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Biases in Long-Horizon Predictive Regressions

Jacob Boudoukh, Ronen Israel, Matthew P. Richardson

NBER Working Paper No. 27410
Issued in June 2020
NBER Program(s):Asset Pricing

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.

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Document Object Identifier (DOI): 10.3386/w27410

 
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