Eviews5 / EViews5 / Example Files / x12 / example3

27 May 1999

Example 3: Total Net Income After Taxes (Total NIAT) is the sum of NIAT
from Nondurable Goods industries (label 11000 or 11) and
Durable Goods industries (label 12000 or 12). Durable goods
are those that last three years or more (e.g. automobiles).
Nondurable goods are those with a shorter expected lifetime
(e.g. food).

Each of these NIAT series is obtained by subtracting expenses
-- Depreciation (D), Operating Costs (OC), and Income
Taxes (IT) -- from Sales (S) and then adding Non-Operating
Income/Expenses (NO), i.e.,

NIAT = S - D - OC - IT + NO.

We had to decide whether to adjust Total NIAT directly or
indirectly and, if indirectly, whether from the directly
adjusted NIATs of Durables and Nondurable, or from the five
components of these NIATs, some of which might not have
acceptable seasonal adjustments. (In this case the unadjusted
series is a component of the Total NIAT.) Revisions histories
helped us decide to use the directly adjusted NIATs from
Durable and Nondurable to obtain the adjusted Total NIAT
instead of using the using the 10 smaller components.

Graphs for comparing direct vs indirect adjustments are
included in X-12-Graph. Suggested graphs: overlay graphs,
seasonal factor by quarter graphs, and history graphs.

The metafiles and a few of the spec files are listed below.



Example 3: i10.mta

The metafile for the direct adjustment of Total NIAT. The unadjusted
series is obtained as the sum of NIAT from Nondurables and NIAT from
Durables. The spec files for these series (i11.spc and i12.spc)
produce seasonal adjustments of these components from which an indirect
adjustment of Total NIAT can be calculated for comparison with the
direct adjustment. X-12-ARIMA will save the output file for the
composite adjustments to nait.out.

i11
i12
i10 nait



Example 3: i10ind3.mta

The metafile for the indirect adjustment of Total NIAT from the
10 component series, one of which, Nonoperating Income/Expenses from
Durable Goods industries, is not seasonally adjusted (no12nsa.spc).

s11
s12
d11
d12
oc11
oc12
it11
it12
no11
no12nsa
i10 niat_3



# Example 3: i11.spc

# For adjustment of Net Income After Taxes from Nondurable Goods
# as a component of Total NIAT.

series{
format='datevalue'
name='i11000'
period=4
title='Net Income after taxes - Nondurable Manufacturing '
comptype=add
print=a1
file='i11.dat'
}
regression{
variables=(
const seasonal ao1992.1 ao1987.4 ao1995.4
)
}
arima{
model=(0 1 0 )
}
check{ }
estimate{
parms=estimated
maxiter=200
}
forecast{
maxlead=12
}
x11{
mode=add
}
history{
start=1993.2
estimates=(sadj sadjchng trend)
print=(sar chr trr)
}



# Example 3: i12.spc

# For adjustment of Net Income After Taxes from Durable Goods
# as a component of Total NIAT.

series{
format='datevalue'
period=4
title='Net Income after taxes - Durable Manufacturing'
comptype=add
name='i12000'
file='i12.dat'
print=a1
}
regression{
variables=(
ls1992.4 ao1992.1
)
}
arima{
model=(0 1 1 )(0 1 1 )
}
check{ }
estimate{
}
forecast{
maxlead=12
}
x11{
mode=add
}
history{
start=1993.2
estimates=(sadj sadjchng trend)
print=(sar chr trr)
}



# Example 3: no12nsa.spc

# For adjustment of Net Income After Taxes from Durable Goods
# as a component of Total NIAT. Note: this is a run with
# type=summary, so this component will NOT be seasonally adjusted.

series{
print=a1
file='no12.dat'
period=4
title='Non Operating Income/Expenses - Durable Manufacturing'
name='no12'
span=(1979.1, )
comptype=add
format='datevalue'
}
x11{
mode=add
type=summary
}
history{
start=1993.2
estimates=(sadj sadjchng trend)
print=(sar chr trr)
}



#Example 3: i10.spc

# For indirect adjustment of Total Net Income After Taxes (Total NIAT) as
# the sum of the direct adjustments of NIAT of Durable Goods Industries (i12.spc)
# and NIAT of Nondurable Goods Industries (i11.spc). The direct adjustment
# and statistics to compare the direct adjustment to the indirect adjustment
# are also calculated.

composite{
title='Net Income After Taxes - Total Manufacturing'
name='i10000'
print=brief
}
regression{
variables=(
ao1987.4 ao1992.1
)
}
arima{model=(0 1 1 )(1 1 1)}
check{print=all}
estimate{ }
forecast{maxlead=12}
x11{
mode=add
save=d11
}
history{
start=1993.2
estimates=(sadj)
print=(sar iar)
save=(iar)
}