Robust Methods for Forecast Aggregation
Jaime Ramos, The Ohio State University
This study introduces a new forecast aggregation technique. Adding to the well- known difficulties and uncertainty involved in the forecasting process, the aggregation of hundreds or thousands of forecasters’ opinions and expert predictions on social, economical and political matters makes the process even more difficult. Simple quantitative data analytics, least squares regression, and maximum likelihood estimations are not sufficient to handle the dynamics of such data, which includes outliers, clusters of opinions, extreme values, and abrupt change of mind and predictions of forecasters influenced by news, recent events, collaboration or feedback from experts. The methods developed in this work are based on a particular minimum-distance technique called L2E, which is popular in nonparametric density estimation that makes the aggregation robust to clusters of opinions and dramatic changes. Variance-stabilizing transformations are introduced to attain homoscedasticity for L2E regression improving parameter estimation and overall aggregation. New normalization approaches are proposed to use when the aggregated values are unsuitable probabilities, such as values outside the [0,1] range and/or do not add to 1.