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Improving equity premium forecasts by incorporating structural break uncertainty
Author(s) -
Tian Jing,
Zhou Qing
Publication year - 2018
Publication title -
accounting and finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.645
H-Index - 49
eISSN - 1467-629X
pISSN - 0810-5391
DOI - 10.1111/acfi.12240
Subject(s) - econometrics , bivariate analysis , equity (law) , structural break , economics , equity premium puzzle , multivariate statistics , computer science , risk premium , machine learning , political science , law
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.