Biases in Long-Horizon Predictive Regressions

40 Pages Posted: 4 Jun 2020

See all articles by Jacob Boudoukh

Jacob Boudoukh

Reichman University - Interdisciplinary Center (IDC) Herzliyah

Ronen Israel

AQR Capital Management, LLC

Matthew P. Richardson

Department of Finance, Leonard N. Stern School of Business, New York University

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Date Written: May 28, 2020

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.

Keywords: Bias in OLS regressions, Predictability, Long Horizon Regressions

JEL Classification: G12

Suggested Citation

Boudoukh, Jacob and Israel, Ronen and Richardson, Matthew P., Biases in Long-Horizon Predictive Regressions (May 28, 2020). NYU Stern School of Business, Available at SSRN: https://ssrn.com/abstract=3612600 or http://dx.doi.org/10.2139/ssrn.3612600

Jacob Boudoukh (Contact Author)

Reichman University - Interdisciplinary Center (IDC) Herzliyah ( email )

P.O. Box 167
Herzliya, 4610101
Israel

Ronen Israel

AQR Capital Management, LLC ( email )

Greenwich, CT
United States

Matthew P. Richardson

Department of Finance, Leonard N. Stern School of Business, New York University ( email )

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Suite 9-190
New York, NY 10012-1126
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