Modelling the accruals process and assessing unexpected accruals*

and
where
t a t
d
t
m
u, t PM
t m t
dm t d t
dd
u, t1
PM
1 t
m u, t
t m t
dm
t
gm u, t1
,
2, t TWC TWC TWC
3, t TWC TWC TWC TWC
,
4, t NCO
5, t NCO NCO NCO
u, t1 REV u, t REV
3, , 3, 4, , 4, 5, , 5,
t, j 0, t, j 0, t 1, t, j 1, t j 2, t, j 2, t j
REV NCO NCO
u, t 1 u, t 1 u, t
t j t j t j t j t j t j
ut ,
ut ,
PM represents the underlying pre-depreciation profit margin, represents the
economic depreciation rate, and the j subscript represents individual observation. The parameters
without the j subscript ( 0,t to 5,t ) are the regression parameters, while the parameters with the j
subscript ( 0, t, j to 5, t, j
(12)
) are the parameters of the individual observation. If this model is
estimated using time-series observations of the same firm, then the estimated parameters will
reflect the time-series averaged parameters. On the other hand, if this model is estimated using
cross-sectional observations for a given year, then the estimated parameters will reflect the cross-
sectional averaged parameters. 16 The implications of my analysis apply to both types of models,
and I will refer to both of these time-series observations and cross-sectional observations as
„benchmark firms‟.
The encompassing model‟s disturbance term in expression (12) for a particular firm, firm j, is
often referred to as the firm j‟s unexpected accruals. It is entirely attributed to the heterogeneity
in the parameters (i.e., the deviation of firm j parameters from parameters of benchmark firms).
This heterogeneity is shown to be due to deviations in accounting and operating policies between
16 The accruals model in Jones (1991) is originally applied in a time-series fashion. However, the cross-sectional
estimation accruals model seems to be common (in Kothari, Leone, Wasley 2004 for instance). This is probably due
to the superiority of the cross-section model over the time-series model (see Bartov, Gui, and Tsui 2000).
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