Revision policy incorporates both – routine and exceptional data revisions.

Routine data revisions include the following events:

  • revision of the published data of a higher aggregation level, adding information of a more detailed aggregation level;
  • further revision of the published data receiving additional or revised information from respondents;
  • further revision of the published data receiving additional or revised information from administrative data sources;
  • revision of the published provisional data;
  • revision of the published data in the result of seasonal adjustment or due to changes in the definition of the base period;
  • revision of the published data in line with changes to the methodology or classifications.

Exceptional data revisions are revisions that cannot be included in the previously set revision cycle. A need for exceptional revision can occur due to errors in data sources or calculations, as well as in the event of extraordinary changes to the methodology or data sources.

Revision policy consists of a range of defined elements:

  • revision cycle - previously scheduled and regular revision, mainly based on the volume of objectively and sequentially expanding available information required for calculations;
  • revision schedule - previously defined terms, when the data revised in the revision cycle are to be published;
  • revision volume - previously defined length of the time series to be revised and defined indicators subjected to the revision;
  • communication with data users:
    • the reasons for scheduled changes in the revision cycle are freely available to the data users;
    • clearly identifiable revised data,
    • in the event the reason for revision is changes to the methodology, definitions or classifications – timely introduction of the expected changes in data files to the data users or information upon interruptions to time series;
  • revision analysis – revision shall be analysed in order to have a deeper understanding of data changes. A set of specific indicators is applied in the revision analysis, of which information is accumulated. If such analysis is carried out in a longer time period and is published, data users can get a notion regarding the volume of the possible future revisions of the data published in the first stages of the revision cycle.

 

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