From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles

TL;DR

This paper investigates the use of reservoir ensembles, specifically Multi-Frequency Echo State Networks, for macroeconomic forecasting, demonstrating their superior performance through theoretical analysis and empirical validation.

Contribution

It introduces ensemble methods for MFESNs, extending online learning guarantees and showing improved forecasting accuracy over single models.

Findings

Ensemble MFESNs outperform individual models in macroeconomic forecasting.

Theoretical guarantees are extended for dependent data settings.

Empirical results confirm significant predictive improvements.

Abstract

Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks (MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series forecasting results (Ballarin et al., 2024a). The Hedge and Follow-the-Leader schemes are discussed, and their online learning guarantees are extended to settings with dependent data. In empirical applications, the proposed Ensemble Echo State Networks demonstrate significantly improved predictive performance relative to individual MFESN models.