Improving equity premium forecasts by incorporating structural break uncertainty

Jing Tian, Qing Zhou

Research output: Contribution to journalArticle


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.
Original languageEnglish
Pages (from-to)619-656
Number of pages38
JournalAccounting and Finance
Issue numberS1
Early online date18 Oct 2016
Publication statusPublished - Nov 2018
Externally publishedYes


  • Structural break uncertainty
  • Out-of-sample forecast
  • Equity premium

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