Structural instability and predictability

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.