Working Paper: CEPR ID: DP15917
Authors: Yinchu Zhu; Allan Timmermann
Abstract: We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the strength of the monitoring instruments. Finite-sample bounds on forecasting performance that account for estimation error are used to compute the expected loss of the competing forecasts as well as for the dynamic rotation strategy. Finally, using Monte Carlo simulations and empirical applications to forecasting inflation and stock returns, we demonstrate the potential gains from using conditioning information to rotate between forecasts
Keywords: forecasting performance; real-time monitoring; finite sample bounds
JEL Codes: C53; C32; C18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| monitoring instruments (C36) | improved predictive accuracy (C52) |
| dynamic rotation strategies (C69) | outperform traditional forecasting methods (C53) |
| monitoring instruments (C36) | time variation in model performance (C22) |
| strength of the monitoring instrument (C26) | ability to identify the best forecast (C53) |
| weak predictor + strong monitoring instrument (C52) | gains in predictive accuracy (C52) |
| use of monitoring instruments (C36) | improved forecasting outcomes (C53) |