Merlin Heidemanns's guest appearances

Merlin Heidemanns

Learning Bayesian Statistics

David Spiegelhalter

Andrew Gelman

Michael Lee

Baba Brinkman

Michael Kennedy

Allen Downey

Sean Taylor

Michael Faber

Matthew Kay

Michael Betancourt

Eric Ma

Karin Knudson

Colin Carroll

Osvaldo Martin

Chris Fonnesbeck

OwlTail

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/ !Links from the show:Andrew's website: http://www.stat.columbia.edu/~gelman/Andrew's blog: https://statmodeling.stat.columbia.edu/Andrew on Twitter: https://twitter.com/statmodelingMerlin's website: https://merlinheidemanns.github.io/website/Merlin on Twitter: https://twitter.com/MHeidemannsThe Economist POTUS forecast: https://projects.economist.com/us-2020-forecast/presidentHow The Economist presidential forecast works: https://projects.economist.com/us-2020-forecast/president/how-this-worksGitHub repo of the Economist model: https://github.com/TheEconomist/us-potus-modelInformation, incentives, and goals in election forecasts (Gelman, Hullman &amp; Wlezien): http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdfHow to think about extremely unlikely events: https://bit.ly/3ejZYyZPostal voting could put America’s Democrats at a disadvantage: https://econ.st/3mCxR0PThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

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 <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer nofollow" target="_blank">https://www.patreon.com/learnbayesstats</a> 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 <a href="https://bababrinkman.com/" rel="noopener noreferrer nofollow" target="_blank">https://bababrinkman.com/</a> !Links from the show:<ul><li>Andrew's website: <a href="http://www.stat.columbia.edu/~gelman/" rel="noopener noreferrer nofollow" target="_blank">http://www.stat.columbia.edu/~gelman/</a></li><li>Andrew's blog: <a href="https://statmodeling.stat.columbia.edu/" rel="noopener noreferrer nofollow" target="_blank">https://statmodeling.stat.columbia.edu/</a></li><li>Andrew on Twitter: <a href="https://twitter.com/statmodeling" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/statmodeling</a></li><li>Merlin's website: <a href="https://merlinheidemanns.github.io/website/" rel="noopener noreferrer nofollow" target="_blank">https://merlinheidemanns.github.io/website/</a></li><li>Merlin on Twitter: <a href="https://twitter.com/MHeidemanns" rel="noopener noreferrer nofollow" target="_blank">https://twitter.com/MHeidemanns</a></li><li>The Economist POTUS forecast: <a href="https://projects.economist.com/us-2020-forecast/president" rel="noopener noreferrer nofollow" target="_blank">https://projects.economist.com/us-2020-forecast/president</a></li><li>How The Economist presidential forecast works: <a href="https://projects.economist.com/us-2020-forecast/president/how-this-works" rel="noopener noreferrer nofollow" target="_blank">https://projects.economist.com/us-2020-forecast/president/how-this-works</a></li><li>GitHub repo of the Economist model: <a href="https://github.com/TheEconomist/us-potus-model" rel="noopener noreferrer nofollow" target="_blank">https://github.com/TheEconomist/us-potus-model</a></li><li>Information, incentives, and goals in election forecasts (Gelman, Hullman &amp; Wlezien): <a href="http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf" rel="noopener noreferrer nofollow" target="_blank">http://www.stat.columbia.edu/~gelman/research/unpublished/forecast_incentives3.pdf</a></li><li>How to think about extremely unlikely events: <a href="https://bit.ly/3ejZYyZ" rel="noopener noreferrer nofollow" target="_blank">https://bit.ly/3ejZYyZ</a></li><li>Postal voting could put America’s Democrats at a disadvantage: <a href="https://econ.st/3mCxR0P" rel="noopener noreferrer nofollow" target="_blank">https://econ.st/3mCxR0P</a></li></ul>Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - <a href="https://podcorn.com/privacy" rel="nofollow" target="_blank">https://podcorn.com/privacy</a>

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