USING L2 ESTIMATION FOR L1 ESTIMATORS: AN APPLICATION TO THE SINGLE‐INDEX MODEL
提出用计算高效的L2范数结合自助法来近似非高斯数据下L1回归参数的标准误，并用日度证券收益数据验证，发现使用普通最小二乘标准误可能改变决策。
ABSTRACT The bootstrap method is used to compute the standard error of regression parameters when the data are non‐Gaussian distributed. Simulation results with L 1 and L 2 norms for various degrees of “non‐Gaussianess” are provided. The computationally efficient L 2 norm, based on the bootstrap method, provides a good approximation to the L 1 norm. The methodology is illustrated with daily security return data. The results show that decisions can be reversed when the ordinary least‐squares estimate of standard errors is used with non‐Gaussian data.