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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 337-364 |
| Number of pages | 28 |
| Journal | Accounting Review |
| Volume | 94 |
| Issue number | 6 |
| DOIs | |
| State | Published - 2019 |
Funding
We thank workshop participants at The University of Arizona, University of California, San Diego, The University of Chicago, Florida Atlantic University, Indiana University, University of Maryland, University of Pennsylvania, The University of Texas at Austin, Texas A&M University, the 2012 Pennsylvania State University Conference, University of Rochester, Tilburg University, the 2012 University of Toronto Rotman Conference, and 2012 University of South Florida Accounting Conference for helpful comments. Special thanks go to Ray Ball, David C. Burgstahler (editor), Christian Leuz, Jeff McMullin, Christopher Parmeter, Dan Taylor, Tjomme Rusticus, Jerry Zimmerman, and two anonymous referees. Financial support provided by the University of Miami and the University of Rochester Simon Business School is gratefully acknowledged.
Keywords
- Extreme observations
- Inference problems
- Influential observations
- Outliers
- Regression diagnostics
- Robust regression
- Truncation
- Winsorization
ASJC Scopus subject areas
- Accounting
- Finance
- Economics and Econometrics