Flexible global forecast combinations

TL;DR

This paper introduces a flexible framework for global forecast combination methods that leverage task-relatedness, demonstrating improved accuracy over traditional local methods in economic forecasting.

Contribution

It develops global versions of forecast combination methods and provides empirical evidence of their superiority in economic forecasting tasks.

Findings

Global forecast combinations outperform local methods in accuracy.

The framework is flexible to different levels of task-relatedness.

Empirical results on Eurozone data support the effectiveness of global methods.

Abstract

Forecast combination -- the aggregation of individual forecasts from multiple experts or models -- is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations.