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Activity Number: 349
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315149 View Presentation
Title: Probability Aggregation in Time-Series: Dynamic Hierarchical Modeling of Sparse Expert Beliefs
Author(s): Ville Satoppa*
Companies: University of Pennsylvania
Keywords: Probability Aggregation ; Dynamic Linear Model ; Hierarchical Modeling ; Expert Forecast ; Subjective Probability ; Bias Estimation

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 dataset 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 dataset that includes over 2,300 experts making multiple probability forecasts over two years on different subsets of 166 international political events.

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