Biases in long-horizon predictive regressions

Jacob Boudoukh, Ronen Israel and Matthew Richardson

Journal of Financial Economics, 2022, vol. 145, issue 3, 937-969

Abstract: Analogous to Stambaugh (1999), this paper derives the small sample bias of estimators in J-horizon predictive regressions, providing a closed-form solution in terms of the sample size, horizon and persistence of the predictive variable. For large J, the bias is linear in JT with a slope that depends on the predictive variable's persistence. The paper offers a number of other useful results, including (i) important extensions to the original Stambaugh (1999) setting, (ii) closed-form bias formulas for popular alternative long-horizon estimators, (iii) out-of-sample analysis with and without bias adjustments, along with new interpretations of out-of-sample statistics, and (iv) a detailed investigation of the bias of the overlapping estimator's standard error based on the methods of Hansen and Hodrick (1980) and Newey and West (1987). The small sample bias adjustments substantially reduce the magnitude of long-horizon estimates of predictability.

Keywords: Predictive regression bias; Standard error bias; Out of sample R2 (search for similar items in EconPapers)
JEL-codes: G11 G12 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304405X21004013
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:145:y:2022:i:3:p:937-969

DOI: 10.1016/j.jfineco.2021.09.013

Access Statistics for this article

Journal of Financial Economics is currently edited by G. William Schwert

More articles in Journal of Financial Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().