B1367
Title: Angular combining of forecasts of probability distributions
Authors: James Taylor - University of Oxford (United Kingdom) [presenting]
Abstract: When multiple forecasts are available for a probability distribution, forecast combining enables a pragmatic synthesis of the available information to extract the wisdom of the crowd. A linear opinion pool has been widely used, whereby the combining is applied to the probability predictions of the distributional forecasts. However, this has been criticised on theoretical grounds, prompting the combination to be applied to the quantile forecasts of the distributional forecasts. But it has been argued that this will deliver poorer empirical results. We seek an alternative to combining probabilities and combining quantiles. Looking at the distributional forecasts, combining the probability forecasts can be viewed as vertical combining, with quantile forecast combining seen as horizontal combining. Our alternative approach is to allow combining to take place on an angle between the extreme cases of vertical and horizontal combining. The angle can be optimised using a proper scoring rule. We provide empirical illustration using weekly distributional forecasts of COVID-19 mortality for locations in the United States.
