 Incorrect Inferences When Using Residuals as Dependent VariablesWei Chen, Paul Hribar and Samuel Melessa Journal of Accounting Research, 2018, vol. 56, issue 3, 751-796 Abstract: We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two‐step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book‐tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:joares:v:56:y:2018:i:3:p:751-796 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Accounting Research is currently edited by Philip G. Berger, Luzi Hail, Christian Leuz, Haresh Sapra, Douglas J. Skinner, Rodrigo Verdi and Regina Wittenberg Moerman More articles in Journal of Accounting Research from Wiley Blackwell |
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