  We propose local prediction pools as a method for combining the predictive
distributions of a set of experts conditional on a set of variables believed to
be related to the predictive accuracy of the experts. This is done in a two
step process where we first estimate the conditional predictive accuracy of
each expert given a vector of covariates$\unicode{x2014}$or pooling
variables$\unicode{x2014}$and then combine the predictive distributions of the
experts conditional on this local predictive accuracy. To estimate the local
predictive accuracy of each expert, we introduce the simple, fast, and
interpretable caliper method. Expert pooling weights from the local prediction
pool approaches the equal weight solution whenever there is little data on
local predictive performance, making the pools robust and adaptive. We also
propose a local version of the widely used optimal prediction pools. Local
prediction pools are shown to outperform the widely used optimal linear pools
in a macroeconomic forecasting evaluation, and in predicting daily bike usage
for a bike rental company.
