Date of Completion
MM-estimation Winsorization Truncation Robust estimator outliers
Field of Study
Doctor of Philosophy
Outliers can lead to incorrect inferences in least squares regression. In this study, I evaluate outlier treatment methods: MM-estimation as named in Yohai (1987), winsorization, and truncation, providing more evidence for outlier treatment in accounting research. Simulation results where I seed known “outliers” suggest that MM-estimation has the best performance. In particular, in the presence of the most influential type of outliers, theory-based MM-estimation provides inferences similar to the ordinary least squares (OLS) estimator, i.e. the best linear unbiased estimator, whereas winsorization and truncation fail to provide right inferences over 99 percent of the time. I also show that MM-estimation provides the most accurate predicted earnings and estimated discretionary accruals: with truncation creating more bias and inference problems than not treating the outliers, the means (medians) of the absolute earnings prediction errors and discretionary accruals using winsorization are higher than those using MM-estimation by more than 21% (48%) on average. In sum, MM-estimation provides more reliable results, which should be an important consideration for future accounting research employing datasets containing outliers.
Qu, Li, "An Evaluation of Outlier Treatment Methods in Accounting Research" (2013). Doctoral Dissertations. 159.