An Approach to Statistical Inference in Cross-Sectional Models with Security Abnormal Returns As Dependent Variable
指出，在事件研究中用三步法将异常收益对公司特征做横截面回归时，若扰动项存在横截面相关或异方差，OLS推断会失效，并探讨了改进的统计推断方法。
Several recent event studies test hypotheses about the association between event-related abnormal security returns and firm characteristics using a three-step procedure.' First, forecast model parameters are estimated, usually parameters of a market model; second, prediction errors or residuals are computed over an event period; third, prediction errors are regressed cross-sectionally on firm characteristics hypothesized to influence the impact of the event on share values. Results of these regressions are used to draw inferences about the relation between abnormal returns and firm characteristics. Researchers who employ three-step procedures acknowledge that crosssectional (OLS) regressions lead to valid inferences if the disturbances are IID (normal) in cross-section; see, for example, Leftwich [1981, p. 23] and Lustgarten [1982, p. 138]. While sufficient assumptions to draw valid inferences from OLS regressions are clear, these assumptions are violated if there is cross-correlation and cross-sectional heteroscedasticity in the firm return processes from which the prediction errors are estimated. Assumptions about the processes generating these prediction