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Structural instability and predictability
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DOI：10.1016/j.intfin.2019.101145.png)
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Abstract
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We propose a structural break predictive regression model that accounts for predictor persistency, endogeneity, heteroscedasticity, and a structural break. Monte Carlo (MC) simulations indicate that this test performs satisfactorily compared to competitor estimators. We employ a popular U.S. data set (the period January 1927 to December 2016) that includes stock market returns and multiple predictors. We show, consistent with the MC results, evidence of a structural break. Our analysis reveals that a structural break-based predictive regression model fits the data reasonably well in predicting stock price returns. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Structural break
Predictability
Monte Carlo simulation
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