1. Imputation in UNECE Statistical Databases: Principles and Practices
Steven Vale and Heinrich Brüngger, UNECE Statistical Division

2. Contents The ECOSOC view of statistical imputation Current practices
Basic principles
Step-by-step implementation
Conclusions and open questions
14 November 2018

3. ECOSOC views Resolution 2006/6 on strengthening statistical capacity
Sets limits for the use of imputation
... but also implicitly endorses it as a statistical technique
Statistical agencies need to review their practices to ensure compliance

4. Defining imputation
“A procedure for entering a value for a specific data item where the response is missing or unusable”
Boundary issues:
Imputing and editing
Imputing and forecasting

5. Current practice in UNECE
Very limited ad-hoc imputation
Four cases:
Account identities
Regional aggregates
Poor quality national data with little impact on region totals
Re-classification
Using imputations from others
Sufficient transparency in source metadata?

6. Basic principles (1) Imputed national data are not published
Avoids the need for consultation
Only official sources used for imputation
Preference for data from same country
Clear distinction between “real” and imputed data
Transparency – imputed data clearly flagged, and methods documented

7. Basic principles (2)
Aggregates must contain > 90% “real” data, covering > 50% of countries
Imputed data are re-calculated periodically to adjust for revisions
Method used defined at the level of the variable and stored as an attribute
Decisions on the use of imputation to be taken with regard to the quality framework

8. Step-by-step application
Automatic imputation routines to extend imputation towards the boundaries set by the ECOSOC Resolution
One step at a time, with pause and review to consider quality and cost / benefit
“Dashboard” to allow statisticians to choose the most appropriate method
Implemented in the context of re-engineering of statistical database system

9. First step Use a linear trend to impute missing values Requirements:
Sufficient time series observations (at least 3 out of previous 5 periods)
Closeness of fit of linear trend (R2 close to 1)
Constraints
Validity of R2 for few observations
Forward imputation only

10. 2000
2001
2002
2003
2004
2005
2006
2007 N Y
Data Available:
Y = Yes
N = No
Imputation:
= Yes
= No

11. Next steps More flexibility:
Longer time series
Imputing values at start and in middle of time series
Non-linear trends?
Cross-country imputation in strictly limited cases?

12. Conclusions Strong links between imputation and quality
Trade-off between accessibility and accuracy
Step-by-step, pause and review approach seems appropriate
Transparency is essential
Standardization of practices between international organizations would help

13. Open questions
Are other organizations interested in defining a common policy on the use of imputation, in response to the ECOSOC Resolution?
Could we go further and consider harmonization of methods and tools?
How should this be done? Is a specific forum needed, or can this be dealt with in combination with work on data quality?
Have other organizations modified their policies on imputation in the light of the ECOSOC Resolution, and if so, how?