Modelling the accruals process and assessing unexpected accruals*

4 Assessing unexpected accruals measures
The identification of earnings management with the unexpected accruals approach critically
depends on the ability of unexpected accruals to capture abnormal accruals. With unexpected
accruals being the empirical estimates of abnormal accruals, the bias in unexpected accruals (i.e.,
the difference between the two) can be determined by three components – measurement error in
variables, heterogeneity in parameters, and omitted variables. Measurement error in variables
arises when accurate measures of accounting variables cannot be obtained. Heterogeneity in
parameters arises when parameters of individual observations differ from the regression
parameters, which reflect the average effect of the parameters of the observations involved in the
regression. Finally, the omission of relevant variables can also cause a difference between
abnormal and unexpected accruals because they will be captured by the regression disturbance
term. Since measurement errors are unavoidable in accounting variables, I only comment on
them whenever appropriate. Much of my analysis will focus on the problems of heterogeneity
and omitted variables.
4.1 Encompassing model
To derive a more general model that encompasses the Jones model, I divide the OIB of short-
term accruals in table 3 by revenue of the same period and divide DA by the lagged non-current
operating assets. Re-arranging yields
WC DA REV REV REV
t t u, t1 u, t u, t1
j j 0, t 1, t j 2, t j 3, t j
where the parameters are represented as
u
C C ,
NCO NCO
u, t1 u, t
4, t j 5, t j t, j
t
a
t
da
u, t1
PM
0, t t t
1, t TWC TWC
(11)
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