Election Forecast Models Are Worth More Attention Than Polls
Picking and weighing different factors for modeling may be an art rather than a science, but its best practitioners get good results.
In the run-up to the U.S. presidential election, I wrote a script that automatically produced a daily news story showing the victory probabilities for the two main candidates according to FiveThirtyEight, the polling aggregation website. Every time the story ran, I would get a few angry emails calling FiveThirtyEight and its election forecast model ugly names and predicting that it would fail as it did in 2016.
I was agnostic about the election’s outcome (and, as a non-American, I didn’t think either of the candidates was worthy of running the world’s most powerful nation). Instead of arguing with the Donald Trump supporters who wrote the emails, I always replied that the forecasts were a historical document. The development of successful multi-factor prediction models is the next step away from the obsolete reliance on public polling that my Bloomberg Opinion colleague Cathy O’Neill persuasively decried right after the election.