Conditional Rotation Between Forecasting Models

Allan Timmermann and Yinchu Zhu

No 15917, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C18 C32 C53 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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