Danny Ebanks
If a Statistical Model Predicts That Common Events Should Occur Only Once in 10,000 Elections, Maybe it’s the Wrong Model

Authors: Danny Ebanks. Jonathan N. Katz. Gary King.
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Political scientists forecast elections, not primarily to satisfy public interest, but to validate statistical models used for estimating many quantities of scholarly interest. Although we have learned a great deal from these models, they can be embarrassingly overconfident: Events that should occur once in 10,000 elections occur almost every year, and even those which should occur once in a trillion-trillion elections are sometimes observed. We develop a novel generative statistical model of US district-level congressional elections, validate it with extensive out-of-sample tests, and use it to compute the first correctly calibrated probabilities of incumbent losses, one of the most important quantities for evaluating a democracy. We find that even when marginals vanish, incumbency advantage grows, and other dramatic changes occur, the risk of an out-party incumbent losing a midterm election contest has been high and essentially constant since the 1950s. We then develop a broader theory of American democracy consistent with the results from our generative model and discuss the broader implications of our generative modeling strategy.

Written by

Danny Ebanks

Hi, my name is Danny! I am a Postdoctoral Fellow for IQSS at Harvard after having recently earned my PhD at Caltech in Quantitative Social Sciences! With a research passion for political methodology and American politics, I strive to develop and implement statistical methods, to understand the latest in machine learning and AI, and innovate in these areas in ways small and large to better understand our political world. I am always eager to chat about research and statistics, so feel free to reach out. Outside of research, I'm lifelong runner who hails from New York.