Local Prediction Pools

TL;DR

This paper introduces local prediction pools that adaptively combine expert forecasts based on covariate-dependent local predictive accuracy, improving forecasting performance in macroeconomics and bike usage prediction.

Contribution

The paper presents a novel local prediction pooling method with a simple caliper approach for estimating local accuracy, enhancing robustness and adaptivity over traditional pooling methods.

Findings

Outperforms traditional optimal linear pools in macroeconomic forecasts.

Improves daily bike usage prediction accuracy.

Provides a fast, interpretable method for local expert weighting.

Abstract

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.