us-potus-model

of elections as Nate Silver said it was?" He would have learned that Australia has had compulsory voting for decades and it is as conservative as ever. Now that "gut checks" have disentangled themselves from New York graphics departments - can you believe its the graphics department, masquerading as a "data science" department, that runs election forecast stories?! - maybe forecasts will be better everywhere. If I were doing this, I'd frame it as a Kaggle competition built on PyroPL. And I'd require top performance in democratic election forecasting everywhere, not just the US. The Economist is right, it's all about fundamentals, but which fundamentals, and can you prevent the choice of fundamentals from reflecting the publisher's political aspirations? Personally I'm skeptical that the Economist's choice of GDP growth makes any sense, and net approval rating sounds like a poll, so... there must be something better.

ediction. I haven't been able to find decent write-ups of this particular part of model forecasting, but from what I've been able to piece together : I believe they quantify the uncertainty of their presidential predictions by building logistic regressions of the output based on highly-correlated predictor data (i.e demographic information, GDP) and then simulating thousands of possible scenarios where demographics trend differently. I don't exactly know how these simulations are set up (I would love to know though). And presumably this is updated in a bayesian matter everytime they get more input data, so the prior probabilities will have an impact on the posterior. If anyone has a different or more detailed idea of how this is done, let me know. In fact, here's the actual code for Elliot Morris' forecasting model: https://github.com/TheEconomist/us-potus-model if anyone can figure it out better then me. [1] https://projects.fivethirtyeight.com/pollster-ratings/ [2] https://www.vox.com/21538156/biden-polls-lead-election-trump...