Outliers and Robust Inference in Archival Accounting Research

This study examines the nature of outliers in archival accounting research and evaluates the merits and limitations of robust estimators in identifying and downweighing their influence. Using simulated and actual data samples, we demonstrate how outliers can result from the data generating process, research design choices such as scaling, and model misspecification. Given the nonrandom nature of outliers, we show that inferences based on robust estimators can be biased when the relation of interest varies with variables correlated with the likelihood of outliers (e.g., firm size). We also find that a failure to account for nonlinear relations can induce biases in robust estimators that are more severe than with OLS. Overall, we advise researchers to acknowledge the nonrandom nature of outliers in their samples, to be cautious in implementing and interpreting robust estimation methods, and to evaluate and report the sensitivity of these methods to critical design choices. We also highlight the usefulness of combining data visualizations with robust estimation techniques to assess both the nature of outliers and their impact on inference