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Conditional Rotation Between Forecasting Models

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  • Timmermann, Allan
  • Zhu, Yinchu

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

Suggested Citation

  • Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:15917
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    Cited by:

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    3. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    4. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).

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    More about this item

    Keywords

    Forecasting performance; Real time monitoring; Finite sample bounds;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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