Probability aggregation in time-series: Dynamic hierarchical modeling of sparse expert beliefs

TL;DR

This paper introduces a hierarchical dynamic model for aggregating sparse, time-updated expert probability beliefs, improving calibration and sharpness in real-world political forecasting.

Contribution

It develops a novel hierarchical model that accounts for expert expertise and updates over time, addressing the challenge of sparse, irregular data in probability aggregation.

Findings

Model produces well-calibrated aggregate probabilities.

Demonstrates effectiveness on real-world political forecast data.

Handles sparse, infrequent updates in expert beliefs.

Abstract

Most subjective probability aggregation procedures use a single probability judgment from each expert, even though it is common for experts studying real problems to update their probability estimates over time. This paper advances into unexplored areas of probability aggregation by considering a dynamic context in which experts can update their beliefs at random intervals. The updates occur very infrequently, resulting in a sparse data set that cannot be modeled by standard time-series procedures. In response to the lack of appropriate methodology, this paper presents a hierarchical model that takes into account the expert's level of self-reported expertise and produces aggregate probabilities that are sharp and well calibrated both in- and out-of-sample. The model is demonstrated on a real-world data set that includes over 2300 experts making multiple probability forecasts over two years on different subsets of 166 international political events.