1 research outputs found
Combining Predictive Distributions
Predictive distributions need to be aggregated when probabilistic forecasts
are merged, or when expert opinions expressed in terms of probability
distributions are fused. We take a prediction space approach that applies to
discrete, mixed discrete-continuous and continuous predictive distributions
alike, and study combination formulas for cumulative distribution functions
from the perspectives of coherence, probabilistic and conditional calibration,
and dispersion. Both linear and non-linear aggregation methods are
investigated, including generalized, spread-adjusted and beta-transformed
linear pools. The effects and techniques are demonstrated theoretically, in
simulation examples, and in case studies on density forecasts for S&P 500
returns and daily maximum temperature at Seattle-Tacoma Airport