Germany's ruling centre-right party has been overtaken in the polls by the Social Democrats, reviving the chance of a left-controlled 'traffic light' or 'Germany coalition with the Greens or classically liberal Free Democrats. 1/2 economist.com/graphic-detail…
2/2 There is still a lot of uncertainty, though — in fact, even more than there was last week (a product of changing correlations in the data). At this point in the campaign (a month out) history advises a pretty strong hedge against the polls using the results of the last elec.
CDU/CSU + SPD is viable, but there are some political obstacles and the probability of the parties winning a combined majority is under 50%. Red-Black-Green (sometimes 'Kenya') is not usually entertained federally, though it has worked in some länders.
This all being said, our model doesn't produce probabilities for coalition outcomes, only for seat totals for the constituent parties — because coalition negotiations are political and unpredictable, and our journalistic goal here is to explain how the polls translate to seats
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Aaand there it is folks! Early vote-by-mail results in California's recall election are way ahead of tied-race benchmarks and signal an imminent victory for Governor Gavin Newsom, possibly by high double digits. I'm going to bed early tonight livevoterturnout.com/sandiegoca/Liv…
At this point, given the CA recall polling and VBM data, all we're looking for in the early vote tonight is confirmation of projected partisan distributions. Returned ballots are sufficiently Dem that we just need to assess loyalty + turnout. LA county VBM +25 Newsom would do it.
The very fuzzy math here is that early mail ballots statewide (+40) were about 10 points more pro-Biden than the final results (+30) in California in 2020, and LA County (+45) was about 15 pts more Biden than the whole state. So +55 early LA = +30 CA-wide in the end. +25 = tied.
You don't call an election just based on one county, of course. But Los Angeles cast 25% of CA's votes in 2020 so it's a good guide.
Other VBM benchmarks could be:
Recall +11 in Orange County (+19D 2020 VBM = 30 overall :: -11D VBM = 0)
Keep +3 in SD (+33D VBM = 30 :: 3D = 0)
Regardless of what happens in the election, the bottom line is that modern statistical methods can provide better analyses of polling data than the stuff available ten years ago, especially (but not only) because of recent problems in the polling industry. Time to do better.
Here's my final update to this model of California recall polls. I'm calculating an aggregate that adjusts polls based on whether they use partisanship in their weighting schemes, and draws different trends for adjusted v unadjusted data. Newsom +18 +/- 10 gist.github.com/elliottmorris/…
The point of this project was to illustrate the different methods we can use to aggregate polling data — esp in how to improve existing popular averages that don't peer under the hood of how pollsters are processing their data, an increasingly important aspect of public polling.
So, note two things:
1. Popular averages magnify unlikely trends in public opinion by being whipsawed by data that is subject to higher standard errors than a decade ago (when the models were first made). Weighting by party flattens trends by decreasing nonresponse
The thing about Rasmussen (and to a lesser extent, some other right-leaning pollsters and aggregators) is that the conservative information ecosystem has provided a top-dollar audience for confirmation bias, and there's not much AAPOR or good political journos can do about it.
Two of the enduring patterns of polling over the past 20 years are (a) that pre-election polls tend to underestimate the dominant party in a given state, especially lopsided ones (like California) & (b) that polls underestimate the status quo option on referenda and recalls. So..
And see the graph from @MHeidemanns for potus polling errors
@MHeidemanns Also, given that the crosstabs for Newsom v Cox pretty much match perfectly with the 2018 results in the state, I'm inclined to believe the media was hoodwinked by partisan non-response and outliers (esp from lower-quality polls) over the last month.