Transcript UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustment 14 – 17 March 2011, Astana, Kazakhstan Why Seasonally Adjust and How? Approaches X-12-ARIMA, TRAMO/SEATS, X-13

UNECE Workshop on Short-Term Statistics (STS)
and Seasonal Adjustment
14 – 17 March 2011, Astana, Kazakhstan
Why Seasonally Adjust
and How?
Approaches X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools
Anu Peltola
Economic Statistics Section, UNECE
Overview






What and why
Basic concepts
Methods
Software
Recommendations
Useful references
March 2011
UNECE Statistical Division
Slide 2
A Coyote Moment
Did We Notice the Turning Point?
March 2011
UNECE Statistical Division
Slide 3
Economic Crises – Statistics

Did we give any warnings?
•
•
A responsibility for the statistical offices? A new task?
Important to all users of statistics


Statistical offices often have monopoly to analyze
detailed data sets
•
•

Not only to politicians, but also to enterpreneurs and citizens
We should not forecast, but draw attention to statistics
Identify changes early, leading indicators, develop more
flash estimates -> quality vs. timeliness
Otherwise, a risk of marginalisation of NSOs
March 2011
UNECE Statistical Division
Slide 4
Economic Crises – Conclusions

Some limits of official statistics were
highlighted by the critics:
•
lack of comparability among countries
• need for more timely key indicators
• need for statistical indicators in areas of
particular importance for the financial and
economic crisis
Source: Status Report on Information Requirements in EMU
March 2011
UNECE Statistical Division
Slide 5
Turning Points
Trend vs. Year-on-Year Rate
Volume of Construction
40%
160
140
30%
120
20%
100
10%
80
0%
60
-10%
40
Change from corresponding month
March 2011
2009
2008
2007
2006
20
2005
-20%
Trend series
UNECE Statistical Division
Slide 6
Why Seasonally Adjust?

Seasonal effects in raw data conceal the true
underlying development
•

To aid in comparing economic development
•


Easier to interpret, reveals long-term development
Including comparison of countries or economic
activities
To aid economists in short-term forecasting
To allow series to be compared from one
month to the next
•
Faster and easier detection of economic cycles
March 2011
UNECE Statistical Division
Slide 7
Why Original Data is Not Enough?

Comparison with the same period of last year
does not remove moving holidays
•



If Easter falls in March (usually April) the level of
activity can vary greatly for that month
Comparison ignores trading day effects, e.g.
different amount of different weekdays
Contains the influence of the irregular
component
Delay in identification of turning points
March 2011
UNECE Statistical Division
Slide 8
Seasonal Adjustment

Seasonal adjustment is an analysis technique
that:
•
•

Estimates seasonal influences using procedures
and filters
Removes systematic and calendar-related
influences
Aims to eliminate seasonal and working day
effects
•
March 2011
No seasonal and working day effects in a perfectly
seasonally adjusted series
UNECE Statistical Division
Slide 9
Interpretation of Seasonally
Adjusted Data

In a seasonally adjusted world:
•
Temperature is exactly the same during
both summer and winter
• There are no holidays
• People work every day of the week with the
same intensity
Source: Bundesbank
March 2011
UNECE Statistical Division
Slide 10
Filter Based Methods




X-11, X-11-ARIMA, X-12-ARIMA
(STL, SABL, SEASABS)
Based on the “ratio to moving average” described
in 1931 by Fredrick R. Macaulay (US)
Estimate time series components (trend and
seasonal factors) by application of a set of filters
(moving averages) to the original series
Filter removes or reduces the strength of
business and seasonal cycles and noise from the
input data
March 2011
UNECE Statistical Division
Slide 11
X-11 and X-11-ARIMA
X-11




Developed by the US Census Bureau
Began operation in the US in 1965
Integrated into software such as SAS and STATISTICA
Uses filters to seasonally adjust data
X-11-ARIMA



Developed by Statistics Canada in 1980
ARIMA modelling reduces revisions in the seasonally
adjusted series and the effect of the end-point problem
No user-defined regressors, not robust against outliers
March 2011
UNECE Statistical Division
Slide 12
X-12-ARIMA
http://www.census.gov/srd/www/x12a/








Developed and maintained by the US Census Bureau
Based on a set of linear filters (moving averages)
User may define prior adjustments
Fits a regARIMA model to the series in order to detect
and adjust for outliers and other distorting effects
Diagnostics of the quality and stability of the adjustments
Ability to process many series at once
Pseudo-additive and multiplicative decomposition
X-12-Graph generates graphical diagnostics
March 2011
UNECE Statistical Division
Slide 13
X-12-ARIMA
Source: David Findley and Deutsche Bundesbank
March 2011
UNECE Statistical Division
Slide 14
Model Based Methods


TRAMO/SEATS, STAMP,
”X-13-ARIMA/SEATS”
Stipulate a model for the data (V. Gómes and A.
Maravall)



Models separately the trend, seasonal and
irregular components of the time series
Components may be modelled directly or
modelling by decomposing other components
from the original series
Tailor the filter weights based on the nature of
the series
March 2011
UNECE Statistical Division
Slide 15
TRAMO/SEATS
www.bde.es






By Victor Gómez & Agustin Maravall, Bank of Spain
Both for in-depth analysis of a few series or for
routine applications to a large number of series
TRAMO preadjusts, SEATS adjusts
Fully model-based method for forecasting
Powerful tool for detailed analyses of series
Only proposes additive/log-additive decomposition
TRAMO = Time Series Regression with ARIMA Noise, Missing Observations and Outliers
SEATS = Signal Extraction in ARIMA Time Series
March 2011
UNECE Statistical Division
Slide 16
DEMETRA software
http://circa.europa.eu/irc/dsis/eurosam/info/data/demetra.htm






By EUROSTAT with Jens Dossé, Servais Hoffmann,
Pierre Kelsen, Christophe Planas, Raoul Depoutot
Includes both X-12-ARIMA and TRAMO/SEATS
Modern time series techniques
to large-scale sets of time series
To ease the access of non-specialists
Automated procedure and
a detailed analysis of single time series
Recommended by Eurostat
March 2011
UNECE Statistical Division
Slide 17
X-12-ARIMA vs. TRAMO/SEATS
Source: Central Bank of Turkey (2002): Seasonal Adjustment in Economic Time Series.
March 2011
UNECE Statistical Division
Slide 18
Demetra+ software

Users can choose:
•
•




Tramo-Seats model-based adjustments
X-12-ARIMA
One interface
Aims to improve comparability
of the two methods
Uses a common set of diagnostics
and of presentation tools
Necmettin Alpay Koçak is
a member of the testing group
March 2011
UNECE Statistical Division
Slide 19
Common Guidelines
1. Use tools and software supported widely
•
•
•
2.
3.
4.
5.
Demetra+ will be supported by Eurostat
Methodological guidelines will be available
Results will be more comparable
Use your national calendars
Dedicate enough human resources to SA
Define a SA strategy
Aim at a clear message to the users
•
•
March 2011
Consider which series serve the purpose of the indicator
Document all relevant choices and events
UNECE Statistical Division
Slide 20
Useful references

Eurostat is preparing a Handbook on Seasonal Adjustment

ESS Guidelines on Seasonal Adjustment
http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-09-006/EN/KSRA-09-006-EN.PDF

Central Bank of the Republic of Turkey (2002). Seasonal Adjustment in
Economic Time Series.
http://www.tcmb.gov.tr/yeni/evds/yayin/kitaplar/seasonality.doc
Hungarian Central Statistical Office (2007). Seasonal Adjustment Methods
and Practices. www.ksh.hu/hosa
US Census Bureau. The X-12-ARIMA Seasonal Adjustment Program.
http://www.census.gov/srd/www/x12a/
Bank of Spain. Statistics and Econometrics Software.
http://www.bde.es/servicio/software/econome.htm
Australian Bureau of Statistics (2005). Information Paper, An Introduction
Course on Time Series Analysis – Electronic Delivery. 1346.0.55.001.
http://www.abs.gov.au/ausstats/[email protected]/papersbycatalogue/7A71E7935D
23BB17CA2570B1002A31DB?OpenDocument




March 2011
UNECE Statistical Division
Slide 21