in several states. The economist model essentially says "A swing like that has never happened, so I'll assume it won't" This results in about 95% chance for Biden. The 538 model says "Very large swings have occurred, if you look back to (for example) Truman v. Dewey. And sometimes something might happen that will completely change the course of the election (War was declared or a major scandal) and comes up with about 88% for Biden.
The change from a few weeks ago to now is that Trump is running out of time to make a big change. Both models were set to wait till after the debate, to see if it could make a change. It didn't (much). The assumption is that the gap will narrow as you get towards polling day. But this narrowing hasn't happened. This leads to greater confidence in a Biden win.
The difference is 538's "fat tails".
The answer(s) could come in at almost any step in their forecasting process, probably several. By breaking down the inputs, the process, and the outputs, we can at least get a sense of the possible answers to the question.
Inputs
These models primarily use polls as input data. They may include other sources like prediction markets. The Economist model may use different sources or put more trust in some polls than others, compared to e.g. 538. There is also historical data, which may be relied on differently by different models.
Process
The Economist model simulates votes randomly to see what might happen. To do that, it needs to make a lot of assumptions about the process, including the distributions of votes for each state, how the states are correlated, and what the sources of error in their forecasts could be and how much impact they could have.
You have to decide your sources of error around all these things. When I look at the Economist's forecast for Michigan, they predict vote totals that match the polls on average, and they predict a very high probability that the outcome will match the majority in the polls. That indicates they think their sources of uncertainty above are low (for some reason). This was a big concern after the 2016 election, and with 538's prominent place in the discussion, they've focused a lot on this issue.
But this is not enough. You also need to decide your sources of uncertainty around correlation between states. If Michigan is an upset, what is the chance that Wisconsin is also an upset? The Economist has a lot of details on their beliefs here, but it could be that they put less uncertainty on these factors than other models.
Outputs
The Economist model may be predicting something different than other models. Consider just the following two examples:
Between 1 and 2 are many sources of uncertainty: How does turnout among poll respondents compare to general population? How is that biased by party? Will there be any game-changing events such as a scandal unfolding in the next week? Etc. There are also sources of uncertainty between 2 and 3, such as whether the Electoral College votes actually match the state votes.
I want to add another point: incentives. It's not clear that the Economist optimizes their own goals by given the best possible forecast (whatever that means). People often evaluate forecasts in a winner-take-all manner: If you predicted Biden at 60% and he loses, you're heavily criticized for being "wrong". And if he wins in a landslide, you might still be considered "wrong". If you predict 90%, then at least if it does end up being a landslide for Biden then people will praise you. Another more nefarious possibility is that a forecaster biases their prediction in hopes of swaying the actual election outcome.
I don't actually think these are happening here, but it is something to be concerned about in election forecasting.
