Revisions in raw, working-day
and seasonally adjusted data
An application to the Italian
industrial production index
Anna Ciammola
ISTAT
Layout
1. Statement of the problem
2. Approaches to analyse revisions in
seasonally adjusted (SA) data
3. The “empirical” approach
4. An application
Steswp meeting - Paris 25-27 June 2007
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Statement of the problem
Revisions of seasonally adjusted data can be
traced back to different sources:
1. Revisions in raw data
Sources data, benchmarking, rebasement,
classification, methodology, …
2. Revisions in working-day factors
Easter, working/trading day, leap-year, holidays
3. Revisions in seasonal factors
Concurrent adjustment, forecasted seasonal
factors
Steswp meeting - Paris 25-27 June 2007
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Statement of the problem
A simple representation of the problem using the
following notation
Ijsa,i
I refers to a specific economic indicator
j refers to the state of raw data 
j=p preliminary data
j=f final data
i refers to the state of seasonally adjusted data 
i=p preliminary seasonal factors
i=f final seasonal factors
Steswp meeting - Paris 25-27 June 2007
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Statement of the problem
Seasonal
revisions
I
Rsa
R
RT total revision
sa, p
p
Revisions in
raw data
I
sa, f
p
R
I
sa, p
f
Revisions in
raw data
I
R’sa
sa, f
f
Seasonal
revisions
Source: Maravall and Pierce, 1983
Steswp meeting - Paris 25-27 June 2007
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Statement of the problem
The revision processes R and Rsa
may be very different
The analysis of revisions only on SA
data is not sufficient to describe the
statistical properties of revisions
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Approaches
1.
“Analytical” approach
Decomposition of the total revision
RT = f (R , Rsa)
Interesting, but not always applicable
Example: Row, WDA and SA Italian QNA
derive from three different process of
temporal disaggregation
Steswp meeting - Paris 25-27 June 2007
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Approaches
2. “Empirical” approach
Analysis of three sets of revisions:
- on raw data
- on WDA data
- on SA data
Less appealing, but:
- always applicable
- simple for dissemination purposes
Steswp meeting - Paris 25-27 June 2007
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The “empirical” approach
Raw data
WDA data
SA data
Revisions as:
• Error
• Relative error
Revisions on:
• Levels
• Y-o-Y changes
Revisions on:
• Levels
• Y-o-Y changes
• P-o-P changes
Indices of:
- location
- variability
- systematic component
Steswp meeting - Paris 25-27 June 2007
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The “empirical” approach
A. Revisions in raw and WDA data
- Stability of WDA factors always checked
in the decomposition processes
- RWDA = g (R , B)
(B changes in the parameter estimates of
regressors in reg-ARIMA models)
- Changes in B should be small
Revisions process in WDA data

Revisions process in raw data
Steswp meeting - Paris 25-27 June 2007
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The “empirical” approach
B. Revisions in raw and SA data
- Stability of SA factors always checked in
the decomposition processes
- Analysis of revisions on raw data is a
helpful tool to understand the size and
other properties of revisions on SA data
Example: Bias in revisions of SA data
 bias in revisions of raw data?
 bias due to the SA process?
Steswp meeting - Paris 25-27 June 2007
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An application
The revision of the Italian industrial production
index (Intermediate goods)
Sources of revision
1. additional data arrived from late respondents (increase in coverage);
2. correction of errors in data already embodied in the estimates
3. revision of statistics (external to the survey) utilised in compiling the
index (e.g. productivity coefficients drawn from national accounts)
Timing of revision (from October 2004)
1. First revision: 1 month after the preliminary release
2. Second revision at fixed points
- April (releasing February data and concerning the previous three years)
- October (releasing August data and concerning the first semester of
current year)
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An application
Revisions 1 month after - levels
Revisions 12 months after - levels
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-1.5
-2
Jan-03
Raw
WDA
Jan-04
Jan-05
Jan-06
Jan-07
-1.5
-2
Jan-03
Revisions 1 month after - YoY
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-2
Jan-03
Raw
WDA
Jan-04
Jan-05
Jan-06
Jan-04
Jan-05
Jan-06
Revisions 12 months after - YoY
1.5
-1.5
Raw
WDA
Jan-07
Steswp meeting - Paris 25-27 June 2007
-1.5
-2
Jan-03
Raw
WDA
Jan-04
Jan-05
Jan-06
13
An application
Revisions 1 month after - levels
Revisions 12 months after - levels
2
2
Raw
SA
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
-1.5
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
-1.5
Jan-03
Revisions 1 month after - YoY
Jan-04
Jan-05
Jan-06
Revisions 12 months after - YoY
2
2
Raw
SA
1.5
1
0.5
0.5
0
0
-0.5
-0.5
-1
-1
Jan-04
Jan-05
Jan-06
Jan-07
Steswp meeting - Paris 25-27 June 2007
Raw
SA
1.5
1
-1.5
Jan-03
Raw
SA
1.5
-1.5
Jan-03
Jan-04
Jan-05
Jan-06
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An application
Period: Jan-03 / Apr-07
Revision indicators (*)
on Y-o-Y changes
n
MAR
RMAR
MR
SD(HAC)
t-value
t(1-0.05/2,n-1)
Significance of MR
MSR
UM
UR
UD
MIN
MAX
RANGE
% L > P
% SL = SP
Raw data
WDA data
SA data
1 month
12 months
1 month
12 months
1 month
12 months
after
after
after
after
after
after
51
40
51
40
51
40
0.22
0.30
0.18
0.39
0.25
0.56
0.07
0.10
0.08
0.21
0.13
0.37
0.16
0.16
0.11
-0.01
0.12
0.11
0.032
0.058
0.026
0.085
0.041
0.086
4.97
2.66
4.36
-0.15
2.95
1.22
2.01
2.02
2.01
2.02
2.01
2.02
YES
YES
YES
NO
YES
NO
0.086
0.174
0.051
0.366
0.111
0.582
0.285
0.138
0.254
0.000
0.129
0.019
0.000
0.002
0.014
0.000
0.058
0.206
0.715
0.859
0.733
1.000
0.813
0.775
-0.3
-0.8
-0.3
-2.1
-0.9
-2.1
1.0
1.4
0.5
1.3
1.2
2.0
1.3
2.2
0.8
3.4
2.1
4.1
68.6
62.5
62.7
55.0
64.7
60.0
98.0
97.5
94.1
90.0
94.1
85.0
(*) Source: User manual and pre-programmed spreadsheets for performing revision analysis (OECD)
Steswp meeting - Paris 25-27 June 2007
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Thank you!