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An Asymptotic Theory for Estimating Beta-Pricing Models Using Cross-Sectional Regression

Ravi Jagannathan and Zhenyu Wang
The Journal of Finance
Vol. 53, No. 4, Papers and Proceedings of the Fifty-Eighth Annual Meeting of the American Finance Association, Chicago, Illinois, January 3-5, 1998 (Aug., 1998), pp. 1285-1309
Published by: Wiley for the American Finance Association
Stable URL: http://www.jstor.org/stable/117402
Page Count: 25
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An Asymptotic Theory for Estimating Beta-Pricing Models Using Cross-Sectional Regression
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Abstract

Without the assumption of conditional homoskedasticity, a general asymptotic distribution theory for the two-stage cross-sectional regression method shows that the standard errors produced by the Fama-MacBeth procedure do not necessarily overstate the precision of the risk premium estimates. When factors are misspecified, estimators for risk premiums can be biased, and the t-value of a premium may converge to infinity in probability even when the true premium is zero. However, when a beta-pricing model is misspecified, the t-values for firm characteristics generally converge to infinity in probability, which supports the use of firm characteristics in cross-sectional regressions for detecting model misspecification.

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