Robust model rankings of forecasting performance

摘要

This paper investigates robust model rankings in out-of-sample, short-horizon forecasting. We provide strong evidence that rolling window averaging consistently produces robust model rankings while improving the forecasting performance of both individual models and model averaging. The rolling window averaging outperforms the (ex post) optimal window forecasts in more than 50% of the times across all rolling windows.