On the Efficiency of Least Squares Regression with Security Abnormal Returns as the Dependent Variable
通过蒙特卡洛模拟，比较了事件聚类场景下多种估计量在横截面回归中的有限样本表现，发现若满足渐近有效条件，普通最小二乘估计量在有限样本中表现良好，且对于足够大的横截面，其他复杂估计量并无优势。
Monte Carlo procedures are used to compare the finite sample performance of several estimators that may be used in cross-sectional regressions with security abnormal returns as the dependent variable. Alternative models of event-induced increases in stock return variance are examined for the “event-clustering” scenario. Event clustering implies crosssectional correlation and heteroskedasticity in market model prediction errors, violating one of the fundamental ordinary least squares (OLS) assumptions (i.i.d. disturbances). Nonetheless, provided that the conditions for asymptotic validity derived by Greenwald (1983) are met, the OLS estimator is well specified in finite samples. Further, for sufficiently large cross sections there is no advantage to several other more complex estimators.