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Seasonal Adjustment of National
Index Data at International Level
Shyam Upadhyaya, Shohreh Mirzaei Yeganeh
United Nations Industrial Development
Organization (UNIDO), Vienna, Austria

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Overview
• What and why
• Basic concepts
• Costs and risks
• Methods
• Software
• UNIDO experience
• Recommendation

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Seasonally adjusted and original series
- Industrial Production Index

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Seasonally adjusted and original series
- Industrial Production Index

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IIP percentage change
QII 2011 to QI 2011QII 2012 to QI 2012
CameroonRussiaCameroonRussia
-17.17%-15.48%-6.86%-1.39%Original
-0.66%-8.51%8.43%-0.66%SA

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Why seasonally adjust?
• To aid in short term forecasting
• To aid in relating time series to other series including
comparison of time series from different countries
• To allow series to be compared from month to month, quarter
to quarter
• to see the real movements and turning points in manufacturing
production, which may be impossible or difficult to see due to
seasonal movements

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Seasonal Adjustment
• The process of estimating and removing the Seasonal Effects
and filtering out the systematic calendar related influences
from the original IIP time series
• One common misconception is that Seasonal Adjustment will
also hide any outliers present. This is not the case: if there is
some kind of unusual event, we need that information for
analysis, and outliers are included in the Seasonally Adjusted
series

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Seasonal Adjustment
• Facilitates the comparison of long-term and short-term
movements among series and countries
• Fluctuations due to exceptionally strong or weak seasonal
influences will continue to be visible in the seasonally adjusted
series. In general, other random disruptions and unusual
movements that are readily understandable in economic terms
(for example the consequences of economic policy, large scale
orders or strikes) will also continue to be visible

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Seasonal Adjustment
• the Seasonally Adjusted results do not show
“normal” and repeating events, they provide an
estimate for what is new in the series which is the
ultimate goal of Seasonal Adjustment

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Costs and Risks
• Seasonal Adjustment is time consuming, significant
computer/human resources must be dedicated to this
task
• Inappropriate or low-quality Seasonal Adjustment can
generate misleading results and increase the
probability of false signals (credibility effects)
• The presence of residual seasonality, as well as over-
smoothing, are concrete risks which could negatively
affect the interpretation of Seasonally Adjusted data

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Seasonal adjustment methods
• Model based method
– TRAMO/SEATS
• Filter based method
– X12-ARIMA

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TRAMO/ SEATS
• TRAMO (Time Series Regression with ARIMA Noise,
Missing Observations and Outliers) and SEATS (Signal
Extraction in ARIMA Time Series) developed by Victor
Gómez and Agustin Maravall at Bank of Spain.
• The two programs are intensively used at present by data-
producing and economic agencies, including Eurostat and the
European Central Bank.
• Programs TRAMO and SEATS provide a fully model-based
method for forecasting and signal extraction in univariate time
series. Due to the model-based features, it becomes a powerful
tool for a detailed analysis of series.

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Demetra+
• When choosing a seasonal adjustment (SA) program, statistical agencies
have had at least two different options in the past: X-12-ARIMA and
TRAMO/ SEATS.
• Nowadays, combined software packages exist which merge functionalities
of X-12-ARIMA and TRAMO/SEATS: Demetra+.
• Users may thus choose between these approaches for each particular time
series under review without switching between different programs.

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System architecture (Cycle)
Raw
Database
TSTools
Demetra+
Output
Database
Publication

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Revision
• Three types of Revision Policy
– Current Adjustment → adjusts with fixed specification,
user defined regression variables can be updated
– Semi-concurrent Revision → re-estimates respective
parameters and factors every time new or revised
observation become available
– Concurrent Adjustment → adjustment performed without
any fixed specifications

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UNIDO experience (IIP)
• 334 time series
• Quality of the time series
– Short time series: minimum 3 years long for monthly and 4 years
long for quarterly
• Revision policy: semi-concurrent revision (once a year)
• 4 quarterly reports on the world manufacturing production
using SA data have been released

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Suggestions and recommendations
• Aggregation approach
– Indirect approach
– Direct adjustment
• It is highly recommended to perform the SA at country level
• Revision policy
• Publication policy
– When seasonality is present and can be identified, series should be
made available in seasonally adjusted form.
– The method and software used should be explicitly mentioned in the
metadata accompanying the series.

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• Countries with no SA experience are encouraged to compile,
maintain and update their national calendars or, as a minimal
alternative, to supply an historical list of public holidays
including, whenever possible, information on compensation
holidays. Moreover providing the calendar for the year t+1 or
the corresponding holidays
• Users of Seasonally Adjusted data should be aware that their
usefulness for econometric modeling purposes needs to be
carefully considered

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Thank you for your attention!