Biases in Long-Horizon Predictive Regressions
40 Pages Posted: 4 Jun 2020
Date Written: May 28, 2020
Analogous to Stambaugh (1999), this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a plug-in adjustment for these estimators. A number of surprising results emerge, including (i) a higher bias for overlapping than nonoverlapping regressions despite the greater number of observations, and (ii) particularly higher bias for an alternative long-horizon predictive regression commonly advocated for in the literature. For large J, the bias is linear in (J/T) with a slope that depends on the predictive variable’s persistence. The bias adjustment substantially reduces the existing magnitude of long-horizon estimates of predictability.
Keywords: Bias in OLS regressions, Predictability, Long Horizon Regressions
JEL Classification: G12
Suggested Citation: Suggested Citation