Fama-MacBeth Two-Pass Regressions: Improving Risk Premia Estimates

15 Pages Posted: 10 Apr 2015 Last revised: 22 Aug 2015

See all articles by Jushan Bai

Jushan Bai

Columbia University

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Date Written: June 2015

Abstract

In this paper, we provide the asymptotic theory for the widely used Fama and MacBeth (1973) two-pass regression in the usual case of a large number of assets. We find that the convergence of the OLS two-pass estimator depends critically on the time series sample size in addition to the number of cross-sections. To accommodate typical relatively small time series length, we propose new OLS and GLS estimators that improve the small sample performances significantly.

Keywords: Fama and MacBeth; two-pass regression; cross section; risk premia

JEL Classification: G11, G14

Suggested Citation

Bai, Jushan and Zhou, Guofu, Fama-MacBeth Two-Pass Regressions: Improving Risk Premia Estimates (June 2015). Available at SSRN: https://ssrn.com/abstract=2591754 or http://dx.doi.org/10.2139/ssrn.2591754

Jushan Bai

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Guofu Zhou (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

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