The robustness of forecast combination in unstable environments: a Monte Carlo study of advanced algorithms

In this paper, we study the behavior and effectiveness of several recently developed forecast combination algorithms in simulated unstable environments, where the performances of individual forecasters are cross-sectionally heterogeneous and dynamically evolving. Our results clearly reveal how different algorithms respond to structural instabilities of different origin, frequency, and magnitude. Accordingly, we propose an improved forecast combination procedure and demonstrate its effectiveness in a real-time forecast combination exercise using the U.S. Survey of Professional Forecasters.