Towards a seasonal adjustment and a revision policy

1. UNECE Workshop on Seasonal Adjustment 20 – 23 February 2012, Ankara, Turkey Towards a seasonal adjustment and a revision policy Anu Peltola Economic Statistics Section, UNECE

2. Overview • How to organize seasonal adjustment? • Designing a policy • Contents of the Policy • ESS Guidelines on Seasonal Adjustment • Examples of policies and changes in them • Revision policy is an essential part • Examples of revision policies

3. Delegated or Centralised? 1) Production units perform seasonal adjustment to their own data • How to ensure sufficient knowledge? • How to avoid mistakes? • How to ensure consistency between statistics? 2) One methodology unit performs all seasonal adjustments that are published • How to manage all series in the hectic schedule? • How to ensure knowledge of industry specific issues, such as causes of outliers? 3) A policy guides the production units and directs them into cooperation with methodology experts

4. Reflect National Conditions • The policy cannot be exactly the same for all offices/countries • Find out about users’ preferences • Consider resources available in your office • Staff time • Computer resources • Release schedules • Consider advantages and disadvantages of seasonal adjustment • Prepare to face them in the policy

5. Prepare a Policy in Stages • Learn and gather experience: • Test seasonal adjustment comprehensively • Involve colleagues of other statistical areas in seasonal adjustment if useful for their statistics • Study international guidelines • Define the basic choices first : • Software and method, timing of revisions, release and metadata guidelines • Expand the policy later: • Guidelines for problematic series, breaks in time series, times of economic uncertainty

6. Contents of the Policy • Method and software choice for seasonal adjustment, dissemination and storage • Methods and timing of re-analysis and revisions • Means of aggregation of series • Treatment of outliers • Requirements for documentation both internal and for users • Guidelines for releasing seasonally adjusted

7. ESS Guidelines on Seasonal Adjustment • Helps define the seasonal adjustment policy • Following them improves international comparability • Gives tips for alternative methods in: • Outlier detection • Calendar adjustment and moving holidays • Seasonal adjustment approach • Consistency between raw and adjusted data • Indirect vs. direct aggregation • Revision of seasonally adjusted data • Quality measures • Touches also the issues of more problematic series

8. Statistics Canada – SA Policy • Scope and purpose • Method chosen • Principles of seasonal adjustment • Seasonal adjustment guidelines • Quality indicators • References http://www.statcan.gc.ca/pub/12-539-x/2009001/seasonal-saisonnal-eng.htm

9. Statistics Canada – Guidelines • Seasonality needs to be identifiable for adjustment • No residual seasonality in the adjusted data • 10 to 15 years of data ideal, 5 years minimum • RegARIMA model to extrapolate the series to reduce revisions • Options reviewed periodically - not in-between • Factors and the regARIMA model parameters recomputed every time • Exceptions only when the most recent observations have been historically subjected to large revisions > forecasted factors • Aggregate (direct or indirect) checked for residual seasonality • Revisions published according to a official revision policy • Month-to-month rates computed on seasonally adjusted data • Use with caution if the time series has high volatility  • Year on year same-month rates computed on calendar adjusted data, or, in absence of calendar effects, on raw data. • Users have access to the historical raw series, seasonally adjusted and, upon request, to the adjustment options

10. Statistics Finland – SA Policy • What is seasonal variation and why should it be removed? • What are the components of a time series? • Functioning principles of the method applied • All forecasts contain statistical uncertainties! • Seasonal adjustment practices applied • Models are kept fixed for one year but parameters of them are re-estimated in each calculation round • Models used checked once a year • Details of the models are freely available to anybody http://www.tilastokeskus.fi/til/tramo_seats_en.html

11. Direct or Indirect Aggregation • Direct approach means that the aggregate time series are seasonally adjusted independently • Indirect approach - by aggregating the seasonally adjusted series of the component time series by using a weighting scheme • Direct approach preferred for transparency and accuracy • Especially if component series show similar seasonal patterns • Indirect approach may be preferred when components show significantly differing seasonal patterns • Useful in addressing strong user requirements for consistency • Presence of residual seasonality needs to be monitored carefully

12. Bank of England – Change in Policy • Started deriving quarterly series from the monthly seasonally adjusted series in 2007 – and stopped separate adjustments • Informed the users with a brief article: • Explained the background • Reasons behind the change: • Users were confused: M and Q series did not match • Deriving Q from M in line with international best practice • No need to review the Q series separately – less resources • Effects on the data • Implementation http://www.bankofengland.co.uk/statistics/ms/articles/art2apr07.pdf

13. Bank of England – Change in Policy Effect of the change in policy on the annual growth rate of household sector

14. Revision policy - OECD/Eurostat Guidelines • Provides the users with the necessary information to cope with revisions: • Defines a predetermined schedule for revisions • Is reasonably stable from year to year • Is transparent • Gives advance notice of larger revisions due to conceptual or methodology changes • Offers adequate documentation of revisions • Carry revisions back several years to give consistent time series

15. Documentation on Revisions Such documentation should include: • Clear identification of preliminary (or provisional) data and revised data • Advance notice of major changes in concepts, definitions, and classification and in statistical methods • The sources of revision explained • Information on breaks in series when consistent series cannot be constructed • Information on the size of possible future revisions based on past history

16. Size of the Likely Revisions • Information to judge reliability and accuracy • Do periodic analyses of revisions • Investigate the sources of revision from earlier estimates • Make statistical measures of the revisions • Publish the historical revision data for major aggregates

17. Canadian System of National Accounts Revision Policy • Status of data clearly indicated – preliminary / final • Revisions are carried out regularly • To incorporate current information from censuses, annual surveys, administrative sources, public accounts, etc. • To implement improved estimation methods • Number of revision times per year • The period open for revision – e.g. four years • Historical revisions conducted periodically • To improve estimation methods • To introduce conceptual and classification changes • To revise data to be in line with new international standards • Dates for revision schedule for the next year • Approximate historical revisions of the aggregates http://www.statcan.gc.ca/pub/13-605-x/2011001/article/11414-eng.htm

18. Statistics Finland – Level Shift • Started to treat the economic slowdown as a level shift • The aim is to improve the quality of seasonal adjustment • The crisis can be seen as abnormal observations • Eurostat and ECB suggest treating the slowdown as outliers

19. Statistics Finland – Level Shift Level shift shows a sharper change in the series

20. Revision Policy for Seasonal Adjustment • The policy should address: • Select methods for refreshing the seasonally adjusted data • Set the timing for refreshing the adjusted data • Define the time period over which the raw and the seasonally adjusted data will be revised • Convey the relative size of revisions of the seasonally adjusted data and the main causes of revisions • Set the timing of publication of revisions to the seasonally adjusted data and publication of the revisions to the raw data

21. Methods for Refreshing the Seasonally Adjusted Data • The quality of forecasts used in seasonal adjustment increases with the frequency of updates • A trade-off between the cost and the quality • Very frequent updates of the seasonal model could also lead to weaker stability of results and revisions in opposing directions • Current adjustment strategy minimizes the frequency of revision - concurrent generates the most accurate data but will lead to more revisions

22. Select Between the Alternative Strategies • Balanced alternatives may provide better quality of adjustment • Partial concurrent adjustment is widely used • Keeps the model, filters, outliers and calendar regressors fixed until the annual or biannual re-identification • Re-identifies parameters and factors every time new or revised data become available • ESS Guidelines suggests using balanced options • But if you find a problem between the updates, it should be promptly corrected • The choice depends on the properties of the series • For series shorter than seven years, re-identification could be done more often, for example twice a year

23. Balance Between Accuracy and Stability • If a different model is selected in the annual update, examine the diagnostics to find out whether it‘s notably better than the previous one • Consider the time period over which the results are revised • A full revision from the beginning of the series, promotes a methodically uniform treatment • Some question whether a new figure contains relevant information for revisions in the historical seasonal pattern • Some offices limit the period of revision of the seasonally adjusted data to a period that is about four years longer than the revision period for the original data

24. Publishing Revisions to the Seasonally Adjusted Series • In general publish revisions to the seasonally adjusted series at the same time as you add new observations • Link a change of the seasonal adjustment method to other methodological revisions of statistics, such as changes of base year or the economic activity classification • Give advance information about the forthcoming methodological changes • To correct notable mistakes additional release may be needed