Biases in Long-Horizon Predictive Regressions

Jacob Boudoukh, Ronen Israel and Matthew P. Richardson

No 27410, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C01 C1 C22 C53 C58 G12 G17 (search for similar items in EconPapers)
Date: 2020-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Published as Jacob Boudoukh & Ronen Israel & Matthew Richardson, 2021. "Biases in long-horizon predictive regressions," Journal of Financial Economics, .

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