1. Internal documentation and user documentation
UNECE Workshop on Seasonal Adjustment
20 – 23 February 2012, Ankara, Turkey
Internal documentation and user documentation
Anu Peltola
Economic Statistics Section, UNECE

2. Overview Main types of documentation Internal documentation
User documentation
Releasing metadata
Communicate quality issues
Assess and explain revisions
Clear presentation
February 2012

3. Main Types of Documentation
Metadata for users
Varying information needs
Documents needed for production
The resulting data itself
At least raw data and seasonally adjusted data to be stored as data vintages
Good for revision analysis
February 2012

4. Internal Documentation
Internal documentation comprises the following:
Step-by-step working instructions for performing seasonal adjustment
Internal quality reports
Regularly updated lists of national holidays
Lists for monitoring and enumerating the reasons for outliers
February 2012

5. Demetra+ Presents a considerable amount of quality diagnostics
For the key series you may store the results of adjustment at least when re-identifying the model
i.e. The summary statistics of the Results panel
Invest more time on documenting the key time series
February 2012

6. Some International Guidelines
The Fundamental Principles of Official Statistics (UNECE, 1992)
The Guidelines for the Modelling of Statistical Data and Metadata (UNECE, 1995)
The ESS Guidelines on Seasonal Adjustment
Some International Guidelines
February 2012

7. Design User Documentation Well
Due to excess of information
Give only relevant information
Direct the user to more detail (link)
Put emphasis on:
1. Understanding the idea of seasonal adjustment
2. Knowing how to use the data
3. For specialists information for replicating the results
You may use the metadata template of the ESS Guidelines for defining the contents
February 2012

8. 1. General Explanation Offer metadata for understanding statistics:
“Seasonal adjustment is the process of estimating and then removing from a time series influences that are systematic and calendar related. Observed data need to be seasonally adjusted, as seasonal effects can conceal both the true underlying movement in the series as well as certain non-seasonal characteristics which may be of interest to analysts.”
- By the Australian Bureau of Statistics
February 2012

9. 2. How to Use Data? For example, explain reading indices to users:
The name “index” comes from Latin and means a pointer
It allows for comparison in time and space.
A cup of coffee cost 3 Swiss francs in 2011, whereas it was 2.4 francs in 2005
Here, the year 2005 is our base period. The index number for 2005 is 100, and the comparable figure for 2011 is 125. The result indicates a price increase of 25 per cent.
February 2012

10. 3. Metadata for Own Seasonal Adjustment
Seasonal adjustment method
(e.g.TRAMO/SEATS or X-12-ARIMA)
Software (e.g. Demetra+)
General decision rules applied
Describe the quality of the raw data
Means for outlier detection and correction
Choices in calendar adjustment and national holidays
Set of core quality diagnostics
Timing and reasons for revisions
Contact information to experts
February 2012

11. Practices for Dissemination of Metadata - OECD
” All statistical agencies should:
Compile metadata required for users to understand the strengths and limitations of the statistics it describes; and
Keep their metadata up-to-date, incorporating the latest changes in definitions, classifications and methodology, etc. “
February 2012

12. Metadata to Accompany Each Release
Ensure an easy access to the metadata
Direct the reader to the more detailed information with a link from releases, tables and graphs + add to printed publications
Lead to a quality report for the indicator
You may offer an archive of historical revisions
Metadata in native and some other languages, such as English
February 2012

13. Metadata to Accompany Each Release – UK example
Extracted from the end of a news release:
February 2012

14. What to Say about Data Quality
In general, metadata for the statistical indicator will:
Describe the quality of the raw data (length and appropriateness for adjustment)
Inform about any breaks in the series
Explain the definitions being measured
Limitations of use
Compilation methods and weighting system
Treatment of changes
Departures from international standards
Provide at least a minimum amount of information to enable assessment of reliability
February 2012

15. Outliers to be Explained
Outliers stay visible in the adjusted data
They contain information about events
Understanding the causes of outliers helps interpret the data
Inform the user about the criteria for identifying outliers
You may publish a list of events that caused outliers
Explain the difficulty with outliers in the end of the series - duration and type isn't known until new observations accumulate
A permanent level change or just one single peak?
February 2012

16. Why Seasonal Adjustment is Revised?
Offer an explanation to users, for example:
“There are two reasons that we revise seasonal factors: We revise factors when we revise the unadjusted data to achieve a better fit to the revised data.” and “…when future data become available, we use them to obtain improved seasonal factor estimates for the most recent years of the series. These revised factors lead to revised seasonal adjustments of higher quality.”
- by the United States Bureau of Economic Analysis
February 2012

17. Assess Revisions Before Publishing
Demetra+ includes a revision history test
Use it to assess the likely size of revisions
Look at revisions to changes (previous/corresponding month)
February 2012

18. Recommended Metadata for Indices - OECD
Precise definitions of the underlying concepts
Index calculation methods - Laspeyres, Paasche, Fisher
Strategy for constructing indices - fixed base or chain index
Weighting system used, revision practices
Aggregation practices
Re-basing practices - procedures for linking
Treatment of changes
February 2012

19. Clear Presentation
Similar but different series should be given different titles
The precise name of the classification used in statistics clearly indicated
When a field of activity is only partially covered it should be clearly indicated for instance with an asterisk or a footnote
Addition: Data corrected after the release could be clearly indicated with an asterisk
February 2012