An asymptotic theory for estimating beta-pricing models using cross-sectional regression

被引：118
|
作者：
Jagannathan, R [1 ]
Wang, ZY
机构：
[1] Northwestern Univ, Kellogg Grad Sch Management, Evanston, IL 60208 USA
[2] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA
[3] Columbia Univ, Grad Sch Business, New York, NY 10027 USA
来源：
JOURNAL OF FINANCE | 1998年 / 53卷 / 04期
关键词：
D O I：
10.1111/0022-1082.00053
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
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.
引用
收藏
页码：1285 / 1309
页数：25
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