Improving Tests of Abnormal Returns by Bootstrapping the Multivariate Regression Model with Event Parameters
针对事件研究中参数检验对残差非正态性不稳健的问题，提出并比较了自举替代方法，能控制交叉相关和时间序列依赖下的第一类错误率，并开发了参数方法无法实现的新检验。
Parametric dummy variable-based tests for event studies using multivariate regression are not robust to nonnormality of the residual, even for arbitrarily large sample sizes. Bootstrap alternatives are described, investigated, and compared for cases where there are nonnormalities, and cross-sectional and time-series dependencies. Independent bootstrapping of residual vectors from the multivariate regression model controls type I error rates in the presence of cross-sectional correlation, and surprisingly, even in the presence of time-series dependence structures. The proposed methods not only improve upon parametric methods, but also allow development of new and powerful event study tests for which there is no parametric counterpart.