Modeling Conditional Distribution Discontinuities
54 Pages Posted: 14 Feb 2018 Last revised: 1 Aug 2018
Date Written: July 30, 2018
We develop new tests of distribution discontinuity conditional on multiple explanatory variables, which can be used to analyze meet-or-beat behavior around benchmarks. These tests combine Burgstahler and Dichev’s (1997) intuition on benchmark-driven earnings management with a flexible statistical model that addresses important limitations of the existing distribution discontinuity tests. Our conditional discontinuity method offers large improvements in test performance relative to both histogram-based tests of the existence of distribution discontinuity and logit-based tests of the determinants of distribution discontinuity, and it changes some of the major findings in the earnings discontinuity literature. Our method is flexible, robust, and easy to implement in standard statistical software. Future research in many fields could benefit by adopting our distribution discontinuity tests.
Keywords: standardized difference test; zero benchmark; smooth distribution; nonlinear interpolation; conditional distribution
JEL Classification: M41; C20; C25
Suggested Citation: Suggested Citation