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Combining multiple probability predictions using a simple logit model
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DOI：10.1016/j.ijforecast.2013.09.009.png)
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Abstract
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This paper begins by presenting a simple model of the way in which experts estimate probabilities. The model is then used to construct a likelihood-based aggregation formula for combining multiple probability forecasts. The resulting aggregator has a simple analytical form that depends on a single, easily-interpretable parameter. This makes it computationally simple, attractive for further development, and robust against overrating. Based on a large-scale dataset in which over 1300 experts tried to predict 69 geopolitical events, our aggregator is found to be superior to several widely-used aggregation algorithms. (C) 2013 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Combining forecasts
Error correction models
Expert forecasts
Logit-normal models
Multinomial events
Probability forecasting
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