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His approach is pretty sound, and straightforward if you listen to his podcast. Proper treatment of fat tails via student t distributions and correlated variabl
by greesil 3y ago
His approach is pretty sound, and straightforward if you listen to his podcast. Proper treatment of fat tails via student t distributions and correlated variables with good (subjective) judgement on polling quality.
I find that most people with negative views of Nate's approach are innumerate with regards to statistics. They don't criticize the model because they can't be bothered to understand it, or even basic probability, and certainly don't rank his performance vs other modelers which wouldn't require understanding the model.
- petulla 3y agoNo: https://statmodeling.stat.columbia.edu/2020/10/24/reverse-engineering-the-problematic-tail-behavior-of-the-fivethirtyeight-presidential-election-forecast/ https://statmodeling.stat.columbia.edu/2020/10/24/reverse-en...
- greesil 3y agoWhat are the consequences of this, in terms of prediction accuracy?
- greesil 3y agoso basically what TFA says is if you build a new model on Nate's model outputs and use that to compute conditional probabilites of various improbable scenarios you see weird shit. State level polling data according to Nate isn't all that great (read: high uncertainty, moves around when a new poll comes out) unless it's a swing state. But hey when you go looking into noise you are going to see.... weird shit. Did you Google any other good blog posts?