1. Anu Peltola Economic Statistics Section, UNECE
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

2. Overview What and why Basic concepts Methods Software Recommendations
Useful references
March 2011

3. A Coyote Moment Did We Notice the Turning Point?
March 2011

4. Economic Crises – Statistics
Did we give any warnings?
A responsibility for the statistical offices? A new task?
Important to all users of statistics
Not only to politicians, but also to enterpreneurs and citizens
Statistical offices often have monopoly to analyze detailed data sets
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

5. 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

6. Turning Points Trend vs. Year-on-Year Rate Volume of Construction
March 2011

7. Why Seasonally Adjust?
Seasonal effects in raw data conceal the true underlying development
Easier to interpret, reveals long-term development
To aid in comparing economic 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

8. 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

9. 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
No seasonal and working day effects in a perfectly seasonally adjusted series
March 2011

10. 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

11. 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

12. X-11 and X-11-ARIMA X-11 X-11-ARIMA 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

13. 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

14. X-12-ARIMA
Source: David Findley and Deutsche Bundesbank
March 2011

15. 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

16. TRAMO/SEATS
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

17. DEMETRA software
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

18. X-12-ARIMA vs. TRAMO/SEATS
Source: Central Bank of Turkey (2002): Seasonal Adjustment in Economic Time Series.
March 2011

19. Demetra+ software Users can choose: One interface
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

20. Common Guidelines Use tools and software supported widely
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
Consider which series serve the purpose of the indicator
Document all relevant choices and events
March 2011

21. Useful references
Eurostat is preparing a Handbook on Seasonal Adjustment
ESS Guidelines on Seasonal Adjustment
Central Bank of the Republic of Turkey (2002). Seasonal Adjustment in Economic Time Series.
Hungarian Central Statistical Office (2007). Seasonal Adjustment Methods and Practices.
US Census Bureau. The X-12-ARIMA Seasonal Adjustment Program.
Bank of Spain. Statistics and Econometrics Software.
Australian Bureau of Statistics (2005). Information Paper, An Introduction Course on Time Series Analysis – Electronic Delivery
March 2011