Persistence in forecasting performance and conditional combination strategies

This paper considers measures of persistence in the (relative) forecasting performance of linear and nonlinear time-series models applied to a large cross-section of economic variables in the G7 countries. We find strong evidence of persistence among top and bottom forecasting models and relate this to the possibility of improving performance through forecast combinations. We propose a new four-stage conditional model combination method that first sorts models into clusters based on their past performance, then pools forecasts within each cluster, followed by estimation of the optimal forecast combination weights for these clusters and shrinkage towards equal weights. These methods are shown to work well empirically in out-of-sample forecasting experiments.