DP15917 Conditional Rotation Between Forecasting Models

Author(s): Allan Timmermann, Yinchu Zhu
Publication Date: March 2021
Keyword(s): finite sample bounds, Forecasting Performance, real time monitoring
JEL(s): C18, C32, C53
Programme Areas: Financial Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15917

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