Influential Observations and Inference in Accounting Research
比较了缩尾、截断、Cook距离和稳健回归四种方法处理极端值的效果，发现稳健回归最优，建议会计研究采用或至少报告稳健回归的敏感性检验。
ABSTRACT Accounting studies often encounter observations with extreme values that can influence coefficient estimates and inferences. Two widely used approaches to address influential observations in accounting studies are winsorization and truncation. While expedient, both depend on researcher-selected cutoffs, applied on a variable-by-variable basis, which, unfortunately, can alter legitimate data points. We compare the efficacy of winsorization, truncation, influence diagnostics (Cook's Distance), and robust regression at identifying influential observations. Replication of three published accounting studies shows that the choice impacts estimates and inferences. Simulation evidence shows that winsorization and truncation are ineffective at identifying influential observations. While influence diagnostics and robust regression both outperform winsorization and truncation, overall, robust regression outperforms the other methods. Since robust regression is a theoretically appealing and easily implementable approach based on a model's residuals, we recommend that future accounting studies consider using robust regression, or at least report sensitivity tests using robust regression. JEL Classifications: C12; C13; C18; C51; C52; M41. Data Availability: Data are available from the public sources cited in the text.