An Asymptotic Theory for Estimating Beta‐Pricing Models Using Cross‐Sectional Regression
推导了无条件同方差假设下两阶段横截面回归的渐近分布理论，发现Fama-MacBeth标准误不一定高估风险溢价估计精度，且模型误设时特征变量的t值会趋于无穷，支持用特征变量检测模型误设。
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.