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.

翻译：我们提出局部预测池作为一种方法，用于在给定一组被认为与专家预测准确性相关的变量的条件下，组合一组专家的预测分布。这通过两步过程实现：首先，在给定协变量向量（或称为池化变量）的条件下，估计每位专家的条件预测准确性；然后，基于这种局部预测准确性，组合专家的预测分布。为估计每位专家的局部预测准确性，我们引入了简单、快速且可解释的卡尺方法。当关于局部预测性能的数据较少时，来自局部预测池的专家池化权重接近等权解，从而使池化结果具有鲁棒性和自适应性。我们还提出了广泛使用的最优预测池的局部版本。在宏观经济预测评估以及预测一家自行车租赁公司的日常自行车使用量中，局部预测池被证明优于广泛使用的最优线性池。