Why Seasonally Adjust and How? Approaches X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools Anu Peltola...

Why Seasonally Adjust and How?

Approaches X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools

Anu PeltolaEconomic Statistics Section, UNECE

UNECE Workshop on Short-Term Statistics (STS) UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustmentand Seasonal Adjustment14 – 17 March 2011, Astana, Kazakhstan

March 2011 UNECE Statistical Division Slide 2

Overview

What and why Basic concepts Methods Software Recommendations Useful references


A Coyote Moment Did We Notice the Turning Point?


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


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


Turning Points Trend vs. Year-on-Year RateVolume of Construction

-20%

-10%

0%

10%

20%

30%

40%

20

05

20

06

20

07

20

08

20

09

20

40

60

80

100

120

140

160

Change from corresponding month Trend series


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


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


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


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


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


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


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


X-12-ARIMA

Source: David Findley and Deutsche Bundesbank


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


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 OutliersSEATS = Signal Extraction in ARIMA Time Series


DEMETRA softwarehttp://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


X-12-ARIMA vs. TRAMO/SEATS

Source: Central Bank of Turkey (2002): Seasonal Adjustment in Economic Time Series.


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


Common Guidelines

1. Use tools and software supported widely• Demetra+ will be supported by Eurostat• Methodological guidelines will be available• Results will be more comparable

2. Use your national calendars3. Dedicate enough human resources to SA4. Define a SA strategy5. Aim at a clear message to the users

• Consider which series serve the purpose of the indicator• Document all relevant choices and events


Useful references Eurostat is preparing a Handbook on Seasonal Adjustment

ESS Guidelines on Seasonal Adjustmenthttp://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-09-006/EN/KS-RA-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/7A71E7935D23BB17CA2570B1002A31DB?OpenDocument

Why Seasonally Adjust and How? Approaches X-12-ARIMA, TRAMO/SEATS, X-13 A/S and Tools Anu Peltola...

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