Models, Markets, and the Forecasting of Elections
- URL: http://arxiv.org/abs/2102.04936v4
- Date: Tue, 25 May 2021 16:34:04 GMT
- Title: Models, Markets, and the Forecasting of Elections
- Authors: Rajiv Sethi, Julie Seager, Emily Cai, Daniel M. Benjamin, Fred
Morstatter
- Abstract summary: We find systematic differences in accuracy over time, with markets performing better several months before the election, and the model performing better as the election approached.
We propose a market design that incorporates model forecasts via a trading bot to generate synthetic predictions.
- Score: 3.8138805042090325
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We examine probabilistic forecasts for battleground states in the 2020 US
presidential election, using daily data from two sources over seven months: a
model published by The Economist, and prices from the PredictIt exchange. We
find systematic differences in accuracy over time, with markets performing
better several months before the election, and the model performing better as
the election approached. A simple average of the two forecasts performs better
than either one of them overall, even though no average can outperform both
component forecasts for any given state-date pair. This effect arises because
the model and the market make different kinds of errors in different states:
the model was confidently wrong in some cases, while the market was excessively
uncertain in others. We conclude that there is value in using hybrid
forecasting methods, and propose a market design that incorporates model
forecasts via a trading bot to generate synthetic predictions. We also propose
and conduct a profitability test that can be used as a novel criterion for the
evaluation of forecasting performance.
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