Biases in Long-Horizon Predictive Regressions / Jacob Boudoukh, Ronen Israel, Matthew P. Richardson.

Format:

Book

Author/Creator:

Boudoukh, Jacob.

Contributor:

National Bureau of Economic Research.

Israel, Ronen.

Richardson, Matthew P.

Series:

Working Paper Series (National Bureau of Economic Research) no. w27410.

NBER working paper series no. w27410

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2020.

Summary:

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.

Notes:

Print version record

June 2020.