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Improving equity premium forecasts by incorporating structural break uncertainty

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  • Jing Tian
  • Qing Zhou

Abstract

This article compares five alternative methods for directly dealing with structural break uncertainty in forecasting the U.S. equity premium using 30 widely used bivariate and multivariate predictive regressions. We find that two recently developed methods – Robust Optimal Weights on Observations and Forecast Combination across Estimation Windows – outperform the conventional rolling window and postbreak estimation methods. This result indicates that very early historical information is beneficial for U.S. equity premium forecasting but should be discounted to incorporate structural break uncertainty.

Suggested Citation

  • Jing Tian & Qing Zhou, 2018. "Improving equity premium forecasts by incorporating structural break uncertainty," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 619-656, November.
    • Handle: RePEc:bla:acctfi:v:58:y:2018:i:s1:p:619-656
    DOI: 10.1111/acfi.12240
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