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.
Original language | English |
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Pages (from-to) | 619-656 |
Number of pages | 38 |
Journal | Accounting and Finance |
Volume | 58 |
Issue number | S1 |
Early online date | 18 Oct 2016 |
DOIs | |
Publication status | Published - Nov 2018 |
Externally published | Yes |
Keywords
- Structural break uncertainty
- Out-of-sample forecast
- Equity premium