#27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns

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 (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/ (https://bababrinkman.com/) ! Links from the show: Andrew's website: http://www.stat.columbia.edu/~gelman/ (http://www.stat.columbia.edu/~gelman/) Andrew's blog: https://statmodeling.stat.columbia.edu/ (https://statmodeling.stat.columbia.edu/) Andrew on Twitter: https://twitter.com/statmodeling (https://twitter.com/statmodeling) Merlin's website: https://merlinheidemanns.github.io/website/ (https://merlinheidemanns.github.io/website/) Merlin on Twitter: https://twitter.com/MHeidemanns (https://twitter.com/MHeidemanns) The Economist POTUS forecast: https://projects.economist.com/us-2020-forecast/president (https://projects.economist.com/us-2020-forecast/president) How The Economist presidential forecast works: https://projects.economist.com/us-2020-forecast/president/how-this-works (https://projects.economist.com/us-2020-forecast/president/how-this-works) GitHub repo of the Economist model: https://github.com/TheEconomist/us-potus-model (https://github.com/TheEconomist/us-potus-model) Information, incentives, and goals in election forecasts (Gelman, Hullman and Wlezien): http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf (http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf) How to think about extremely unlikely events: https://bit.ly/3ejZYyZ (https://bit.ly/3ejZYyZ) Postal voting could put America’s Democrats at a disadvantage: https://econ.st/3mCxR0P (https://econ.st/3mCxR0P)

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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!