In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States?
But how do these statistical models work? How do you account for the different sources of uncertainty, be it polling errors, unexpected turnout or media events? How do you model covariation between States? How do you even communicate the model’s results and afterwards assess its performance? To talk about all this, I had the pleasure to talk to Andrew Gelman and Merlin Heidemanns.
Andrew was already on episode 20, to talk about his recent book with Jennifer Hill and Aki Vehtari, “Regression and Other Stories”. He’s a professor of statistics and political science at Columbia University and works on a lot of topics, including: why campaign polls are so variable while elections are so predictable, the statistical challenges of estimating small effects, and methods for surveys and experimental design.
Merlin is a PhD student in Political Science at Columbia University, and he specializes in political methodology. Prior to his PhD, he did a Bachelor's in Political Science at the Freie Universität Berlin.
I hope you’ll enjoy this episode where we dove into the Bayesian model they helped develop for The Economist, and talked more generally about how to forecast elections with statistical methods, and even about the incentives the forecasting industry has as a whole.
Thank you to my Patrons for making this episode possible! Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
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Thank you to my Patrons for making this episode possible!
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