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TYPOLOGY OF PRODUCTS IN OFFICIAL STATISTICS Thomas Burg Marcus Hudec.

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Presentation on theme: "TYPOLOGY OF PRODUCTS IN OFFICIAL STATISTICS Thomas Burg Marcus Hudec."— Presentation transcript:

1 TYPOLOGY OF PRODUCTS IN OFFICIAL STATISTICS Thomas Burg Marcus Hudec

2 Content 2  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions & Next Steps © Burg & Hudec Vienna, June 3rd 2014

3 Content 3  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

4 Starting Point 4 Classical Approach: One-dimensional Type of Statistics Primary Statistics – Secondary Statistics Deviation between official statistics and academic statistics Eurostat handbook on Quality Reports - Sample Survey - Census - Statistical process using administrative sources - Statistical process involving multiple data sources - Price or any other economic index processes - Statistical Compilation Vienna, June 3rd 2014 © Burg & Hudec

5 Types of Statistics 5 Vienna, June 3rd 2014 © Burg & Hudec

6 Content 6  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

7 Canonical Dimensions 7  Three dimensional approach  Data Collection  Data Processing  Data Presentation ?? Each dimension having its own characterization © Burg & Hudec Vienna, June 3rd 2014

8 Data Collection 8 Data can be collected having in mind two different purposes: 1. 1. Subject of Statistic 2. Auxiliary Information Possible data sources for Statistical Products Survey  Respondents Administrative Data  Non-Statistical purpose Register Data  Maintained by NSI Existing Data  Collected for other product New Data Sources  Big Data“ © Burg & Hudec Vienna, June 3rd 2014

9 Data Processing (I) 9 Simple Aggregation(„Normal processing“) Modell Based Calculations Accounting Data Matching © Burg & Hudec Vienna, June 3rd 2014

10 Data Processing (II) 10 Model Based processing  Can be used for direct calculation but as well at certain product steps aiming to enhance quality  Broad variety but some are typical in official statistics © Burg & Hudec Vienna, June 3rd 2014 Weighting of Sampling Schemes Small Area Estimation Index Calculations Forecasting Methods Index Calculations Data Validation Techniques Disaggregation Statistical Disclosure Control Flash Estimation Backcasting Methods Imputation Techniques

11 Data Presentation 11 © Burg & Hudec Vienna, June 3rd 2014  Classical Statistical Tables  Maps  Indicators  Systems of Accounts Difficult to assign or rather „not to assign“!

12 Content 12  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

13 Template (I) 13 © Burg & Hudec Vienna, June 3rd 2014

14 Template (II) 14 © Burg & Hudec Vienna, June 3rd 2014

15 Content 15  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

16 EU SILC (I) 16 © Burg & Hudec Vienna, June 3rd 2014

17 EU SILC (II) 17 © Burg & Hudec Vienna, June 3rd 2014

18 Register Based Labour Market Statistics (I) 18 © Burg & Hudec Vienna, June 3rd 2014

19 Register Based Labour Market Statistics (II) 19 © Burg & Hudec Vienna, June 3rd 2014

20 Content 20  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

21 Impact on Quality Reporting 21 Set of Metadata relevant for user depends on characteristics of the Statistical Product All quality dimensions are concerned but first of all accuracy is a topic. Certain expectations on quality reporting © Burg & Hudec Vienna, June 3rd 2014

22 Data Sources 22 © Burg & Hudec Vienna, June 3rd 2014

23 Processing 23 © Burg & Hudec Vienna, June 3rd 2014 Simple Aggregation Model Based Processing AccountingData Matching AvailabilityModel Diganositcs Measurement Errors Matching rates Completness of Metadata Goodness of FitTop Down vs. Bottom up Adequcy of Units Description of Methods Misclassification errors Homogeneity of underlying concepts Analysis of sensitivity Strength of association

24 Data Presentation 24 © Burg & Hudec Vienna, June 3rd 2014 Contents of Quality report not dependent on characteristics  Accessibility  Clarity  Timeliness  Revisions  Restrictions caused by Statistical disclosure control

25 Content 25  Starting Point  Canonical Dimensions for a Typology of Statistical Products  Template  Examples  Impact on Quality Reporting  Conclusions © Burg & Hudec Vienna, June 3rd 2014

26 Main Conclusions 26 © Burg & Hudec Vienna, June 3rd 2014  One dimensional approach of assigning a type of statistics is not sufficient  Canonical dimensions can describe the characteristics of a statistical product  Characterization of product has impact on set of metadata and expectations on quality reporting

27 Next Steps 27 © Burg & Hudec Vienna, June 3rd 2014  Sharpening the proposal Completeness, exact definition etc..  Applying the concept to Standard-Documentation of Statistics Austria

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